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使用深度学习方法对野豆叶病的分类

Dagne Walle Girmaw¹, Tsehay Wasihun Muluneh²

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

|July 25, 2024

概括

这项研究引入了一种自动化方法,用于通过转移学习来诊断田间豆叶病. 这种方法可以准确地识别像阿斯科奇塔虫 (Ascochyta blight) 这样的疾病,从而减少农民的成本和时间.

科学领域:

农业科学农业科学
植物病理学植物病理学
计算机视觉计算机视觉

背景情况:

田间豆是埃塞俄比亚小农的重要作物,为农民提供食物,料和收入.
叶子疾病,如阿斯科奇塔,粉和叶子斑,显著降低了田间豆的产量和质量.
目前的疾病检测方法依赖于视觉观察,这是昂贵的,劳动密集型,缺乏精度.

研究的目的:

开发一种自动化,准确和具有成本效益的方法来诊断田间豆叶病.
为了分类三种常见的田间豆叶病:阿斯科奇塔,叶斑和粉.

主要方法:

转移学习方法用于自动疾病诊断.
一个带有软max分类器的DenseNet121模型在1600张健康和患病的田间豆叶的图像上进行了训练和验证.
该模型在分类Ascochyta病,叶子斑和粉状菌方面的准确性进行了测试.

主要成果:

在100个时代后,DenseNet121模型实现了高准确率:培训99.73%,验证99.16%,测试98.33%.
开发的模型显示了对准确地检测野病的显著潜力.

结论:

转移学习方法提供了一种快速,自动化和更便宜的替代方法,用于检测田间病的传统视觉检查.
这项研究为农民和农业专家提供了有价值的工具,以改善作物管理和产量.

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