通过实时分类葡萄叶病通过具有轻量级CNN架构和Grad-CAM的边缘设备来增强农业
Md Jawadul Karim1, Md Omaer Faruq Goni1, Md Nahiduzzaman1
1Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Scientific reports
|July 11, 2024
概括
这项研究引入了修改后的MobileNetV3Large模型,用于在边缘设备上早期检测葡萄叶病. 人工智能模型实现了高精度,帮助农民在疾病管理和促进粮食安全.
科学领域:
- 农业技术 农业技术
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 农作物疾病显著影响产量,质量和成本.
- 早期和精确的疾病检测对于有效的作物管理至关重要.
- 机器学习和图像分析为疾病识别提供了先进的解决方案.
研究的目的:
- 开发和部署一个高效的AI模型,在边缘设备上实时检测葡萄叶病.
- 增强MobileNetV3Large架构,以提高性能和减少计算负载.
- 验证模型在实际农业场景中的准确性和适用性.
主要方法:
- 使用了一个经过修改的MobileNetV3Large模型,具有自定义的密集和丢弃层.
- 该模型接受了葡萄叶疾病分类的训练和测试.
- 在Nvidia Jetson Nano边缘设备上部署,配备自定义GUI应用程序进行实时监控.
- 使用Grad-CAM可视化来解释模型决策.
主要成果:
- 拟议的模型实现了高平均训练 (99.66%) 和测试 (99.42%) 的准确性.
- 精度,回忆和F1分数约为99.42%.
- 该模型展示了对保存和实时数据的高可靠性预测.
- Grad-CAM有效地可视化了影响分类的图像区域.
结论:
- 开发的边缘部署人工智能模型可以实现高效准确的实时葡萄叶疾病监测.
- 这项技术支持自主农业实践,增强疾病缓解策略.
- 这项研究有助于推进植物疾病分类,以改善全球粮食安全.
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