Research on an Apple Recognition and Yield Estimation Model Based on the Fusion of Improved YOLOv11 and DeepSORT

Zhanglei Yan, Yuwei Wu, Wenbo Zhao, Shao Zhang and Xu Li ()
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Zhanglei Yan: Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, College of Information Engineering, Tarim University, Alar 843300, China
Yuwei Wu: Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, College of Information Engineering, Tarim University, Alar 843300, China
Wenbo Zhao: Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, College of Information Engineering, Tarim University, Alar 843300, China
Shao Zhang: College of Information and Electrical Engineering, China Agricultural University (CAU), Beijing 100107, China
Xu Li: Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, College of Information Engineering, Tarim University, Alar 843300, China

Agriculture, 2025, vol. 15, issue 7, 1-25

Abstract: Accurate apple yield estimation is essential for effective orchard management, market planning, and ensuring growers’ income. However, complex orchard conditions, such as dense foliage occlusion and overlapping fruits, present challenges to large-scale yield estimation. This study introduces APYOLO, an enhanced apple detection algorithm based on an improved YOLOv11, integrated with the DeepSORT tracking algorithm to improve both detection accuracy and operational speed. APYOLO incorporates a multi-scale channel attention (MSCA) mechanism and an enhanced multi-scale prior distribution intersection over union (EnMPDIoU) loss function to enhance target localization and recognition under complex environments. Experimental results demonstrate that APYOLO outperforms the original YOLOv11 by improving mAP@0.5, mAP@0.5–0.95, accuracy, and recall by 2.2%, 2.1%, 0.8%, and 2.3%, respectively. Additionally, the combination of a unique ID with the region of line (ROL) strategy in DeepSORT further boosts yield estimation accuracy to 84.45%, surpassing the performance of the unique ID method alone. This study provides a more precise and efficient system for apple yield estimation, offering strong technical support for intelligent and refined orchard management.

Keywords: YOLOv11; DeepSORT; crop detection; crop counting (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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