Mobile Application for Enhance Sustainable Tea Farming in Sri Lanka

Abstract

Ensuring sustainable tea farming requires intensive monitoring of plant conditions, nutritional status, and disease infections. To that end, this research presents a smartphone application that is powered by machine learning to assist Sri Lankan tea farmers in identifying fertilizer and chemical deficiencies, predicting tea yield quality, and detecting diseases at early stages. The system makes use of a trained machine-learning model to scan images of leaves for relevant characteristics to provide instant feedback through a user-friendly smartphone interface. The app offers advice to farmers to improve yield and reduce crop loss. This approach enhances accuracy in farming, minimizes reliance on over-fertilization, and assists in efficient farming methods. The given system is designed to target small scale and far-away farmers to make it more popular in diversified agricultural lands. The research involves mass-scale agricultural image dataset collection and processing, deep learning model training, and deployment of a robust mobile application for field implementation. Outputs strive to contribute to Sri Lankan smart agriculture by allowing farmers to make data-driven decisions to ultimately improve productivity and sustainability.