Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method
Accurate and effective fruit tracking and counting are crucial for estimating tomato yield. In complex field environments, occlusion and overlap of tomato fruits and leaves often lead to inaccurate counting. To address these issues, this study proposed an improved lightweight YOLO11n network and an...
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MDPI AG
2025-06-01
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| Series: | Agriculture |
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| author | Aichen Wang Yuanzhi Xu Dong Hu Liyuan Zhang Ao Li Qingzhen Zhu Jizhan Liu |
| author_facet | Aichen Wang Yuanzhi Xu Dong Hu Liyuan Zhang Ao Li Qingzhen Zhu Jizhan Liu |
| author_sort | Aichen Wang |
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| description | Accurate and effective fruit tracking and counting are crucial for estimating tomato yield. In complex field environments, occlusion and overlap of tomato fruits and leaves often lead to inaccurate counting. To address these issues, this study proposed an improved lightweight YOLO11n network and an optimized region tracking-counting method, which estimates the quantity of tomatoes at different maturity stages. An improved lightweight YOLO11n network was employed for tomato detection and semantic segmentation, which was combined with the C3k2-F, Generalized Intersection over Union (GIoU), and Depthwise Separable Convolution (DSConv) modules. The improved lightweight YOLO11n model is adaptable to edge computing devices, enabling tomato yield estimation while maintaining high detection accuracy. An optimized region tracking-counting method was proposed, combining target tracking and region detection to count the detected fruits. The particle swarm optimization (PSO) algorithm was used to optimize the detection region, thus enhancing the counting accuracy. In terms of network lightweighting, compared to the original, the improved YOLO11n network significantly reduces the number of parameters and Giga Floating-point Operations Per Second (GFLOPs) by 0.22 M and 2.5 G, while achieving detection and segmentation accuracies of 91.3% and 90.5%, respectively. For fruit counting, the results showed that the proposed region tracking-counting method achieved a mean counting error (MCE) of 6.6%, representing a reduction of 5.0% and 2.1% compared to the Bytetrack and cross-line counting methods, respectively. Therefore, the proposed method provided an effective approach for non-contact, accurate, efficient, and real-time intelligent yield estimation for tomatoes. |
| format | Article |
| id | doaj-art-79281bc60aa94c30b3b2ea333936c14a |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-79281bc60aa94c30b3b2ea333936c14a2025-08-20T03:28:24ZengMDPI AGAgriculture2077-04722025-06-011513135310.3390/agriculture15131353Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting MethodAichen Wang0Yuanzhi Xu1Dong Hu2Liyuan Zhang3Ao Li4Qingzhen Zhu5Jizhan Liu6School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaCollege of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaAccurate and effective fruit tracking and counting are crucial for estimating tomato yield. In complex field environments, occlusion and overlap of tomato fruits and leaves often lead to inaccurate counting. To address these issues, this study proposed an improved lightweight YOLO11n network and an optimized region tracking-counting method, which estimates the quantity of tomatoes at different maturity stages. An improved lightweight YOLO11n network was employed for tomato detection and semantic segmentation, which was combined with the C3k2-F, Generalized Intersection over Union (GIoU), and Depthwise Separable Convolution (DSConv) modules. The improved lightweight YOLO11n model is adaptable to edge computing devices, enabling tomato yield estimation while maintaining high detection accuracy. An optimized region tracking-counting method was proposed, combining target tracking and region detection to count the detected fruits. The particle swarm optimization (PSO) algorithm was used to optimize the detection region, thus enhancing the counting accuracy. In terms of network lightweighting, compared to the original, the improved YOLO11n network significantly reduces the number of parameters and Giga Floating-point Operations Per Second (GFLOPs) by 0.22 M and 2.5 G, while achieving detection and segmentation accuracies of 91.3% and 90.5%, respectively. For fruit counting, the results showed that the proposed region tracking-counting method achieved a mean counting error (MCE) of 6.6%, representing a reduction of 5.0% and 2.1% compared to the Bytetrack and cross-line counting methods, respectively. Therefore, the proposed method provided an effective approach for non-contact, accurate, efficient, and real-time intelligent yield estimation for tomatoes.https://www.mdpi.com/2077-0472/15/13/1353deep learningnetwork lightweightingregion tracking-countingtomatoyield estimation |
| spellingShingle | Aichen Wang Yuanzhi Xu Dong Hu Liyuan Zhang Ao Li Qingzhen Zhu Jizhan Liu Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method Agriculture deep learning network lightweighting region tracking-counting tomato yield estimation |
| title | Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method |
| title_full | Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method |
| title_fullStr | Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method |
| title_full_unstemmed | Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method |
| title_short | Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method |
| title_sort | tomato yield estimation using an improved lightweight yolo11n network and an optimized region tracking counting method |
| topic | deep learning network lightweighting region tracking-counting tomato yield estimation |
| url | https://www.mdpi.com/2077-0472/15/13/1353 |
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