Predictive Model for Rice Blast Disease on Climate Data Using Long Short-Term Memory and Multi-Layer Perceptron: An Empirical Study on Davangere District

Abstract

Among various diseases of paddy affecting rice production and cultivation, blast majorly called as rice blast disease has the predominant impact. Thus, monitoring and early prediction of the occurrence of rice blast disease are very important and would be largely helpful for prevention of  blast disease. Here, we have proposed LSTM and MLP based machine learning models for rice blast disease prediction and prevention. Historical seven metrological data are used to make prediction of blast disease, two days before its actual occurrence. According to the literature survey conducted in this study, we have made an observation that rice blast disease would outbreak when Minimum Temperature is between 20-26C and Maximum Relative Humidity is ≥90%, hence region specific models are developed for four regions of Davangere district: Chanagiri, Davangere, Harihara, Honnalli. We have adopted curve shift method and two more user defined functions namely temporalize and scale in LSTM model. Performance of the proposed models are evaluated considering classification metrics such as accuracy, precision and recall. In the study conducted, dropout rate is varied from 0.1 to 0.9 for LSTM model and number of hidden layer are added from 1 to 4 in MLP model. For all the regions, both LSTM and MLP model predictions are accurate, and compared to LSTM model, performance of MLP model accuracy is high. These models will be very helpful for rice cultivator and researchers than using regular blast disease prediction model