Paddy Disease Classifier using Deep learning Techniques

Agriculture is an important sector for self-sustainability and plays a major role in a nation's economy and growth. Lack of timely identification of plant disease may result in huge loss in yield and in the economy. The objective of the research work is to support a large community of farmers particularly involved in paddy farming to understand and predict the disease affected to the crop. This research work demonstrates the robustness of classifying the paddy leaf disease using deep neural networks. Pre-processing techniques such as data augmentation and median filter have been applied to the dataset to avoid overfitting and to improve the model performance and accuracy. A model has been generated and analyzed its performance using deep learning approach. Also, the feature extracted, and preprocessed data set was fed to several models and analyzed their performance using various accuracy metrics.

I. Introduction

The commencement of agriculture-the organized cultivation is regarded as the major milestone in the development of human civilization. Agriculture is the science and art of cultivating plants and livestock which is an important sector for self-sustainability, more precisely the economy of a nation. The contribution of agriculture in Indian scenario accounts for a fair percentage of gross domestic product (GDP). Agriculture exacted a vital role in a developing nation like India for self-sustainability. Approximately, two third of Indian population rely on agriculture for their livelihood. Also the surge of the sector has been greatly fueled by the disparity in production and demand in the country. The availability of major hectares of agro-climatic land and timely rains could contribute to substantial production leading to export benefits for the economy of the country. In 2020–21, the country's rice production is expected to target a record hit of 118 million tones [11].