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Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage

Author

Listed:
  • Xiang Yue

    (College of Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Kai Qi

    (College of Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Xinyi Na

    (College of Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yang Zhang

    (College of Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yanhua Liu

    (College of Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Cuihong Liu

    (College of Engineering, Shenyang Agricultural University, Shenyang 110866, China)

Abstract

The spread of infections and rot are crucial factors in the decrease in tomato production. Accurately segmenting the affected tomatoes in real-time can prevent the spread of illnesses. However, environmental factors and surface features can affect tomato segmentation accuracy. This study suggests an improved YOLOv8s-Seg network to perform real-time and effective segmentation of tomato fruit, surface color, and surface features. The feature fusion capability of the algorithm was improved by replacing the C2f module with the RepBlock module (stacked by RepConv), adding SimConv convolution (using the ReLU function instead of the SiLU function as the activation function) before two upsampling in the feature fusion network, and replacing the remaining conventional convolution with SimConv. The F1 score was 88.7%, which was 1.0%, 2.8%, 0.8%, and 1.1% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm, respectively. Meanwhile, the segment mean average precision (segment mAP @0.5 ) was 92.2%, which was 2.4%, 3.2%, 1.8%, and 0.7% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm. The algorithm can perform real-time instance segmentation of tomatoes with an inference time of 3.5 ms. This approach provides technical support for tomato health monitoring and intelligent harvesting.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1643-:d:1221598
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    References listed on IDEAS

    as
    1. 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.
    2. Jaesu Lee & Haseeb Nazki & Jeonghyun Baek & Youngsin Hong & Meonghun Lee, 2020. "Artificial Intelligence Approach for Tomato Detection and Mass Estimation in Precision Agriculture," Sustainability, MDPI, vol. 12(21), pages 1-15, November.
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