Smart Farming: Enhancing Crop Recommendation and Price Prediction with Advanced Machine Learning

This paper examines the innovative advancements in crop recommendation and price prediction, focusing on how cutting-edge machine learning techniques are reshaping the agricultural landscape. Traditional methods of crop selection often fall short, providing generalized advice that doesn't account for the unique conditions of each farm, leading to inefficiencies and increased risk for farmers. The enhanced models discussed in this paper address these shortcomings by utilizing algorithms such as Random Forest, Naïve Bayes, K-Nearest Neighbor, and XGBoost. These tools analyse a range of factors including soil characteristics, weather patterns, and historical crop performance to offer precise and specific recommendations. By predicting crop prices alongside these recommendations, the models enable farmers to make informed decisions that maximize yield, profitability, and sustainability. These also highlights the importance of continuous data integration, which ensures that the models remain relevant and accurate over time. Additionally, the use of visualization tools like Tableau allows farmers to easily interpret complex data, empowering them to adopt resource-efficient practices and reduce financial risks. This paper underscores the potential of these enhanced models to transform agriculture, making it more efficient, sustainable, and economically viable for farmers around the world.