人工智能驱动的检测和决策支持系统,用于精确管理玉米菌
Jadesha G1, Anurag Dhole2, Deepak D2
1Plant Pathologist, College of Agriculture, GKVK, University of Agricultural Sciences, Bangalore, Karnataka, India.
PloS one
|March 9, 2026
概括
人工智能 (AI) 使用VGG16精确检测玉米菌 (MDM),改善作物保护. 一个人工智能决策支持工具在实地试验中提高了产量和利能力.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 玉米菌 (MDM) 导致大量的作物损失,需要早期检测.
- 传统的疾病检测方法可能耗时且不太准确.
- 人工智能为快速准确地识别植物疾病提供了潜力.
研究的目的:
- 评估机器学习 (ML) 和深度学习 (DL) 算法用于玉米菌检测.
- 开发一个可解释和准确的AI模型来分类健康和受感染的玉米叶.
- 创建一个决策支持系统 (DSS),用于农场层面的咨询和疾病管理.
主要方法:
- 在玉米叶图像的现场数据集上评估了13个ML/DL算法.
- 使用VGG16因为其在分类任务中的卓越性能.
- 采用t-SNE用于特征可视化和Grad-CAM用于模型可解释性.
- 开发了一个基于网络的应用程序,将AI分类与咨询措施集成在一起.
- 进行了为期两年的实地试验,以评估DSS引导的真菌杀菌剂应用.
主要成果:
- VGG16实现了高分类准确度 (97%),精度 (0.98),回忆 (0.95),F1得分 (0.97) 和AUC-ROC (0.99).
- 特征可视化和Grad-CAM证实了模型的准确性,并专注于相关的疾病症状.
- 基于网络的DSS指导了杀菌剂的应用,显著降低了疾病的严重程度,增加了谷物产量和经济回报.
- 用DSS指导的治疗提高了195-289%的产量和经济回报 (B:C比为3.36-3.57).
结论:
- 人工智能模型,特别是VGG16,为玉米菌检测提供了准确和可解释的解决方案.
- 集成的人工智能决策支持系统增强了精准农业,从而提高了作物产量和利能力.
- 这种方法通过有效的疾病管理,有助于实现可持续的农业实践.
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