Image contrast enhancement by constrained local histogram equalization
Computer Vision and Image Understanding
Knowledge-based machine vision system for outdoor plant identification
Knowledge-based machine vision system for outdoor plant identification
Content-based image classification using a neural network
Pattern Recognition Letters
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Segmentation and classification of tobacco seedling diseases
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Detection and classification of areca nuts with machine vision
Computers & Mathematics with Applications
The Visual Computer: International Journal of Computer Graphics
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In this study, we present an application of neural network and image processing techniques for detecting and classifying Phalaenopsis seedling diseases, including bacterial soft rot (BSR), bacterial brown spot (BBS), and Phytophthora black rot (PBR). The lesion areas with BSR, PBR, and BBS of Phalaenopsis seedlings were segmented by an exponential transform with an adjustable parameter and image processing techniques. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the lesion area. These texture features and three color features (the mean gray level of lesion area on the R, G, and B bands) were used in the classification procedure. A back-propagation neural network classifier was employed to classify BSR, BBS, PBR, and OK (uninfected area of leaf) of Phalaenopsis seedlings. The methodology presented herein effectively detected and classified these Phalaenopsis seedling lesions to an accuracy of 89.6%. The detection capability of the system, without classifying the disease type, is as high as 97.2%.