Rainfall-Driven Smart Fertilization System for Sustainable Crop Growth
Abstract
Managing fertilizer application is often a complex challenge for farmers, requiring precise guidance to maximize crop productivity and minimize nutrient waste. While moderate, well-timed precipitation is beneficial for dissolving dry fertilizers and helping nutrients reach the root zone, heavy rainfall can be counterproductive. Excessive moisture often leads to increased surface runoff and the leaching of vital soil components, including primary macronutrients like nitrogen (N), phosphorus (P), and potassium (K), as well as essential micronutrients such as manganese (Mn) and boron (B). This study introduces a sophisticated nutrient recommendation system that utilizes an updated XGBoost algorithm designed for time-series data. By analyzing the relationship between historical rainfall patterns and specific crop fertility requirements, the proposed model provides tailored recommendations to optimize plant growth. Ultimately, this approach seeks to improve soil health and agricultural efficiency by ensuring nutrients are applied in a way that minimizes environmental loss.
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