洞察网:一个深度学习框架,用于增强植物疾病检测和可解释的洞察力
Mubasshar U I Tamim1, Sultanul A Hamim1, Sumaiya Malik1
1Department of Computer Science and Engineering American International University-Bangladesh Dhaka Bangladesh.
Plant direct
|May 7, 2025
概括
先进的深度学习模型准确地检测植物疾病,提高作物产量和质量. 这项技术通过自动化,可靠的植物健康诊断来支持可持续农业和精准农业.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 植物病严重威胁全球粮食安全和农业可持续性.
- 目前的疾病检查方法是手动的,耗时的,并且缺乏现代农业的可扩展性.
- 开发自动化,准确的植物疾病检测对于有效的作物管理至关重要.
研究的目的:
- 开发和评估用于检测和分类植物叶病的先进深度学习模型.
- 提高关键作物物种植物病诊断的准确性和效率.
- 为促进精准农业和可持续农业实践的发展做出贡献.
主要方法:
- 使用MobileNet架构的深度学习模型被设计成具有更深层的卷积层和脱落规范化.
- 该模型在番茄,豆类和植物疾病的数据集上进行了训练和验证.
- 使用梯度加权类激活映射 (Grad-CAM) 来实现模型的可解释性.
主要成果:
- 拟议的深度学习模型实现了高分类准确度:97.90%的西红,98.12%的豆类,97.95%的花.
- 该模型在多种植物物种中展示了有效的疾病检测和分类能力.
- 格拉德-CAM提供了对模型诊断决策过程的见解.
结论:
- 深度学习模型,特别是基于MobileNet的架构,为准确和可扩展的植物疾病诊断提供了强大的解决方案.
- 这项研究支持将人工智能整合到农业中,以改善作物健康监测和可持续粮食生产.
- 开发的模型通过及时准确地识别植物疾病,为精准农业做出贡献.
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