Green Tea Leaves Disease Analyser: Web Based Disease Detecting and Solutions Providing System

Abstract

"In response to the pressing challenges faced by green tea plantations in Sri Lanka, this project proposes a novel solution—a web application leveraging image processing and machine learning. The agricultural sector in Sri Lanka is grappling with various plantation ailments influenced by environmental variables such as rainfall, temperature, and pests, significantly impacting crop productivity. To address these issues, a self-usable web application is designed, aiming to detect and diagnose diseases in green tea leaves at their early stages, providing crucial information for effective treatment.

The primary objective of this application is to enhance tea cultivation and production in Sri Lanka by preemptively identifying diseases in tea leaves before they can spread. Beyond identification, the application is equipped with a feature to suggest appropriate treatments for diagnosed diseases, contributing to the overall health and productivity of tea plantations. The integration of image processing and machine learning techniques ensures the accuracy of disease detection, offering a reliable tool for tea farmers.

The project employs image processing and machine learning techniques to analyze tea leaf images for disease detection. The web-based interface ensures accessibility for end-users, including tea farmers and researchers. The system is tested rigorously using a comprehensive tea leaf image dataset to validate its effectiveness.

The outcomes of this project are expected to contribute significantly to the improvement of tea cultivation in Sri Lanka. By offering a user-friendly tool for early disease detection and providing actionable solutions, the system has the potential to enhance crop yield, reduce losses, and promote sustainable tea production practices.

In conclusion, the Green Tea Leaves Disease Analyzer represents a valuable innovation in the field of agriculture technology. Its integration of image processing, machine learning, and web-based accessibility positions it as a promising solution for the challenges faced by the green tea industry in Sri Lanka.

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