Diseases and pests are main stresses to crop production. It is necessary to accurately and quickly monitor and control the stresses dynamically, so as to ensure the food security and the quality and safety of agricultural products, protect the ecological environment, and promote the sustainable development of agriculture. In recent years, with the rapid development of the unmanned aerial vehicle (UAV) industry, UAV agricultural remote sensing has played an important role in the application of crop diseases and pests monitoring due to its high image spatial resolution, strong data acquisition timeliness and low cost. The relevant background of UAV remote sensing monitoring of crop disease and pest stress was introduced, then the current methods commonly used in remote sensing monitoring of crop disease and pest stress by UAV was summarized. The data acquisition method and data processing method of UAV remote sensing monitoring of crop disease and pest stress were mainly discussed. Then, from the six aspects of visible light imaging remote sensing, multispectral imaging remote sensing, hyperspectral imaging remote sensing, thermal infrared imaging remote sensing, LiDAR imaging remote sensing and multiple remote sensing fusion and comparison, the research progress of remote sensing monitoring of crop diseases and pests by UAV worldwide was reviewed. Finally, the unresolved key technical problems and future development directions in the research and application of UAV remote sensing monitoring of crop disease and pest stress were proposed. Such as, the performance of the UAV flight platform needs to be optimized and upgraded, as well as the development of low-cost, lightweight, modular, and more adaptable airborne sensors. Convenient and automated remote sensing monitoring tasks need to be designed and implemented, and more remote sensing monitoring information can be obtained. Data processing algorithms or software should be designed and developed with greater applicability and wider applicability, and data processing time should be shortened by using 5G-based communication networks and edge computing devices. The applicability of the algorithm or model for UAV remote sensing monitoring of crop disease and pest stress needs to be stronger, so as to build a corresponding method library. We hope that this paper can help Chinese UAV remote sensing monitoring of crop diseases and pests to achieve more standardization, informatization, precision and intelligence.
Since powdery mildew (Blumeria graminis f. sp. tritici) mainly infects the foliar of wheat, satellite remote sensing technology can be used to monitor and assess it on a large scale. In this study, multisource and multitemporal satellite images were used to monitor the disease and improve the classification accuracy. Specifically, four Landsat-8 thermal infrared sensor (TIRS) and twenty MODerate-resolution imaging spectroradiometer (MODIS) temperature product (MOD11A1) were used to retrieve the land surface temperature (LST), and four Chinese Gaofen-1 (GF-1) wide field of view (WFV) images was used to identify the wheat-growing areas and calculate the vegetation indices (VIs). ReliefF algorithm was first used to optimally select the vegetation index (VIs) sensitive to wheat powdery mildew, spatial-temporal fusion between Landsat-8 LST and MOD11A1 data was performed using the spatial and temporal adaptive reflectance fusion model (STARFM). The Z-score standardization method was then used to unify the VIs and LST data. Four monitoring models were then constructed through a single Landsat-8 LST, multitemporal Landsat-8 LSTs (SLST), cumulative MODIS LST (MLST) and the combination of cumulative Landsat-8 and MODIS LST (SMLST) using the Support Vector Machine (SVM) classifier, that were LST-SVM, SLST-SVM, MLST-SVM and SMLST-SVM. Four assessment indicators including user accuracy, producer accuracy, overall accuracy and Kappa coefficient were used to compare the four models. The results showed that, the proposed SMLST-SVM obtained the best identification accuracies. The overall accuracy and Kappa coefficient of the SMLST-SVM model had the highest values of 81.2% and 0.67, respectively, while they were respectively 76.8% and 0.59 for the SLST-SVM model. Consequently, multisource and multitemporal LSTs can considerably improve the differentiation accuracies of wheat powdery mildew.
Accurate detection and recognition of plant diseases is the key technology to early diagnosis and intelligent monitoring of plant diseases, and is the core of accurate control and information management of plant diseases and insect pests. Deep learning can overcome the disadvantages of traditional diagnosis methods and greatly improve the accuracy of diseases detection and recognition, and has attracted a lot of attention of researchers. This paper collected the main public plant diseases image data sets all over the world, and briefly introduced the basic information of each data set and their websites, which is convenient to download and use. And then, the application of deep learning in plant disease detection and recognition in recent years was systematically reviewed. Plant disease target detection is the premise of accurate classification and recognition of plant disease and evaluation of disease hazard level. It is also the key to accurately locate plant disease area and guide spray device of plant protection equipment to spray drug on target. Plant disease recognition refers to the processing, analysis and understanding of disease images to identify different kinds of disease objects, which is the main basis for the timely and effective prevention and control of plant diseases. The research progress in early disease detection and recognition algorithm was expounded based on depth of learning research, as well as the advantages and existing problems of various algorithms were described. It can be seen from this review that the detection and recognition algorithm based on deep learning is superior to the traditional detection and recognition algorithm in all aspects. Based on the investigation of research results, it was pointed out that the illumination, sheltering, complex background, different disorders with similar symptoms, different changes of disease symptoms in different periods, and overlapping coexistence of multiple diseases were the main challenges for the detection and recognition of plant diseases. At the same time, the establishment of a large-scale and more complex data set that meets the specific research needs is also a difficulty that need to face together. And at further, we point out that the combination of the better performance of the neural network, large-scale data set and agriculture theoretical basis is a major trend of the development of the future. It is also pointed out that multimodal data can be used to identify early plant diseases, which is also one of the future development direction.
Timely detection and treatment of tomato diseases can effectively improve the quality and yield of tomato. In order to realize the real-time and non-destructive detection of tomato diseases, a tomato leaf disease classification and recognition method based on improved MobileNetV3 was proposed in this study. Firstly, the lightweight convolutional neural network MobileNetV3 was used for transfer learning on the image net data set. The network was initialized according to the weight of the pre training model, so as to realize the transfer and fine adjustment of large-scale shared parameters of the model. The training method of transfer learning could effectively alleviate the problem of model over fitting caused by insufficient data, realized the accurate classification of tomato leaf diseases in a small number of samples, and saved the time cost of network training. Under the same experimental conditions, compared with the three standard deep convolution network models of VGG16, ResNet50 and Inception-V3, the results showed that the overall performance of MobileNetV3 was the best. Next, the impact of the change of loss function and the change of data amplification mode on the identification of tomato leaf diseases were observed by using MobileNetV3 convolution network. For the test of loss value, focal loss and cross entropy function were used for comparison, and for the test of data enhancement, conventional data amplification and mixup hybrid enhancement were used for comparison. After testing, using Mixup enhancement method under focal loss function could improve the recognition accuracy of the model, and the average test recognition accuracy of 10 types of tomato diseases under Mixup hybrid enhancement and focal loss function was 94.68%. On the basis of transfer learning, continue to improve the performance of MobileNetV3 model, the dilated convolution convolution with expansion rate of 2 and 4 was introduced into convolution layer, 1×1 full connection layer after deep convolution of 5×5 was connected to form a perceptron structure in convolution layer, and GLU gating mechanism activation function was used to train the best tomato disease recognition model. The average test recognition accuracy was as high as 98.25%, the data scale of the model was 43.57 MB, and the average detection time of a single tomato disease image was only 0.27s, after ten fold cross validation, the recognition accuracy of the model was 98.25%, and the test results were stable and reliable. The experiment showed that this study could significantly improve the detection efficiency of tomato diseases and reduce the time cost of disease image detection.
Plant income insurance has become an important part of agricultural insurance in China. It has been recommended to pilot since 2016 by Chinese government in several counties, and is now (2022) required to be implemented in all major grain producing counties in the 13 major grain producing provinces. The measurement of yield for plant income insurance in such huge volume urgently needs the support of remote sensing technology. Therefore, the development history and application status of remote sensing technology in the whole agricultural insurance industry was reviewed to help understanding the whole context circumstances of plant income insurance firstly. Then, the application scenarios of remote sensing technology were analyzed, and the key remote sensing technologies involved were introduced. The technologies involved include crop field plot extraction, crop classification, crop disaster estimation, and crop yield estimation. Research progress of these technologies were reviewed and summarized,and the satellite data sources that most commonly used in plant income insurance were summarized as well. It was found that to obtain a better support for a development of plant income insurance as well as all crop insurance from remote sensing communities, issues existed not only in the involved remote sensing technologies, but also in the remote sensing industry as well as the insurance industry. The most two important technical problems in the current application scenario of planting income insurance are that: the plot extraction and crop classification are not automated enough; the yield estimation mechanism is not strong, and the accuracy is not high. At the industry level, the first issue is the limitation of the remote sensing technology itself in that the remote sensing is not almighty, suffering from limited data source, either from satellite or from other platform, laborious data preprocessing, and pricey data fees for most of the data, and the second is the compatibility between the current business of the insurance industry and the combination of remote sensing. In this regard, this paper proposed in total five specific suggestions, which are: 1st, to establish a data distribution platform to solve the problems of difficult data acquisition and processing and standardization of initial data; 2nd, to improve the sample database to promote the automation of plot extraction and crop classification; 3rd, to achieve faster, more accurate and more scientific yields through multidisciplinary research; 4th, to standardize remote sensing technology application in agricultural insurance, and 5th, to write remote sensing applications in crop insurance contract. With these improvements, the application mode of plant income insurance and probably the whole agriculture insurance would run in a way with easily available data, more automated and intelligent technology, standards to follow, and contract endorsements.
In order to realize the rapid and non-destructive monitoring of wheat biomass, field wheat trials were conducted based on different densities, nitrogen fertilizers and varieties, and unmanned aerial vehicle (UAV) was used to obtain RGB images in the pre-wintering stage, jointing stage, booting stage and flowering stage of wheat. The color and texture feature indices of wheat were obtained using image processing, and wheat biomass was obtained by manual field sampling in the same period. Then the relationship between different color and texture feature indices and wheat biomass was analyzed to select the suitable feature index for wheat biomass estimation. The results showed that there was a high correlation between image color index and wheat biomass in different stages, the values of r were between 0.463 and 0.911 (P<0.05). However, the correlation between image texture feature index and wheat biomass was poor, only 5 index values reached significant or extremely significant correlation level. Based on the above results, the color indices with the highest correlation to wheat biomass or the combining indices of color and texture features in different growth stages were used to construct estimation model of wheat biomass. The models were validated using independently measured biomass data, and the correlation between simulated and measured values reached the extremely significant level (P<0.01), and root mean square error (RMSE) was smaller. The R2 of color index model in the four stages were 0.538, 0.631, 0.708 and 0.464, and RMSE were 27.88, 516.99, 868.26 and 1539.81 kg/ha, respectively. The R2 of the model combined with color and texture index were 0.571, 0.658, 0.753 and 0.515, and RMSE were 25.49, 443.20, 816.25 and 1396.97 kg/ha, respectively. This indicated that the estimated results using the models were reliable and accurate. It also showed that the estimation models of wheat biomass combined with color and texture feature indices of UAV images were better than the single color index models.
Ground-penetrating radar (GPR) is one of the emerging technologies for soil moisture measurement. However, the measurement accuracy is difficult to determine due to some influence factors including radar wave frequency, soil texture type, etc. The GPR equipment with 1000 MHz center frequency and the measurement method of common midpoint (CMP) were adopted in the research to collect radar wave raw data in the selected field area under arid soil and moist soil conditions. The transmitter and receiver antennas of the GPR equipment were moved 0.01 m respectively in opposite directions on each radar wave raw data collection. Therefore, a CMP radar image consisted of 100 pieces of radar wave raw data by increasing the antenna distance from 0 m to 2 m. Each radar wave raw data indicated that the radar waves were reflected in the reflective layer with different dielectric constant under the same antenna distance. And the reflected and refracted radar waves were acquired by the receiving antenna at different two-way travel time respectively, and recorded in the computer. The collection of CMP soundings aimed to determine the inversion accuracy, optimum inversion depth, effective inversion depth and optimal inversion model of soil moisture content at different depth ranges and adjacent reflective layers by GPR at field scale. The reflected and refracted radar wave data were extracted from the raw data. The velocities of the surface waves and reflected waves were obtained respectively from the line slope of the surface wave data and the hyperbolic curves fitting of the reflected wave data. In addition, the relative dielectric constant of the soil at specified depth were deduced according to the soil dielectric constant and its reflected wave velocity. Moreover, 4 different models including Topp, Roth, Herkelrath and Ferre were used to figure out the soil volumetric water content inversion. Meanwhile, the measured data of soil volumetric moisture content obtained by oven drying method were used to verify the accuracy of the inversion results. The results showed that the effective inversion depth of 1000 MHz GPR ranged from 0 to 50 cm. The best inversion depth was 50 cm in arid soil and 40 cm in moist soil. The Roth model had the best correlation and stability with the highest R2 was 0.750, the Root Mean Square Error (RMSE) was 0.0114 m3/m3 and the lowest Relative Error (RE) was 3.0%. The GPR could possess the capacity of quick, precise and non-destructive measurement of specified depth soil moisture in field scale. The inversion model of soil moisture content needs to be calibrated according to different soil conditions.
Intercropping creates a heterogeneous canopy and triggers plastic responses in plant growth and structural development. In order to quantify the effect of planting pattern, strip width and row position on the structural development and light capture of maize and soybean in simultaneous intercropping, both experimental and modelling approaches were used. Field experiments were conducted in 2017－2018 with two sole crops (maize and soybean) and two intercrops: Two rows of maize alternating with two rows of soybeans (2:2 MS) and three rows of maize alternating with six rows of soybean (3:6 MS). The morphological traits of maize and soybean e.g., leaf length and width, internode length and diameter, leaf and petiole declination angle in different rows and different planting patterns, and photosynthetically active radiation (PAR) above and below the canopy of 2:2 MS were measured throughout the growing season. A functional-structural plant model of maize-soybean intercropping was developed in the GroIMP platform. The model was parameterized based on the morphological data set of 2017, and was validated with the leaf area index (LAI), plant height and PAR data set of 2018. The model simulated the morphological development of individual organs based on growing degree days (thermal time) and calculated the light capture at leaf level. The model well reproduced the observed dynamics of leaf area index and plant height (RMSE: 0.24－0.70 m2/m2 for LAI and 0.06－0.17 m for plant height), and the fraction of light capture in the 2:2 MS intercropping (RMSE: 0.06－0.10). Maize internode diameter in intercrops increased, but the internode length did not change. Soybean internodes in intercrops became longer and thinner compared to sole soybean probably caused by the shading imposed by maize, and the 2:2 MS had longer internodes than the 3:6 MS, indicating the effects of strip width. Simulated light capture of maize in 2:2 MS intercropping was 35.6% higher than sole maize. For maize in 3:6 MS intercropping, the light capture of the border rows and inner row were 27.8% and 20.3% higher than sole maize, respectively. Compared to sole soybean, the simulated light capture of soybean in border rows was 36.0% lower in 2:2 MS intercropping, and was 28.8% lower in 3:6 MS intercropping. For 3:6 MS intercropping, light capture of soybean in inner rows I and inner rows II were 4.1% and 1.8% lower than sole soybean, respectively. In the future, the model could be further developed and used to explore and optimize the planting patterns of maize soybean intercropping under different environmental conditions using light capture as an indicator.
Abscisic acid (ABA) is an important plant hormone, which can control seed and bud dormancy, organ size control, senescence and death, and participate in both biological and abiotic stress, inhibit plant growth, and participate in plant disease resistance. In order to determine the content of ABA in plants quickly and accurately, a new type of ABA immunosensor was developed. To improve the detection performance of the sensor, the detection performance of the sensor was increased by modifying GR-COOH and SA on the electrode surface. The concentration of GR-COOH, SA, and ABA-Antibody were optimized, the optimal conditions for the three materials were 1.5 mg/ml, 1.25 mg/ml and 0.5 mg/ml. The immunosensor was constructed based on the electrode impedance changes (△Z )due to the binding reaction of ABA antibody and antigen. It was found that the sensor showed linear relationship with ABA in the response range of 10 pmol/L~1 μmol/L, R2 was 0.99927, and the detection limit was about 10 pmol/L. The sensor also had good selectivity and stability. Using the electrochemical immunosensor, the content of ABA in navel orange leaf that have been successfully inoculated with citrus Huanglongbing by PCR was determined, and healthy plants were used as control. The test results showed that the impedance changes(△Z ) of healthy leaves and diseased leaves were 72 and 823, respectively, which indicated that the level of ABA in the infected plants increased significantly. The sensor provides a tool for the detection of plant hormone levels under disease stress. The results showed that the content of ABA increased in the leaves of navel orange infected by citrus Huanglongbing, which indicated that ABA played an important role in plant disease resistance. Furthermore, the changes of gene expression of key enzymes CitZEP in ABA synthesis pathway were studied, The results showed that the expression of CitZEP increased in plants infected with Huanglongbing disease, and the results were consistent with the detection results of the sensor, which indicated that the sensor had good practicability.
Plant hormone Abscisic Acid (ABA) plays an important role in regulating plant growth. However, the content of ABA in plant tissues is very low, and rapid and sensitive detection methods are urgently needed. In this study, a rapid and quantitative ABA detection method was established based on aptamer recognition and surface-enhanced Raman spectroscop (SERS). The gold nanoparticles modified by ABA aptamer had the characteristics of SERS signal enhancement and selective recognition, realizing the rapid and sensitive detection of trace ABA in complex plant sample matrix. When ABA molecules appeared in detect system, the aptamer would specifically bind with ABA molecules, and the aptamer folded into G-tetrad structure at same time, which wrapped ABA molecules in the tetrad structure, shortened the distance between ABA molecules and gold nanoparticles, and the enhanced and stable ABA molecules SERS signal were obtained. Under the condition of optimized aptamer concentration at 0.12 μmol/L, different concentrations of ABA solutions in the detection system were detected. Within the concentration range of 0.1－100 μmol/L, the SERS intensity of ABA presented a good linear relationship with the concentration. The detection limit of this method was 0.1 μmol/L and the linear correlation coefficient R2 was 0.9855. The repeatability test of 20 points randomly on SERS substrate showed that the relative standard deviation (RSD) was 6.71%, indicating the stability of SERS substrate was well. Furthermore, the substrate of gold nanoparticles modified by the ABA aptamer terminal with sulfhydryl group (SH-Apt) could be stored in the refrigerator for more than half a year, indicating that the substrate has good stability. Once the preparation of the synthesized SH-Apt modified gold nanoparticles was completed. It could be used on demand without the need to prepare SERS substrate for every detection. In this sense, the constructed aptamer SERS biosensor could realize the rapid and quantitative detection of ABA. The method was used for the determination of ABA in wheat leaves, and the result was in good agreement with the Enzyme Linked Immunosorbent Assay (ELISA) (The max relative error was 9.13%). This biosensor is an exploratory study on the detection of plant hormones by SERS, and the results of the study will have important reference value for the subsequent quantitative and on-site detection of ABA, as well as the detection of other plant hormones.
Convolutional neural network models have different advantages and disadvantages, it is becoming more and more difficult to select an appropriate convolutional neural network model in an actual fish identification project. The identification of underwater fish is a challenge task due to varies in illumination, low contrast, high noise, low resolution and sample imbalance between each type of image from the real underwater environment. In addition, deploying models to mobile devices directly will reduce the accuracy of the model sharply. In order to solve the above problems, Fish Recognition Ground-Truth dataset was used to training model in this study, which is provided by Fish4Knowledge project from University of Edinburgh. It contains 27,370 images with 23 fish species, and has been labeled manually by marine biologists. AlexNet, GoogLeNet, ResNet and DenseNet models were selected initially according to the characteristics of real-time underwater fish identification task, then a comparative experiment was designed to explore the best network model. Random image flipping, rotation and color dithering were used to enhance data based on ground-truth fish dataset in response to the limited number of underwater fish images. Considering that there was a serious imbalance in the number of samples in each category, the label smoothing technology was used to alleviate model overfitting. The Ranger optimizer and Cosine learning rate attenuation strategy were used to further improve the training effect of the models. The accuracy and recall rate information of each model were recorded and counted. The results showed that, the accuracy and recall rate of the fish recognition model based on DenseNet reached 99.21% and 96.77% in train set and validation set respectively, its F1 value reached 0.9742, which was the best model obtained in the experiment. Finally, a remote fish identification system was designed based on Python language, in this system the model was deployed to linux server and the Android APP was responsible for uploading fish images via http to request server to identify the fishes and displaying the identification information returned by server, such as fish species, profiles, habits, distribution, etc. A set of recognition tests were performed on real Android phone and the results showed that in the same local area net the APP could show fish information rapidly and exactly within 1 s.
Objects in farmlands often have characteristic of small volume and high density with variable light and complex background, and the available object detection models could not get satisfactory recognition results. Taking bees as research objects, a method that could overcome the influence from the complex backgrounds, the difficulty in small object feature extraction was proposed, and a detection algorithm was created for small objects irrelevant to image size. Firstly, the original image was split into some smaller sub-images to increase the object scale, and the marked objects were assigned to the sub-images to produce a new dataset. Then, the model was trained again using transfer learning to get a new object detection model. A certain overlap rate was set between two adjacent sub-images in order to restore the objects. The objects from each sub-image was collected and then non-maximum suppression (NMS) was performed to delete the redundant detection boxes caused by the network, an improved NMS named intersection over small NMS (IOS-NMS) was then proposed to delete the redundant boxes caused by the overlap between adjacent sub-images. Validation tests were performed when sub-image size was set was 300×300, 500×500 and 700×700, the overlap rate was set as 0.2 and 0.05 respectively, and the results showed that when using single shot multibox detector (SSD) as the object detection model, the recall rate and precision was generally higher than that of SSD with the maximum difference 3.8% and 2.6%, respectively. In order to further verify the algorithm in small target recognition with complex background, three bee images with different scales and different scenarios were obtained from internet and test experiments were conducted using the new proposed algorithm and SSD. The results showed that the proposed algorithm could improve the performance of target detection and had strong scale adaptability and generalization. Besides, the new algorithm required multiple forward reasoning for a single image, so it was not time-efficient and was not suitable for edge calculation.
Realizing the construction of intelligent farming by using advanced information technology, thus improving the living welfare of dairy cows and the economic benefits of dairy farms has become an important goal and task in dairy farming research field. Computer vision technology has the advantages of non-contact, stress-free, low cost and high throughput, and has a broad application prospect in animal production. On the basis of describing the importance of computer vision technology in the development of intelligent farming industry, this paper introduced the cutting-edge technology of cow physiological parameters and disease diagnosis based on computer vision, including cow temperature monitoring, body size monitoring, weight measurement, mastitis detection and lameness detection. The introduction coverd the development process of these studies, the current mainstream techniques, and discussed the problems and challenges in the research and application of related technology, aiming at the problem that the current computer vision-based detection methods are susceptible to individual difference and environmental changes. Combined with the development status of farming industry, suggestions on how to improve the universality of computer vision technology in intelligent farming industry, how to improve the accuracy of monitoring cows' physiological parameters and disease diagnosis, and how to reduce the influence of environment on the system were put forward. Future research work should focus on research and developmentof algorithm, make full use of computer vision technology continuous detection and the advantage of large amount of data, to ensure the accuracy of the detection, and improve the function of the system integration and data utilization, expand the computer vision system function. Under the premise that does not affect the ability of the system, to improve the study on the number of function integration and system function and reduce equipment costs.
Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary.
The motion behavior of dairy cows contains much of health information. The application of information and intelligent technology will help farms grasp the health status of dairy cows in time and improve breeding efficiency. In this paper, the development trend of intelligent morning technology of cow's motion behavior was mainly analyzed. Firstly, on the basis of expounding the significance of monitoring the basic motion (lying, walking, standing), oestrus, breathing, rumination and limping of dairy cows, the necessity of behavior monitoring of dairy cows was introduced. Secondly, the current research status was summarized from contact monitoring methods and non-contact monitoring methods in chronological order. The principle and achievements of related research were introduced in detail and classified. It is found that the current contact monitoring methods mainly rely on acceleration sensors, pedometers and pressure sensors, while the non-contact monitoring methods mainly rely on video images, including traditional video image analysis and video image analysis based on deep learning. Then, the development status of cow behavior monitoring industry was analyzed, and the main businesses and mainstream products of representative livestock farm automation equipment suppliers were listed. Industry giants, such as Afimilk and DeLaval, as well as their products such as intelligent collar (AfiCollar), pedometer (AfiActll Tag) and automatic milking equipment (VMS™ V300) were introduced. After that, the problems and challenges of current contact and non-contact monitoring methods of dairy cow motion behavior were put forward. The current intelligent monitoring methods of dairy cows' motion behavior are mainly wearable devices, but they have some disadvantages, such as bring stress to dairy cows and are difficult to install and maintain. Although the non-contact monitoring methods based on video image analysis technology does not bring stress to dairy cows and is low cost, the relevant research is still in its infancy, and there is still a certain distance from commercial use. Finally, the future development directions of relevant key technologies were prospected, including miniaturization and integration of wearable monitoring equipment, improving the robustness of computer vision technology, multi-target monitoring with limited equipment and promoting technology industrialization.
The gait phase of dairy cows is an important indicator to reflect the severity of lameness. IThe accuracy of available gait segmentation methods was not enough for lameness detection. In this study, a gait phase recognition method based on Gaussian mixture model (GMM) and hidden Markov model (HMM) was proposed and tested. Firstly, wearable inertial sensors LPMS-B2 were used to collect the acceleration and angular velocity signals of cow hind limbs. In order to remove the noise of the system and restore the real dynamic data, Kalman filter was used for data preprocessing. The first-order difference of the angular velocity of the coronal axis was selected as the eigenvalue. Secondly, to analyze the long-term continuous recorded gait sequences of dairy cows, the processed data was clustered by GMM in the unsupervised way. The clustering results were taken as the input of the HMM, and the gait phase recognition of dairy cows was realized by decoding the observed data. Finally, the cow gait was segmented into 3 phases, including the stationary phase, standing phase and swing phase. At the same time, gait segmentation was achieved according to the standing phase and swing phase. The accuracy, recall rate and F1 of the stationary phase were 89.28%, 90.95% and 90.91%, respectively. The accuracy, recall rate and F1 of the standing phase recognition in continuous gait were 91.55%, 86.71% and 89.06%, respectively. The accuracy, recall rate and F1 of the swing phase recognition in continuous gait were 86.67%, 91.51% and 89.03%, respectively. The accuracy of cow gait segmentation was 91.67%, which was 4.23% and 1.1 % higher than that of the event-based peak detection method and dynamic time warping algorithm, respectively. The experimental results showed that the proposed method could overcome the influence of the cow's walking speed on gait phase recognition results, and recognize the gait phase accurately. This experiment provides a new method for the adaptive recognition of the cow gait phase in unconstrained environments. The degree of lameness of dairy cows can be judged by the gait features.
Non-contact measurement based on the point cloud acquisition technology is able to alleviate the stress responses among beef cattle while collecting core body dimension data, but the current 3D data collection for beef cattle is usually time-consuming and easily influenced by the environment, which is in fact inapplicable to the actual breeding environment. In order to overcome the difficulty in obtaining the complete beef cattle point clouds, a non-contact phenotype data acquisition equipment was developed with a 3D reconstruction function, which can provide a large amount of standardized 3D quantitative phenotype data for beef cattle breeding and fattening process. The system is made up of a Kinect DK depth camera, an infrared grating trigger, and an Radio Frequency Identification (RFID) trigger, which enables the multi-angle instantaneous acquisition of beef cattle point clouds when the beef cattle pass through the walkway. The point cloud processing algorithm was developed based on the C++ platform and Point Cloud Library (PCL), and 3D reconstruction of beef cattle point clouds was achieved through spatial and outlier point filtering, Random Sample Consensus (RANSAC) shape fitting, point cloud thinning, and perceptual box filtering based on the dimensionality reduction density clustering to effectively filter out the interference, such as noises from the railings close to the beef cattle, without destroying the integrity of the point clouds. In the present work, a total of 124 sets of point clouds were successfully collected from 20 beef cattles on the actual farm using this system, and the target extraction experiments were completed. Notably, the beef cattle passed through the walkway in a natural state without any intervention during the whole data collection process. The experimental results showed that the acquisition success rate of this device was 91.89%. The coordinate system of the collected point cloud was consistent with the real situation and the body dimension reconstruction error was 0.6%. This device can realize the automatic acquisition and 3D reconstruction of beef cattle point cloud data from multiple angles without human intervention, and can automatically extract the target beef cattle point clouds from a complex environment. The point cloud data collected by this system help to restore the body size and shape of beef cattle, thereby provide solid support for the measurement of core parameters such as body height, body width, body oblique length, chest circumference, abdominal circumference, and body weight.
Identifying of yak is indispensable for individual documentation, behavior monitoring, precise feeding, disease prevention and control, food traceability, and individualized breeding. Aiming at the application requirements of animal individual identification technology in intelligent informatization animal breeding platforms, a yak face recognition algorithm based on transfer learning and multiscale feature fusion, i.e., transfer learning-multiscale feature fusion-VGG(T-M-VGG) was proposed. The sample data set of yak facial images was produced by a camera named GoPro HERO8 BLACK. Then, a part of dataset was increased by the data enhancement ways that involved rotating, adjusting the brightness and adding noise to improve the robustness and accuracy of model. T-M-VGG, a kind of convolutional neural network based on pre-trained visual geometry group network and transfer learning was input with normalized dataset samples. The feature map of Block3, Block4 and Block5 were considered as F3, F4 and F5, respectively. What's more, F3 and F5 were taken by the structure that composed of three parallel dilated convolutions, the dilation rate were one, two and three, respectively, to dilate the receptive filed which was the map size of feature map. Further, the multiscale feature maps were fused by the improved feature pyramid which was in the shape of stacked hourglass structure. Finally, the fully connected layer was replaced by the global average pooling to classify and reduce a large number of parameters. To verify the effectiveness of the proposed model, a comparative experiment was conducted. The experimental results showed that recognition accuracy rate in 38,800 data sets of 194 yaks reached 96.01%, but the storage size was 70.75 MB. Twelve images representing different yak categories from dataset were chosen randomly for occlusion test. The origin images were masked with different shape of occlusions. The accuracy of identifying yak individuals was 83.33% in the occlusion test, which showed that the model had mainly learned facial features. The proposed algorithm could provide a reference for research of yak face recognition and would be the foundation for the establishment of smart management platform.
The production mode of livestock breeding has changed from extensive to intensive, and the production level is improved. However, low labor productivity and labor shortage have seriously restricted the rapid development of China's livestock breeding industry. As a new intelligent agricultural machinery equipment, agricultural robot integrates advanced technologies, such as intelligent monitoring, automatic control, image recognition technology, environmental modeling algorithm, sensors, flexible execution, etc. Using modern information and artificial intelligence technology, developing livestock feeding and pushing robots, realizing digital and intelligent livestock breeding, improving livestock breeding productivity are the main ways to solve the above contradiction. In order to deeply analyze the research status of robot technology in livestock breeding, products and literature were collected worldwide. This paper mainly introduced the research progress of livestock feeding robot from three aspects: Rail feeding robot, self-propelled feeding robot and pushing robot, and analyzed the technical characteristics and practical application of feeding robot.The rail feeding robot runs automatically along the fixed track, identifies the target animal, positions itself, and accurately completes feed delivery through preset programs to achieve accurate feeding of livestock. The self-propelled feeding robot can walk freely in the farm and has automatic navigation and positioning functions. The system takes single chip microcomputer as the control core and realizes automatic walking by sensor and communication module. Compared with the rail feeding robot, the feeding process is more flexible, convenient and technical, which is more conducive to the promotion and application of livestock farms. The pushing robot will automatically push the feed to the feeding area, promote the increase of feed intake of livestock, and effectively reduce the labor demand of the farm. By comparing the feeding robots of developed countries and China from two aspects of technology and application, it is found that China has achieved some innovation in technology, while developted countries do better in product application. The development of livestock robot was prospected. In terms of strategic planning, it is necessary to keep up with the international situation and the trend of technological development, and formulate the agricultural robot development strategic planning in line with China's national conditions. In terms of the development of core technologies, more attention should be paid to the integration of information perception, intelligent sensors and deep learning algorithms to realize human-computer interaction. In terms of future development trends, it is urgent to strengthen innovation, improve the friendliness and intelligence of the robot, and improve the learning ability of the robot. To sum up, intelligent livestock feeding and pushing machine operation has become a cutting-edge technology in the field of intelligent agriculture, which will surely lead to a new round of agricultural production technology reform, promote the transformation and upgrading of China's livestock industry. .
Focusing on the low level of management and informatization and intelligence of the beef cattle industry in China, a big data platform for commercial beef cattle breeding, referring to the experience of international advanced beef cattle breeding countries, was proposed in this research. The functions of the platform includes integrating germplasm resources of beef cattle, automatic collecting of key beef cattle breeding traits, full-service support for the beef cattle breeding process, formation of big data analysis and decision-making system for beef cattle germplasm resources, and joint breeding innovation model. Aiming at the backward storage and sharing methods of beef cattle breeding data and incomplete information records in China, an information resource integration platform and an information database for beef cattle germplasm were established. Due to the vagueness and subjectivity of the breeding performance evaluation standard, a scientific online evaluation technology of beef cattle breeding traits and a non-contact automatic acquisition and intelligent calculation method were proposed. Considering the lack of scientific and systematic breeding planning and guidance for farmers in China, a full-service support system for beef cattle breeding and nanny-style breeding guidance during beef cattle breeding was developed. And an interactive progressive decision-making method for beef cattle breeding to solve the lack of data accumulation of beef cattle germplasm was proposed. The main body of breeding and farming enterprises was not closely integrated, according to that, the innovative breeding model of regional integration was explored. The idea of commercial beef cattle breeding big data software platform and the technological and model innovation content were also introduced. The technology innovations included the deep mining of germplasm resources data and improved breed management pedigree, the automatic acquisition and evaluation technology of non-contact breeding traits, the fusion of multi-source heterogeneous information to provide intelligent decision support. The future goal is to form a sustainable information solution for China's beef cattle breeding industry and improve the overall level of China's beef cattle breeding industry.
The shortage of feed grain is continually worsening in China, which leads to the transformation of feed grain security into national food security. Therefore, comprehensively integrating the basic data resources of feed nutrition and improving the nutritional value of all available feed resources will be one of the key technical strategies to ensure national food security in China. In this study, based on the description specification and attribute data standard of 16 categories of Chinese feed raw materials, more than 500,000 pieces of data on the types, spatial distribution, chemical composition and nutritional value characteristics of existing feed resources, which were accumulated through previous projects from the sixth Five-Year Plan to the thirteenth Five-Year Plan period, were digitally collected, recorded, categorized and comprehensively analyzed. By using MySQL relational database technology and PHP program, a new generation of feed nutrition big data online platform (http://www.chinafeeddata.org.cn/) was developed and web data sharing service was provided as well. First of all, the online platform provided visual analysis of all warehousing data, which could realize the visual comparison of a single or multiple feed nutrients in various graphic forms such as scatter diagram, histogram, curve line and column chart. By using two-dimensional code technology, all feed nutrition attribute data and feed entity sample traceability data could be shared and downloaded remotely in real-time on mobile phones. Secondly, the online platform also incorporated various regression models for prediction of feed effective nutrient values using readily available feed chemical composition in the datasets, providing dynamic analysis for feed raw material nutrient variation. Finally, based on Geographic Information System technology, the online platform integrated the data of feed chemical composition and major mineral element concentrations with their geographical location information, which was able to provide the distribution query and comparative analysis of the geographic information map of the feed raw material nutrition data at both provincial and national level. Meanwhile, the online platform can also provide a download service of the various datasets, which brought convenience to the comprehensive application of existing feed nutrition data. This research also showed that expanding feed resource data and providing prediction and analysis models of feed effective nutrients could maximize the utilization of the existing feed nutrition data. After embedding online calculation modules of various feed formulation software, this platform would be able to provide a one-stop service and optimize the utilization of the feed nutrition data.
Raman spectroscopy is a type of scattering spectroscopy with features such as rapid, less susceptible to moisture interference, no sample pre-treatment and in vivo detection. As a powerful characterization tool for analyzing and testing the molecular composition and structure of substances, Raman spectroscopy is also playing an extremely important role in the detection of plant and animal phenotypes, food safety, soil and water quality in the agricultural field with the continuous improvement of Raman spectroscopy technology. In this paper, the detection principles of Raman spectroscopy are introduced, and the new progresses of eight Raman spectroscopy technology are summarized, including confocal microscopy Raman spectroscopy, Fourier transform Raman spectroscopy, surface-enhanced Raman spectroscopy, tip-enhanced Raman spectroscopy, resonance Raman spectroscopy, spatially shifted Raman spectroscopy, frequency-shifted excitation Raman difference spectroscopy and Raman spectroscopy based on nonlinear optics, etc. And their advantages and disadvantages and application scenarios are prerented, respectively. The applications of Raman spectroscopy in plant detection, soil detection, water quality detection, food detection, etc. are summarized. It can be specifically subdivided into plant phenotype, plant stress, soil pesticide residue detection, soil colony detection, soil nutrient detection, food pesticide detection, food quality detection, food adulteration detection, and water quality detection. In future agricultural applications, the elimination of fluorescence background due to complex living organisms in Raman spectroscopy is the next research direction. The study of stable enhanced substrates is an important direction in the application of Surface Enhanced Raman Spectroscopy (SERS). In order to meet the measurement of different scenarios, portable and telemetric Raman spectrometers will also play an important role in the future. Raman spectroscopy needs to be further explored for a wide variety of research objects in agriculture, especially for applications in animal science, for which there is still a paucity of relevant studies up to now. In the existing field of agricultural research, it is necessary to pursue the characterization of more specific substances by Raman spectroscopy, which can prompt the application of Raman spectroscopy for a wider range of uses in agriculture. Further, the pursuit of lower detection limits and higher stability for practical applications is also the direction of development of Raman spectroscopy in the field of agriculture. Finally, the challenges that need to be solved and the future development directions of Raman spectroscopy are proposed in the field of agriculture in order to bring more inspiration to future agricultural production and research.
Intelligent equipment is necessary to ensure stable, high-quality, and efficient production of facility agriculture. Among them, intelligent harvesting equipment needs to be designed and developed according to the characteristics of fruits and vegetables, so there is little large-scale mechanization. The intelligent harvesting equipment in Japan has nearly 40 years of research and development history since the 1980s, and the review of its research and development products has specific inspiration and reference significance. First, the preferential policies that can be used for harvesting robots in the support policies of the government and banks to promote the development of facility agriculture were introduced. Then, the development of agricultural robots in Japan was reviewed. The top ten fruits and vegetables in the greenhouse were selected, and the harvesting research of tomato, eggplant, green pepper, cucumber, melon, asparagus, and strawberry harvesting robots based on the combination of agricultural machinery and agronomy was analyzed. Next, the commercialized solutions for tomato, green pepper, and strawberry harvesting system were detailed and reviewed. Among them, taking the green pepper harvesting robot developed by the start-up company AGRIST Ltd. in recent years as an example, the harvesting robot developed by the company based on the Internet of Things technology and artificial intelligence algorithms was explained. This harvesting robot can work 24 h a day and can control the robot's operation through the network. Then, the typical strawberry harvesting robot that had undergone four generations of prototype development were reviewed. The fourth-generation system was a systematic solution developed by the company and researchers. It consisted of high-density movable seedbeds and a harvesting robot with the advantages of high space utilization, all-day work, and intelligent quality grading. The strengths, weaknesses, challenges, and future trends of prototype and industrialized solutions developed by universities were also summarized. Finally, suggestions for accelerating the development of intelligent, smart, and industrialized harvesting robots in China's facility agriculture were provided.
Aiming at the difficulty of accurately extract the phenotypic traits of plants and organs from images or point clouds caused by the multiple tillers and serious cross-occlusion among organs of wheat plants, to meet the needs of accurate phenotypic analysis of wheat plants, three-dimensional (3D) digitization was used to extract phenotypic parameters of wheat plants. Firstly, digital representation method of wheat organs was given and a 3D digital data acquisition standard suitable for the whole growth period of wheat was formulated. According to this standard, data acquisition was carried out using a 3D digitizer. Based on the definition of phenotypic parameters and semantic coordinates information contained in the 3D digitizing data, eleven conventional measurable phenotypic parameters in three categories were quantitative extracted, including lengths, thicknesses, and angles of wheat plants and organs. Furthermore, two types of new parameters for shoot architecture and 3D leaf shape were defined. Plant girth was defined to quantitatively describe the looseness or compactness by fitting 3D discrete coordinates based on the least square method. For leaf shape, wheat leaf curling and twisting were defined and quantified according to the direction change of leaf surface normal vector. Three wheat cultivars including FK13, XN979, and JM44 at three stages (rising stage, jointing stage, and heading stage) were used for method validation. The Open3D library was used to process and visualize wheat plant data. Visualization results showed that the acquired 3D digitization data of maize plants were realistic, and the data acquisition approach was capable to present morphological differences among different cultivars and growth stages. Validation results showed that the errors of stem length, leaf length, stem thickness, stem and leaf angle were relatively small. The R2 were 0.93, 0.98, 0.93, and 0.85, respectively. The error of the leaf width and leaf inclination angle were also satisfactory, the R2 were 0.75 and 0.73. Because wheat leaves are narrow and easy to curl, and some of the leaves have a large degree of bending, the error of leaf width and leaf angle were relatively larger than other parameters. The data acquisition procedure was rather time-consuming, while the data processing was quite efficient. It took around 133 ms to extract all mentioned parameters for a wheat plant containing 7 tillers and total 27 leaves. The proposed method could achieve convenient and accurate extraction of wheat phenotypes at individual plant and organ levels, and provide technical support for wheat shoot architecture related research.
Factory-like rearing of silkworm (Bombyx mori) using artificial diet for all instars is a brand-new rearing mode of silkworm. Accurate feeding is one of the core technologies to save cost and increase efficiency in factory silkworm rearing. Automatic identification and counting of silkworm play a key role to realize accurate feeding. In this study, a machine vision system was used to obtain digital images of silkworms during main instars, and an improved Mask R-CNN model was proposed to detect the silkworms and residual artificial diet. The original Mask R-CNN was improved using the noise data of annotations by adding a pixel reweighting strategy and a bounding box fine-tuning strategy to the model frame. A more robust model was trained to improve the detection and segmentation abilities of silkworm and residual feed. Three different data augmentation methods were used to expand the training dataset. The influences of silkworm instars, data augmentation, and the overlap between silkworms on the model performance were evaluated. Then the improved Mask R-CNN was used to detect silkworms and residual feed. The AP50 (Average Precision at IoU=0.5) of the model for silkworm detection and segmentation were 0.790 and 0.795, respectively, and the detection accuracy was 96.83%. The detection and segmentation AP50 of residual feed were 0.641 and 0.653, respectively, and the detection accuracy was 87.71%. The model was deployed on the NVIDIA Jetson AGX Xavier development board with an average detection time of 1.32 s and a maximum detection time of 2.05 s for a image. The computational speed of the improved Mask R-CNN can meet the requirement of real-time detection of the moving unit of the silkworm box on the production line. The model trained by the fifth instar data showed a better performance on test data than the fourth instar model. The brightness enhancement method had the greatest contribution to the model performance as compared to the other data augmentation methods. The overlap between silkworms also negatively affected the performance of the model. This study can provide a core algorithm for the research and development of the accurate feeding information system and feeding device for factory silkworm rearing, which can improve the utilization rate of artificial diet and improve the production and management level of factory silkworm rearing.
To further improve the simulation and estimation accuracy of the supply and demand process of agricultural products, a large number of agricultural data at the national and provincial levels since 1980 were used as the basic research sample, including production, planted area, food consumption, industrial consumption, feed consumption, seed consumption, import, export, price, GDP, population, urban population, rural population, weather and so on, by fully considering the impact factors of agricultural products such as varieties, time, income and economic development, a multi-agricultural products supply and demand forecasting model based on long short-term memory neural network (LSTM) was constructed in this study. The general thought of supply and demand forecasting model is packaging deep neural network training model as an I/O-opening modular model, reserving control interface for input of outside data, and realizing the indicators forecasting of supply and demand and matrixing of balance sheet. The input of model included forecasting balance sheet data of agricultural products, annual price data, general economic data, and international currency data since 2000. The output of model was balance sheet data of next decade since forecasting time. Under the premise of fully considering the mechanical constraints, the model used the advantages of deep learning algorithms in nonlinear model analysis and prediction to analyze and predict supply and demand of 9 main types of agricultural products, including rice, wheat, corn, soybean, pork, poultry, beef, mutton, and aquatic products. The production forecast results of 2019－2021 based on this model were compared and verified with the data published by the National Bureau of Statistics, and the mean absolute percentage error was 3.02%, which meant the average forecast accuracy rate of 2019－2021 was 96.98%. The average forecast accuracy rate was 96.10% in 2019, 98.26% in 2020, and 96.58% in 2021, which shows that with the increase of sample size, the prediction effect of intelligent learning model would gradually get better. The forecasting results indicate that the multi-agricultural supply and demand prediction model based on LSTM constructed in this study can effectively reflect the impact of changes in hidden indicators on the prediction results, avoiding the uncontrollable error introduced by manual experience intervention. The model can provide data production and technical support such as market warning, policy evaluation, resource management and public opinion analysis for agricultural production and management and macroeconomic regulation, and can provide intelligent technical support for multi-regional and inter-temporal agricultural outlook work by monitoring agricultural operation data in a timely manner.
Artificial intelligence (AI) assisted planting can improve in the precise management of protected horticultural crops while also alleviating the increasingly prevalent problem of labor shortage. As a typical representative of labor-intensive industries, the strawberry industry has a growing need for intelligent technology. To assess the regulatory effects of various AI strategies and key technologies on strawberry production in greenhouse, as well as provide valuable references for the innovation and industrial application of AI in horticultural crops, four AI planting strategies were evaluated. Four 96 m2 modern greenhouses were used for planting strawberry plants. Each greenhouse was equipped with standard sensors and actuators, and growers used artificial intelligence algorithms to remotely control the greenhouse climate and crop growth. The regulatory effects of four different AI planting strategies on strawberry growth, fruit yield and qualitywere compared and analyzed. And human-operated cultivation was taken as a reference to analyze the characteristics, existing problems and shortages. Each AI planting strategy simulated and forecast the greenhouse environment and crop growth by constructing models. AI-1 implemented greenhouse management decisions primarily through the knowledge graph method, whereas AI-2 transferred the intelligent planting model of Dutch greenhouse tomato planting to strawberry planting. AI-3 and AI-4 created growth and development models for strawberries based on World Food Studies (WOFOST) and Product of Thermal Effectiveness and Photosynthesis Active Radiation (TEP), respectively. The results showed that all AI supported strategy outperformed a human-operated greenhouse that served as reference. In comparison to the human-operated cultivation group, the average yield and output value of the AI planting strategy group increased 1.66 and 1.82 times, respectively, while the highest Return on Investment increased 1.27 times. AI can effectively improve the accuracy of strawberry planting management and regulation, reduce water, fertilizer, labor input, and obtain higher returns under greenhouse production conditions equipped with relatively complete intelligent equipment and control components, all with the goal of high yield and quality. Key technologies such as knowledge graphs, deep learning, visual recognition, crop models, and crop growth simulators all played a unique role in strawberry AI planting. The average yield and Return on Investment (ROI) of the AI groups were greater than those of the human-operated cultivation group. More specifically, the regulation of AI-1 on crop development and production was relatively stable, integrating expert experience, crop data, and environmental data with knowledge graphs to create a standardized strawberry planting knowledge structure as well as intelligent planting decision-making approach. In this study, AI-1 achieved the highest yield, the heaviest average fruit weight, and the highest ROI. This group's AI-assisted strategy optimized the regulatory effect of growth, development, and yield formation of strawberry crops in consideration of high yield and quality. However, there are still issues to be resolved, such as the difficulty of simulating the disturbance caused by manual management and collecting crop ontology data.
Traditional orchard production is facing problems of labor shortage due to the aging, difficulties in the management of agricultural equipment and production materials, and low production efficiency which can be expected to be solved by building a smart orchard that integrates technologies of Internet of Things(IoT), big data, equipment intelligence, et al. In this study, based on the objectives of full mechanization and intelligent management, a smart orchard was built in Pinggu district, an important peaches and pears fruit producing area of Beijing. The orchard covers an aera of more than 30 hm2 in Xiying village, Yukou town. In the orchard, more than 10 kinds of information acquisition sensors for pests, diseases, water, fertilizers and medicines are applied, 28 kinds of agricultural machineries with intelligent technical support are equipped. The key technologies used include: intelligent information acquisition system, integrated water and fertilizer management system and intelligent pest management system. The intelligent operation equipment system includes: unmanned lawn mower, intelligent anti-freeze machine, trenching and fertilizer machine, automatic driving crawler, intelligent profiling variable sprayer, six-rotor branch-to-target drone, multi-functional picking platform and finishing and pruning machine, etc. At the same time, an intelligent management platform has been built in the smart orchard. The comparison results showed that, smart orchard production can reduce labor costs by more than 50%, save pesticide dosage by 30% ~ 40%, fertilizer dosage by 25% ~ 35%, irrigation water consumption by 60% ~ 70%, and comprehensive economic benefits increased by 32.5%. The popularization and application of smart orchards will further promote China's fruit production level and facilitate the development of smart agriculture in China.
To solve the problems of low level of digitalization of orchard management and relatively single construction method, a three-dimensional virtual orchard construction method based on laser point cloud was proposed in this research. First, the hand-held 3D point cloud acquistion equipment (3D-BOX) combined with the lidar odometry and mapping (SLAM-LOAM) algorithm was used to complete the acquisition of the point cloud data set of orchard; then the outliers and noise points of the point cloud data were removed by using the statistical filtering algorithm, which was based on the K-neighbor distance statistical method. To achieve this, a distance threshold model for removing noise points was established. When a discrete point exceeded, it would be marked as an outlier, and the point was separated from the point cloud dataset to achieve the effect of discrete point filtering. The VoxelGrid filter was used for down sampling, the cloth simulation filtering (CSF) cloth simulation algorithm was used to calculate the distance between the cloth grid points and the corresponding laser point cloud, and the distinction between ground points and non-ground points was achieved by dividing the distance threshold, and when combined with the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, ground removal and cluster segmentation of orchard were realized; finally, the Unity3D engine was used to build a virtual orchard roaming scene, and convert the real-time GPS data of the operating equipment from the WGS-84 coordinate system to the Gauss projection plane coordinate system through Gaussian projection forward calculation. The real-time trajectory of the equipment was displayed through the LineRenderer, which realized the visual display of the motion trajectory control and operation trajectory of the working machine. In order to verify the effectiveness of the virtual orchard construction method, the test of orchard construction method was carried out in the Begonia fruit and the mango orchard. The results showed that the proposed point cloud data processing method could achieve the accuracy of cluster segmentation of Begonia fruit trees and mango trees 95.3% and 98.2%, respectively. Compared with the row spacing and plant spacing of fruit trees in the actual mango orchard, the average inter-row error of the virtual mango orchard was about 3.5%, and the average inter-plant error was about 6.6%. And compared the virtual orchard constructed by Unity3D with the actual orchard, the proposed method can effectively reproduce the actual three-dimensional situation of the orchard, and obtain a better visualization effect, which provides a technical solution for the digital modeling and management of the orchard.
As a pillar industry of economic development in the main apple-producing areas, apple industry has made important contributions to the increase of local farmers' income. With the transformation and upgrading of apple industry, the mechanization and intelligence level would be directly related to economic benefits. To promote the research of apple production intelligent technology and the development of intelligent equipment, in this paper, the current level of mechanization in each step of apple production was first introduced. Then, the main characteristics of the main apple orchard machinery, such as power chassis, weeding machinery, and harvesting equipment, were demonstrated. The application progress of automatic leveling and control, automatic navigation, automatic obstacle avoidance, weed identification, weed removal, apple identification, apple positioning, apple separation, and other technologies in intelligent power chassis, intelligent weeding machines, and apple harvesting robots, were summarized. The basic principles and characteristics of the above three key technologies of intelligent equipment were expounded in combination with different application environments. Intelligent control is the key technology for the intelligentization of orchard power chassis. The post of chassis adaptive control technology and autonomous navigation technology were discussed. In addition, a chassis intelligent perception and intelligent decision-making system should be established. Orchard chassis safe, accurate, efficient, and stable driving and operation is the future development trend of orchard intelligent chassis. The lack of robust weed sensing technology is the main limitation to the commercial development of a robotic weed control system. To improve the level of weed detection and weeding, machine vision and multi-sensor fusion methods have been proposed to solve the practical problems, such as illumination, overlapping leaves, occlusion, and classifier or network structure optimization. Robotic apple harvesting has proven to be a highly challenging task due to environmental complexities, sensor reliability, and robot stability. To improve the accuracy and efficiency of harvest mechanization applications in apples, apple quick identification under complex scenes, apple picking path planning, and materials and structure of manipulator for apple picking must all be optimized accordingly. Finally, the challenges of intelligent equipment technologies in apple production were analyzed, and the developing suggestions were put forward. This research can provide references and ideas for the advancement of intelligent technology research in apple production and the research and development of intelligent equipment.
In order to solve the problems of high labor intensity, low efficiency of manual operation and lack of supporting machinery in the fruit harvesting of modern orchards, a self-propelled orchard multi-station harvesting equipment was designed in combination with the fruit tree dwarf anvil wide-row dense planting mode and agronomic planting requirements. The whole machine structure and working principle of the self-propelled orchard multi-station harvesting equipment were expounded. According to the environmental conditions of mountainous orchards, the crawler chassis structure was designed, and the working speed was 0~2 km/h. The operating platform including left extension platform and right extension platform was designed according to the difference of fruit tree row spacing, and the working width of the operating platform was 1500~2700 mm. In order to improve the working efficiency and ensure the same picking speed of upper and lower operators, the picking operation mode of "two sides, two heights and six stations" was proposed by comparing the difference in the working flexibility between the operator on the platform and the operator on the ground during the operation of the machine, and the in-and-out channels of fruit boxes and the automatic collection and packing device were designed. The front and rear unobstructed fruit box access system was composed of the front loading and unloading mechanism, the rear loading and unloading mechanism and the fruit box slide rail, which was convenient for the empty fruit box to enter the fruit loading station of the working platform from the front and unloading from the rear after the fruit was filled. Six sub-conveyor belts were designed to handle apples harvested by six non interacting operators at the same time. The prototype was test in the field, and the packing uniform distribution coefficient calculation method was proposed to evaluate the uniformity of fruit packing, and the performance of the prototype was comprehensively evaluated in combination with the fruit damage rate and packing speed. The results showed that, the designed self-propelled orchard multi-station harvesting equipment could synchronize with the six stations manual harvesting speed. At the same time, with the help of the expansion platform, the apple picking range covered the entire canopy of the fruit tree. The prototype worked smoothly, and the speed of each conveyor belt was in good coordination with manual picking, and there was no apple congestion occurred. The apple harvest damage rate was 4.67%, the packing uniform distribution coefficient was 1.475, and the packing speed was 72.9 apples per minute, which could meet the requirements of orchard harvest operation.
In order to solve the problems of pesticides abuse, nonuniformity deposition and low operating efficiency, build up the smart mango orchard, sedimentary properties of liquids in mango canopy of two orchard pesticide machinery, i.e., orchard caterpillar mist sprayer and six-rotor unmanned aerial vehicle (UAV) of were compared. Mango canopy was divided into upper, middle and lower canopy, tartrazine wsa selected as the tracer, high-definition printing paper and filter paper were used to collect pesticide droplets, the image processing methods such as deposition distribution uniformity were used to analyze the droplets. The experimental results showed that, for the surface droplets coverage rate of upper canopy leaf, unmanned aerial vehicle (UAV) was significantly higher than the cartipillar mist sprayer, there was no significant difference for the middle and lower canopy leaf. The the average coverage rate of both the front and back of leaves in UAV treatment group were 1.5~2 times for cartipillar mist sprayer, and got more deposition in back of leaves compare with caterpillar mist sprayer. The density of droplets on the front of the leaves of the mist sprayer treatment was significantly higher than that of the UAV treatment, but there was no significant difference on the back of the leaves. Both the front and back of the leaves of the plant protection UAV did not meet the requirements of disease and pest control with a low spray amount of 20/cm2. The liquid deposition of mist sprayer concentrated in the middle and lower canopy (61.1%), and while for the UAVs, it concentrated in the upper canopy (43.0%). The proportion of the deposition in the canopy was higher than that of the UAVs (48.6%), but the deposition capacity of mist sprayer in the upper canopy was insufficient, accounting for only 17%. The research shows that, compared with UAV, caterpillar mist sprayer is more suitable for the pest control of lower and middlein canopy, at the same time, the high density of droplets cover also has obvious advantages when spraying fungicide. UAV is more suitable for the external tidbits pest control of upper mango canopy, such as thrips, anthrax. According to the experimental results, a stereoscopic plant protection system can be built up in which can use the advantages of both caterpillar mist sprayer and UAV to achieve uniform coverage of pesticide in the mango tree canopy.
To realize the autonomous navigation and automatic target spraying of intelligent plant protect machinery in orchard, in this study, an autonomous navigation and automatic target spraying robot for orchards was developed. Firstly, a single 3D light detection and ranging (LiDAR) was used to collect fruit trees and other information around the robot. The region of interest (ROI) was determined using information on the fruit trees in the orchard (plant spacing, plant height, and row spacing), as well as the fundamental LiDAR parameters. Additionally, it must be ensured that LiDAR was used to detect the canopy information of a whole fruit tree in the ROI. Secondly, the point clouds within the ROI was two-dimension processing to obtain the fruit tree center of mass coordinates. The coordinate was the location of the fruit trees. Based on the location of the fruit trees, the row lines of fruit tree were obtained by random sample consensus (RANSAC) algorithm. The center line (navigation line) of the fruit tree row within ROI was obtained through the fruit tree row lines. The robot was controlled to drive along the center line by the angular velocity signal transmitted from the computer. Next, the ATRS's body speed and position were determined by encoders and the inertial measurement unit (IMU). And the collected fruit tree zoned canopy information was corrected by IMU. The presence or absence of fruit tree zoned canopy was judged by the logical algorithm designed. Finally, the nozzles were controlled to spray or not according to the presence or absence of corresponding zoned canopy. The conclusions were obtained. The maximum lateral deviation of the robot during autonomous navigation was 21.8 cm, and the maximum course deviation angle was 4.02°. Compared with traditional spraying, the automatic target spraying designed in this study reduced pesticide volume, air drift and ground loss by 20.06%, 38.68% and 51.40%, respectively. There was no significant difference between the automatic target spraying and the traditional spraying in terms of the percentage of air drift. In terms of the percentage of ground loss, automatic target spraying had 43% at the bottom of the test fruit trees and 29% and 28% at the middle of the test fruit trees and the left and right neighboring fruit trees. But in traditional spraying, the percentage of ground loss was, in that sequence, 25%, 38%, and 37%. The robot developted can realize autonomous navigation while ensuring the spraying effect, reducing the pesticides volume and loss.
In view of the uneven distribution of airflow inside the multi-air-duct sprayer, the air flow caused by the air outlet is disturbed and the droplet can not be evenly deposited on the fruit tree canopy. In this research, the length parameter of the inner baffle plate of the multi-duct sprayer was optimized. The Computational Fluid Dynamics (CFD) was used to simulate and analyze the internal airflow of the air supply system of the multi-duct sprayer based on Star-CCM+ software. The standard deviations of the wind speed of the wind outlet 1~6 at different guide plates were 0.7468, 0.6776, 1.4441, 5.1305, 4.5768 and 0.8209, respectively. Among them, the standard deviations of wind speed value at Point 1, Point 2 and Point 6 were less than 1, indicating that the change of deflector length has little impact on the speed change. The standard deviations of wind speed value at Point 3, Point 4 and Point 5 were large, indicating that with the change of deflector length, the wind speed at Air outlet 3, Air outlet 4, Air outlet 5 were greatly affected. On this basis, through the response surface analysis of Air outlet 3, Air outlet 4 and Air outlet 5, it was determined that, the length of Deflector 1 as 200 mm, the length of Deflector 2 as 60 mm and the length of Deflector 3 as 50 mm, was the optimal parameter combination. Under the optimal combination parameters, the wind speed values of symmetrical Air outlet 3 and Air outlet 6 were 39.135 and 41.320 m/s, respectively, with a relative deviations of 5.58%. The wind speed values of air outlet 4 and air outlet 5 were 33.022 and 34.328 m/s, respectively, with a relative deviation of 3.95%, which meeting the design requirements of sprayer. The indoor wind speed test results showed that the average wind speed of the upper layer was 15.75 m/s, the average wind speed of the middle layer was 20.83 m/s, and the average wind speed of the lower layer was 28.27 m/s, which met the end speed principle. The wind field was distributed according to the shape of the fruit tree canopy. The wind field of the left and right sides of the sprayer was symmetrical distributed and the air distribution was uniform. The work can provide a reference for the design of multi-duct sprayer.
The orchard in the mountainous area is rugged and steep, and there is no road for large-scale plant protection machinery traveling in the orchard, so it is difficult for mobile spraying machinery to enter. In order to solve the above problems, the automatic pipeline spraying technology and facilities were studied. A pipeline automatic spraying facility suitable for mountainous orchards was designed, which included spraying head, field spraying pipeline, automatic spraying controller and spraying groups. The spraying head was composed of a spraying unit and a constant pressure control system, which pressurized the pesticide liquid and stabilized the liquid pressure according to the preset pressure value to ensure a better atomization effect. Field spraying pipeline consisted of main pipeline, valves and spraying groups. In order to perform automatic spraying, a solenoid valve was installed between the main pipeline and each spraying group, and the automatic spraying operation of each spraying group was controlled automatically by the opening or closing of the solenoid valve. An automatic spraying controller composed of main controller, solenoid valve driving circuit, solenoid valve controlling node and power supplying unit was developed, and the controlling software was also programmed in this research. The main controller had manual and automatic two working modes. The solenoid valve controlling node was used to send wireless signals to the main controller and receive wireless signals from the main controller, and open or close the corresponding solenoid valve according to the received control signal. During the spraying operation, the pesticide liquid flowed into the orchard from the spray head through the pipeline. The automatic spray controller was used to control the solenoid valve to open or close the spray group one by one, and implement manual control or automatic control of spraying. In order to determine the continuous opening time of the solenoid valve, an effectiveness of the spray test was carried out. The spraying test results showed that spraying effectiveness could be guaranteed by opening solenoid valve for 8 s continuously. The efficiency of this pipeline automatic spraying facility was 2.61 hm2/h, which was 45－150 times that of manual spraying, and 2.1 times that of unmanned aerial vehicle spraying. The automatic pipeline spraying technology in mountainous orchards had obvious advantages in the timeliness of pest controlling. This research can provide references and ideas for the development of spray technology and intelligent spraying facilities in mountainous orchards.
The rapid increasing of apple planting area on the Loess Plateau has exerted an important influence on the regional eco-hydrology and socio-economic development. However, the orchards in this area are small and complex, and there are only county or city scale statistical data, lack of actual spatial distribution information. To this end, for the extraction of apple orchards on the Loess Plateau, in this study, a professional dataset of low-altitude remote sensing images acquired by unmanned aerial vehicle was firstly established. The R_34_Linknet network and other five commonly used deep learning semantic segmentation models SegNet, FCN_8s, DeeplabV3+, UNet and Linknet were applied to the spatial distribution extraction of apple orchards on the Loess Plateau, and the best-performing model was R_34_Linknet, with a F1 score of 87.1%, a pixel accuracy (PA) of 92.3%, an mean intersection over union (MioU) of 81.2%, a frequency weighted intersection over union (FWIoU) of 85.7%, and the mean pixel accuracy (MPA) was 89.6%. The spatial pyramid pool structure (ASPP) and R_34_Linknet network was combined to expand the receptive field of the network and get R_34_Linknet_ASPP network, and then ASPP structure was improved. Combining the spatial pyramid pooling (ASPP) with the R_34_Linknet network to expand the receptive field of the network and obtain a R_34_Linknet_ASPP network; Then the ASPP structure was improved to get a R_34_Linknet_ASPP+ network. The performance of the three networks were compared. R_34_Linknet_ASPP+ got the best performance, with 86.3% for F1, 94.7% for PA, 82.7% for MIoU, 89.0% for FWIoU, and 92.3% for MPA on the test set. The accuracy of apple orchard extraction in Wangdonggou, Changwu County and Tongji Village, Baishui County using R_34_Linknet_ASPP+ were 94.22% and 95.66%, respectively. In Wangdonggou, it was 1.21% and 0.58% higher than R_34_Linknet and R_34_Linknet_ASPP, respectively. In Tongji village, it was 1.70% and 0.90% higher than R_34_Linknet and R_34_Linknet_ASPP, respectively. The results show that the proposed R_34_Linknet_ASPP+ method can extract apple orchards accurately, the edge treatment of apple orchard plots is better, the method can be used as the technical support and theoretical basis for research on the spatial distribution mapping of apple orchards on the Loess Plateau.
Mechanized and intelligent flower thinning is a high-speed flower thinning method nowadays. The classification and detection of flowers and flower buds are the basic requirements to ensure the normal operation of the flower thinning machine. Aiming at the problems of pear inflorescence detection and classification in the current intelligent production of pear orchards, a Y-shaped shed pear orchard inflorescence recognition algorithm Ghost-YOLOv5s-BiFPN based on improved YOLOv5s was proposed in this research. The detection model was obtained by labeling and expanding the pear tree bud and flower images collected in the field and sending them to the algorithm for training. The Ghost-YOLOv5s-BiFPN algorithm used the weighted bidirectional feature pyramid network to replace the original path aggregation network structure, and effectively fuse the features of different sizes. At the same time, ghost module was used to replace the traditional convolution, so as to reduce the amount of model parameters and improve the operation efficiency of the equipment without reducing the accuracy. The field experiment results showed that the detection accuracy of the Ghost-YOLOv5s-BiFPN algorithm for the bud and flower in the pear inflorescence were 93.21% and 89.43%, respectively, with an average accuracy of 91.32%, and the detection time of a single image was 29 ms. Compared with the original YOLOv5s algorithm, the detection accuracy was improved by 4.18%, and the detection time and model parameters were reduced by 9 ms and 46.63% respectively. Compared with the original YOLOV5s network, the mAP and recall rate were improved by 4.2% and 2.7%, respectively; the number of parameters, model size and floating point operations were reduced by 46.6%, 44.4% and 47.5% respectively, and the average detection time was shortened by 9 ms. With Ghost convolution and BIFPN adding model, the detection accuracy has been improved to a certain extent, and the model has been greatly lightweight, effectively improving the detect efficiency. From the thermodynamic diagram results, it can be seen that BIFPN structure effectively enhances the representation ability of features, making the model more effective in focusing on the corresponding features of the target. The results showed that the algorithm can meet the requirements of accurate identification and classification of pear buds and flowers, and provide technical support for the follow-up pear garden to achieve intelligent flower thinning.
Dragon fruit detection in natural environment is the prerequisite for fruit harvesting robots to perform harvesting. In order to improve the harvesting efficiency, by improving YOLOX (You Only Look Once X) network, a target detection network with an attention module was proposed in this research. As the benchmark, YOLOX-Nano network was chose to facilitate deployment on embedded devices, and the convolutional block attention module (CBAM) was added to the backbone feature extraction network of YOLOX-Nano, which improved the robustness of the model to dragon fruit target detection to a certain extent. The correlation of features between different channels was learned by weight allocation coefficients of features of different scales, which were extracted for the backbone network. Moreover, the transmission of deep information of network structure was strengthened, which aimed at reducing the interference of dragon fruit recognition in the natural environment as well as improving the accuracy and speed of detection significantly. The performance evaluation and comparison test of the method were carried out. The results showed that, after training, the dragon fruit target detection network got an AP0.5 value of 98.9% in the test set, an AP0.5:0.95 value of 72.4% and F1 score was 0.99. Compared with other YOLO network models under the same experimental conditions, on the one hand, the improved YOLOX-Nano network model proposed in this research was more lightweight, on the other hand, the detection accuracy of this method surpassed that of YOLOv3, YOLOv4 and YOLOv5 respectively. The average detection accuracy of the improved YOLOX-Nano target detection network was the highest, reaching 98.9%, 26.2% higher than YOLOv3, 9.8% points higher than YOLOv4-Tiny, and 7.9% points higher than YOLOv5-S. Finally, real-time tests were performed on videos with different input resolutions. The improved YOLOX-Nano target detection network proposed in this research had an average detection time of 21.72 ms for a single image. In terms of the size of the network model was only 3.76 MB, which was convenient for deployment on embedded devices. In conclusion, not only did the improved YOLOX-Nano target detection network model accurately detect dragon fruit under different lighting and occlusion conditions, but the detection speed and detection accuracy showed in this research could able to meet the requirements of dragon fruit harvesting in natural environment requirements at the same time, which could provide some guidance for the design of the dragon fruit harvesting robot.
The detecting and grading of the internal quality of apples is an effective means to increase the added value of apples, protect the health of residents, meet consumer demand and improve market competitiveness. Therefore, an apple internal quality detecting module and a grading module were developed in this research to constitute a movable apple internal quality orchard origin grading system, which could realize the detection of apple sugar content and apple moldy core in orchard origin and grading according to the set grading standard. Based on this system, a multiplicative effect elimination (MEE) based spectral correction method was proposed to eliminate the multiplicative effect caused by the differences in physical properties of apples and improve the internal quality detection accuracy. The method assumed that the multiplication coefficient in the spectrum was closely related to the spectral data at a certain wavelength, and divided the original spectrum by the data at this wavelength point to achieve the elimination of the multiplicative scattering effect of the spectrum. It also combined the idea of least-squares loss function to set the loss function to solve for the optimal multiplication coefficient point. To verify the validity of the method, after pre-processing the apple spectra with multiple scattering correction (MSC), standard normal variate transform (SNV), and MEE algorithms, the partial least squares regression (PLSR) prediction models for apple sugar content and partial least squares-discriminant analysis (PLS-DA) models for apple moldy core were developed, respectively. The results showed that the MEE algorithm had the best results compared to the MSC and SNV algorithms. The correlation coefficient of correction set (Rc), root mean square error of correction set (RMSEC), the correlation coefficient of prediction set (Rp), and root mean square error of prediction set (RMSEP) for sugar content were 0.959, 0.430%, 0.929, and 0.592%, respectively; the sensitivity, specificity, and accuracy of correction set and prediction set for moldy core were 98.33%, 96.67%, 97.50%, 100.00%, 90.00%, and 95.00%, respectively. The best prediction model established was imported into the system for grading tests, and the results showed that the grading correct rate of the system was 90.00% and the grading speed was 3 pcs/s. In summary, the proposed spectral correction method is more suitable for apple transmission spectral correction. The mobile orchard local grading system of apple internal quality combined with the proposed spectral correction method can accurately detect apple sugar content and apple moldy core. The system meets the demand for internal quality detecting and grading of apples in orchard production areas.
Phytophthora strawberries, as a kind of plant pathogenic fungi, can cause strawberry skin and crown rot without safe and effective treatment, which affect the economic benefits of planting strawberries. Therefore, it is urgent to use low-cost diagnostic methods to achieve early prevention. Strawberry plants infected with Phytophthora cactorum would release a unique organic volatile gas, 4-ethylphenol, with a concentration ranging from 1.12 to 22.56 mg/kg, which could be used as a marker gas for rapid diagnosis of the disease. In this study, semiconducting single-walled carbon nanotubes (SWNT) and field effect sensors (FET) were used to prepare semiconductor field effect gas sensors (SWNT/FET) without selectivity. And then the metal porphyrin MnOEP with high sensitivity and selectivity to 4-ethylphenol was immoblized on the SWNT's surface to obtain MnOEP-SWNT/FET. MnOEP-SWNT/FET has the advantages of low cost, low power consumption, small size, high sensitivity and easy integration, which can effectively overcome the shortcomings of gas chromatography-mass spectrometry, high-performance liquid chromatography and other analytical methods. By comparing the sensitivity and selectivity of different sensors, MnOEP-SWNT/FET is very suitable for real-time monitoring of 4-ethylphenol. The key reasons for the high sensitivity and selectivity are: MnOEP is a macromolecular heterocyclic compound formed by four pyrrole rings connected together by methylene and manganese ion(Mn), each pyrrole ring consists of four carbons and one nitrogen, and all nitrogen atoms inside the ring form a central cavity; the coordination metal ions of MnOEP are in an unsaturated state, gas molecules can interact with the central metal ions through van der Waals force and hydrogen bond at the axial position of MnOEP to change their own optical or electrical properties. MnOEP-SWNT/FET was studied by Raman spectrum, UV spectrum and voltammetry. The physical and chemical properties were analyzed and the detection conditions were optimized to improve the gas sensitivity of MnOEP-SWNT/FET to 4-ethylphenol. Under the optimal detection conditions, MnOEP-SWNT/FET has a good linear relationship with 0.25% ~100% saturated vapor of 4-ethylphenol (20 ℃) and the detection limit is 0.15% saturated vapor of 4-ethylphenol. The relative standard error of different concentrations was less than 10%. By measuring the actual samples, it has high detection accuracy for strawberry plants infected with Phytophthora, but it still exists false positive for healthy strawberry.
