As a pillar industry of economic development in the main apple-producing areas, apple industry has made important contributions to the increase of local farmers' income. With the transformation and upgrading of apple industry, the mechanization and intelligence level would be directly related to economic benefits. To promote the research of apple production intelligent technology and the development of intelligent equipment, in this paper, the current level of mechanization in each step of apple production was first introduced. Then, the main characteristics of the main apple orchard machinery, such as power chassis, weeding machinery, and harvesting equipment, were demonstrated. The application progress of automatic leveling and control, automatic navigation, automatic obstacle avoidance, weed identification, weed removal, apple identification, apple positioning, apple separation, and other technologies in intelligent power chassis, intelligent weeding machines, and apple harvesting robots, were summarized. The basic principles and characteristics of the above three key technologies of intelligent equipment were expounded in combination with different application environments. Intelligent control is the key technology for the intelligentization of orchard power chassis. The post of chassis adaptive control technology and autonomous navigation technology were discussed. In addition, a chassis intelligent perception and intelligent decision-making system should be established. Orchard chassis safe, accurate, efficient, and stable driving and operation is the future development trend of orchard intelligent chassis. The lack of robust weed sensing technology is the main limitation to the commercial development of a robotic weed control system. To improve the level of weed detection and weeding, machine vision and multi-sensor fusion methods have been proposed to solve the practical problems, such as illumination, overlapping leaves, occlusion, and classifier or network structure optimization. Robotic apple harvesting has proven to be a highly challenging task due to environmental complexities, sensor reliability, and robot stability. To improve the accuracy and efficiency of harvest mechanization applications in apples, apple quick identification under complex scenes, apple picking path planning, and materials and structure of manipulator for apple picking must all be optimized accordingly. Finally, the challenges of intelligent equipment technologies in apple production were analyzed, and the developing suggestions were put forward. This research can provide references and ideas for the advancement of intelligent technology research in apple production and the research and development of intelligent equipment.
Traditional orchard production is facing problems of labor shortage due to the aging, difficulties in the management of agricultural equipment and production materials, and low production efficiency which can be expected to be solved by building a smart orchard that integrates technologies of Internet of Things(IoT), big data, equipment intelligence, et al. In this study, based on the objectives of full mechanization and intelligent management, a smart orchard was built in Pinggu district, an important peaches and pears fruit producing area of Beijing. The orchard covers an aera of more than 30 hm2 in Xiying village, Yukou town. In the orchard, more than 10 kinds of information acquisition sensors for pests, diseases, water, fertilizers and medicines are applied, 28 kinds of agricultural machineries with intelligent technical support are equipped. The key technologies used include: intelligent information acquisition system, integrated water and fertilizer management system and intelligent pest management system. The intelligent operation equipment system includes: unmanned lawn mower, intelligent anti-freeze machine, trenching and fertilizer machine, automatic driving crawler, intelligent profiling variable sprayer, six-rotor branch-to-target drone, multi-functional picking platform and finishing and pruning machine, etc. At the same time, an intelligent management platform has been built in the smart orchard. The comparison results showed that, smart orchard production can reduce labor costs by more than 50%, save pesticide dosage by 30% ~ 40%, fertilizer dosage by 25% ~ 35%, irrigation water consumption by 60% ~ 70%, and comprehensive economic benefits increased by 32.5%. The popularization and application of smart orchards will further promote China's fruit production level and facilitate the development of smart agriculture in China.
To solve the problems of low level of digitalization of orchard management and relatively single construction method, a three-dimensional virtual orchard construction method based on laser point cloud was proposed in this research. First, the hand-held 3D point cloud acquistion equipment (3D-BOX) combined with the lidar odometry and mapping (SLAM-LOAM) algorithm was used to complete the acquisition of the point cloud data set of orchard; then the outliers and noise points of the point cloud data were removed by using the statistical filtering algorithm, which was based on the K-neighbor distance statistical method. To achieve this, a distance threshold model for removing noise points was established. When a discrete point exceeded, it would be marked as an outlier, and the point was separated from the point cloud dataset to achieve the effect of discrete point filtering. The VoxelGrid filter was used for down sampling, the cloth simulation filtering (CSF) cloth simulation algorithm was used to calculate the distance between the cloth grid points and the corresponding laser point cloud, and the distinction between ground points and non-ground points was achieved by dividing the distance threshold, and when combined with the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, ground removal and cluster segmentation of orchard were realized; finally, the Unity3D engine was used to build a virtual orchard roaming scene, and convert the real-time GPS data of the operating equipment from the WGS-84 coordinate system to the Gauss projection plane coordinate system through Gaussian projection forward calculation. The real-time trajectory of the equipment was displayed through the LineRenderer, which realized the visual display of the motion trajectory control and operation trajectory of the working machine. In order to verify the effectiveness of the virtual orchard construction method, the test of orchard construction method was carried out in the Begonia fruit and the mango orchard. The results showed that the proposed point cloud data processing method could achieve the accuracy of cluster segmentation of Begonia fruit trees and mango trees 95.3% and 98.2%, respectively. Compared with the row spacing and plant spacing of fruit trees in the actual mango orchard, the average inter-row error of the virtual mango orchard was about 3.5%, and the average inter-plant error was about 6.6%. And compared the virtual orchard constructed by Unity3D with the actual orchard, the proposed method can effectively reproduce the actual three-dimensional situation of the orchard, and obtain a better visualization effect, which provides a technical solution for the digital modeling and management of the orchard.
In order to solve the problems of high labor intensity, low efficiency of manual operation and lack of supporting machinery in the fruit harvesting of modern orchards, a self-propelled orchard multi-station harvesting equipment was designed in combination with the fruit tree dwarf anvil wide-row dense planting mode and agronomic planting requirements. The whole machine structure and working principle of the self-propelled orchard multi-station harvesting equipment were expounded. According to the environmental conditions of mountainous orchards, the crawler chassis structure was designed, and the working speed was 0~2 km/h. The operating platform including left extension platform and right extension platform was designed according to the difference of fruit tree row spacing, and the working width of the operating platform was 1500~2700 mm. In order to improve the working efficiency and ensure the same picking speed of upper and lower operators, the picking operation mode of "two sides, two heights and six stations" was proposed by comparing the difference in the working flexibility between the operator on the platform and the operator on the ground during the operation of the machine, and the in-and-out channels of fruit boxes and the automatic collection and packing device were designed. The front and rear unobstructed fruit box access system was composed of the front loading and unloading mechanism, the rear loading and unloading mechanism and the fruit box slide rail, which was convenient for the empty fruit box to enter the fruit loading station of the working platform from the front and unloading from the rear after the fruit was filled. Six sub-conveyor belts were designed to handle apples harvested by six non interacting operators at the same time. The prototype was test in the field, and the packing uniform distribution coefficient calculation method was proposed to evaluate the uniformity of fruit packing, and the performance of the prototype was comprehensively evaluated in combination with the fruit damage rate and packing speed. The results showed that, the designed self-propelled orchard multi-station harvesting equipment could synchronize with the six stations manual harvesting speed. At the same time, with the help of the expansion platform, the apple picking range covered the entire canopy of the fruit tree. The prototype worked smoothly, and the speed of each conveyor belt was in good coordination with manual picking, and there was no apple congestion occurred. The apple harvest damage rate was 4.67%, the packing uniform distribution coefficient was 1.475, and the packing speed was 72.9 apples per minute, which could meet the requirements of orchard harvest operation.
In order to solve the problems of pesticides abuse, nonuniformity deposition and low operating efficiency, build up the smart mango orchard, sedimentary properties of liquids in mango canopy of two orchard pesticide machinery, i.e., orchard caterpillar mist sprayer and six-rotor unmanned aerial vehicle (UAV) of were compared. Mango canopy was divided into upper, middle and lower canopy, tartrazine wsa selected as the tracer, high-definition printing paper and filter paper were used to collect pesticide droplets, the image processing methods such as deposition distribution uniformity were used to analyze the droplets. The experimental results showed that, for the surface droplets coverage rate of upper canopy leaf, unmanned aerial vehicle (UAV) was significantly higher than the cartipillar mist sprayer, there was no significant difference for the middle and lower canopy leaf. The the average coverage rate of both the front and back of leaves in UAV treatment group were 1.5~2 times for cartipillar mist sprayer, and got more deposition in back of leaves compare with caterpillar mist sprayer. The density of droplets on the front of the leaves of the mist sprayer treatment was significantly higher than that of the UAV treatment, but there was no significant difference on the back of the leaves. Both the front and back of the leaves of the plant protection UAV did not meet the requirements of disease and pest control with a low spray amount of 20/cm2. The liquid deposition of mist sprayer concentrated in the middle and lower canopy (61.1%), and while for the UAVs, it concentrated in the upper canopy (43.0%). The proportion of the deposition in the canopy was higher than that of the UAVs (48.6%), but the deposition capacity of mist sprayer in the upper canopy was insufficient, accounting for only 17%. The research shows that, compared with UAV, caterpillar mist sprayer is more suitable for the pest control of lower and middlein canopy, at the same time, the high density of droplets cover also has obvious advantages when spraying fungicide. UAV is more suitable for the external tidbits pest control of upper mango canopy, such as thrips, anthrax. According to the experimental results, a stereoscopic plant protection system can be built up in which can use the advantages of both caterpillar mist sprayer and UAV to achieve uniform coverage of pesticide in the mango tree canopy.
To realize the autonomous navigation and automatic target spraying of intelligent plant protect machinery in orchard, in this study, an autonomous navigation and automatic target spraying robot for orchards was developed. Firstly, a single 3D light detection and ranging (LiDAR) was used to collect fruit trees and other information around the robot. The region of interest (ROI) was determined using information on the fruit trees in the orchard (plant spacing, plant height, and row spacing), as well as the fundamental LiDAR parameters. Additionally, it must be ensured that LiDAR was used to detect the canopy information of a whole fruit tree in the ROI. Secondly, the point clouds within the ROI was two-dimension processing to obtain the fruit tree center of mass coordinates. The coordinate was the location of the fruit trees. Based on the location of the fruit trees, the row lines of fruit tree were obtained by random sample consensus (RANSAC) algorithm. The center line (navigation line) of the fruit tree row within ROI was obtained through the fruit tree row lines. The robot was controlled to drive along the center line by the angular velocity signal transmitted from the computer. Next, the ATRS's body speed and position were determined by encoders and the inertial measurement unit (IMU). And the collected fruit tree zoned canopy information was corrected by IMU. The presence or absence of fruit tree zoned canopy was judged by the logical algorithm designed. Finally, the nozzles were controlled to spray or not according to the presence or absence of corresponding zoned canopy. The conclusions were obtained. The maximum lateral deviation of the robot during autonomous navigation was 21.8 cm, and the maximum course deviation angle was 4.02°. Compared with traditional spraying, the automatic target spraying designed in this study reduced pesticide volume, air drift and ground loss by 20.06%, 38.68% and 51.40%, respectively. There was no significant difference between the automatic target spraying and the traditional spraying in terms of the percentage of air drift. In terms of the percentage of ground loss, automatic target spraying had 43% at the bottom of the test fruit trees and 29% and 28% at the middle of the test fruit trees and the left and right neighboring fruit trees. But in traditional spraying, the percentage of ground loss was, in that sequence, 25%, 38%, and 37%. The robot developted can realize autonomous navigation while ensuring the spraying effect, reducing the pesticides volume and loss.
In view of the uneven distribution of airflow inside the multi-air-duct sprayer, the air flow caused by the air outlet is disturbed and the droplet can not be evenly deposited on the fruit tree canopy. In this research, the length parameter of the inner baffle plate of the multi-duct sprayer was optimized. The Computational Fluid Dynamics (CFD) was used to simulate and analyze the internal airflow of the air supply system of the multi-duct sprayer based on Star-CCM+ software. The standard deviations of the wind speed of the wind outlet 1~6 at different guide plates were 0.7468, 0.6776, 1.4441, 5.1305, 4.5768 and 0.8209, respectively. Among them, the standard deviations of wind speed value at Point 1, Point 2 and Point 6 were less than 1, indicating that the change of deflector length has little impact on the speed change. The standard deviations of wind speed value at Point 3, Point 4 and Point 5 were large, indicating that with the change of deflector length, the wind speed at Air outlet 3, Air outlet 4, Air outlet 5 were greatly affected. On this basis, through the response surface analysis of Air outlet 3, Air outlet 4 and Air outlet 5, it was determined that, the length of Deflector 1 as 200 mm, the length of Deflector 2 as 60 mm and the length of Deflector 3 as 50 mm, was the optimal parameter combination. Under the optimal combination parameters, the wind speed values of symmetrical Air outlet 3 and Air outlet 6 were 39.135 and 41.320 m/s, respectively, with a relative deviations of 5.58%. The wind speed values of air outlet 4 and air outlet 5 were 33.022 and 34.328 m/s, respectively, with a relative deviation of 3.95%, which meeting the design requirements of sprayer. The indoor wind speed test results showed that the average wind speed of the upper layer was 15.75 m/s, the average wind speed of the middle layer was 20.83 m/s, and the average wind speed of the lower layer was 28.27 m/s, which met the end speed principle. The wind field was distributed according to the shape of the fruit tree canopy. The wind field of the left and right sides of the sprayer was symmetrical distributed and the air distribution was uniform. The work can provide a reference for the design of multi-duct sprayer.
The orchard in the mountainous area is rugged and steep, and there is no road for large-scale plant protection machinery traveling in the orchard, so it is difficult for mobile spraying machinery to enter. In order to solve the above problems, the automatic pipeline spraying technology and facilities were studied. A pipeline automatic spraying facility suitable for mountainous orchards was designed, which included spraying head, field spraying pipeline, automatic spraying controller and spraying groups. The spraying head was composed of a spraying unit and a constant pressure control system, which pressurized the pesticide liquid and stabilized the liquid pressure according to the preset pressure value to ensure a better atomization effect. Field spraying pipeline consisted of main pipeline, valves and spraying groups. In order to perform automatic spraying, a solenoid valve was installed between the main pipeline and each spraying group, and the automatic spraying operation of each spraying group was controlled automatically by the opening or closing of the solenoid valve. An automatic spraying controller composed of main controller, solenoid valve driving circuit, solenoid valve controlling node and power supplying unit was developed, and the controlling software was also programmed in this research. The main controller had manual and automatic two working modes. The solenoid valve controlling node was used to send wireless signals to the main controller and receive wireless signals from the main controller, and open or close the corresponding solenoid valve according to the received control signal. During the spraying operation, the pesticide liquid flowed into the orchard from the spray head through the pipeline. The automatic spray controller was used to control the solenoid valve to open or close the spray group one by one, and implement manual control or automatic control of spraying. In order to determine the continuous opening time of the solenoid valve, an effectiveness of the spray test was carried out. The spraying test results showed that spraying effectiveness could be guaranteed by opening solenoid valve for 8 s continuously. The efficiency of this pipeline automatic spraying facility was 2.61 hm2/h, which was 45－150 times that of manual spraying, and 2.1 times that of unmanned aerial vehicle spraying. The automatic pipeline spraying technology in mountainous orchards had obvious advantages in the timeliness of pest controlling. This research can provide references and ideas for the development of spray technology and intelligent spraying facilities in mountainous orchards.
The rapid increasing of apple planting area on the Loess Plateau has exerted an important influence on the regional eco-hydrology and socio-economic development. However, the orchards in this area are small and complex, and there are only county or city scale statistical data, lack of actual spatial distribution information. To this end, for the extraction of apple orchards on the Loess Plateau, in this study, a professional dataset of low-altitude remote sensing images acquired by unmanned aerial vehicle was firstly established. The R_34_Linknet network and other five commonly used deep learning semantic segmentation models SegNet, FCN_8s, DeeplabV3+, UNet and Linknet were applied to the spatial distribution extraction of apple orchards on the Loess Plateau, and the best-performing model was R_34_Linknet, with a F1 score of 87.1%, a pixel accuracy (PA) of 92.3%, an mean intersection over union (MioU) of 81.2%, a frequency weighted intersection over union (FWIoU) of 85.7%, and the mean pixel accuracy (MPA) was 89.6%. The spatial pyramid pool structure (ASPP) and R_34_Linknet network was combined to expand the receptive field of the network and get R_34_Linknet_ASPP network, and then ASPP structure was improved. Combining the spatial pyramid pooling (ASPP) with the R_34_Linknet network to expand the receptive field of the network and obtain a R_34_Linknet_ASPP network; Then the ASPP structure was improved to get a R_34_Linknet_ASPP+ network. The performance of the three networks were compared. R_34_Linknet_ASPP+ got the best performance, with 86.3% for F1, 94.7% for PA, 82.7% for MIoU, 89.0% for FWIoU, and 92.3% for MPA on the test set. The accuracy of apple orchard extraction in Wangdonggou, Changwu County and Tongji Village, Baishui County using R_34_Linknet_ASPP+ were 94.22% and 95.66%, respectively. In Wangdonggou, it was 1.21% and 0.58% higher than R_34_Linknet and R_34_Linknet_ASPP, respectively. In Tongji village, it was 1.70% and 0.90% higher than R_34_Linknet and R_34_Linknet_ASPP, respectively. The results show that the proposed R_34_Linknet_ASPP+ method can extract apple orchards accurately, the edge treatment of apple orchard plots is better, the method can be used as the technical support and theoretical basis for research on the spatial distribution mapping of apple orchards on the Loess Plateau.
Mechanized and intelligent flower thinning is a high-speed flower thinning method nowadays. The classification and detection of flowers and flower buds are the basic requirements to ensure the normal operation of the flower thinning machine. Aiming at the problems of pear inflorescence detection and classification in the current intelligent production of pear orchards, a Y-shaped shed pear orchard inflorescence recognition algorithm Ghost-YOLOv5s-BiFPN based on improved YOLOv5s was proposed in this research. The detection model was obtained by labeling and expanding the pear tree bud and flower images collected in the field and sending them to the algorithm for training. The Ghost-YOLOv5s-BiFPN algorithm used the weighted bidirectional feature pyramid network to replace the original path aggregation network structure, and effectively fuse the features of different sizes. At the same time, ghost module was used to replace the traditional convolution, so as to reduce the amount of model parameters and improve the operation efficiency of the equipment without reducing the accuracy. The field experiment results showed that the detection accuracy of the Ghost-YOLOv5s-BiFPN algorithm for the bud and flower in the pear inflorescence were 93.21% and 89.43%, respectively, with an average accuracy of 91.32%, and the detection time of a single image was 29 ms. Compared with the original YOLOv5s algorithm, the detection accuracy was improved by 4.18%, and the detection time and model parameters were reduced by 9 ms and 46.63% respectively. Compared with the original YOLOV5s network, the mAP and recall rate were improved by 4.2% and 2.7%, respectively; the number of parameters, model size and floating point operations were reduced by 46.6%, 44.4% and 47.5% respectively, and the average detection time was shortened by 9 ms. With Ghost convolution and BIFPN adding model, the detection accuracy has been improved to a certain extent, and the model has been greatly lightweight, effectively improving the detect efficiency. From the thermodynamic diagram results, it can be seen that BIFPN structure effectively enhances the representation ability of features, making the model more effective in focusing on the corresponding features of the target. The results showed that the algorithm can meet the requirements of accurate identification and classification of pear buds and flowers, and provide technical support for the follow-up pear garden to achieve intelligent flower thinning.
Dragon fruit detection in natural environment is the prerequisite for fruit harvesting robots to perform harvesting. In order to improve the harvesting efficiency, by improving YOLOX (You Only Look Once X) network, a target detection network with an attention module was proposed in this research. As the benchmark, YOLOX-Nano network was chose to facilitate deployment on embedded devices, and the convolutional block attention module (CBAM) was added to the backbone feature extraction network of YOLOX-Nano, which improved the robustness of the model to dragon fruit target detection to a certain extent. The correlation of features between different channels was learned by weight allocation coefficients of features of different scales, which were extracted for the backbone network. Moreover, the transmission of deep information of network structure was strengthened, which aimed at reducing the interference of dragon fruit recognition in the natural environment as well as improving the accuracy and speed of detection significantly. The performance evaluation and comparison test of the method were carried out. The results showed that, after training, the dragon fruit target detection network got an AP0.5 value of 98.9% in the test set, an AP0.5:0.95 value of 72.4% and F1 score was 0.99. Compared with other YOLO network models under the same experimental conditions, on the one hand, the improved YOLOX-Nano network model proposed in this research was more lightweight, on the other hand, the detection accuracy of this method surpassed that of YOLOv3, YOLOv4 and YOLOv5 respectively. The average detection accuracy of the improved YOLOX-Nano target detection network was the highest, reaching 98.9%, 26.2% higher than YOLOv3, 9.8% points higher than YOLOv4-Tiny, and 7.9% points higher than YOLOv5-S. Finally, real-time tests were performed on videos with different input resolutions. The improved YOLOX-Nano target detection network proposed in this research had an average detection time of 21.72 ms for a single image. In terms of the size of the network model was only 3.76 MB, which was convenient for deployment on embedded devices. In conclusion, not only did the improved YOLOX-Nano target detection network model accurately detect dragon fruit under different lighting and occlusion conditions, but the detection speed and detection accuracy showed in this research could able to meet the requirements of dragon fruit harvesting in natural environment requirements at the same time, which could provide some guidance for the design of the dragon fruit harvesting robot.
The detecting and grading of the internal quality of apples is an effective means to increase the added value of apples, protect the health of residents, meet consumer demand and improve market competitiveness. Therefore, an apple internal quality detecting module and a grading module were developed in this research to constitute a movable apple internal quality orchard origin grading system, which could realize the detection of apple sugar content and apple moldy core in orchard origin and grading according to the set grading standard. Based on this system, a multiplicative effect elimination (MEE) based spectral correction method was proposed to eliminate the multiplicative effect caused by the differences in physical properties of apples and improve the internal quality detection accuracy. The method assumed that the multiplication coefficient in the spectrum was closely related to the spectral data at a certain wavelength, and divided the original spectrum by the data at this wavelength point to achieve the elimination of the multiplicative scattering effect of the spectrum. It also combined the idea of least-squares loss function to set the loss function to solve for the optimal multiplication coefficient point. To verify the validity of the method, after pre-processing the apple spectra with multiple scattering correction (MSC), standard normal variate transform (SNV), and MEE algorithms, the partial least squares regression (PLSR) prediction models for apple sugar content and partial least squares-discriminant analysis (PLS-DA) models for apple moldy core were developed, respectively. The results showed that the MEE algorithm had the best results compared to the MSC and SNV algorithms. The correlation coefficient of correction set (Rc), root mean square error of correction set (RMSEC), the correlation coefficient of prediction set (Rp), and root mean square error of prediction set (RMSEP) for sugar content were 0.959, 0.430%, 0.929, and 0.592%, respectively; the sensitivity, specificity, and accuracy of correction set and prediction set for moldy core were 98.33%, 96.67%, 97.50%, 100.00%, 90.00%, and 95.00%, respectively. The best prediction model established was imported into the system for grading tests, and the results showed that the grading correct rate of the system was 90.00% and the grading speed was 3 pcs/s. In summary, the proposed spectral correction method is more suitable for apple transmission spectral correction. The mobile orchard local grading system of apple internal quality combined with the proposed spectral correction method can accurately detect apple sugar content and apple moldy core. The system meets the demand for internal quality detecting and grading of apples in orchard production areas.
Phytophthora strawberries, as a kind of plant pathogenic fungi, can cause strawberry skin and crown rot without safe and effective treatment, which affect the economic benefits of planting strawberries. Therefore, it is urgent to use low-cost diagnostic methods to achieve early prevention. Strawberry plants infected with Phytophthora cactorum would release a unique organic volatile gas, 4-ethylphenol, with a concentration ranging from 1.12 to 22.56 mg/kg, which could be used as a marker gas for rapid diagnosis of the disease. In this study, semiconducting single-walled carbon nanotubes (SWNT) and field effect sensors (FET) were used to prepare semiconductor field effect gas sensors (SWNT/FET) without selectivity. And then the metal porphyrin MnOEP with high sensitivity and selectivity to 4-ethylphenol was immoblized on the SWNT's surface to obtain MnOEP-SWNT/FET. MnOEP-SWNT/FET has the advantages of low cost, low power consumption, small size, high sensitivity and easy integration, which can effectively overcome the shortcomings of gas chromatography-mass spectrometry, high-performance liquid chromatography and other analytical methods. By comparing the sensitivity and selectivity of different sensors, MnOEP-SWNT/FET is very suitable for real-time monitoring of 4-ethylphenol. The key reasons for the high sensitivity and selectivity are: MnOEP is a macromolecular heterocyclic compound formed by four pyrrole rings connected together by methylene and manganese ion(Mn), each pyrrole ring consists of four carbons and one nitrogen, and all nitrogen atoms inside the ring form a central cavity; the coordination metal ions of MnOEP are in an unsaturated state, gas molecules can interact with the central metal ions through van der Waals force and hydrogen bond at the axial position of MnOEP to change their own optical or electrical properties. MnOEP-SWNT/FET was studied by Raman spectrum, UV spectrum and voltammetry. The physical and chemical properties were analyzed and the detection conditions were optimized to improve the gas sensitivity of MnOEP-SWNT/FET to 4-ethylphenol. Under the optimal detection conditions, MnOEP-SWNT/FET has a good linear relationship with 0.25% ~100% saturated vapor of 4-ethylphenol (20 ℃) and the detection limit is 0.15% saturated vapor of 4-ethylphenol. The relative standard error of different concentrations was less than 10%. By measuring the actual samples, it has high detection accuracy for strawberry plants infected with Phytophthora, but it still exists false positive for healthy strawberry.
