AI-Driven Agronomy: Enhancing Crop Yield and Market Profits

The agriculture sector plays an important role to the nation's economy, contributing significantly to GDP and employing a sizable section of the labour force. Nonetheless, precisely projecting food production and demand remains a difficult task. Traditional prediction approaches are time-consuming and prone to errors, resulting in inefficient resource allocation and financial losses. To solve these issues, we offer an innovative platform that uses machine learning algorithms to predict the best crops for production based on environmental conditions, as well as forecast market demand and pricing patterns for those crops.When comparing the effectiveness of various crop forecast classification models, the Voting Classifier, which incorporates numerous models, scored an amazing accuracy rate of 0.986. This predictive power can help farmers and stakeholders make more informed decisions, resulting to better profitability and resource efficiency. Furthermore, for price estimate, we used a Stacking Regressor model, which combines the top two models, the Random Forest Regressor and the XGBoosting Regressor. The Stacking Regressor performed better than the individual regression models, earning an optimised MSE of 277.58. This ensemble approach greatly improved price estimation accuracy, allowing stakeholders to make more informed decisions and increase profitability in the agriculture industry. Finally, the suggested intelligent platform offers a promising option for precise crop forecast and price estimation, benefiting farmers and stakeholders by optimising resource allocation, increasing profitability, and contributing to the agriculture sector's longterm growth.