一个智能深度增强模型用于检测香叶病
Amjad Rehman1, Ibrahim Abunadi2, Faten S Alamri3
1Department of Information Systems CCIS Prince Sultan University, Riyadh, Saudi Arabia.
Microscopy research and technique
|August 23, 2024
概括
本研究引入了使用VGG19和被动攻击分类器 (PAC) 检测香叶病的AI模型. 该模型在各种视觉类型中实现了高精度,有助于早期识别作物疾病.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 准确识别香病对全球粮食安全和农业利能力至关重要.
- 四种常见的香叶病 (健康,Cordana,Sigatoka,Pestalotiopsis) 需要精确的分类.
- 多种视觉类型 (RGB,夜间,红外,热) 提供不同的数据进行分析.
研究的目的:
- 开发和评估一个智能深度增强学习模型来对四种香叶病进行分类.
- 评估模型在RGB,夜间,红外和热视觉类型中的性能.
- 为了验证该模型对大规模无人机疾病检测的有效性.
主要方法:
- 提出了一种混合深度学习模型,将VGG19和被动攻击分类器 (PAC) 结合起来.
- 该模型经过Kaggle数据集的训练和测试,每种视觉类型 (分辨率为224x224) 包含1600张图像.
- 显微镜被用来验证疾病的存在和程度.
主要成果:
- 该模型实现了高平均准确率:99.16% (RGB),98.02% (夜视),96.05% (红外视) 和96.10% (热视).
- 训练测试方法证明了模型在不同视觉频谱的强大性能.
- 显微镜验证证实了该模型的疾病检测准确性.
结论:
- 开发的智能模型有效地将香叶疾病分类为多种视觉类型.
- 这项技术可以与基于物联网 (IoT) 的无人机集成,以实现高效,大规模的作物监测.
- 通过这种人工智能模型早期发现疾病可以显著减少作物损失并提高产量.
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