DETECTION DISEASE AND AUTOMATIC MEDICATION COMPOSITION ON PLANT LEAFS USING STRUCTURE ALGORITHM
Abstract
The foremost difficulties facing by the agricultural crops in India are due to pests disturbing their roots and leaves. Plant diseases cause significant damage and economic losses in crops. The improvement is necessary consequently for the reduction in plant diseases by early diagnosis, results for the production to improve. Huge crop get wasted every year, because of rapid infestation by pests and other insects. Diagnosis is really difficult for infected cotton plants, reason is variety of symptoms for the diseases. In this research, we have used a new technique to identify the pest & type of disease in cotton plants. Images of leaves affected with some disease will be done first with pre-processing. Images are then subject find Edge detection. Edge detected images will be given to Advanced fuzzy K-means clustering for the segmentation. Then, the feature extraction will be done for the color features, correlation, entropy, texture features such as energy, contrast, edges are extracted from the leaf image, then compared with normal cotton leaf image. Finally decision of exact disease will be shown. So, it is very easy for the diagnosis. The proposed technique is very advanced and is compared with existing techniques, the time taken to analyze has decreased by 40% if we compare with C-means, 52% if we compare with the K-means clustering. Code is written Matlab and simulated in Matlab IDE.
Keywords: Cotton Plants, Segmentation, the K-means clustering, C-means clustering, Matlab.Full Text:
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