Precision agriculture includes the recommendation of fertilizer based on the soil. We provide a machine learning-based strategy for recommending soil-based fertilizer in this research. Our method makes fertilizer recommendations that are relevant to particular soil types and crops by utilizing data on soil characteristics, weather patterns, and crop output. We investigate various machine learning techniques and assess their effectiveness using data from the real world, including decision trees, random forests, and neural networks. Our findings demonstrate that our method can provide precise fertilizer recommendations, resulting in higher crop yields and less waste.

Keywords—Machine learning techniques, Decision trees, Random forests, and Neural networks.