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提高深度学习的实用性,以在现场条件下识别作物疾病:从模型评估和作物特定方法的见解

Qi Tian¹, Gang Zhao^2,3, Changqing Yan⁴

¹College of Natural Resources and Environment, Northwest A&F University, Xianyang, China.

Pest management science

|July 20, 2024

概括

自动化作物疾病识别模型在实验室环境中表现最好,在现场条件下准确性明显下降. 增强数据表示和体积对于实际的现场应用至关重要,而不是仅依赖复杂的模型架构.

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:

农作物疾病导致大量的收获损失和粮食短缺.
卷积神经网络 (CNN) 和智能手机使作物疾病的自动识别成为可能.
现有的模型在实验室条件下表现出色,但在现实世界的现场环境中却扎不堪.

研究的目的:

在实验室,现场和混合条件下评估疾病识别准确性.
评估模型架构,参数大小和作物特定模型 (CSM) 的影响.
研究使用DenseNets,ResNets,MobileNetV3,EfficientNet和VGG Nets的模型,用于果,马和西红的14种疾病.

主要方法:

收集了14种来自果,土豆和西红的疾病的数据集.
测试了多个CNN架构 (DenseNets,ResNets,MobileNetV3,EfficientNet,VGG Nets) 的使用情况.
在实验室,混合 (实验室和现场) 和现场条件下评估模型性能.

主要成果:

精度从98.22% (实验室) 下降到91.76% (混合) 到71.55% (现场).
模型架构和参数大小对各种条件的准确性影响最小.
特定作物模型 (CSM) 减少了作物间的错误分类,但稍微增加了作物内部的错误分类.

结论:

数据表示和体积对于现场性能比新型模型架构更为关键.
增加特定场景图像的收集对于提高模型准确性至关重要.
这些发现支持农民实际实施农作物疾病识别工具.

关键词:

农业应用农业应用农作物疾病植物疾病深度学习是一种深度学习.疾病鉴定疾病鉴定

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