Leaf Disease Detection in Paddy Using Inception-V3

Rice is a common food in more than a hundred countries throughout the world. The production of rice is essential to the expansion of the world economy. In India, a very big portion of the population relies heavily on agriculture for both their livelihood and the consumption of the products produced. As a result, it is a crucial industry. Crops are seriously threatened by diseases. Rice leaf disease is the biggest problem that the agriculture sector is dealing with. Diseases decimate the crop, which lowers the production and quality of the crops and lowers profitability. Farmers in any nation cannot accurately diagnose rice leaf disease because they lack expertise about the condition. They are unable to properly care for rice leaves as a result. The use of technology in agriculture will lessen the need for physical labor and improve precision by reducing errors. The CNN model is selected for this purpose. There are alternative resources, such as machine learning technology, for identifying diseases, but deep learning is now commonly employed because of recent technological advancements and accuracy measures. There are more than 30 different diseases that affect the paddy crop and cause agricultural loss. Three of these are more frequently seen in the crop, Leaf Blast, Brown Spot, and Bacterial Leaf Blight. This paper is focused on using deep learning algorithm, namely, Inception-v3 for classification and detecting rice leaf diseases. The algorithm gave an accuracy of 92%.