Regularization Proximity Censored Regressive Deep Reinforcement Learning for IoT aware Plant Disease Prediction

Agriculture is critical to food production worldwide and is an important part of the global economy, but is faced with significant threats to plants from diseases affected by climate change. Early and accurate prediction of plant diseases is more critical now than ever. The existing methods are not being able to address the complicated problems of multi label disease classification, we propose a new methodological approach to address these limits by using a new Regularized Proximity Censor Regressive Deep Reinforcement Learning (RPCRDRL) model. The new proposed model contains five stages: image acquisition using an IoT Framework, preprocessing using Hamann indexive total regularization filtering in order to eliminate “noise” to help provide accurate images, image segmentation using Moore proximity censored regression to isolate segments that suggest plant disease reference and isolate the ERGs (effectively removing other plant parts), 4 - feature extraction from shape, color, and texture features, and 5 - classification of plant diseases using Deep Reinforcement Learning (DRL) with the Ruzicka similarity index to improve accuracy and computing time. Our experimental results indicate that the RPCRDRL model improved disease identification of plant disease compared to traditional methods while also improving computation time. The RPCRDRL model significantly improved the model performance and real-time organization process for a real-time plant disease prediction with real-world data sets with accurate testing in agricultural scenarios including accuracy rates, precision rates, recall rates, F1 scores and prediction times.