Rice Leaf Disease Detection using Machine Learning

Abstract

Agriculture has a critical part in the country's economic development; hence it is critical to ensure its advancement. The rice is prime meals of more than 60% of the Indians as it is the key grains in India. The reach of varied diseases in rice plant’s have increased from past some years. There's a diversity of pathogens such as Bacterial, Fungal, Viral and they can damages the plant parts like leafs from above and the bottom side. The factors like light, water, temperature, radiation, atmosphere, humidity, acidity of soil and water affects natural growth of plants. It's observed that, the Rice plant’s diseases are the main contributors in the reduction of production and quality of food. Recognition of such diseases may improve Production. These crop diseases are creating troubles for farmers for low output and economic loss and agriculture industry. So, it's need of ours to detect these diseases as early as possible. However, image processing backgrounds hinder the diagnosis of rice plant illnesses. A new study could use CNN to identify rice leaf disease. To diagnose rice leaf diseases, we present a 6 Layered CNN based model. We use here a novel dataset of field data and Kaggle dataset for rice leaf disease images.