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使用深度学习和无人机图像用于精确的除草剂应用,检测teff作物中的杂草

Alemu Setargew Kebede¹, Tsehay Wasihun Muluneh¹, Abebe Belay Adege^2,3

¹Department of Information Technology, University of Gondar, Gondar, Ethiopia.

Scientific reports

|August 21, 2025

概括

这项研究开发了一种人工智能模型,用于精确地检测Teff田中的杂草,并通过无人机实现有针对性的除草剂应用. 这提高了埃塞俄比亚的作物生产率和粮食安全.

科学领域:

农业科学
计算机科学
人工智能

背景情况:

由于杂草和肥料管理效率低下,
传统的除草是劳动密集的,不准确的,
尤其是在大田里, 区分Teff和类似的杂草是很困难的.

研究的目的:

开发一个优化的深度学习模型来准确地检测Teff领域的杂草.
通过无人驾驶飞行器 (UAV) 实现选择性和高效的除草剂应用.
提高特夫农作物的生产率,并促进粮食安全.

主要方法:

我们收集了1308张高分辨率无人机图像.
使用基于形状的特征 (面积比,坚度) 和数据增强.
评估了包括MobileNetV2,InceptionResNetV2,DenseNet201,VGG16,Resnet50,Fast R-CNN和YOLOv8在内的深度学习模型
微调模型和各种优化器进行了实验.

主要成果:

微调的MobileNetV2在杂草检测方面达到最高准确率96.40%.
该模型表现出适应不同数据特性的能力,并尽量减少过度拟合.
这种方法可以通过无人机选择性有效地应用除草剂.

结论:

人工智能驱动的杂草检测为Teff种植中的精准农业提供了变革性的解决方案.
优化的深度学习模型显示出实践实施的巨大潜力.
这项技术可以改善杂草管理,提高Teff作物的产量,

关键词:

深度学习无人机技术精准农业检测杂草

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