强大的CRW作物在农业中使用混合深度学习模型检测和分类叶病
B V Baiju1, Nancy Kirupanithi2, Saravanan Srinivasan3
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Plant methods
|February 13, 2025
概括
这项研究引入了Slender-Convolutional神经网络 (CNN),用于检测玉米,大米和小麦作物的疾病. 该模型准确识别植物疾病,为资源有限的农民提供实用解决方案.
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:
- 植物病严重影响作物产量和质量,需要有效的诊断工具.
- 目前的特定作物机器学习 (ML) 和深度学习 (DL) 模型往往对资源贫困,数字素养较低的农民来说是不切实际的.
- 需要多功能且易于使用的植物疾病检测系统.
研究的目的:
- 开发和评估一种用于多作物植物疾病检测的新型苗条卷积神经网络 (CNN) 模型.
- 通过为玉米,大米和小麦创造一个通用的解决方案来解决特定作物模型的局限性.
- 提供一个准确和有效的工具,用于在农场疾病的识别,即使在资源有限的环境中.
主要方法:
- 一个Slender-CNN架构被设计成具有不同尺寸的平行卷积层,用于多尺度的病变定位.
- 该模型在玉米,大米和小麦作物的数据集上进行了训练和验证,包括健康和受感染的样本.
- 性能与VGG19,EfficientNetb6和YOLOv5.5等已建立的CNN模型进行了基准测试.
主要成果:
- 斯伦德-CNN模型的整体准确率为88.54%,超过了几种基准模型.
- 该模型在分类单个作物类型方面表现出高度准确性:玉米为99.81%,大米为87.11%,小麦为98.45%.
- 与VGG19,EfficientNetb6,ResNeXt,DenseNet201,AlexNet,YOLOv5和MobileNetV3.3相比,拟的网络表现出了更好的表现.
结论:
- 开发的Slender-CNN模型为多作物植物疾病检测提供了有效和准确的解决方案.
- 它的紧设计和高性能使其适合在资源有限的农业环境中部署.
- 该模型的多功能性和准确性支持改善了农场疾病管理和作物生产.
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