A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model
With the rapid development of agriculture, tomatoes, as an important economic crop, require accurate ripeness recognition technology to enable selective harvesting. Therefore, intelligent tomato ripeness recognition plays a crucial role in agricultural production. However, factors such as lighting c...
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MDPI AG
2024-12-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/11/1/15 |
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| author | Zhanshuo Yang Yaxian Li Qiyu Han Haoming Wang Chunjiang Li Zhandong Wu |
| author_facet | Zhanshuo Yang Yaxian Li Qiyu Han Haoming Wang Chunjiang Li Zhandong Wu |
| author_sort | Zhanshuo Yang |
| collection | DOAJ |
| description | With the rapid development of agriculture, tomatoes, as an important economic crop, require accurate ripeness recognition technology to enable selective harvesting. Therefore, intelligent tomato ripeness recognition plays a crucial role in agricultural production. However, factors such as lighting conditions and occlusion lead to issues such as low detection accuracy, false detections, and missed detections. Thus, a deep learning algorithm for tomato ripeness detection based on an improved YOLOv8n is proposed in this study. First, the improved YOLOv8 model is used for tomato target detection and ripeness classification. The RCA-CBAM (Region and Color Attention Convolutional Block Attention Module) module is introduced into the YOLOv8 backbone network to enhance the model’s focus on key features. By incorporating attention mechanisms across three dimensions—color, channel, and spatial attention—the model’s ability to recognize changes in tomato color and spatial positioning is improved. Additionally, the BiFPN (Bidirectional Feature Pyramid Network) module is introduced to replace the traditional PANet connection, which achieves efficient feature fusion across different scales of tomato skin color, size, and surrounding environment and optimizes the expression ability of the feature map. Finally, an Inner-FocalerIoU loss function is designed and integrated to address the difficulty of ripeness classification caused by class imbalance in the samples. The results show that the improved YOLOv8+ model is capable of accurately recognizing the ripeness level of tomatoes, achieving relatively high values of 95.8% precision value and 91.7% accuracy on the test dataset. It is concluded that the new model has strong detection performance and real-time detection. |
| format | Article |
| id | doaj-art-9d6c550ca5054b1baa79551b7a358217 |
| institution | Kabale University |
| issn | 2311-7524 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj-art-9d6c550ca5054b1baa79551b7a3582172025-01-24T13:34:28ZengMDPI AGHorticulturae2311-75242024-12-011111510.3390/horticulturae11010015A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 ModelZhanshuo Yang0Yaxian Li1Qiyu Han2Haoming Wang3Chunjiang Li4Zhandong Wu5Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650504, ChinaWith the rapid development of agriculture, tomatoes, as an important economic crop, require accurate ripeness recognition technology to enable selective harvesting. Therefore, intelligent tomato ripeness recognition plays a crucial role in agricultural production. However, factors such as lighting conditions and occlusion lead to issues such as low detection accuracy, false detections, and missed detections. Thus, a deep learning algorithm for tomato ripeness detection based on an improved YOLOv8n is proposed in this study. First, the improved YOLOv8 model is used for tomato target detection and ripeness classification. The RCA-CBAM (Region and Color Attention Convolutional Block Attention Module) module is introduced into the YOLOv8 backbone network to enhance the model’s focus on key features. By incorporating attention mechanisms across three dimensions—color, channel, and spatial attention—the model’s ability to recognize changes in tomato color and spatial positioning is improved. Additionally, the BiFPN (Bidirectional Feature Pyramid Network) module is introduced to replace the traditional PANet connection, which achieves efficient feature fusion across different scales of tomato skin color, size, and surrounding environment and optimizes the expression ability of the feature map. Finally, an Inner-FocalerIoU loss function is designed and integrated to address the difficulty of ripeness classification caused by class imbalance in the samples. The results show that the improved YOLOv8+ model is capable of accurately recognizing the ripeness level of tomatoes, achieving relatively high values of 95.8% precision value and 91.7% accuracy on the test dataset. It is concluded that the new model has strong detection performance and real-time detection.https://www.mdpi.com/2311-7524/11/1/15YOLOv8 modeltomato detectionripeness recognitiondeep learning |
| spellingShingle | Zhanshuo Yang Yaxian Li Qiyu Han Haoming Wang Chunjiang Li Zhandong Wu A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model Horticulturae YOLOv8 model tomato detection ripeness recognition deep learning |
| title | A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model |
| title_full | A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model |
| title_fullStr | A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model |
| title_full_unstemmed | A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model |
| title_short | A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model |
| title_sort | method for tomato ripeness recognition and detection based on an improved yolov8 model |
| topic | YOLOv8 model tomato detection ripeness recognition deep learning |
| url | https://www.mdpi.com/2311-7524/11/1/15 |
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