Automatic diagnosis of plant disease: recognition between healthy and diseased leaf

Yutaka Sasaki; Tsuguo Okamoto; Kenji Imou; Toru TorⅡ

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Automatic diagnosis of plant disease: recognition between healthy and diseased leaf

机译：植物病害自动诊断：健康叶片和病叶片之间的识别

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Diagnosis of plant disease is important for environmental preservation. It must be designed for labor-saving and progress of work efficiency in the actual management system, so this automation is a significant theme for the future. The final goalis the construction of an automatic diagnosis system for plant disease. This article reports on the automatic diagnosis that uses spectral reflectance and filtered images. The results obtained were as follows; 1) It was found that 500, 600, 650 and 700nmwere good for the recognition of visible injury by spectral reflectance. 2) Diagnosis by images using 500,600 and 650nm band-pass filter showed better performance than the use of other band-pass filters. 3) After making distinction parameter thatdistinguished healthy and diseased leaves, the distinction rate of error was under 5%.

机译：植物病害的诊断对于环境保护很重要。必须在实际的管理系统中为节省劳动力和提高工作效率而设计，因此这种自动化是未来的重要主题。最终目标是构建植物病害自动诊断系统。本文报告了使用光谱反射率和滤波图像的自动诊断。得到的结果如下。 1）发现500、600、650和700nm有利于通过光谱反射识别可见损伤。 2）使用500,600和650nm带通滤波器对图像的诊断显示出比其他带通滤波器更好的性能。 3）在确定区分健康叶和病叶的判别参数后，判别误差在5％以下。

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《配管·装置·プラント技術》 |1999年第2期|共8页
作者
Yutaka Sasaki; Tsuguo Okamoto; Kenji Imou; Toru TorⅡ;
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正文语种 jpn
中图分类管道运输;
关键词
Plant disease; Automatic diagnosis; Spectral reflectance; Filtered image; Distinction index;

机译：植物病害;自动诊断;光谱反射率;滤波图像;分辨指数;

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1. Automatic diagnosis of plant disease: recognition between healthy and diseased leaf [J] . Yutaka Sasaki, Tsuguo Okamoto, Kenji Imou, 配管·装置·プラント技術 . 1999,第2期

机译：植物病害自动诊断：健康叶片和病叶片之间的识别
2. Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks [J] . Tetila Everton Castelao, Machado Bruno Brandoli, Menezes Gabriel Kirsten, IEEE Geoscience and Remote Sensing Letters . 2020,第5期

机译：使用UAV图像和深卷积神经网络自动识别大豆叶疾病
3. RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition [J] . Jorge Parraga-Alava, Kevin Cusme, Angélica Loor, Data in Brief . 2019,第1期

机译：RoCoLe： robusta 咖啡叶图像数据集，用于评估基于机器学习的植物病害识别方法
4. Automatic diagnosis of plant disease - Spectral reflectance of healthy and diseased leaves - [C] . Yutaka Sasaki, Tsuguo Okamoto, Kenji Imou, In-ternational Federation of Auto-matic Control/International Commission of Agricultural and Biosystems Engineering Workshop . 1998

机译：自动诊断植物病 - 健康和患病的光谱反射率 -
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6. RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition [O] . Jorge Parraga-Alava, Kevin Cusme, Angélica Loor, 2019

机译：RoCoLe：罗布斯塔咖啡叶图像数据集用于评估基于机器学习的植物病害识别方法
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8. Physical and Physiological Basis for Differences in Reflectance of Healthy and Diseased Plants [R] . Knipling, E. B. 1967

机译：健康和病害植物反射差异的物理和生理基础

Automatic diagnosis of plant disease: recognition between healthy and diseased leaf

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