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

Xiang Yue, Kai Qi, Xinyi Na, Yang Zhang, Yanhua Liu and Cuihong Liu ()
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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

Agriculture, 2023, vol. 13, issue 8, 1-15

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.

Keywords: YOLOv8; instance segmentation; disease detection; maturity segmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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