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Farmland Soil Block Identification and Distribution Statistics Based on Deep Learning

Author

Listed:
  • Lichao Liu

    (College of Engineering, Anhui Agricultural University, Hefei 230036, China
    Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment, Hefei 230036, China
    Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230036, China)

  • Quanpeng Bi

    (College of Engineering, Anhui Agricultural University, Hefei 230036, China)

  • Jing Liang

    (College of Engineering, Anhui Agricultural University, Hefei 230036, China)

  • Zhaodong Li

    (College of Engineering, Anhui Agricultural University, Hefei 230036, China
    Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment, Hefei 230036, China)

  • Weiwei Wang

    (College of Engineering, Anhui Agricultural University, Hefei 230036, China
    Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment, Hefei 230036, China
    Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230036, China)

  • Quan Zheng

    (College of Engineering, Anhui Agricultural University, Hefei 230036, China
    Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment, Hefei 230036, China)

Abstract

Soil block distribution is one of the important indexes to evaluate the tillage performance of agricultural machinery. The traditional manual screening methods have the problems of low efficiency and damaging the original surface of the soil. This study proposes a statistical method of farmland soil block distribution based on deep learning. This method combines the adaptive learning rate and squeeze-and-excitation networks channel attention mechanism based on the original Mask-RCNN and uses the improved model to identify, segment and distribute statistics of the farmland soil blocks. Firstly, the influence of different learning rates and an improved Mask-RCNN algorithm model on training results were analyzed. Secondly, the effectiveness of the model in soil block identification and size measurement was analyzed. Finally, the identified soil blocks were classified accordingly, and the scale problem of soil block distribution after removing edge soil blocks was analyzed. The results show that with the decrease of learning rate, the loss value of model training decreases and the prediction accuracy of model is improved. The average precision value of the improved model increased by 25.29 %, and the recall value increased by 8.92%. The correlation coefficient of the maximum diameter measured by manual measurement and the maximum diameter measured by model algorithm was 0.99, which verifies the feasibility of the algorithm model. The prediction error of the model is the smallest when the camera height is 40 cm. Large-scale detection of soil block size in an experimental field in Hefei, Anhui, with an average confidence of over 97%. At the same time, the soil block is effectively classified according to the set classification standard. This study can provide an effective method for the accurate classification of soil block size and can provide a quantitative basis for the control of farmland cultivation intensity.

Suggested Citation

  • Lichao Liu & Quanpeng Bi & Jing Liang & Zhaodong Li & Weiwei Wang & Quan Zheng, 2022. "Farmland Soil Block Identification and Distribution Statistics Based on Deep Learning," Agriculture, MDPI, vol. 12(12), pages 1-17, November.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2038-:d:987156
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    References listed on IDEAS

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    1. Md. Reduanul Haque & Ferdous Sohel, 2022. "Deep Network with Score Level Fusion and Inference-Based Transfer Learning to Recognize Leaf Blight and Fruit Rot Diseases of Eggplant," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    2. Qiang Cui & Baohua Yang & Biyun Liu & Yunlong Li & Jingming Ning, 2022. "Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning," Agriculture, MDPI, vol. 12(8), pages 1-16, July.
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    Cited by:

    1. Xiang Yue & Kai Qi & Xinyi Na & Yang Zhang & Yanhua Liu & Cuihong Liu, 2023. "Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage," Agriculture, MDPI, vol. 13(8), pages 1-15, August.

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