EcoGrow : Smart Farming App for Climate Informed Crop Selection Using Machine Learning

Abstract

My thesis focuses on developing a farmer-centric application aimed at providing crucial weather information and crop recommendations to optimize agricultural practices. By incorporating real time weather data, and location-specific tracking, the app aims to empower farmers with valuable insights for decision-making. The integration of a chatbot further enhances user interaction, offering solutions to agricultural queries. The system relies on a comprehensive dataset for weather conditions and crop information. The agricultural sector faces numerous challenges, including unpredictable weather conditions and the need for precise crop planning. Recognizing these issues, my project aims to create a farmer-centric app addressing the lack of easily accessible, real-time, and location-specific agricultural information. By offering comprehensive weather insights and crop recommendations, solution seeks to empower farmers and enhance the efficiency of their decision-making processes. To tackle the identified problems, methodology revolves around the development of a user friendly mobile application. I leverage a dataset encompassing various weather parameters, including temperature, rainfall and humidity. The app dynamically displays current weather conditions upon user login and allows users to track specific locations for detailed forecasts. Crop recommendations are categorized as short-term, mid-term, and long-term based on the gathered data. The incorporation of a chatbot enriches user experience, providing instant solutions to agricultural queries. The prototype of farmer-centric app demonstrates promising results. Users can seamlessly access real-time weather conditions, empowering them to make informed decisions about crop selection and cultivation practices. The location-specific tracking feature ensures personalized recommendations, enhancing the adaptability of the system. The chatbot has proven effective in providing quick and relevant solutions to users' agricultural queries, contributing to a comprehensive and user-friendly platform. A.E.W Jayatilake - w1761374