Detection and tracking of agricultural spray droplets using GSConv-enhanced YOLOv5s and DeepSORT

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2025-04-09 DOI:10.1016/j.compag.2025.110353

Chen Shengde , Liu Junyu , Xu Xiaojie , Guo Jianzhou , Hu Shiyun , Zhou Zhiyan , Lan Yubin

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引用次数: 0

Abstract

Accurate detection and tracking of agricultural spray droplets are crucial for optimizing spraying efficiency and ensuring uniform pesticide application. This study presents an improved droplet detection and tracking framework by enhancing the YOLOv5s model with GSConv technology, thereby improving droplet detection accuracy. To enhance tracking robustness, DeepSORT was integrated with Kalman filtering, effectively incorporating motion and appearance information. Experimental results demonstrate that the proposed method achieves a detection frame rate of 105 fps and an [email protected] of 0.9184, indicating high precision across different recall rates. Additionally, tracking performance was evaluated against manual droplet counting across five test videos, yielding a mean absolute percentage error (MAPE) of 6.434 %, further validating the accuracy and reliability of the system. These results highlight the potential of the proposed approach for real-time monitoring of spray quality, facilitating precise control of spraying parameters, and contributing to advancements in precision agriculture.

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基于gsconvs增强的YOLOv5s和DeepSORT的农业喷雾液滴检测与跟踪

农业喷雾液滴的准确检测和跟踪是优化喷洒效率和保证均匀施药的关键。本研究通过GSConv技术增强YOLOv5s模型，提出了一种改进的液滴检测和跟踪框架，从而提高了液滴检测精度。为了增强跟踪鲁棒性，将深度排序与卡尔曼滤波相结合，有效地融合了运动和外观信息。实验结果表明，该方法的检测帧率为105 fps， [email protected]的检测帧率为0.9184，在不同的召回率下具有较高的检测精度。此外，通过5个测试视频对人工液滴计数的跟踪性能进行了评估，平均绝对百分比误差（MAPE）为6.434%，进一步验证了系统的准确性和可靠性。这些结果突出了该方法在实时监测喷雾质量、促进喷雾参数精确控制以及促进精准农业发展方面的潜力。

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

期刊介绍： Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.