LAUSR

Plant disease detection attracts significant attention in the field of agriculture where image based disease detection plays an important role. To improve the yield of plants, it is necessary to detect the onset of diseases in plants and advice the farmers to act based on the suggestions. In this paper, a novel web enabled disease detection system (WEDDS) based on compressed sensing (CS) is proposed to detect and classify the diseases in leaves. Statistical based thresholding strategy is proposed for segmentation of the diseased leaf. CS measurements of the segmented leaf are uploaded to the cloud to reduce the storage complexity. At the monitoring site, the measurements are retrieved and the features are extracted from the reconstructed segmented image. The analysis and classification is done using support vector machine classifier. The performance of the proposed WEDDS has been evaluated in terms of accuracy and is compared with the existing techniques. The WEDDS was also evaluated experimentally using Raspberry pi 3 board. The results show that the proposed method provides an overall detection accuracy of 98.5% and classification accuracy of 98.4%.