An effective segmentation and limo classification for Paddy disease detection using deep learning

Abstract

Rice crop disease detection and its diagnosis methods are vitally important for newlinethe agriculture field to be sustainable. For that many researchers finding solutions to newlineminimize or avoid the rice plant disease to take the best yield for formers. Because newlinethis disease led to a more than 38% yearly drop in paddy production. Due to a lack of newlineawareness and digital knowledge in fast identifying and best remedy for rice crop newlinediseases. In that, automated and artificial intelligence (AI) based rice crop disease newlinedetection and prevention method is a key research solution needed for the current newlineagriculture field. The internet of things (IoT), has plenty of opportunities and newlinecontributing a vital role in wireless networks, especially in the last 15 years. Using newlineIoT in the agriculture industry is growing up rapidly as it receives complex contextual newlineinformation about water irrigation, crop disease detection, fertilizer utilization, and newlinesoil rate. Various crop disease detection methods need more accuracy and newlinedimensionality corrections. Disease detection is indispensable for agriculture to be newlinemaintainable. Meantime automated rice plant disease detection systems also face newlinevarious problems to detect diseases in the current situation. Regular machine learningbased newlineimage-wise disease detection methods are following preprocessing input values, newlinenecessary feature extraction, image segmentation, and disease classifications steps. newline newline