FGA-Corn:使用深度学习视觉,在中叶区域精确应用农药的综合系统
Zhongqiang Song1,2, Wenqiang Li2, Xuehang Song2
1School of Physics and Electronic Information, Weifang University, Weifang, Shandong, China.
Frontiers in plant science
|July 23, 2025
概括
本研究介绍了FGA-Corn系统,用于在玉米中精确地应用农药,减少浪费和污染. 该系统在检测中心叶子和有效地输送农药方面实现了高精度.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 精准农业 精准农业 精准农业
背景情况:
- 在玉米种植中传统的农药喷导致了严重的环境污染和资源浪费.
- 越来越需要智能农业设备,以实现精确高效的害虫和疾病预防.
研究的目的:
- 开发和评估一个综合系统,FGA-Corn,用于在玉米中精确地应用农药.
- 提高农药输送的准确性和效率,最大限度地减少对环境的影响.
主要方法:
- FGA-Corn系统集成了前摄像头后道 (FCRF) 机械结构,农业喷雾决策系统 (ASDS) 算法和GMA-YOLOv8检测算法.
- 该GMA-YOLOv8算法使用了新的GHG2S骨干与GhostConv和SimAM进行特征提取,以及混合本地频道注意模块用于多尺度特征融合.
主要成果:
- 在两个数据集上,GMA-YOLOv8算法实现了94.5%和90.1%的mAP@0.5得分,模型大小减少了23.3%.
- 实地实验表明,中叶检测准确度为91.3 ± 1.9%,农药输送率为84.1 ± 3.3%,输送精度为92.2 ± 2.9%.
结论:
- FGA-Corn系统为玉米种植中精密喷提供了有效和准确的解决方案.
- 这项研究为可持续农业实践的智能农业机械技术进步做出了贡献.
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