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When using deep neural networks for the unmanned aerial vehicle remote sensing image detection and recognition of pine wilt disease (PWD), it could be found that the model is vulnerable to adversarial samples and may lead to abnormal recognition results. That is, serious errors in model classification and localization can be caused by adding minor perturbations, which are difficult for the human eye to detect, to the original samples. Traditional defense strategies rely heavily on adversarial training, but this defense always lags behind the pace of attack. In order to solve this problem, based on the YOLOv5 model, an improved YOLOV5-DRCS model with an adaptive shrinkage filtering network is proposed as follows, which enables the model to maintain relatively stable robustness after being attacked: soft threshold filtering is used in the feature extraction module, the threshold value is calculated based on the adaptive structural unit for denoising, and a SimAM attention mechanism is added in the feature layer fusion so that the final result has more global attention. In order to evaluate the effectiveness of this method, the fast gradient symbol method with white-box attacks was used to conduct an attack test on the remote sensing image dataset of pine wood nematode disease. The results showed that when the number of samples increased by 40%, the average accuracy of 92.5%, 92.4%, 91.0%, and 90.1% on the counter disturbance coefficients ϵ ∈ {2,4,6,8} was maintained, respectively, indicating that the proposed method could significantly improve the robustness and accuracy of the model when faced with the challenge of counter samples. [ABSTRACT FROM AUTHOR]

Different agronomic requirements, production conditions, and crop species result in varying row spacings. To address the issue of seedling damage caused by pressure when a fixed track width sprayer operates in different row spacings and enhance the accuracy of track width adjustment, this study designed a track width adjustment system for a sprayer based on the agronomic requirements for field management during the early and mid-stages of corn growth and the entire growth period of wheat in Henan Province, China. The designed track width adjustment system for the sprayer comprised transmission mechanisms, telescopic track width adjustment mechanisms, and an electro-hydraulic control system. The control system achieved a precise track width adjustment by controlling the movement of the hydraulic cylinders through electrical signals, forming a closed-loop adjustment system with the aid of sensors. Four control schemes are proposed: classical PID, integral separated PID, fuzzy adaptive PID, and integral separated fuzzy PID. Simulation experiments were conducted using MATLAB to compare these schemes. The results indicated that the integral separated fuzzy PID exhibited the fastest response and highest steady-state accuracy. The performance of the track width adjustment system was validated through field experiments. The results demonstrate that the stability coefficient of variation for the track width adjustment was 3.04%, which is below the 10% threshold required by agricultural machinery standards. Additionally, the average error of the track width adjustment was 13.42 mm, indicating high precision and effectively reducing seedling compression damage during plant protection operations. [ABSTRACT FROM AUTHOR]