Abstract:
Millions of people rely on the production of tea for their livelihoods, making it an essential component of the worldwide economy. Unfortunately, tea plants can become infected with a number of diseases and pests, which can seriously reduce their yield and degrade their quality. Tea plants must be manually inspected for diseases, which takes time, is labor-intensive, and is frequently error prone. This study employs image classification techniques to construct an automated disease detection and classification system for tea plants in answer to this problem. Our approach seeks to improve the efficacy and precision of illness diagnostics in tea plantations by leveraging artificial intelligence and computer vision, consequently supporting sustainable practices in tea production.
