使用先进图像处理和YOLOv8识别作物疾病的有效方法
Muhammad Nouman Noor1, Muhammad Masab2, Farah Haneef3
1Department of AI and Data Science National University of Computer and Emerging Sciences (FAST-NUCES) Islamabad Pakistan.
Food science & nutrition
|February 12, 2026
概括
这项研究引入了一种使用深度学习 (YOLOv8) 的计算机辅助方法,用于在作物中早期检测植物疾病. 人工智能模型准确识别了32种疾病,改善了农业监测,减少了作物损失.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 植物疾病威胁到经济作物 (番茄,咖啡,小麦) 和全球粮食安全,特别是在亚洲.
- 传统的疾病检测是缓慢的,劳动密集的,缺乏可访问的数据,阻碍了实际应用.
- 计算机辅助检测对于有效和可扩展的作物疾病管理至关重要.
研究的目的:
- 开发和评估一个计算机辅助系统,用图像分析来检测和分类作物疾病.
- 利用深度学习,特别是YOLOv8,进行植物疾病的准确细分和分类.
- 通过自动识别改进早期疾病诊断并最大限度地减少作物损失.
主要方法:
- 应用了图像处理技术 (局部对比增强,波形变换,中间选).
- YOLOv8深度学习模型是使用转移学习对32种作物疾病的混合数据集进行训练的.
- 绩效使用诸如回忆和整体准确度等指标进行评估.
主要成果:
- YOLOv8模型在细分和分类作物疾病方面取得了高性能.
- 实现了0.94的回忆和92.567%的整体准确性.
- 在各种疾病识别场景中表现出可靠的性能.
结论:
- 开发的计算机辅助系统增强了对关键作物的早期疾病检测.
- 人工智能方法减少了对疾病诊断的专家干预的依赖.
- 这项技术有助于防止重大作物损失,并加强粮食安全.
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