Proceedings ArticleDOI
Image recognition of plant diseases based on principal component analysis and neural networks
Haiguang Wang,Guanlin Li,Zhanhong Ma,Xiaolong Li +3 more
- pp 246-251
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TLDR
The results showed that neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained.Abstract:
Plant disease identification based on image processing could quickly and accurately provide useful information for the prediction and control of plant diseases. In this study, 21 color features, 4 shape features and 25 texture features were extracted from the images of two kinds wheat diseases (wheat stripe rust and wheat leaf rust) and two kinds of grape diseases (grape downy mildew and grape powdery mildew), principal component analysis (PCA) was performed for reducing dimensions in feature data processing, and then neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used as the classifiers to identify wheat diseases and grape diseases, respectively. The results showed that these neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained. For the two kinds of wheat diseases, the optimal recognition result was obtained when image recognition was conducted based on PCA and BP networks, and the fitting accuracy and the prediction accuracy were both 100%. For the two kinds of grape diseases, the optimal recognition results were obtained when GRNNs and PNNs were used as the classifiers after reducing the dimensions of feature data with PCA, and the prediction accuracies were 94.29% with the fitting accuracies equal to 100%.read more
Citations
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Basic Investigation on a Robust and Practical Plant Diagnostic System
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Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model
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Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions
Artzai Picon,Maximiliam Seitz,Aitor Alvarez-Gila,Patrick Mohnke,Amaia Ortiz-Barredo,Jone Echazarra +5 more
TL;DR: Three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network are proposed that combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks.
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Feature decision-making ant colony optimization system for an automated recognition of plant species
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Proceedings ArticleDOI
Image recognition of plant diseases based on backpropagation networks
TL;DR: In this paper, two kinds of grape diseases (grape downy mildew and grape powdery mildow) and wheat diseases (wheat stripe rust and wheat leaf rust) were selected as research objects, and the image recognition of the diseases was conducted based on image processing and pattern recognition.
References
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Journal ArticleDOI
Image pattern classification for the identification of disease causing agents in plants
Anyela Camargo,Jeremy S. Smith +1 more
TL;DR: In this paper, a machine vision system for the identification of the visual symptoms of plant diseases, from coloured images, was reported, where Diseased regions shown in digital pictures of cotton crops were enhanced, segmented, and a set of features were extracted from each of them.
Journal ArticleDOI
Color Classifier for Symptomatic Soybean Seeds Using Image Processing.
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