Hybridization of resnet yolo classifier for paddy leaf disease classification using fitness sorted shark smell optimizatio

Abstract

Plant disease is one of the major factors that may cause negative newlineimpacts on the agricultural production. Therefore, plant disease needs to be newlineidentified with their location and group of plant diseases have to be detected newlinefor ensuring the plant profitability and also for financial development. In newlinegeneral, the conventional method of determining the diseases is done through newlinethe support of plant pathologists and rangers, where the pesticide is used in newlinethe fields for preventing the pest diseases. This manual way of detecting the newlineplant disease is observed to be tedious and time-consuming process as well as newlineit needs more knowledge to tract the plant disease in the regular interval of newlinetime. These problems on the manual detection can be solved with the newlinedevelopment of some automatic plant disease recognition models that may newlineincrease the food productivity by incurring the timely prevention at the earlier newlinestage of disease occurrence. Hence, the image processing techniques are newlineutilized for efficiently solving these issues on classifying and recognizing newlineplant diseases. Further, when observing various kinds of crops, the rice and newlinepaddy are considered to be utilized by a high number of people in various newlinecountries. newlineThe process of recognizing the paddy leaf diseases is done through newlineimage processing techniques, where the diseased leaf part gets separated from newlinethe background leaf images. However, they may face certain challenges like newlinetexture of diseased regions, illumination problem, morphological changes in newlinediseased regions and colour changes of disease. These existing challenges are newlineconsidered and this thesis aims to establish the paddy disease recognition newlineusing diverse image processing methods for preventing paddy diseases in the newlineinitial stage. newline