Real-Time Strawberry Ripeness Classification and Counting: An Optimized YOLOv8s Framework with Class-Aware Multi-Object Tracking

Oluwasegun Moses Ogundele, Niraj Tamrakar, Jung-Hoo Kook, Sang-Min Kim, Jeong-In Choi, Sijan Karki, Timothy Denen Akpenpuun and Hyeon Tae Kim ()
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Oluwasegun Moses Ogundele: Department of Bio-Systems Engineering, Gyeongsang National University, (Institute of Smart Space Agriculture (ISSA)), Jinju 52828, Republic of Korea
Niraj Tamrakar: Department of Bio-Systems Engineering, Gyeongsang National University, (Institute of Smart Space Agriculture (ISSA)), Jinju 52828, Republic of Korea
Jung-Hoo Kook: Department of Smart Farm, Gyeongsang National University, (Institute of Smart Space Agriculture (ISSA)), Jinju 52828, Republic of Korea
Sang-Min Kim: Department of Smart Farm, Gyeongsang National University, (Institute of Smart Space Agriculture (ISSA)), Jinju 52828, Republic of Korea
Jeong-In Choi: Department of Smart Farm, Gyeongsang National University, (Institute of Smart Space Agriculture (ISSA)), Jinju 52828, Republic of Korea
Sijan Karki: Department of Bio-Systems Engineering, Gyeongsang National University, (Institute of Smart Space Agriculture (ISSA)), Jinju 52828, Republic of Korea
Timothy Denen Akpenpuun: Department of Agricultural and Biosystems Engineering, University of Ilorin, PMB 1515, Ilorin 240103, Nigeria
Hyeon Tae Kim: Department of Bio-Systems Engineering, Gyeongsang National University, (Institute of Smart Space Agriculture (ISSA)), Jinju 52828, Republic of Korea

Agriculture, 2025, vol. 15, issue 18, 1-28

Abstract: Accurate fruit counting is crucial for data-driven decision-making in modern precision agriculture. In strawberry cultivation, a labor-intensive sector, automated, scalable yield estimation is especially critical. However, dense foliage, variable lighting, visual ambiguity of ripeness stages, and fruit clustering pose significant challenges. To overcome these, we developed a real-time multi-stage framework for strawberry detection and counting by optimizing a YOLOv8s detector and integrating a class-aware tracking system. The detector was enhanced with a lightweight C3x module, an additional detection head for small objects, and the Wise-IOU (WIoU) loss function, thereby improving performance against occlusion. Our final model achieved a 92.5% mAP@0.5, outperforming the baseline while reducing the number of parameters by 27.9%. This detector was integrated with the ByteTrack multiple object tracking (MOT) algorithm. Our system enabled accurate, automated fruit counting in complex greenhouse environments. When validated on video data, results showed a strong correlation with ground-truth counts (R 2 = 0.914) and a low mean absolute percentage error (MAPE) of 9.52%. Counting accuracy was highest for ripe strawberries (R 2 = 0.950), confirming the value for harvest-ready estimation. This work delivers an efficient, accurate, and resource-conscious solution for automated yield monitoring in commercial strawberry production.

Keywords: multi-object tracking; occlusion; strawberry detection; YOLOv8s; yield estimation (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: 2025
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