Crop Disease Prediction with Convolution Neural Network (CNN) Augmented With Cellular Automata

Abstract

Food security is the primary concern of any country, and crop diseases are the major threats to this. Each stage of the crop will be affected by various diseases starting from seeding to ripeness. The spread of the crop diseases is very rapid, and identification of this is challenging as the infrastructure is very less to monitor the same. After a thorough literature survey, we understood there are several ways of predicting the disease and yield prediction. We have developed two new and robust classifiers, one which processes images to predict the crop's diseases, and the second one uses the weather data to predict the same. Both classifiers use deep-learning technique Convolution Neural Networks (CNN) augmented with six neighborhood cellular automata to predict the crop disease and yield. This work will be first of its kind to develop two classifiers for six crop disease prediction. The average time to compute the yield of a particular crop is less than 0.5 nanoseconds. The first classifier is named as CNN-CA-I, which was trained/tested to process 245 different crop species and 132 diseases associated with these crops where image segmentation is done with higher accuracy, thus strengthening the disease recognition system. We gave collected public datasets of 12, 45,678 images diseases and leaves of healthy plants taken in ideal conditions. This model reports an accuracy of 92.6% on a tested standard dataset for disease and yield prediction. The second classifier is CNN-CA-W that predicts crop disease trained and tested with environment data.8,52.624 datasets are collected from ECMWF for processing the weather data to predict the crop's condition and thus reporting the yield of the crop. This model reports an accuracy of 90.1% on a tested standard dataset.