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    Design and simulation of attitude adjustment system for tillage equipment in hilly and mountainous areas
    SHI Zheng-fa, YUAN Kui, YU Jia-bin, HU Yong, HE Cheng-long, ZHANG Fu-gui, AI Yong-feng
    HUBEI AGRICULTURAL SCIENCES    2024, 63 (6): 207-212.   DOI: 10.14088/j.cnki.issn0439-8114.2024.06.034
    Abstract206)      PDF (3541KB)(12)       Save
    In response to the problem of low adjustment and control accuracy of the existing attitude adjustment system for tillage equipment in hilly and mountainous areas under complex operating conditions, which led to poor stability and safety of the equipment operation,based on the lateral attitude adjustment system of the equipment, a set of attitude adjustment system suitable for hilly and mountainous tillage operations was designed using the fuzzy PID control algorithm. Compared with the conventional PID control algorithm, based on the dynamic modeling of the attitude adjustment system, simulation analysis was conducted using MATLAB. The results showed that the lateral attitude adjustment time of the conventional PID control algorithm was 4.5 seconds, and the overshoot of the lateral attitude angle was 0.89°;the lateral attitude adjustment time of the implement using the fuzzy PID control algorithm was 1.9 seconds, and there was basically no overshoot of the lateral attitude angle. The simulation test results had verified the feasibility, correctness, and scientificity of the attitude adjustment system of the equipment. The accuracy and stability performance of the adjustment system met the requirements of complex working conditions in hilly and mountainous cross slope operations.
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    Real-time segmentation model for abnormal growth areas in farmland based on multispectral aerial images
    HU Hai-yang, CHEN Jian, ZHANG Li-lian, YANG Lin-nan
    HUBEI AGRICULTURAL SCIENCES    2024, 63 (6): 198-203.   DOI: 10.14088/j.cnki.issn0439-8114.2024.06.032
    Abstract197)      PDF (5089KB)(34)       Save
    In response to the problem of abnormal segmentation in farmland, a feature fusion skip connection module and a global-local attention module were used to improve the UNet network model. A real-time segmentation network for abnormal farmland areas was proposed, which achieved fine segmentation of various abnormal farmland areas. The results showed that the Mean Intersection Union ratio (MIoU) of the real-time segmentation model for abnormal growth areas in farmland was significantly better than that of other models, with a MIoU of 41.24%;compared to the model using UNet as the baseline, although the number of parameters in this study model had slightly increased, the farmland segmentation effect had significantly improved, with an increase of 4.16 percentage points in MIoU;compared with the SegFormer model based on Transformer encoder, the parameter count of this study model was basically the same, with an increase of 2.50 percentage points in MIoU. This research model ensured excellent segmentation performance in each category by using adaptive sampling training methods. Using multispectral aerial images to train a real-time segmentation model for abnormal growth areas in farmland could help achieve real-time monitoring and early warning of farmland growth by drones, promote the development of smart agriculture, and provide new methods and ideas for automatic monitoring of farmland growth.
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