Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/8/1643 |
| _version_ | 1797585823619612672 |
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| author | Xiang Yue Kai Qi Xinyi Na Yang Zhang Yanhua Liu Cuihong Liu |
| author_facet | Xiang Yue Kai Qi Xinyi Na Yang Zhang Yanhua Liu Cuihong Liu |
| author_sort | Xiang Yue |
| collection | DOAJ |
| description | 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<sub>@0.5</sub>) 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. |
| first_indexed | 2024-03-11T00:12:31Z |
| format | Article |
| id | doaj.art-78d931473ede4e778c1ced16118be87d |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T00:12:31Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-78d931473ede4e778c1ced16118be87d2023-11-18T23:53:02ZengMDPI AGAgriculture2077-04722023-08-01138164310.3390/agriculture13081643Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth StageXiang Yue0Kai Qi1Xinyi Na2Yang Zhang3Yanhua Liu4Cuihong Liu5College of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaThe 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<sub>@0.5</sub>) 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.https://www.mdpi.com/2077-0472/13/8/1643YOLOv8instance segmentationdisease detectionmaturity segmentation |
| spellingShingle | Xiang Yue Kai Qi Xinyi Na Yang Zhang Yanhua Liu Cuihong Liu Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage Agriculture YOLOv8 instance segmentation disease detection maturity segmentation |
| title | Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage |
| title_full | Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage |
| title_fullStr | Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage |
| title_full_unstemmed | Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage |
| title_short | Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage |
| title_sort | improved yolov8 seg network for instance segmentation of healthy and diseased tomato plants in the growth stage |
| topic | YOLOv8 instance segmentation disease detection maturity segmentation |
| url | https://www.mdpi.com/2077-0472/13/8/1643 |
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