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：植物疾病的自动诊断：健康和患病叶之间的识别

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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：植物病的诊断对于环境保护很重要。它必须设计用于实际管理系统中的劳动力节约和工作效率的进步，因此这种自动化是未来的重要主题。最终守门员植物疾病自动诊断系统的构建。本文报告了使用光谱反射和滤波图像的自动诊断。获得的结果如下; 1）发现，500,600,650和700nmwere通过光谱反射率识别可见损伤。 2）通过使用500,600和650nm带通滤波器的图像诊断显示出比其他带通滤波器的性能更好。 3）在做出区分的分别的情况后，Duts Tuinding健康和患病的叶片，区别率低于5％。

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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：植物疾病;自动诊断;光谱反射率;过滤图像;区别指数;

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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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