Predictive Market-Aware Farming: A Machine Learning Approach for Crop and Market Optimization

Abstract

This article explores the complexity of crop farming planning, where optimizing the parameters including the soil quality, market demand, and crop rotation to optimize profitability. Farmers generally find it extremely difficult predicting shifts in market demand and controlling the probability of crop damage due to unforeseen circumstances like pests, disease, and weather. In this research, we employ predictive analytics and machine learning algorithms for processing past market data, to calculate patterns, and forecast future trends. Our methodology combines current market information such as prices and demand trends, past crop performance data, providing farmers with a complete decision-making package. We constructed an online platform that gathers information from various sources, such as agricultural research institutions and locations, providing area-specific and crop-specific data. Through market demand analysis, seasonal variations, and price patterns, the website enables farmers to select the most lucrative crops for their regions and seasons. Random models are employed. Forest, XGBoost, and Support Vector Machines, with performance based on measures such as accuracy and mean absolute error. The results show that these machine learning habits can greatly enhance crop planning by removing uncertainty in market demand and reduces the likelihood of crop loss. This research highlights the potential of data-driven farming practices to improve profitability and lower risk, offering the capability to upcoming technology in precision agriculture.