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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.

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or to different parts of the same target. To tackle this problem, we designed a variable fruit tree spraying system based on the ExG-AABB (excess green and axis-aligned bounding box) algorithm. We used a Kinect depth camera to capture information about the fruit tree canopy and constructed a spray flow model using pulse width modulation and variable spray control technology. Variable multi-nozzle spraying was guided by combining this canopy data. We evaluated the accuracy of each model in calculating canopy volume by comparing the coefficient of determination (R2) and root mean square error (RMSE) of the ExG-AABB with the slice convex hull method, voxel method, three-dimensional alpha-shape method, and QuickHull method. The ExG-AABB algorithm had the highest R2 value (0.9334) and the lowest RMSE value (0.0353 m3) among the five models, indicating that it most accurately reflects the true volume of the fruit tree canopy. This validates the effectiveness of the ExG-AABB algorithm in calculating canopy volume. We established a correlation model between canopy volume and spray volume, designed a canopy-adaptive layering method based on point cloud processing, and achieved precise calculation of nozzle flow. Comparative field experiments were conducted to analyze the spray coverage rate and observed flow, thereby evaluating the spraying effect of this variable spraying system. The experimental results showed that compared to conventional continuous spraying, this variable spraying system not only achieves more uniform spray coverage but also significantly reduces pesticide usage by 48.1%. Furthermore, through system optimization, the average coverage rate of the middle layer of the canopy decreased by 17.53%, effectively reducing the phenomenon of overlapping spraying from multiple nozzles and improving spraying efficiency. [ABSTRACT FROM AUTHOR]

Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco pest identification leveraging enhancements in YOLOv8 technology. Using YOLOv8 large (YOLOv8l) as the base, the neck layer of the original network is replaced with an asymptotic feature pyramid network (AFPN) network to reduce model parameters. A SimAM attention mechanism, which does not require additional parameters, is incorporated to improve the model's ability to extract features. The backbone network's C2f model is replaced with the VoV-GSCSP module to reduce the model's computational requirements. Experiments show the improved YOLOv8 model achieves high overall performance. Compared to the original model, model parameters and GFLOPs are reduced by 52.66% and 19.9%, respectively, while mAP@0.5 is improved by 1%, recall by 2.7%, and precision by 2.4%. Further comparison with popular detection models YOLOv5 medium (YOLOv5m), YOLOv6 medium (YOLOv6m), and YOLOv8 medium (YOLOv8m) shows the improved model has the highest detection accuracy and lightest parameters for detecting four common tobacco pests, with optimal overall performance. The improved YOLOv8 detection model proposed facilitates precise, instantaneous pest detection and recognition for tobacco and other crops, securing high-accuracy, comprehensive pest identification. [ABSTRACT FROM AUTHOR]

The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables. [ABSTRACT FROM AUTHOR]

The rapid and accurate identification of citrus leaf diseases is crucial for the sustainable development of the citrus industry. Because citrus leaf disease samples are small, unevenly distributed, and difficult to collect, we redesigned the generator structure of FastGAN and added small batch standard deviations to the discriminator to produce an enhanced model called FastGAN2, which was used for generating citrus disease and nutritional deficiency (zinc and magnesium deficiency) images. The performance of the existing model degrades significantly when the training and test data exhibit large differences in appearance or originate from different regions. To solve this problem, we propose an EfficientNet-B5 network incorporating adaptive angular margin (Arcface) loss with the adversarial weight perturbation mechanism, and we call it EfficientNet-B5-pro. The FastGAN2 network can be trained using only 50 images. The Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) are improved by 31.8% and 59.86%, respectively, compared to the original FastGAN network; 8000 images were generated using the FastGAN2 network (2000 black star disease, 2000 canker disease, 2000 healthy, 2000 deficiency). Only images generated by the FastGAN2 network were used as the training set to train the ten classification networks. Real images, which were not used to train the FastGAN2 network, were used as the test set. The average accuracy rates of the ten classification networks exceeded 93%. The accuracy, precision, recall, and F1 scores achieved by EfficientNet-B5-pro were 97.04%, 97.32%, 96.96%, and 97.09%, respectively, and they were 2.26%, 1.19%, 1.98%, and 1.86% higher than those of EfficientNet-B5, respectively. The classification network model can be successfully trained using only the images generated by FastGAN2, and EfficientNet-B5-pro has good generalization and robustness. The method used in this study can be an effective tool for citrus disease and nutritional deficiency image classification using a small number of samples. [ABSTRACT FROM AUTHOR]

Some problems of facility agriculture machinery locating system still exist now, such as poor stability, low positioning accuracy and high cost. In addition, agricultural machinery navigation and positioning system based on Global Position System (GPS) is widely used at home and abroad. But it is not applicable to the agricultural facilities and the scenes of more buildings. In order to resolve these problems, a wireless sensor network (WSN) locating system based on NanoPAN5375 module was designed. NanoPAN5375 module is based on the technology of chirp spread spectrum (CSS); it not only has advantages of reliable communication, good stability, long transmission distance, low system power consumption, and so on, but also adopts the principle of the time difference of arrival (TDOA) to calculate the distance between nodes, so that its ranging accuracy is high. Therefore, NanoPAN5375 module was chosen as positioning node of facility agriculture vehicle. In the locating system, the 3 beacon nodes were responsible for the wireless ranging, and the car installed with location node was responsible for the wireless ranging and location coordinates calculation. WD5032N module based on NanoPAN5375 was used as WSN node. And microprocessor STM32F103CB and NanoPAN5375 module were integrated on WD5032N module. NanoPAN5375 communicated with the microprocessor through the serial peripheral interface (SPI), so it could realize the function of wireless communication and wireless ranging. A power supply backplane circuit of WD5032N module was designed to meet the power supply requirement of its microprocessor and NanoPAN5375 module, and location coordinates X and Y were displayed in real time through the serial port communicating with liquid crystal display (LCD). The node software, which was compiled by C Language taking the Keil MDK as development environment, could complete data acquisition and processing, wireless transmission, serial communication and other functions. The distances between unknown nodes and each beacon node were calculated by using Symmetrical double-sided two way ranging (SDSTWR) algorithm which was based on CSS technology in location node. And then the coordinate information of the unknown node was calculated by the triangle centroid localization algorithm. Finally in order to resolve the effects of measurement noise on positioning accuracy, the iterating based on Kalman filter algorithm was applied to observed values of target state. The antennas of 3 and 5 DB were respectively used on WD5032N to do ranging and positioning experiments in the indoor and outdoor. On one hand, the results of ranging experiments have shown that the ranging accuracy of the nodes based on NanoPAN5375 module is high. Within the scope of the appropriate distance, point-to-point ranging accuracy between the nodes can reach 1.02 m or less. On the other hand, the results of positioning experiments indicate that the stability of WSN positioning system based on NanoPAN5375 is good and the positioning accuracy is high. And the positioning precision could reach about less than 1 m within the scope of 3 edges of equilateral triangle formed by 3 beacon nodes. And the positioning accuracy was about 1-2 m close to the outside of the triangle edges. So it can satisfy the requirement of positioning accuracy of general facility agriculture vehicle, and can be applied to other agricultural machines' positioning. [ABSTRACT FROM AUTHOR]

In order to achieve a rapid, accurate, non-destructive, in-situ detection of Panonychus citri (McGregor) infestation levels to the fruit trees, a Panonychus citri (McGregor) fast detector based on optical measurement techniques was designed and developed in this study. The instrument's working effect was tested through experiments. The optical reflection method was adopted by the detector as the detection principle, and the tree canopy's light reflectance in the red and near-infrared light zone was measured. The Panonychus citri (McGregor) index (PCI) was introduced in this study as the equipment output and was calculated in accordance with the measured reflectance. Furthermore, the Panonychus citri (McGregor) infestation status at a tree's selected canopy outer area was divided into three levels as None, Light, and Heavy based on the number of eggs on top of the leaves within a specific region at the tree canopy's outer area. The relationship between the infestation levels of the regions and their corresponding PCI readings was analyzed. In the hardware design, light emitting diode (LED) arrays having center wavelengths of 665nm and 815nm were selected as the red and near-infrared light sources. Natural light interference at the detector was mostly reduced by using a 40 kHz light modulation/demodulation technology, a specially designed optical path, and a photoelectric detection adapting circuit. Results from the instrument's working effect test indicated that: the detection coefficient k equals to 2.622; the detection was less slightly affected by the foliage density; the detection was less affected by the natural environmental light, but was affected by the temperature to some extent; the three Panonychus citri (McGregor) infestation levels as None, Light and Heavy could be distinguished by the detector, and when the infestation level was low, the result was stable. High correlation was achieved between the detector's PCI reading and the number of eggs on top of a piece of leaf, which indicated that the relationship between Panonychus citri (McGregor) infestation level and the egg number achieved in this study was consistent with previous experiments. Further research will test a temperature-compensation method to reduce the influence of temperature on the detection results. Reasons for lower-stability detection in the Heavy level of infestation will also be analyzed. [ABSTRACT FROM AUTHOR]

This study examines the impact of canopy density, side wind speed, nozzle tilt angle, and droplet size on droplet penetration during plant protection spraying operations. Experiments conducted in citrus orchards evaluated how side wind speed and nozzle tilt angle influence droplet penetration across various canopy densities. A Phase Doppler Analyzer (PDA) was used to assess droplet size variations under different nozzle tilt angles and side wind speeds, yielding a multiple linear regression equation (R 2 = 0.866) that links nozzle tilt angle and side wind speed with droplet size. Results showed that droplet size decreases with increasing nozzle tilt angle at a constant crosswind speed. Further experiments investigated the effects of droplet size and canopy leaf area density on droplet penetration, involving three canopy leaf area densities, four wind speeds, and six nozzle tilt angles. Droplet deposition and canopy coverage were measured under various spraying parameters, with conventional operations (0° nozzle tilt and orthogonal wind speeds) serving as controls. The study found that adjusting nozzle tilt angle and wind speed enhances droplet penetration in different canopy structures. Optimal parameters varied with leaf area density (LAD): an 18° tilt angle and 3 m/s wind speed for a LAD of 5.94 m 3 /m 3 , a 45° tilt angle and 2 m/s wind speed for a LAD of 8.47 m 2 /m 3 , and a 36° tilt angle and 3 m/s wind speed for a LAD of 11.12 m 2 /m 3 . At 1 m/s, droplet deposition followed a downward parabolic trend with changes in nozzle tilt angle, whereas at 2 m/s, deposition followed an upward parabolic trend. At a side wind speed of 3 m/s, droplet deposition remained unchanged with nozzle tilt angle but decreased with increasing canopy density. Nonlinear regression analysis indicated that leaf area density had a greater impact on deposition differences than droplet size, with droplet penetration decreasing as leaf area density increased. This study provides a reference for enhancing fog droplet penetration techniques in plant protection operations, offering practical guidelines for optimizing spraying conditions and improving pesticide use efficiency in different canopy structures., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sun, Huang, Hu, Jiang, Song and Xue.)