Agro System for Sustainable Farming Solutions

Abstract

Agriculture plays a crucial role in ensuring food security and economic stability. However, farmers often face challenges related to unpredictable weather conditions, soil health, and market demand, leading to financial losses. This paper presents an integrated system for crop prediction using XGBoost and a farmer marketplace to optimize agricultural productivity and profitability. The proposed model leverages XGBoost, an advanced machine learning algorithm, to analyze key agricultural parameters such as soil composition, temperature, rainfall, humidity, and historical yield data to predict the most suitable crops for a given region. The model is trained on diverse datasets to enhance accuracy and provide actionable insights to farmers.

Additionally, a farmer marketplace platform is developed to connect farmers directly with consumers, retailers, and wholesalers. This platform facilitates real-time price discovery, demand forecasting, and direct trade, ensuring fair prices and reducing dependency on intermediaries. The integration of predictive analytics with a marketplace helps farmers make informed decisions about crop selection and sales strategies. The system aims to enhance agricultural efficiency, improve farmer incomes, and promote sustainable farming practices through data-driven decision-making and direct market access.