Web enabled paddy disease detection using Compressed Sensing.

Abstract

In agricultural industry, paddy diseases play a vital role to cause economic losses. Hence, the detection of diseases of paddy plants and give suggestions to the peasants is beneficial to increase the yield quantity of rice. In this paper, a novel web-based paddy disease detection using Compressed Sensing is proposed to detect and classify paddy diseases. First, the diseased leaf is pre-processed using contrast enhancement, and then LAB color space is applied. The segmentation is done using K-Means clustering. The storage complexity is reduced using the Compressed Sensing technique. The segmented leaf images are compressed and then uploaded to the cloud. In the transmitter section, the Compressed Sensing recovery algorithm is used to reconstruct the segmented image. Then Statistical Gray Level Co-occurrence Matrix (GLCM) method is used to extract the features from the segmented image. Support Vector Machine classifier is used to classify the diseases. The performance of the proposed method is compared with other existing techniques. The proposed system is also experimentally tested with Arduino board. The proposed system achieves the disease recognition rate of 98.38%.