A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction
Accurate tomato yield estimation and ripeness monitoring are critical for optimizing greenhouse management. While manual counting remains labor-intensive and error-prone, this study introduces a novel vision-based framework for automated tomato counting in standardized greenhouse environments. The p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1135 |
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| author | Wanli Zheng Guanglin Dai Miao Hu Pengbo Wang |
| author_facet | Wanli Zheng Guanglin Dai Miao Hu Pengbo Wang |
| author_sort | Wanli Zheng |
| collection | DOAJ |
| description | Accurate tomato yield estimation and ripeness monitoring are critical for optimizing greenhouse management. While manual counting remains labor-intensive and error-prone, this study introduces a novel vision-based framework for automated tomato counting in standardized greenhouse environments. The proposed method integrates YOLOv8-based detection, depth filtering, and an inter-frame prediction algorithm to address key challenges such as background interference, occlusion, and double-counting. Our approach achieves 97.09% accuracy in tomato cluster detection, with mature and immature single fruit recognition accuracies of 92.03% and 91.79%, respectively. The multi-target tracking algorithm demonstrates a MOTA (Multiple Object Tracking Accuracy) of 0.954, outperforming conventional methods like YOLOv8 + DeepSORT. By fusing odometry data from an inspection robot, this lightweight solution enables real-time yield estimation and maturity classification, offering practical value for precision agriculture. |
| format | Article |
| id | doaj-art-60c86e1ae1ff4317bd8eab9122a293f5 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-60c86e1ae1ff4317bd8eab9122a293f52025-08-20T01:57:04ZengMDPI AGAgronomy2073-43952025-05-01155113510.3390/agronomy15051135A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame PredictionWanli Zheng0Guanglin Dai1Miao Hu2Pengbo Wang3Jiangsu Key Laboratory of Embodied Intelligent Robot Technology, College of Mechanical And Electrical Engineering, Soochow University, Suzhou 215123, ChinaJiangsu Key Laboratory of Embodied Intelligent Robot Technology, College of Mechanical And Electrical Engineering, Soochow University, Suzhou 215123, ChinaJiangsu Key Laboratory of Embodied Intelligent Robot Technology, College of Mechanical And Electrical Engineering, Soochow University, Suzhou 215123, ChinaJiangsu Key Laboratory of Embodied Intelligent Robot Technology, College of Mechanical And Electrical Engineering, Soochow University, Suzhou 215123, ChinaAccurate tomato yield estimation and ripeness monitoring are critical for optimizing greenhouse management. While manual counting remains labor-intensive and error-prone, this study introduces a novel vision-based framework for automated tomato counting in standardized greenhouse environments. The proposed method integrates YOLOv8-based detection, depth filtering, and an inter-frame prediction algorithm to address key challenges such as background interference, occlusion, and double-counting. Our approach achieves 97.09% accuracy in tomato cluster detection, with mature and immature single fruit recognition accuracies of 92.03% and 91.79%, respectively. The multi-target tracking algorithm demonstrates a MOTA (Multiple Object Tracking Accuracy) of 0.954, outperforming conventional methods like YOLOv8 + DeepSORT. By fusing odometry data from an inspection robot, this lightweight solution enables real-time yield estimation and maturity classification, offering practical value for precision agriculture.https://www.mdpi.com/2073-4395/15/5/1135agricultural roboticscomputer visionmultiple objects trackingtomato countingyield estimation |
| spellingShingle | Wanli Zheng Guanglin Dai Miao Hu Pengbo Wang A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction Agronomy agricultural robotics computer vision multiple objects tracking tomato counting yield estimation |
| title | A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction |
| title_full | A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction |
| title_fullStr | A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction |
| title_full_unstemmed | A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction |
| title_short | A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction |
| title_sort | robust tomato counting framework for greenhouse inspection robots using yolov8 and inter frame prediction |
| topic | agricultural robotics computer vision multiple objects tracking tomato counting yield estimation |
| url | https://www.mdpi.com/2073-4395/15/5/1135 |
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