Addressing Fertilization Challenges to Enhanced Crop yields using Machine Learning

Fertilizers, often called plant food, supply vital nutrients for healthy plant development. Farmers consistently face the challenge of effectively managing fertilizer usage, which directly influences crop yields and the preservation of essential soil nutrients. Farmers require precise advice on the practical application of fertilizers to enhance agricultural efficiency and conserve resources. Rainfall patterns have a significant impact on nutrient retention post-application: moderate rainfall facilitates nutrient absorption and fertilizer dissolution, whereas excessive rain may cause runoff, leading to the loss of key elements like nitrogen (N), phosphorus (P), potassium (K), manganese (Mn), and boron (B) from the soil.To tackle these challenges, customized nutrient suggestions have been formulated using a sophisticated random forest algorithm that analyzes time-series data. By evaluating rainfall patterns and recognizing the unique fertility needs of different crops, this model determines the precise nutrient amounts necessary for optimal growth. The algorithm aims to boost soil fertility, create favorable crop growth conditions, and mitigate the risks of nutrient loss from leaching and runoff. This advanced method offers farmers essential, tailored guidance to enhance crop yields while fostering sustainable practices. By aligning fertilizer application with the specific needs of crops and rainfall trends, it aids in conserving soil nutrients, optimizing resource usage, reducing environmental impact, and supporting a resilient agricultural framework.