Hybrid Machine Learning Algorithm-Based Paddy Leave Disease Detection System - Payal; Kukana, Poonam (2020 International Conference on Smart Electronics and Communication (ICOSEC), 2020)
The agriculture field is one of the current examination subjects is classification, detection, and recognition of paddy leave disease images of a plant. The research methodology gives paddy leave image Acquisition, paddy leaf disease pre-processing, Segmentation, and MSVM Classification method of paddy Leave diseases. It is employing the OMSVM classification method, the Paddy Leave Disease images are classifying or detecting and calculated the accuracy rate and reduce the error rates such as FDR and TNR parameters. The research parameter has attained the accuracy rate of up to 98.8 per cent, TPR up to 0.99 per cent, TNR up to 0.9910 percent, FDR value up to 0.0029 percent and FPR value up to 0.0090 percent and compared with existing parameters and methods.