Ensemble Based ERDNet model for Leaf Disease Detection in Rice and Maize Crops

As per the report from Statista, the total global consumption of rice is approximately 497 million metric tons in 2019-2020, and 149 million metric tons is consumed by China alone. Worldwide (Over 170 countries), maize is cultivated in nearly 1147.7 metric tons as per the report of FOSTAT, 2020. Crop leaf disease detection is a critical issue faced by farmers in the field of agriculture. In this research, we are dealing with multiclass leaf disease classification of rice and maize. For this purpose, rice and maize leaf disease image has been taken from the plant village dataset. Rice and Maize are the most popular crops in the sub-continental scenario are produced in bulk and suffer from several diseases reasoning both natural and chemical factors. To precisely handle this problem, we propose an Ensemble-based framework comprising of DenseNet121 and ResNet50, called EDRNet. The proposed method produces a very high accuracy of 96.7 % on rice and 90.9% for maize. The comparative analysis of the proposed EDRNet method with other methods shows that the proposed method has better performance over the Rice and Maize disease classification.