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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Deep-learning-based in-field citrus fruit detection and tracking.

Wenli Zhang¹, Jiaqi Wang¹, Yuxin Liu¹

¹Information department, Beijing University of Technology, Beijing, 100022, China.

Horticulture Research

|February 11, 2022

Summary

This summary is machine-generated.

This study introduces an improved deep learning algorithm for accurate orange counting in videos, addressing issues of inconsistent detection and double-counting. The new method enhances fruit yield estimation for better harvesting and marketing strategies.

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Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Accurate fruit yield estimation is vital for agricultural logistics and market planning.
Existing computer vision methods for citrus fruit counting face challenges with detection accuracy and fruit duplication.

Purpose of the Study:

To develop a robust deep learning algorithm for precise orange counting from video sequences.
To overcome limitations of inconsistent detection and double-counting in automated fruit counting systems.

Main Methods:

A novel algorithm combining OrangeYolo (for fruit detection) and OrangeSort (for fruit tracking) was developed.
OrangeYolo utilizes an improved YOLOv3 architecture with attention mechanisms for multi-scale small object detection.
OrangeSort employs a tracking strategy based on motion displacement to prevent double-counting occluded fruits.

Main Results:

OrangeYolo achieved a mean Average Precision (mAP) of 0.957 on a citrus dataset, outperforming YOLOv3, YOLOv4, and YOLOv5.
The integrated OrangeYolo and OrangeSort method demonstrated superior performance with a Mean Absolute Error (MAE) of 0.081 and Standard Deviation (SD) of 0.08.
The proposed method significantly outperformed manual counting and existing tracking algorithms like Sort and DeepSort.

Conclusions:

The developed deep learning algorithm effectively enhances the accuracy of orange fruit counting from video data.
This approach offers a reliable solution for automated fruit yield estimation, improving agricultural management practices.