The "Common Rice Disease Detector" effort aims to tackle the vital problem of rice crop health, which has a substantial effect on the security of food worldwide. By providing farmers and agricultural specialists with a tool for quickly and accurately identifying common rice illnesses, this research hopes to reduce losses in rice quality and output. Among our goals is the creation of a user-friendly application for data analysis and image recognition using deep learning methods, including Convolutional Neural Networks (CNNs). By taking a picture of a leaf, users can quickly and accurately diagnose rice illnesses thanks to the usage of a smartphone application. We were able to precisely classify both healthy leaves and illnesses such as Hispa, Brownspot, and leaf blast by utilizing CNNs.

As part of the research approach, photos of healthy and sick rice leaves were collected from Kaggle. React was used in the development of the mobile app prototype, which integrated the CNN illness detection algorithm. Although the three diseases stated above are the main emphasis of this version, future updates will try to add more rice diseases to the app's capabilities. Through the provision of current and useful information to stakeholders, this effort helps to improve food security through crop management methods.