Estimation of NPK requirements using random forest

Fertilizer use is usually under the limited control of the farmer. Competent advice on the optimal use of these fertilizers is needed so that farmers can achieve higher yields and reduce fertilizer losses. There is a correlation between nutrient losses. The right amount of rainfall at the right time allows nutrients to penetrate the root zone of the soil and loosen the dry manure. When there is an excess of rain, it can cause more runoff, and soils contain important nutrients like manganese (Mn), phosphorus (P), boron (B), nitrogen (N), and potassium (K). The study employs iterative random forest algorithms that are regularly updated with time-series data. These algorithms are used to determine nutrient recommendations by considering rainfall patterns and soil fertility. The aim is to estimate the nutrient requirements for various crops. The method proposed in this study can help improve soil fertility by providing nutrient recommendations for optimal conditions for plant growth and reducing the potential for leaching and runoff. The proposed system is able to achieve 92% accuracy. A user-friendly system has been implemented in the form of a website to provide cross-platform functionality and suggest appropriate timings and amounts of nutrients required for an inputted crop, with an alert system for heavy rainfall.