Apple Trajectory Prediction in Orchards: A YOLOv8-EK-IPF Approach

Jinxing Niu, Zhengyi Liu, Shuo Wang, Jiaxi Huang and Junlong Zhao ()
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Jinxing Niu: School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Zhengyi Liu: School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Shuo Wang: School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Jiaxi Huang: School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Junlong Zhao: School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China

Agriculture, 2025, vol. 15, issue 11, 1-23

Abstract: To address the challenge of accurate apple harvesting by orchard robots, which is hindered by dynamic changes in apple position due to wind interference and branch swaying, this study proposes an optimized prediction algorithm based on an integration of the extended Kalman filter (EKF) and an improved particle filter (IPF), built upon initial apple detection and recognition using YOLOv8. The algorithm first employs spatial partitioning according to the cyclical motion patterns of apples to constrain the prediction results. Subsequently, it optimizes the rationality of particle weights within the particle filter (PF) and reduces its computational resource consumption by implementing historical position weighting and an adaptive particle number strategy. Finally, an adaptive error correction mechanism dynamically adjusts the respective weights of the EKF and IPF components, continuously enhancing the algorithm’s prediction accuracy. Experimental results demonstrate that, compared to the classic unscented Kalman filter (UKF) and unscented particle filter (UPF), the proposed EK-IPF algorithm reduces the mean absolute error (MAE) by 22.25% and 10.89%, respectively, and the root mean square error (RMSE) by 23.70% and 13.25%, respectively, indicating a significant improvement in overall prediction accuracy. This research provides technical support for dynamic apple trajectory prediction in orchard environments.

Keywords: extended Kalman filter; particle filter; trajectory prediction; state estimation; YOLOv8; apple (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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