Research on Apple Detection and Tracking Count in Complex Scenes Based on the Improved YOLOv7-Tiny-PDE

Dongxuan Cao, Wei Luo, Ruiyin Tang, Yuyan Liu (), Jiasen Zhao, Xuqing Li and Lihua Yuan
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Dongxuan Cao: North China Institute of Aerospace Engineering, Langfang 065000, China
Wei Luo: North China Institute of Aerospace Engineering, Langfang 065000, China
Ruiyin Tang: North China Institute of Aerospace Engineering, Langfang 065000, China
Yuyan Liu: North China Institute of Aerospace Engineering, Langfang 065000, China
Jiasen Zhao: North China Institute of Aerospace Engineering, Langfang 065000, China
Xuqing Li: North China Institute of Aerospace Engineering, Langfang 065000, China
Lihua Yuan: North China Institute of Aerospace Engineering, Langfang 065000, China

Agriculture, 2025, vol. 15, issue 5, 1-26

Abstract: Accurately detecting apple fruit can crucially assist in estimating the fruit yield in apple orchards in complex scenarios. In such environments, the factors of density, leaf occlusion, and fruit overlap can affect the detection and counting accuracy. This paper proposes an improved YOLOv7-Tiny-PDE network model based on the YOLOv7-Tiny model to detect and count apples from data collected by drones, considering various occlusion and lighting conditions. First, within the backbone network, we replaced the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv), reducing the network parameters and computational redundancy while maintaining the detection accuracy. Second, in the neck network, we used a dynamic detection head to replace the original detection head, effectively suppressing the background interference and capturing the background information more comprehensively, thus enhancing the detection accuracy for occluded targets and improving the fruit feature extraction. To further optimize the model, we replaced the boundary box loss function from CIOU to EIOU. For fruit counting across video frames in complex occlusion scenes, we integrated the improved model with the DeepSort tracking algorithm based on Kalman filtering and motion trajectory prediction with a cascading matching algorithm. According to experimental results, compared with the baseline YOLOv7-Tiny, the improved model reduced the total parameters by 22.2% and computation complexity by 18.3%. Additionally, in data testing, the p -value improved by 0.5%; the R-value rose by 2.7%; the mAP and F1 scores rose by 4% and 1.7%, respectively; and the MOTA value improved by 2%. The improved model is more lightweight and can preserve a high detection accuracy well, and hence, it can be applied to detection and counting tasks in complex orchards and provides a new solution for fruit yield estimation using lightweight devices.

Keywords: smart orchard; improved YOLOv7-Tiny; occlusion; DyHead; EIoU; DeepSort; lightweight (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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