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A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards.

Yaning Zhai1, Ling Zhang1, Xin Hu2

  • 1Guangxi Technological College of Machinery and Electricity, Nanning 530007, China.

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Summary

This study introduces a new method for tracking and counting fruits in orchards using an improved YOLO object detection and a Kalman filter. The approach enhances precision agriculture by enabling accurate, continuous fruit monitoring in dynamic video scenes.

Keywords:
Kalman filteringfruit countingintelligent orchardmulti-object trackingtarget detection

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Area of Science:

  • Agricultural intelligence
  • Computer vision
  • Precision agriculture

Background:

  • Deep learning models struggle with dynamic orchard scenes for fruit detection and counting.
  • Existing methods are limited to static, single-frame image processing.
  • Optimizing orchard management requires advanced automated monitoring solutions.

Purpose of the Study:

  • To develop a robust multi-object fruit tracking and counting method for dynamic orchard environments.
  • To improve the accuracy and stability of fruit detection and counting in video sequences.
  • To advance precision agriculture through intelligent automated fruit monitoring.

Main Methods:

  • Integration of an improved YOLO object detection algorithm for high-quality initial detection.
  • Application of a dynamically optimized Kalman filter with a variable forgetting factor for adaptive tracking.
  • Utilizing a combined Intersection over Union (IoU) and Re-Identification (Re-ID) strategy for accurate target association.

Main Results:

  • Achieved robust and continuous fruit tracking with a Multiple Object Tracking Accuracy (MOTA) of 95.0% and a Higher Order Tracking Accuracy (HOTA) of 82.4%.
  • Demonstrated high accuracy and stability in fruit counting, with a coefficient-of-determination of 0.85 and a root-mean-square error (RMSE) of 1.57.
  • Validated the method's effectiveness in complex orchard environments using video sequences.

Conclusions:

  • The proposed method effectively addresses the limitations of static image analysis for fruit monitoring.
  • The integration of improved YOLO and Kalman filtering provides accurate and stable fruit detection, tracking, and counting.
  • This technology significantly contributes to optimizing orchard management and advancing precision agriculture.