Plant height is a key indicator to dynamically measure crop health and overall growth status, which is widely used to estimate the biological yield and final grain yield of crops. The traditional manual measurement method is subjective, inefficient, and time-consuming. And the plant height obtained by sampling cannot evaluate the height of the whole field. In the last decade, remote sensing technology has developed rapidly in agriculture, which makes it possible to collect crop height information with high accuracy, high frequency, and high efficiency. This paper firstly reviewed the literature on obtaining plant height by using remote sensing technology for understanding the research progress of height estimation in the field. Unmanned aerial vehicle (UAV) platform with visible-light camera and light detection and ranging (LiDAR) were the most frequently used methods. And main research crops included wheat, corn, rice, and other staple food crops. Moreover, crop height measurement was mainly based on near-field remote sensing platforms such as ground, UAV, and airborne. Secondly, the basic principles, advantages, and limitations of different platforms and sensors for obtaining plant height were analyzed. The altimetry process and the key techniques of LiDAR and visible-light camera were discussed emphatically, which included extraction of crop canopy and soil elevation information, and feature matching of the imaging method. Then, the applications using plant height data, including the inversion of biomass, lodging identification, yield prediction, and breeding of crops were summarized. However, the commonly used empirical model has some problems such large measured data, unclear physical significance, and poor universality. Finally, the problems and challenges of near-field remote sensing technology in plant height acquisition were proposed. Selecting appropriate data to meet the needs of cost and accuracy, improving the measurement accuracy, and matching the plant height estimation of remote sensing with the agricultural application need to be considered. In addition, we prospected the future development was prospected from four aspects of 1) platform and sensor, 2) bare soil detection and interpolation algorithm, 3) plant height application research, and 4) the measurement difference of plant height between agronomy and remote sensing, which can provide references for future research and method application of near-field remote sensing height measurement.
Plant micro-phenotype mainly refers to the phenotypic information at the tissue, cell, and subcellular levels, which is an important part of plant phenomics research. In view of the problems of low efficiency, large error, and few traits of traditional methods for detecting kernel microscopic traits, Micro-CT scanning technology was used to carry out precise identification of micro-phenotype on 11 varieties of maize kernels. A total of 34 microscopic traits were obtained based on CT sequence images of 7 tissues, including seed, embryo, endosperm, cavity, subcutaneous cavity, endosperm cavity and embryo cavity. Among the 34 microscopic traits, 4 traits, including endosperm cavity surface area, kernel volume, endosperm volume ratio and endosperm cavity specific surface area, were significantly different among maize types (P-value<0.05). The surface area of endosperm cavity and kernel volume of common maize were significantly higher than those of other types of maize. The specific surface area of endosperm cavity of high oil maize was the largest. The endosperm cavity of sweet corn had the smallest specific surface area. The endosperm volume ration of popcorn was the largest. Furthermore, 34 traits were used for One-way ANOVA and cluster analysis, and 11 different maize varieties were divided into four categories, of which the first category was mainly common maize, the second category was mainly popcorn, the third category was sweet corn, and the fourth category was high oil maize. The results indicated that Micro-CT scanning technology could not only achieve precise identification of micro-phenotype of maize kernels, but also provide supports for kernel classification and variety detection, and so on.
In order to assess maize growth status accurately and quickly for improving maize precise management, field experiment was conducted in Gongzhuling research station, Jilin Academy of Agricultural Sciences, Jilin province. Experimental design included 3 planting densities and 5 maize materials. The near-ground hyperspectral data and the unmanned aerial vehicle (UAV) hyperspectral images were obtained when maize were during V11－V12 stage. The application abilities of the hyperspectral data obtained from the two phenotyping platforms were compared and analyzed in the estimation of maize leaf area index (LAI) and aboveground biomass. In this study, 21 commonly used spectral vegetation indices were constructed based on ground hyperspectral data, and then the estimation models of maize LAI and aboveground biomass were established based on ground hyperspectral full-bands, UAV hyperspectral full-bands and vegetation indices and partial least square regression method, respectively. According to the variance estimation of regression coefficients, the important bands of LAI and aboveground biomass were selected, and the partial least square method was also used to establish the estimation model of maize LAI and aboveground biomass based on important bands. The results showed that the canopy spectral reflectance of the same maize material increased with the increase of planting density in the near infrared bands. Among the 5 maize materials under the same planting density, the canopy spectral reflectance of wild type material was the lowest in the visible and near infrared bands. For LAI, the model constructed based on vegetation indices had the best estimation result, with R2, RMSE and rRMSE values of 0.70, 0.92 and 15.94%. For aboveground biomass, the model constructed based on the sensitive spectral bands (839－893 nm and 1336－1348 nm) had the best estimation results, with R2, RMSE and rRMSE values of 0.71, 12.31 g and 15.89%, which showed that there was information redundancy in hyperspectral bands in the estimation of aboveground biomass, and the estimation accuracy could be improved by reducing the number of spectral bands and selecting sensitive spectral bands. In summary, the UAV hyperspectral images have a good application ability in the estimation of maize LAI and aboveground biomass, and can quickly and effectively extract the parameters information of maize growth. For specific parameters, sensitive spectral bands selected can provide reliable basis for the development and practical application of multi-spectrum in the future. The study can provide a reference for the use of hyperspectral technology in the management of precision agriculture at the community scale.
Accurate acquisition of crop canopy biochemical information is of great significance for monitoring crop growth and guiding precise fertilization. Previous vertical distribution researches of crop biochemical information were mainly based on hyperspectral inversion, which was lack of the association of plant photosynthesis physiology. This study mainly investigated the vertical distribution characteristics of biochemical parameters such as chlorophyll, carotenoid, dry matter, and water content in the oilseed rape canopy under different nitrogen treatments at the mid-seedling stage. The photosynthetic performance of leaves was measured by using fast chlorophyll fluorescence technology, and linear regression and principal component analysis were further implemented to explore the internal relationship between fluorescence response and biochemical parameters. The results showed that: (1) The chlorophyll content, carotenoid content, dry matter and water content of the rape canopy at the mid-seedling stage all showed a parabolic vertical distribution, while the ratio of chlorophyll to carotenoids content gradually decreases with the leaf position and nitrogen treatments, which was the same as the vertical distribution pattern of fluorescence parameters such as driving force comprehensive performance (DFTotal) and end electron chain quantum yield (φRo) and other fluorescence parameters could be used to diagnose nitrogen stress; (2) JIP-test parameters, especially DFTotal, had a good performance to evaluate the chlorophyll/carotenoids, chlorophyll and dry matter content of oilseed rape leaves; (3) Nitrogen deficiency would weaken the PSII and PSI performance of oilseed rape leaves at the mid-seedling stage, and the maximum photochemical efficiency (φPo) could be used to diagnose nitrogen stress. There was a significant difference in the PSI performance, namely electron transfer efficiency at the end acceptors of leaves in the different leaf position, hence the comprehensive performance parameter DFTotal could be an effective characterization of the vertical heterogeneity of canopy biochemical parameters. These findings indicated the feasibility of applying the rapid chlorophyll fluorescence technology to crop biochemical information heterogeneity monitoring and provided new ideas and technical support for guiding precise fertilization and achieving high-quality and high-yield.
To cope with the challenges posed by the rapid growth of world population and global environmental changes, scholars should employ genetic and phenotypic analyses to breed crop varieties with improved responses to limited resource environments and soil conditions to increase crop yield and quality. Therefore, the efficient, accurate, and non-destructive measurement of crop phenotypic traits, and the dynamic quantification of phenotypic traits are urgently needed for crop phenotypic research, and breeding as well as for modern agricultural development. In this study, cotton plants were taken as research objects, and the multi-temporal point cloud data of cotton plants were collected by using three-dimensional laser scanning technology. The multi-temporal point clouds of three cotton plants at four time points were collected. First, RANSAC algorithm was implemented for main stem extraction on the original point cloud data of cotton plants, then region growing based clustering was carried out for leaf segmentation. Plant height was estimated by calculating the end points of the segmented main stem. Leaf length and width measurements were conducted on the segmented leaf parts. In addition, the volume was also estimated through the convex hull of the original point cloud of plant cotton. Then, multi-temporal point clouds of plants were registered, and organ correspondence was constructed with the Hungarian method. Finally, dynamic quantification of phenotypic traits including plant volume, plant height, leaf length, leaf width, and leaf area were calculated and analyzed. The overall performance of the approaches achieved a matching rate through a series of experiments, and the traits extracted by using of point cloud showed high correlation with the manually measured ones. The relative error between plant height and manual measurement results did not exceed 1.0%. The estimated leaf length and width on point clouds were highly correlated with the manually measured ones, and the coefficient of determination was nearly 1.0. The proposed 3D phenotyping methodology can be introduced and used to other crops for phenotyping.
谷穗的检测和计数对于预测谷子产量和育种至关重要。但是，传统的谷穗计数主要基于人工统计，既费时又费力。为解决上述问题，本研究首先建立了一个包含784张图像和10,000个谷穗样本的谷穗检测数据集。提出了一种基于YOLOv4和自适应锚框调整的谷穗检测方法，可快速准确地检测特定框中的谷穗。通过自适应地调整锚框，可生成符合谷穗目标的候选框，从而提升检测的准确率。为验证该方法的有效性，采用了多个标准，包括平均精度（mAP），F1得分（F1-Score），精度（Precision）和召回率（Recall）进行评价。此外，设计了对比试验验证所提出方法的有效性，包括与其他模型（YOLOv2，YOLOv3和Faster-RCNN）进行比较来评估模型的性能，评估模型在不同交并比（IOU）取值下的性能，评估模型在自适应锚框调整下的谷穗检测性能，评估引起模型评价标准变化的原因，以及评估模型在不同原始输入图像尺寸下的性能。试验结果表明，YOLOv4获得了良好的谷穗检测性能。YOLOv4的mAP达到78.99%，F1-score达到83.00%，Precision达到87%和Recall达到79.00%，在所有评价标准上均比其他比较模型高出8%。试验结果表明，该方法具有较好的准确性和高效性。
Accurate and high-throughput maize plant phenotyping is vital for crop breeding and cultivation research. Tassel-related phenotypic parameters are important agronomic traits. However, fully automatic and fine tassel organ segmentation of maize shoots from three-dimensional (3D) point clouds is still challenging. To address this issue, a tassel point cloud segmentation method based on point cloud super voxels clustering and local geometric features was proposed in this study. Firstly, the undirected graph of the maize plant point cloud was established, the edge weights were calculated by using the difference of normal vectors, and the spectral clustering method was used to cluster the point cloud to form multiple super voxel sub-regions. Then, the principal component analysis method was used to find the two end regions of the plant and based on the observation of the straight direction of the bottom stem regions, the top and bottom regions were distinguished by the point cloud linear features. Finally, the tassel points were identified based on the plane local features of the point cloud. The sub-regions of the top region of the plant were classified into leaf regions, tassel regions, and mixed regions by plane local features of the point cloud, the tassel points in the tassel sub-region, and the mixed region were the finally segmented tassel point clouds. In this study, 15 mature maize plants with 3 point cloud densities were tested. Compared with the ground truth segmented manually, the average F1 scores of the tassel segmentation were 0.763, 0.875 and 0.889 when the point cloud density was 0.8/cm, 1.3/cm, and 1.9/cm, respectively. The segmentation accuracy of this method increased with the increase of plant point cloud density. The increase of point cloud density and the number of point clouds mainly affected the calculation results of point cloud plane features in tassel segmentation. When the number of point clouds was small, the top leaf point cloud was relatively sparse. Therefore, the difference between the plane feature of the leaf point and the plane feature of the tassel point was not obvious, which led to the increase of the misclassification of the point cloud. However, the time complexity of the algorithm was O(n3), so the increase in the density and number of point clouds would lead to a significant increase in the running time. Considering the segmentation accuracy and running time, the research obtained the best effect on the mature maize plants with a point cloud density of 1.3/cm and an average number of 15,000. The segmentation F1 score reached 0.875 and the running time was 6.85 s. The results showed that this method could extract tassels from maize plant point cloud, and provided technical support for the research and application of high-throughput phenotyping and three-dimensional reconstruction of maize.
Automatic grading method of pomelo fruit according to the shape and size is urgently needed in the industry since the work mainly depends on artificial judgment currently. In this research, a method, which detected the vertical and horizontal size of pomelo by using contour coordinate transformation fitting, fruit shape feature extraction and direction angle compensation algorithm, while it determined the shape defects based on fruit shape index, was proposed. The image acquisition system was self-designed and built up with a CMOS camera, a dot matrix LED light source, a plane mirror, the computer, a box and brackets. The image data containing whole surface information of Shatian pomelo samples with different sizes and shapes were collected by this system. The G-B component grayscale image was chosen for denoising and segmentation. The Laplacian edge detection algorithm was implemented to extract the edge pixels of the fruit. The polynomial fitting method was applied to converse the rectangular coordinates to polar coordinates so that the fruit shape description was simplified. The characteristic point polar angle value was used to compensate the random direction of the vertical and horizontal diameters of the sample. Then the vertical and horizontal diameters of fruit were calculated after classifying the sample shapes into the spherical and the pear-like categories. For the involved 168 pomelo samples, the average error, maximum absolute error and average relative error of the vertical diameters were 2.23 mm, 7.39 mm and 1.6% respectively, while these parameters of the horizontal diameters were 2.21 mm, 7.66 mm and 1.4% respectively. The fruit shape discriminant model was established by using BP neural network algorithm based on the seven features extracted from the fitting function and verified by independent validation set including 3 peak heights, 3 peak widths and 1 trough value difference. The total recognition rate of shape identification was 83.7%. The results illustrated that the method had the potential to measuring the pomelo size and shape for grading fast and non-destructively.
To satisfy the communication needs of multiple robots working in orchards, an improved Ad Hoc on-demand distance vector routing protocol based on signal strength threshold and priority nodes (AODV-SP), and the prediction model of Wi-Fi signal reception in peach orchards, was proposed in this study. Different from the traditional AODV protocol, AODV-SP utilizes the idea of priority nodes and strength thresholds to construct a discovery routing algorithm and a selection routing algorithm by seeking priority nodes and calculating the maximum strength threshold between nodes, respectively. The discovery routing message and selection routing message of the AODV-SP protocol were designed according to the discovery routing and selection routing algorithms. To verify the performance of the AODV-SP protocol, the performance of the protocol with different maximum movement speeds of nodes was analyzed by using NS2 simulation software and the performance was compared with the traditional AODV protocol. The simulation results showed that the average end-to-end delay, route initiation frequency, and route overhead of AODV-SP protocol with the introduction of priority node and path signal strength thresholds were smaller than those of the traditional AODV protocol, and the packet delivery rate improved significantly compared with that of AODV protocol. Among them, when the maximum node movement speed was 5 m/s, the route initiation frequency and route overhead of AODV-SP protocol reduced by 3.65% and 7.09%, respectively, compared with AODV protocol. When the maximum node movement speed was 8 m/s, the packet delivery rate of AODV-SP protocol improved by 0.59% and the average end-to-end delay reduced by 13.09%. To further verify the simulation results of AODV-SP making AODV-SP protocol applicable to a multi-robot wireless communication system and ensure the normal operation of multi-robot wireless communication in orchards, a physical platform for multi-robot wireless communication was built in a laboratory environment, and software was designed to enable the physical platform to communicate properly under the AODV-SP protocol. And the physical platform for multi-robot wireless communication using the AODV-SP protocol was tested under static and dynamic conditions, respectively. The experiment results showed that, under static condition, when distance between nodes was less than or equal to 25 m, the packet loss rate of the robot was 0; when distance between nodes was 100 m, tthe packet loss rate of the robot was 21.01%, and the following robots could maintain the chain topology with the leader robot in dynamic conditions. Simulation and physical platform experiments results showed that the AODV-SP protocol could be used for the construction of multi-robot communication systems in orchard.
The development of convolutional neural networks(CNN) has brought a large number of network parameters and huge model volumes, which greatly limites the application on devices with small computing resources, such as single-chip microcomputers and mobile devices. In order to solve the problem, a structured model compression method was studied in this research. Its core idea was using knowledge distillation to transfer the knowledge from the complex integrated model to a lightweight small-scale neural network. Firstly, VGG16 was used to train a teacher model with a higher recognition rate, whose volume was much larger than the student model. Then the knowledge in the model was transfered to MobileNet by using distillation. The parameters number of the VGG16 model was greatly reduced. The knowledge-distilled model was named Distilled-MobileNet, and was applied to the classification task of 38 common diseases (powdery mildew, Huanglong disease, etc.) of 14 crops (soybean, cucumber, tomato, etc.). The performance test of knowledge distillation on four different network structures of VGG16, AlexNet, GoogleNet, and ResNet showed that when VGG16 was used as a teacher model, the accuracy of the model was improved to 97.54%. Using single disease recognition rate, average accuracy rate, model memory and average recognition time as 4 indicators to evaluate the accuracy of the trained Distilled-MobileNet model in a real environment, the results showed that, the average accuracy of the model reached 97.62%, and the average recognition time was shortened to 0.218 s, only accounts for 13.20% of the VGG16 model, and the model size was reduced to only 19.83 MB, which was 93.60% smaller than VGG16. Compared with traditional neural networks, distilled-mobile model has a significant improvement in reducing size and shorting recognition time, and can provide a new idea for disease recognition on devices with limited memory and computing resources.
With the development of smart agriculture, automatic question and answer (Q&A) of agricultural knowledge is needed to improve the efficiency of agricultural information acquisition. Agriculture named entity recognition plays a key role in automatic Q&A system, which helps obtaining information, understanding agriculture questions and providing answer from the knowledge graph. Due to the scarcity of labeled ANE data, some existing open agricultural entity recognition models rely on manual features, can reduce the accuracy of entity recognition. In this work, an approach of model distillation was proposed to recognize agricultural named entity data. Firstly, massive agriculture data were leveraged from Internet, an agriculture knowledge graph (AgriKG) was constructed. To overcome the scarcity of labeled named agricultural entity data, weakly named entity recognition label on agricultural texts crawled from the Internet was built with the help of AgriKG. The approach was derived from distant supervision, which was used to solve the scarcity of labeled relation extraction data. Considering the lack of labeled data, pretraining language model was introduced, which is fine tuned with existing labeled data. Secondly, large scale pretraining language model, BERT was used for agriculture named entity recognition and provided a pretty well initial parameters containing a lot of basic language knowledge. Considering that the task of agriculture named entity recognition relied heavily on low-end semantic features but slightly on high-end semantic features, an Attention-based Layer Aggregation mechanism for BERT(BERT-ALA) was designed in this research. The aim of BERT-ALA was to adaptively aggregate the output of multiple hidden layers of BERT. Based on BERT-ALA model, Bidirectional LSTM (BiLSTM) and conditional random field (CRF) were coupled to further improve the recognition precision, giving a BERT-ALA+BiLSTM+CRF model. Bi-LSTM improved BERT's insufficient learning ability of the relative position feature, while conditional random field models the dependencies of entity recognition label. Thirdly, since BERT-ALA+BiLSTM+CRF model was difficult to serve online because of the extremely high time and space complexity, BiLSTM+CRF model was used as student model to distill BERT-ALA+BiLSTM+CRF model. It fitted the BERT-ALA+BiLSTM+CRF model's output of BiLSTM layer and CRF layer. The experiment on the database constructed in the research, as well as two open datasets showed that (1) the macro-F1 of the BERT-ALA + BiLSTM + CRF model was improved by 1% compared to the baseline model BERT + BiLSTM + CRF, and (2) compared with the model trained on the original data, the macro-F1 of the distilled student model BiLSTM + CRF was increased by an average of 3.3%, the prediction time was reduced by 33%, and the storage space was reduced by 98%. The experimental results verify the effectiveness of the BERT-ALA and knowledge distillation in agricultural entity recognition.
Solar insecticidal lamps (SILs) are widely used in agriculture for the purpose of effectively controlling pests and reducing pesticide dosage. With the increasing deployment of SILs, there are more and more reports about theft and destruction of SILs, seriously affecting the pest control effect and leading to great economic losses. Unfortunately, many efforts remain unsuccessful, since people can destruct the components of SIL in part but not steal the whole SIL, which cannot be detected by GPRS module or can only be labeled as a fault of component. To realize the broader effect of anti-theft and anti-destruction in the scenario of Solar Insecticidal Lamps Internet of Things (SIL-IoTs), there were two types of designs which would enable substantial improvements. On one hand, SIL was reformed and designed to obtain more information from different kinds of sensors and increase the difficulty of theft and destruction of SILs. Four modules were equipped including gated switch, voltage and current module, emergency power module, acceleration sensor module. Gated switch was used to judge whether the gate of power was open or closed. Voltage and current module of battery, solar panel, lamp, and metal mesh were used to judge whether the components were stolen or destructed. Emergency power module was used for communication module after the battery being stolen. Acceleration sensor module was used to judge whether the SIL was shaking by stealer. On the other hand, the auxiliary equipment of SIL, i.e., unmanned aerial vehicle insecticidal lamp (UAV-IL), was put forward for emergency applications after theft and destruction of SIL, e.g., deployment, tracking, patrol inspection, and so on. Through the above-mentioned hardware design and application of UAV-IL, more information from different kinds of sensors could be obtained to make judgements about the situation of theft and destruction. However, considering the short occurrence time of theft and destruction, the design was not enough to realize fast and accurate judgments. Therefore, six key research issues in the design of internal hardware, software algorithm and appearance structure design level were discussed, including 1) optimal design of anti-theft and anti-destruction of SILs; 2) establishment of anti-theft and anti-destruction judgment rules of SILs; 3) fast and accurate judgments of theft and destruction of SILs; 4) emergency measures after theft and destruction of SILs; and 5) prediction and prevention of theft and destruction of SILs; 6) optimal calculation to reduce the load of network data transmission. The anti-theft and anti-destruction have crucial roles in equipment safety, which can be extended to various agricultural applications.
Agricultural drought is a major factor that affects agricultural production. Traditional agricultural drought monitoring is mainly based on meteorological and hydrological data, and although it can provide more accurate drought monitoring results at the point level, there are still limitations in monitoring agricultural drought at the regional scale. The rapid development of remote sensing technology has provided a new mean of monitoring agricultural droughts at the regional scale, especially since the electromagnetic wavelengths sensed by satellite sensors in orbit now cover visible, near-infrared, thermal infrared and microwave wavelengths. It is important to make full use of the rich surface information obtained from satellite remote sensing data for agricultural drought monitoring and forecasting. This paper described the research progress of agricultural drought monitoring based on satellite remote sensing from three aspects: remote sensing index-based method, soil water content method and crop water demand method. The research progress of agricultural drought monitoring based on remote sensing index-based method was elaborated from five aspects: vegetation drought index, temperature drought index, integrated vegetation and temperature drought index, water drought index and microwave drought index; the research progress of agricultural drought monitoring based on soil water content method was elaborated from two aspects: soil water content retrieval based on visible to thermal infrared data and soil water content retrieval based on microwave data; the research progress of agricultural drought monitoring based on crop water demand method was elaborated from two aspects: agricultural drought monitoring based on crop canopy water content retrieval method and crop growth model method. Agricultural drought forecasting is a timeline prediction based on drought monitoring. Based on the summary of the progress of drought monitoring, the research progress of agricultural drought forecasting by the drought index method and the crop growth model method was further briefly described. The existing agricultural drought monitoring methods based on satellite remote sensing were summarized, and its shortcomings were sorted out, and some prospects were put forward. In the future, different remote sensing data sources can be used to combine deep learning methods with crop growth models and based on data assimilation methods to further explore the potential of satellite remote sensing data in the monitoring of agricultural drought dynamics, which can further promote the development of smart agriculture.
With the rapid development of economy and people's living standards, people's demands for crops have changed from quantity to quality. The rise and rapid development of remote sensing technology provides an effective method for crop monitoring. Accurately predicting wheat quality before harvest is highly desirable to optimize management for farmers, grading harvest and categorized storage for the enterprise, future trading price, and policy planning. In this research, the main producing areas of winter wheat (Henan, Shandong, Hebei, Anhui and Jiangsu provinces) were chosed as the research areas, with collected 898 samples of winter wheat over growing seasons of 2008, 2009 and 2019. A Hierarchical Linear model (HLM) for estimating grain protein content (GPC) of winter wheat at heading-flowering stage was constructed to estimate the GPC of winter wheat in 2019 by using meteorological factors, remote sensing imagery and gluten type of winter wheat, where remote sensing data and gluten type were input variables at the first level of HLM and the meteorological data was used as the second level of HLM. To solve the problem of deviation in interannual and spatial expansion of GPC estimation model, maximum values of Enhanced Vegetation Index (EVI) from April to May calculated by moderate-resolution-imaging spectroradiometer were computed to represent the crop growth status and used in the GPC estimation model. Critical meteorological factors (temperature, precipitation, radiation) and their combinations for GPS estimation were compared and the best estimation model was used in this study. The results showed that the accuracy of GPC considering three meteorological factors performed higher accuracy (Calibrated set: R2 = 0.39, RMSE = 1.04%; Verification set: R2 = 0.43, RMSE = 0.94%) than the others GPC model with two meteorological factors or single meteorological factor. Therefore, three meteorological factors were used as input variables to build a winter wheat GPC forecast model for the regional winter wheat GPC forecast in this research. The GPC estimation model was applied to the GPC remote sensing estimation of the main winter wheat-producing areas, and the GPC prediction map of the main winter wheat producing areas in 2019 was obtained, which could obtain the distribution of winter wheat quality in the Huang-Huai-Hai region. The results of this study could provide data support for subsequent wheat planting regionalization to achieve green, high-yield, high-quality and efficient grain production.
Wheat lodging is a negative factor affecting yield production. Obtaining timely and accurate wheat lodging information is critical. Using unmanned aerial systems (UASs) images for wheat lodging detection is a relatively new approach, in which researchers usually apply a manual method for dataset generation consisting of plot images. Considering the manual method being inefficient, inaccurate, and subjective, this study developed a new image processing-based approach for automatically generating individual field plot datasets. Images from wheat field trials at three flight heights (15, 46, and 91 m) were collected and analyzed using machine learning (support vector machine, random forest, and K nearest neighbors) and deep learning (ResNet101, GoogLeNet, and VGG16) algorithms to test their performances on detecting levels of wheat lodging percentages: non- (0%), light (<50%), and severe (>50%) lodging. The results indicated that the images collected at 91 m (2.5 cm/pixel) flight height could yield a similar, even slightly higher, detection accuracy over the images collected at 46 m (1.2 cm/pixel) and 15 m (0.4 cm/pixel) UAS mission heights. Comparison of random forest and ResNet101 model results showed that ResNet101 resulted in more satisfactory performance (75% accuracy) with higher accuracy over random forest (71% accuracy). Thus, ResNet101 is a suitable model for wheat lodging ratio detection. This study recommends that UASs images collected at the height of about 91 m (2.5 cm/pixel resolution) coupled with ResNet101 model is a useful and efficient approach for wheat lodging ratio detection.
The frequent occurrence of waterlogging stress in winter wheat not only seriously affects regional food security and ecological security, but also threatens social and economic stability and sustainable development. In order to identify the waterlogging stress level of winter wheat, a waterlogging stress gradient pot experiment was set up in this research. Three factors were controlled: waterlogging stress level (control, slight waterlogging, severe waterlogging), stress duration (5 days, 10 days, 15 days) and wheat variety (YF4, JM31, JM38). Leaf and canopy hyperspectral data were measured by using ASD Field Spec3 and Gaiasky-mini2 imaging spectrometer, respectively. The data were collected from the first waterlogging day of winter wheat. The sunny and windless weather was selected and measured every 7 days until the wheat was mature. Combined with vegetation index, normalized mean distance and spectral derivative difference entropy, if winter wheat was under waterlogging stress was monitored and stress level was identified. The results showed that: 1) the spectral response characteristics of winter wheat under waterlogging stress changed significantly in RW, RE, NIR and 1650－1800 nm region, which may be due to the sensitivity of these regions to physiological parameters affecting the spectral response characteristics, such as pigment, nutrient, leaf internal structure, etc; 2) the simple ratio pigment index SRPI was the optimal vegetation index for identifying the waterlogging stress of winter wheat. The excellent performance of this vegetation index may come from its extreme sensitivity to the epoxidation state and photosynthetic efficiency of the xanthophyll cycle pigment; 3) the red light absorption valley (RW: 640－680 nm) region was the optimal region for identifying waterlogging stress level. In RW region, waterlogging stress level of winter wheat could be determined by the spectral derivative difference entropy at heading, flowering and filling stages. The greater the level of waterlogging stress, the greater the spectral derivative difference entropy. This may be due to the fact that the RW region was more sensitive to pigment content, and the spectral derivative difference entropy could reduce the effects of spectral noise and background. This study could provide a new method for monitoring waterlogging stress, and would have a good application prospect in the precise prevention and control of waterlogging stress. There are still shortcomings in this study, such as the difference between the pot experiment and the actual field environment, the lack of independent experimental verification, etc. Next research could add pot and field experiments, combine with cross-validation, to further verify the feasibility of this research method.
Leaf area index (LAI) is an important index to describe the growth status and canopy structure of vegetation, is of great theoretical and practical significance to quickly obtain LAI of large area vegetation and crops for ecosystem science research and agricultural & forestry production guidance. In this study, the typical tropical crop rubber tree in Hainan Island was selected as the research area, the LAI estimation model of rubber plantation based on satellite remote sensing vegetation indices was constructed, and its spatiotemporal variation was analyzed. The results showed that, compared with correlations between LAI and the indices of normalized difference vegetation index (NDVI), green NDVI (GNDVI), ratio vegetation index (RVI) and wide dynamic range vegetation index (WDRVI), correlations were higher between LAI and the indices of enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI), difference vegetation index (DVI) and modified soil adjusted vegetation index (MSAVI). Among the LAI estimation models based on different vegetation indices (linear, exponential and logarithmic models), the linear estimation model based on EVI index was the best, and its coefficient of determination (R2) was 0.69. The accuracy of LAI estimation model was high. The linear fitting R2 of observed and simulated LAI was 0.67, the root mean square error (RMSE) was 0.16, and the average relative error (RE) was -0.25%. However, there was underestimation in the middle value and overestimation in the high and low value area of LAI. The high LAI values (4.40－6.23) were mainly distributed in Danzhou and Baisha in the west of Hainan Island, the middle LAI values (3.80－4.40) were mainly distributed in Chengmai, Tunchang and Qiongzhong in the middle of Hainan Island, and the low LAI values (2.69－3.80) were mainly distributed in Ding'an, Qionghai, Wanning, Ledong and Sanya in the east and south of Hainan Island. In summary, the linear estimation model for rubber plantation LAI based on EVI index obtained high accuracy, and has good values of popularization and appliance.
Irrigation schemes determined based on statistical analysis of field trials are usually only applicable to specific soils and meteorological environments. It is difficult to quantitatively analyze the impact of irrigation strategies on the growth of jujube trees. In order to realize the quantitative analysis of the influence of temperature, light and water resources on the growth of fruit trees, WOrld FOod Studies (WOFOST) model parameters were calibrated to simulate the jujube tree growth and water migration process. Firstly, the observed data obtained from field trials in 2016 and 2017 were used to calibrate the phenology development, initialization, green leaf, CO2 assimilation, dry matter partitioning, respiration, and water use parameters of the WOFOST model. Secondly, the time series of total above-ground biomass, leaf area index (LAI) and soil moisture content in field trials were dynamically simulated, and accuracy verification and analysis were also performed. Finally, the maximum LAI, yield, actual evapotranspiration (ETa) and water use efficiency (WUE) data of 55 orchards were employed to evaluate the performance of the calibrated model at the county scale. The results showed that the coefficient of determination R2 of TAGP simulated in the field test area was between 0.92 and 0.98, and the normalized root mean square error (NRMSE) was between 8.7% and 20.5%, the R2 of simulated LAI ranged from 0.79 to 0.97, and the NRMSE ranged from 8.3% to 21.1%. The R2 of the simulated soil moisture content was between 0.29 and 0.75, and the NRMSE ranged from 4.1% and 6.1%. The model could well simulate the time series of jujube tree growth dynamics and soil moisture content changes. At the county scale, the R2 between the simulated and measured maximum LAI were 0.64 and 0.78, and the NRMSE were 13.3% and 10.7% in 2016 and 2017, respectively. The simulated yield showed R2 value of 0.48 and 0.60, and NRMSE of 12.1% and 11.9%, respectively. RMSE of the simulated versus measured ETa were 36.1 mm (7.9%) and 30.8 mm (7.4%), respectively. The model also showed high WUE simulation accuracy (10%<NRMSE<20%) with RMSE values of 0.23 and 0.28 kg/m3 in 2016 and 2017, respectively. In short, WOFOST model achieved accurate simulation of jujube tree growth and water transport at the field and county scales, which may provide new ideas for the quantitative and mechanism analysis of the coupled effects of soil, weather, irrigation management and jujube tree growth.
Climate change requires crop adaptation. Plantint at the suitable date is a key management technology to promote crop yield and address the impact of climate change. Wheat is one of the most important staple crops in China. Huang-Huai-Hai and Jiang-Huai regions are high-quality and high-quantity planting areas for wheat. To deal with the adverse effects of climate change and promote the winter wheat yield in Huang-Huai-Hai and Jiang-Huai regions, the optimum sowing date was identified by creating a wheat simulation with DSSAT CERES-Wheat model. The simulation experiment was designed with 51 management inputs of sowing date and 4 climate scenarios (RCPs) under baseline period (1985－2004) and 40 years in future for three representative stations in the study region. The optimum sowing data of winter wheat was corresponding to the simulation set with highest yield in each site. The characters of changes in climate factors during the growth period and the optimum sowing date among the different period were detected, and the yield increase planted at the optimum sowing date was quantified. The results showed that, in the future, the climate during winter wheat growth period showed a trend of warming and drying would shorten the growth period. The optimum sowing date would be postponed with the rise of temperature, and the decrease of latitude in all periods and under various climate scenarios. Relative to the baseline period, the maximum delay days of the optimal sowing date increased from north to south during 2030s, which were 5 days, 8 days and 13 days at the three representative stations, respectively. The optimum sowing times in 2050s were delayed in different degrees compared with that in 2030s. The largest postponed days at each station was at the RCP8.5 scenario in 2050s. Adopting the management of optimum planting date could mitigate climatic negative effects and was in varying degrees of yield increasing effect at three sites. The smallest increase occurred in Huang-Huai-Hai north region, while Huang-Huai-Hai south region and Jiang-Huai region had the relatively higher yield increasement about 2%－4%. Therefore, the present study demonstrated an effective management of optimum sowing date to promote winter wheat yield under climate change in Huang-Huai-Hai and Jiang-Huai regions.
Crop models involve complex plant processes, which can be built in different scales of space and time, from molecule, cell, organ, tissue, individual to stand in space and from second to year in time. Based on different research requirements, switching the model scales can make the applicability of the model more extensive and flexible. How to switch the crop model from stand level to organ level is the content of this research. The DSSAT software (stand level) and functional-structural plant model 'GreenLab' (organ level) were chosen to explore the possibility to switch the crop model from stand to organ level. The DSSAT can simulate the growth and development processes of crops in detail according to the growth period by taking the data of weather, soil, crop management, and observational data as input. The GreenLab can simulate the growth and development and their interaction of crops by considering plant structure, and the model parameters can be estimated according to the measurements. In this study, the experimental data contains two parts: the measurements of four maize cultivars with two treatments (irrigated and rainfed) in DSSAT, and the simulations including the weights of leaves, internodes and fruits per day using DSSAT based on the measurements. The simulation results of DSSAT were used to calibrate the parameters of the environmental (E), sink strength (Po), and remobilization (kb and ki) in GreenLab, and to compute the weights of leaves, internodes and fruits for each phytomer. The simulation results of the GreenLab model were compared and analyzed with the experimental data and the simulations of DSSAT. The consistency of calculation results could be an indicator to explore the method of building an interface between different-scale crop models, and to compare the characteristics of different models. The results showed that the GreenLab model could reproduce the simulation data of the DSSAT and the measurement data, including the leaf area index (LAI) and the total weight of the plants, and further could compute the biomass for each organ (leaf, internode and fruit), and the biomass distribution among organs, the biomass production (Q), the demand (D) and the ratio between Q and D during the growth. Therefore, the detailed information of organ growth and development could be reproduced and the 3D structures of plant could be given. Finally, the advantages and application fields of different-scale model integration were discussed.
At present, the dynamic detection and monitoring of maize seedling mainly rely on manual observation, which is time-consuming and laborious, and only small quadrats can be selected to estimate the overall emergence situation. In this research, two kinds of data sources, the high-time-series RGB images obtained by the plant high-throughput phenotypic platform (HTPP) and the RGB images obtained by the unmanned aerial vehicle (UAV) platform, were used to construct the image data set of maize seedling process under different light conditions. Considering the complex background and uneven illumination in the field environment, a residual unit based on the Faster R-CNN was built and ResNet50 was used as a new feature extraction network to optimize Faster R-CNN to realize the detection and counting of maize seedlings in complex field environment. Then, based on the high time series image data obtained by the HTPP, the dynamic continuous monitoring of maize seedlings of different varieties and densities was carried out, and the seedling duration and uniformity of each maize variety were evaluated and analyzed. The experimental results showed that the recognition accuracy of the proposed method was 95.67% in sunny days and 91.36% in cloudy days when it was applied to the phenotypic platform in the field. When applied to the UAV platform to monitor the emergence of maize, the recognition accuracy of sunny and cloudy days was 91.43% and 89.77% respectively. The detection accuracy of the phenotyping platform image was higher, which could meet the needs of automatic detection of maize emergence in actual application scenarios. In order to further verify the robustness and generalization of the model, HTPP was used to obtain time series data, and the dynamic emergence of maize was analyzed. The results showed that the dynamic emergence results obtained by HTPP were consistent with the manual observation results, which shows that the model proposed in this research is robust and generalizable.
Yield estimation of fruit tree is one of the important works in orchard management. In order to improve the accuracy of in-situ yield estimation of apple trees in orchard, a method for the yield estimation of single apple tree, which includes an improved YOLOv5 fruit detection network and a yield fitting network was proposed. The in-situ images of the apples without bags at different periods were acquired by using an unmanned aerial vehicle and Raspberry Pi camera, formed an image sample data set. For dealing with no attention preference and the parameter redundancy in feature extraction, the YOLOv5 network was improved by two approaches: 1) replacing the depth separable convolution, and 2) adding the attention mechanism module, so that the computation cost was decreased. Based on the improvement, the quantity of fruit was estimated and the total area of the bounding box of apples were respectively obtained as output. Then, these results were used as the input of the yield fitting network and actual yields were applied as the output to train the yield fitting network. The final model of fruit tree production estimation was obtained by combining the improved YOLOv5 network and the yield fitting network. Yield estimation experimental results showed that the improved YOLOv5 fruit detection algorithm could improve the recognition accuracy and the degree of lightweight. Compared with the previous algorithm, the detection speed of the algorithm proposed in this research was increased by up to 15.37%, while the mean of average accuracy (mAP) was raised up to 96.79%. The test results based on different data sets showed that the lighting conditions, coloring time and with white cloth in background had a certain impact on the accuracy of the algorithm. In addition, the yield fitting network performed better on predicting the yield of apple trees. The coefficients of determination in the training set and test set were respectively 0.7967 and 0.7982. The prediction accuracy of different yield samples was generally stable. Meanwhile, in terms of the with/without of white cloth in background, the range of relative error of the fruit tree yield measurement model was respectively within 7% and 13%. The yield estimation method of apple tree based on improved lightweight YOLOv5 had good accuracy and effectiveness, which could achieve yield estimation of apples in the natural environment, and would provide a technical reference for intelligent agricultural equipment in modern orchard environment.
Dissolved oxygen is an important environmental factor for prawn breeding. In order to improve the prediction accuracy of dissolved oxygen concentration in prawn pond, and solve the problem of low prediction accuracy of different frequency domain modal classification after empirical modal decomposition of nonlinear time series data when there are few training samples, an combination prediction model based on empirical mode decomposition (EMD), random forest (RF) and long short term memory neural network (LSTM) was proposed in this research. Firstly, the time series data of prawn breeding dissolved oxygen concentration were decomposed at multiple scales by EMD to obtain a set of stationary intrinsic mode function (IMF). Secondly, with fewer training samples, poor predicts effects on the low-frequency were verified component by LSTM. Then, IMF1－IMF4 were divided into high-frequency components through test results and used for LSTM model. IMF5－IMF7, Rn were divided for RF model, the EMD-RF-LSTM combination model was constructed to improve the prediction accuracy. Modeled low-frequency and high-frequency components IMF using RF and LSTM, then predictions of each component were accumulated and the prediction value of dissolved oxygen of sequence data were got. Finally, the performance of the model was compared with the limit learning machine (ELM), RF, standard LSTM, EMD-ELM and EMD-RF, EMD-LSTM, etc. In the test based on real dataset, the EMD-ELM model contrasted with ELM model, reduced the mean absolute error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) by 30.11%, 29.60% and 32.95%, respectively. The MAPE, RMSE, MAE for the proposed models were 0.0129,0.1156,0.0844, respectively. MAPE decreased by 84.07%, 57.57%, and 49.81% compared with EMD-ELM, EMD-RF and EMD-LSTM, respectively, the prediction accuracy was significantly improved. The results show that the proposed model EMD-RF-LSTM has good prediction performance and generalization ability, which is meets the actual demand of accurate prediction of dissolved oxygen concentration in prawn culture, and can provide reference for the prediction and early warning of prawn pond water quality.
With the change of global climate, extreme weather events such as drought and flood disasters occur frequently. These have a great impact on crop yields. As an important main grain producing area, the impact of drought and flood on the agricultural production of the three provinces in three northeast provinces of China cannot be ignored. Based on historic meteorological data such as daily precipitation, maximum temperature, minimum temperature, 2 m average wind speed, sunshine hours and relative humidity, etc., the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) during 1988－2017 in three northeast provinces of China were calculated with different time scales. Through comparing with characterization of drought and flood disasters in history, SPEI was chosen to judge drought and flood in the growth season of spring maize. With the purpose of evaluating the effects of drought and flood on spring maize yield, based on the distance correlation analysis method, the index of reasonable time scale and key month were selected to analyze the relationship between the index and the relative meteorological yield of spring maize. The relationship between water conditions at different growth stages and the yield was also analyzed. The results showed that: (1) both SPI and SPEI could represent the drought and flood conditions in three northeast provinces of China. Compared with SPI, SPEI had higher correlation with the drought and flood disaster rate, and SPEI was more advantageous in characterizing the drought and flood conditions in the study area; (2) relative meteorological yield was significantly correlated with drought disaster rate in all three provinces (P<0.01), and reached 0.05 significant level with flood disaster rate in Liaoning province, but not significant in Jilin and Heilongjiang province; (3) the distance correlation coefficient between SPEI3-8 and relative meteorological in Liaoning province was the largest, and that between SPEI6-8 and relative meteorological yield in Jilin and Heilongjiang province was the largest. SPEI and relative meteorological yield showed a downward parabolic trend. Overall, the impact of waterlogging on the yield in Liaoning was slightly less than that of drought, mild drought or moderate wet could lead to a decrease in yield. The impact of drought disaster in Jilin and Heilongjiang was much greater than that of flood, but severe humidity could lead to a decrease in yield. Compared with other provinces, the maize yield in Liaoning province fluctuated more sharply with the change of dry and wet; (4) in Liaoning province, maize may reach the highest yield when the jointing-heading period was close to severe wet, which was mainly affected by drought. In the late growing season, the impact of flood disasters was more severe than that of the early growing season, and both drought and flood disasters had effects on the yield. In Jilin province, the highest yield of spring maize was reached when SPEI was about 1.0 during the period of emergence-jointing and jointing-heading, and the effect of drought was more serious during the period heading-milking. The key growth periods in Heilongjiang province were mainly affected by drought, and the maximum yield was reached in the normal-wet years of emergence-jointing and jointing-heading stages, but medium-scale size or more severe floods still led to the decrease of maize yield. The high yield could be achieved in the slightly wet years in period of heading-milking stage, while the decrease could be caused by flood when it was severely wet. This research can provide a reference for estimating the impact of drought and flood disasters on spring maize and taking disaster prevention measures in three northeast provinces of China.
With the increasing application of plant protection unmanned aerial vehicle (UAV) in precision agriculture, the numerical simulation methods for the development of the downwash flow field of the plant protection UAV and the deposition and drift process of droplets affected by the downwash flow field have achieved rapid and diversified development, but the advantages, disadvantages, scope of application, and verification of each method still lack a systematic review. This article discusses the inviscid model, computational fluid dynamics model and lattice Boltzmann model (LBM) respectively. The advantage of the inviscid wake vortex model based on the vortex element method is that the calculation process is simple. Moreover, integrated with the most widely used aerial spray drift prediction software AGricultural DISPersal (AGDISP), it can be a promising way to do real-time UAV spray drift prediction. But due to lack of viscosity and turbulence models, the droplet deposition and drift simulation accuracy of inviscid model is relatively lower than other models. The computational fluid dynamics (CFD) model includes the finite volume method (FVM) and the finite difference method (FDM). The FVM in the computational fluid dynamics model has high robustness and can be applied to the simulation of various complex environments. Many commercial CFD software are based on FVM and achieved a fast development in aerial spray modeling recently. However, the FVM is greatly affected by the quality of the mesh, and its commonly used upwind style has limited accuracy (second-order accuracy). Under the same mesh density, it is easier to generate artificial dissipation when simulating the rotor tip vortex than the finite difference method. As a result, the simulated rotor tip vortex dissipation speed is much faster than the actual situation. Compared with the FVM, the structured grid used in the FDM is easier to construct a high-order precision numerical format. Which can reach 4-5 orders of accuracy, and with adaptive grid technology, FDM can simulate the evolution of rotor tip vortex with high temporal and spatial accuracy, and can reproduce the typical flow structure development process of the real rotor downwash flow field. However, it also has problems such as high grid structure requirements and excessive computing power requirements. LBM has advantages in computing three-dimensional flow field problems with complex boundary conditions and non-stationary moving objects. However, there are still shortcomings in its functional diversity and completeness. The accuracy of the numerical models mentioned above still needs field test and indoor experiment such as high-speed Particle Image Velocimetry (PIV)/ Phase Doppler Interferometry (PDI) method to verify and optimize. The authors finally pointed out the future direction of plant protection UAV application simulation and verification.
Unmanned Aerial Vehicle(UAV) application in agriculture and forestry has the unique advantages of high efficiency, water and pesticide saving, and strong adaptability to complex terrain. The application research of UAV in agriculture and forestry has shown a fast growing trend. In order to explore the research hotspots and the scientific impact of countries/regions and institutions on UAV application in agriculture and forestry, the relevant literatures in the Web of Science(WoS) core collection database (2011-2020) were collected. The bibliometrics analysis was performed on the journal articles of UAV application in agriculture and forestry based on VOSviewer, WoS analysis tools and Microsoft Excel. The analysis results showed that the number of published papers increased rapidly since 2017, the researches on UAV application in agriculture and forestry were carried out in 94 countries/regions, including1778 institutions. Due to the strong scientific research group in the application of UAV in agriculture and forestry of the United States, China and Australia, a large number of papers had been published, resulting in a great academic influence. Remote sensing was the most widely used technology field of UAV application in agriculture and forestry, mainly involving remote sensing technology, ecological environment science, image processing technology, geological science, etc. Engineering was an important technical field of UAV application in agriculture and forestry, mainly involving control technology, sensor technology and fluid computing modeling technology related to UAV aerial pesticide application.There were 1508 articles and reviews been published in 398 journals, about 1.90% of all journals included in WoS core collection database, indicating that more and more journals paid attention to the application research of UAV in agriculture and forestry. Remote Sensing sponsored by MDPI (Multidisciplinary Digital Publishing Institute) was the journal that published the most of papers, the most cited paper mainly focused on the research status of UAV system in photogrammetry and remote sensing, including sensing, navigation, positioning and general data processing, etc. In addition, the analysis of the research hotspots of UAV application in agriculture and forestry showed that UAV pesticide application, UAV remote sensing of diseases and pests, plant phenotype acquisition were the research hotspots. This study can provide references for innovation research and cooperation between research teams of UAV application in agriculture and forestry.
With the continuous increase of the spraying area, the problem of droplet drift risk in the spraying process of UAV is becoming increasingly prominent, especially the herbicide drift. In order to clarify the effect of the herbicide solution on the droplet size and the deposition and drift distribution characteristics sprayed by UAVs, the droplet sizes of 15 herbicide solutions sprayed by the centrifugal rotary atomizer nozzle installed in the plant protection UAV were measured in the laboratory, and the distribution of droplet deposition and drift in the spraying area and drift area were measured by adding a fluorescent tracer (60 g/hm2) to the tank in the field. The results showed that the herbicide solution had a significant effect on the droplet size distribution. The DV50 of all the other solutions was reduced after sprayed by the centrifugal atomizer except the Carfentrazone-ethyl water dispersible granule, and the maximum decrease ratio was 22.0%. The proportion of small droplets (V<150 μm) increased, with the maximum value of 50.8%. When the environmental crosswind speed was 3.76 m/s, the coverage and number of droplets in the spraying area were only 41.3% and 42.2% of that at 0.74 m/s, and the deposition uniformity was significantly reduced. In the drift zone, the deposition amount of droplets was under 10% of in-swath zone at the downwind of 12 m, and the deposition of all the treatments at 50 m was lower than detection limits (0.0002 μL/cm2). The drift ratio increased with the wind speed increased. When the crosswind speed reached 3.76 m/s, the drift ratio of droplets was 46.4%. Under different crosswind, 90% of the total measured spray drift were 4.8?22.4 m. By fitting the deposition in the drift zone with drift distance and crosswind speed, the downwind deposition was proportional to the crosswind speed. This study provides data support for droplet drift distance of plant protection UAV spraying in wheat fields at different wind speeds in winter and provides a basis for spray drift buffer zone, drift risk assessment, and relevant standard formulation.
To explore the effect of pesticide-adjuvants mixture on the control efficacy against Rice Chilo Suppressalis(walker). This study designed a three-factor, three-level orthogonal experiments with pesticides (10% emamectin benzoate·indoxacard SC, 5% chlorantraniliprole SC and 0.8% rotenone SC), adjuvants(organosilicon, lecithin and mineral Oil) , spray volume (21,24 and 27 L/hm2), referred to the three-factor, three-level orthogonal experimental scheme. And made the blank factor the deviation to analyze its rationality. Analysis of variance (ANOVA) statistical method was used to analyze the significance level of each factor. Duncan's new multiple range test (DMRT) method was used to analyze the order of the influence of different levels of each factor on the control efficacy against Rice Chilo Suppressalis(walker). The results showed that, under the experiment conditions of this research, the mean square value of the deviation factor was smaller than the mean square value of the pesticides, the adjuvants and the spray volume, and the deviation of the orthogonal experiment was within a reasonable range. The main order of the effect of the three factors on the control efficacy of Rice Chilo Suppressalis(walker) was: adjuvants > pesticides > spray volumn. On the 14th day after spraying, pesticides showed a significant effect on the control efficacy (P<0.05) and adjuvants showed a highly significant effect on the control efficacy (P<0.01), and spray volume showed no significant effect on the control efficacy. On the 14th day after spraying, the level 3 of the factor "pesticides" was more effective, in the order of Rotenone > Chlorantraniliprole > Emamectin Benzoate·Indoxacard. The level 1 of the factor "adjuvants" was more effective, in the order of Organosilicon > Lecithin > Mineral Oil. The level 3 of the factor "spray volume" was more effective, in the order of 27 L/hm2 > 24 L/hm2 > 21 L/hm2. Therefore, a preferred pesticide-adjuvants mixture method was 0.8% rotenone SC, organosilicon adjuvants and 27 L/hm2 of spray volume, which had a rapid and long-lasting control efficacy, and its control efficacy in the field reached 81.45% on the 14th day after spraying. Additionally, there was also a satisfactory pesticide-adjuvants mixture method that was 5% Chlorantraniliprole, organosilicon adjuvants and 24 L/hm2 of spray volume. This mixture method also performed well, achieving 79.3% control efficacy in the field on the 14th day after spraying. This study could provide a reference for the optimization of the mixture methods of solutions (pesticides, adjuvants and spray volume) for controlling Rice Chilo Suppressalis(walker).
Variable spray technology is an important means to improve pesticide utilization rate and save pesticide. Fruit tree is a kind of three-dimensional space, and the densities of branches and leaves in the canopy of fruit trees at different locations are different at the same time. The ideal state of spray is to adjust the amount of spray according to local characteristics, so as to realize the application of the spray on the canopy of fruit trees as required and improve the utilization rate of pesticide. In order to achieve the effect of reducing the dosage and increasing the efficiency of pesticide application, a variable spray control system was developed and the methods for computing leaf area density parameter and pulse width modulation(PWM)'s duty ratio of actuators were proposed. As the dosage parameter, the leaf area density was derived based on the point cloud density detected by LiDAR sensor on the upper computer. Then PWM's duty ratio was calculated based on the leaf area density and sent to the slave computer-PLC in real time. The communication between upper and slave computer was carried out through RS485 standard. So the spray flow of each nozzle was controlled by the switching frequency of the solenoid valve with PWM's duty ratio signal. Key parameters were obtained by the test including the net size of spray unit, delay time of the system and the function relationship between the PWM's duty ratio and the spray flow of nozzle. The test results showed that there was a linear relationship between the PWM's duty ratio and the spray flow of nozzle under the pressure of 0.2, 0.3 and 0.4 MPa, and the linear goodness of fit were all above 0.98. Finally, the effectiveness of the variable spray system was verified by the spray test. The test results showed that the minimum number of droplets per unit area (cm2) on the water-sensitive paper was 35 drops at the sampling point, which was higher than the 25 drops defined by the common method for the spray amplitude of aerosol in the air supply spray. Under 39.9% of the canopy ratio between the target canopy area and the whole area, the variable spraying mode saved 71.96% of the pesticide dosage compared with the continuous spraying mode, and 29.72% compared with the target spraying mode, achieving the dose reduction effect.
The tower-type sprayer produces swirling and irregular vertical airstream. The complex swirling results in airflow asymmetry between sides of the sprayer, and the vertical air velocity profile can be unpredictable when the rotational speed of the fan changes. The spray deposition is directly linked to the airflow pattern obtained from the sprayers. In order to study airflow field of this type of air-assisted sprayer, a CFD (Computational Fluid Dynamics) model for the tower-type sprayer was developed. A boundary condition setting method of UDF (User-Defined Function) sectional 3D air velocity was proposed. And the influences of turbulence models and the size of computational domain on CFD airflow simulation were studied. Using Fluent software, three different CFD models were established. The Model 1 took the average air velocity of 11 regions as the velocity inlet. The Model 2 used UDF segmented three-dimension air velocity line as the boundary condition. In order to further study the influence of the computational domain size on simulation, the Model 3 with a smaller computational domain was established. The turbulence model based on reynolds-averaged navier-stokes (RANS) control equation was used to calculate the airflow field in all models. In order to verify the reliability of the model, a three-dimensional measurement system of airflow field was designed, which was used for accurate and fast velocity measurement. The results showed that the Standard k-ε turbulence model, Realizable k-ε turbulence model, BSL k-w turbulence model, SST k-w turbulence model were suitable, and the Standard k-ε turbulence model was the best one. The CFD boundary condition setting method of UDF sectional three-dimension air velocity could improve the accuracy of simulation, and reduce the calculation complexity. With the same settings of other parameters, the performance of the CFD model with larger scale calculation domain was slightly better than that with smaller computational domain. The size of computational domain should be set to the appropriate extent, considering the calculation capacity and practical requirements of modelling. The research results could provide an important reference for CFD modeling of spray airflow field.
In order to solve the problems of low transmission efficiency, high carbon emissions, environmental pollution, low intelligence, and poor flexibility in traditional fuel-driven and front-wheel steering high ground clearance sprayers, a new type of high ground clearance four-wheel independent drive (4WID) sprayer which was suitable for the unmanned driving was proposed in this research. The sprayer adopted the hybrid power of fuel and battery and was steered by the 4WID driving mode of the front and rear double steering axles. For this reason, the turning radius of the proposed 4WID sprayer was small, and the running track of the front and rear wheels were uniform in height, which reduced the phenomenon of seedling crushing during field plant protection operations. Considering the slippage and sinking of the driving wheel in the extremely complex operating environment of the paddy field, based on the linear time-varying kinematics model (LTV) of the sprayer, a layered path tracking control considering the slippage of the driving wheel was constructed. The upper model predictive controller (MPC) obtained the steering angle and movement speed of the sprayer according to the expected path and the current position of the vehicle to realize path tracking. The lower layer used fuzzy control and integral separation PID control to construct a driving wheel slip controller, so as to achieve effective control of path tracking, speed, and driving wheel slip, which improved the stability and path tracking accuracy of the sprayer in a complex operating environment. The co-simulation results of Adams and Matlab showed that under complex working conditions, the slip rate of the driving wheel of the sprayer was controlled within ±20%, so as to prevent excessive slip of the driving wheel from having adverse effects on the speed and steering angle, which was conducive to the improvement of the stability of the sprayer. The sprayer could be tracked quickly and accurately the desired path, the path tracking in road conditions outside attached coefficients were 0.3 and 0.7 of the lateral deviation could be controlled within ±0.018 m. In stage C roughness 3D road conditions, the sprayer could adjust the steering angle of the front wheels in time to stabilize the body posture and the lateral deviation could be controlled within ±0.054 m. Compared with the controller that didn't consider the slip of the driving wheel, the stability and path tracking accuracy of the sprayer had been significantly improved.
Crop 3-dimensional (3D) reconstruction is one of the most fundamental techniques in crop phenomics, and is an important tool to accurately describe the holographic structure of crop morphology. 3D reconstruction models of crops are important for high-throughput crop phenotype acquisition, crop plant characteristics evaluation, and plant structure and phenotype correlation analysis. In order to promote and popularize the 3D reconstruction technology in crop phenotype research, the basic methods and application characteristics, the current advances of research and the prospects of 3D reconstruction in crops were review in this paper. Firstly, the existing methods of crop 3D reconstruction were summarized, the basic principles of each method were reviewed, the characteristics, advantages and disadvantages of each method were analyzed, the applicability of each method on the basis of the general process of crop 3D reconstruction methods were introduced, and the specific process and considerations for the implementation of each method were summarized. Secondly, the application of crop 3D reconstruction were divided into three parts: single crop reconstruction, field group reconstruction and root system, according to different target objects, and the applications of crop 3D reconstruction technology from these three perspectives were reviewed, the research advances of each method for different crop 3D reconstruction based on accuracy, speed and cost were explored, and the problems and challenges of crop 3D reconstruction in the context of different reconstruction objects were organized. Finally, the prospects of crop 3D reconstruction technology were analyzed.
Irrigation is an important factor that affects crop yield. In order to control irrigation of facility crops more effectively and accurately, this study took "Zhangji" strawberry as an example, introduced crop real-time growth characteristics into irrigation decision-making, and combined Penman-Monteith (P-M) model and knowledge reasoning to study the irrigation of strawberry. In the first step, the influencing factors and expert experience in identifying strawberry growth period of "Zhangji" strawberry irrigation were standardized, and the strawberry irrigation data structure based on Resource Description Framework (RDF) was established. The second step was to collect expert experience of strawberry irrigation according to the standardized knowledge structure model. Firstly, all data were unified into structured data, and then were stored in *.csv format together with expert experience, and strawberry irrigation knowledge map based on Neo4j was constructed. The third step was to collect the environmental data and plant data of strawberry in each growth period. The fourth step was using P-M model to calculate the initial irrigation value of strawberry, and then adjusted the initial irrigation value by knowledge reasoning.The fifth step was to conduct experimental planting and evaluate the sampled fruits. In knowledge reasoning, irrigation adjustment strategies of each expert was different. In strawberry irrigation experiment based on P-M model and path sorting algorithm, a group of irrigation reasoning values with the highest probability value were selected to adjust irrigation with the goal of maximizing strawberry yield. The experimental results showed that under the condition of harvesting at a specified time, The total fruit yield, average fruit yield per plant and average fruit weight percentage increased by 2478.5 g, 20.65 g and 12.15% (average fruit weight increased by 1.65 g per fruit) based on P-M model and path sorting algorithm compared with traditional P-M model, respectively. First, on the basis of P-M model, the yield-first irrigation adjustment strategy was adopted. Based on knowledge reasoning, the irrigation frequency and amount were adjusted timely according to the crop growth situation, which improved the yield. Second, under the condition of harvesting and recording yield at a specified time, the experiment accurately controlled the growth period to ensure early fruit ripening, while the other three groups of fruits were not fully mature and the yield of immature fruits were not calculated. Under the method of strawberry irrigation based on Penman-Monteith model and path sorting algorithm, the fruit was picked within a fixed time and reached 0.39 kg/cm2, which increased by 0.1 kg/cm2. Because the planting goal of this study was yield first, only the influence of irrigation on yield was considered. The experimental resulted show that the irrigation method based on model and knowledge reasoning could improve the yield of strawberry, and can provide a new idea for precise irrigation.
The digital elevation model (DEM) of maize population in field was constructed by using optical imaging equipment mounted on unmanned aerial vehicle (UAV) to study the accuracy difference of maize population height monitoring at different growth stages. Three cultivars and eight sowing date treatments were set up to structure maize population with different plant heights. A multi-rotor UAV with high-definition digital camera and multispectral imaging sensor was used to take RGB images and multispectral images in the experiment area on July 25th and August 27th, 2018, which were the biggest and smallest differences in plant height. The DEM data of maize population and canopy height were obtained with image pose correction, image mosaic, point cloud generation, and space reconstruction, et al. The canopy height and plant height were normalized, and the correlation between different cultivars and sowing date was analyzed based on UAV and manual plant height measurement. The feasibility of using DEM data of maize canopy to monitor the difference of plant height was clarified. The results showed that the height difference of maize population could be reflected by the digital elevation information obtained from high-definition RGB camera and multispectral camera. The plant height monitoring accuracy of HD RGB camera was higher than that of multispectral camera. However, the monitoring accuracy of plant height was not enough under the ready-made image equipment and treatment method. So, it was difficult to reflect the smaller plant height difference of maize population. Growth stage had a great influence on the monitoring of maize plant height. When the canopy of early growth stage has not completely covered the surface or the leaf yellow and withered in the later stage of growth. The plant height of the population affected by the exposed surface was seriously underestimated. In this study, the effects of UAV imaging equipment on monitoring maize plant height were analyzed. The influence factors can be used as reference for the application of the method in field production.
In view of the problems of constant speed and single carbon emission calculation method in the distribution model of fresh agricultural products in the transportation link of agricultural supply chain, combined with the time-varying characteristics of road network and the new multi vehicle carbon emission calculation method, this study put forward the distribution route optimization model of fresh agricultural products with four optimization objectives, which were the distribution distance, multi vehicle carbon emission, goods loss and vehicle fixed cost. In this model, the calculation of fuel consumption and carbon emission in the model would be affected by many factors, among which the load is the most important factor: Firstly, the average fuel consumption per 100 km of different trucks was calculated, then the CO2 emission factors of various trucks were calculated according to the carbon balance principle, and finally the average value of the results of each truck was taken as the carbon emission factor of the vehicle. According to those characteristics of the model, an improved double strategies co-evolutionary ant colony system (DC-ACS) was proposed. In this study, the main method was used to transform the problem into a solvable single objective problem. Then, the ant colony algorithm combined the coevolution mechanism, adaptive pheromone update strategy and local search mechanism were used to improve the solution effect of the algorithm. Finally, an appropriate fitness calculation method and stagnation avoidance strategy were designed to enhance the ability of the algorithm to jump out of local optimization. The C105 example of Solomon dataset was solved by using the improved ant colony algorithm. The optimal solutions on the four optimization objectives were 937.94 km, 4961.48 CNY, 4081.78 CNY and 7500.87 CNY respectively, which proved the effectiveness of the model proposed in this study. Based on the effectiveness of the model, the experimental results showed that the total distribution cost of the improved ant colony algorithm reduced by more than 14% on average compared with the basic ant colony algorithm on the four optimization objectives, which proved that the improved ant colony algorithm had more advantages. The improved ant colony algorithm was used to solve large-scale examples with different distributions: centralized, random and mixed. The optimal total costs were 19939.53 CNY, 24095 CNY and 24397.58 CNY, respectively. To sum up, the proposed model and algorithm could provide a good reference for the urban distribution path decision-making of cold chain logistics enterprises, a new idea to improve the distribution path optimization model and optimization method of smart agricultural supply chain, and a reference for enterprises to further expand their scale.
There are higher requirements for the timeliness of vegetable transportation and distribution. In order to solve the problems of long transportation time, high total transportation cost and short preservation time of vegetables during transportation, considering the constraints such as vehicle load and time window, this study proposed a genetic simulated annealing algorithm (GA-SA) for multi-objective vegetable distribution path optimization with time windows. That was, the simulated annealing algorithm (SA) adaptive (Metropolis) acceptance criterion was introduced into the operation process of genetic algorithm (GA). The basic idea was: First, the original population was selected, crossed and mutated by genetic algorithm to form a new generation of path population. At this time, by introducing metropolis acceptance criterion, and then, after modifying the sub situation of the new generation path population and selecting cross mutation, a new target path population was obtained. The improved algorithm retained the excellent individual, and the convergence speed, jumped out of the local optimal solution found based on genetic algorithm, and then found the global optimal solution. Then, the multi-objective of returning all vehicles to the distribution center after distribution was the least time-consuming, the lowest cost and the least use of vehicles was achieved, and the optimal path of vegetable transportation was obtained. Taking Baoding city in Hebei province as the distribution center and some towns under the jurisdiction of Baoding city as the distribution points, the experiment of vegetable transportation path optimization was designed. The experiments of genetic algorithm, simulated annealing algorithm and genetic simulated annealing algorithm were carried out, respectively. The comparative analysis was carried out from the aspects of convergence speed, total distance, total time, vehicles and total cost. The experimental results showed that, compared with the genetic algorithm and simulated annealing algorithm, GA-SA could effectively accelerate its convergence speed. The total cost of the optimized distribution route reduced by about 23.7% and 4% respectively, the total distance reduced by 22.6% and 3% respectively, the time consumption reduced by 26.2 and 2.6 hours respectively, and 2 and 1 vehicles were used less respectively. This study could also provide reference for the research of cold fresh food and other transportation path optimization.
The storage and processing of grain are the basis for economic and social stability and development. As a new heating treatment technology based on electromagnetic wave, radio frequency technology has the characteristics of large penetration depth, rapid heating, volumetric heating and no chemical residue. It has been widely used in post-harvest research of grain and has potential industrial application prospects in some fields. To expound the research progress of the application of radio frequency heating technology in grain storage and processing, this review briefly described the basic principle and characteristics of radio frequency heating as well as the current commercial radio frequency heating system including free oscillation type and 50 Ω type. The basic research of radio frequency heating in grain storage and processing was summarized from three aspects: Dielectric properties of grain and pests, heat resistance of stored grain pests and heating uniformity of sample. The dielectric properties refer to the interaction between materials and electromagnetic waves in an electromagnetic field and determines the absorption and conversion of electromagnetic energy. It can predict the heating characteristics of grain and provide basic data for computer simulation to optimize process during radio frequency treatment. The heat resistance data of pests are necessary for the establishment and optimization of dis-infestations technology, so the kinetic date of thermal death of common stored grain pests were reported in this review. As a main hinder in the commercial application of radio frequency treatments, the heating uniformity has significant effect on heat treatment quality and results in potential food safety problems. The major factors causing heating non-uniformity are the non-uniformity of electromagnetic field, runaway heating and the sample shape effect. The improvement methods of heating uniformity were summarized from three aspects in this article including changing the electromagnetic field distribution, sample position, and optimizing the radio frequency working parameters. Based on the above basic research of radio frequency technology and combining with the practical problems in grain storage and processing, the applications of radio frequency heating in the fields of dis-infestations, sterilizing, enzyme inactivation and drying were also summarized. Finally, some suggestions on the application of this technology in grain storage and processing and future research directions were proposed. This review may play a certain guiding role for the application of radio frequency technology in grain storage and processing.
Vegetable and fruit planting areas and products of China have always ranked first in the world, and the vegetable and fruit industry is respectively the second and third largest agricultural planting industry after grain. Vegetables and fruits are prone to quality deterioration during postharvest storage and transportation, resulting in reduced edible value and huge economic losses. To ensure fruit and vegetable quality and reduce the waste of resources caused by postnatal deterioration, this paper summarizes the latest research status of sensor detection and monitoring technology for fruit and vegetable quality deterioration and analyzed the principle, characteristics, advantages, and disadvantages of various detection technology. Among them, machine vision can detect the external quality and surface defects of fruits and vegetables, but fruits and vegetables are different from the standard machined products, and they are affected by many factors in the growth process, which seriously interfere with the image collection work and easily lead to misjudgment. An electronic nose equipped with expensive gas sensors can monitor the odor deterioration of fruits and vegetables but would require improved sensitivity and durability. Near-infrared can detect the internal quality and recessive defects of fruits and vegetables, but the applicability of the model needs to be improved. Hyperspectral imaging can visually detect the internal and external quality of fruits and vegetables and track the deterioration process, but the huge amount of data obtained leads to data redundancy, which puts forward higher requirements for system hardware. Therefore, low-cost multispectral imaging systems should be developed and characteristic wavelength extraction algorithms should be optimized. Raman spectroscopy can detect fruit and vegetable spoilage bacteria and their metabolites, but there is no effective Raman enhanced substrate production and accurate Raman standard spectrogram database. The comprehensive evaluation of fruit and vegetable deterioration can be realized by multi-technology and multi-information fusion. It can overcome the limitation of single sensor information analysis, improve the robustness and parallel processing ability of the detection model, and provide a new approach for high-precision detection or monitoring of fruit and vegetable quality deterioration. The Internet of Things monitoring system is constructed with various sensors as the sensing nodes to realize the intelligent real-time monitoring of fruit and vegetable quality deterioration information, provide a reference for solving the technical limitation of quality deterioration control in the processing of fruit and vegetable. This is of great significance for reducing the postpartum economic loss of fruits and vegetables and promoting the sustainable development of the fruit and vegetable industry.
With the continuous increase of import and export of various countries, people have put forward higher requirements on the efficiency and accuracy of meat and meat products safety indicators detection. Since electronic nose technology is simple to operate and allows rapid and nondestructive testing, it can meet today's need for efficient test of meat and meat products. In this paper, the detection principle of electronic nose technology was introduced firstly, and its development process was described from two aspects of hardware and software system. Then, the application research progress of electronic nose technology in meat and meat products detection in recent years from the aspects of freshness, adulteration, flavor evaluation and microbial contamination of meat and meat products was analyzed. Different electronic nose instruments and equipment or different pattern recognition algorithms result in different analysis results. Therefore, it highlighting the feasibility and advancement of electronic nose technology application in various aspects of meat and meat products detection. At the same time, in view of the application research results of electronic nose technology in the detection of meat and meat products, the paper pointed out the shortcomings of electronic nose technology, for example: The analysis effect of electronic nose technology was uneven, the price of electronic nose equipment was relatively expensive, and the application range of large electronic nose equipment was limited. Therefore, there were still some difficulties and problems of electronic nose technology in the aspects of universality and popularization. Finally, in view of the shortcomings of the current electronic nose technology, the development and application prospects of the electronic nose technology in the future were prospected. In terms of hardware system, with the research and development continuously of new gas sensitive materials, the durability and sensitivity to smell recognition of the electronic nose by improving the performance of the electrode film material of the electronic nose sensor array was enhanced. In terms of software system, with the upgrading continuously of computer systems, a supporting platform for the emerging and complex pattern recognition algorithms was provided. New pattern recognition algorithms in the pattern recognition system of electronic nose technology were explored and introduced, so that electronic nose technology can achieve faster and more accurate recognition and analysis of odors.
