Numerical Data Based Fertilizer Recommendation System for Farmer

Abstract

In modern agriculture, optimizing fertilizer use is crucial for maximizing crop yields, maintaining soil health, and reducing environmental impact. The Numerical Data-Based Fertilizer Recommendation System (NDFRS) for farmers addresses these needs by leveraging data-driven approaches to provide precise fertilizer recommendations tailored to specific crop and soil conditions. This system integrates soil nutrient analysis, crop nutrient requirements, and environmental factors to generate customized fertilizer plans. The NDFRS employs advanced machine learning algorithms to analyze extensive datasets, including soil test results, historical crop yield data, weather patterns, and geographical information. By incorporating these variables, the system can predict the optimal type and amount of fertilizers required for different crops at various growth stages. This ensures that nutrients are supplied efficiently, enhancing crop productivity while minimizing excess fertilizer use and associated environmental risks. The user- friendly interface of the NDFRS allows farmers to input relevant data easily and receive actionable recommendations. Additionally, the system offers real-time updates and adaptive recommendations based on changing environmental conditions and crop growth stages. Field trials and validation studies demonstrate the system's efficacy in improving crop yields and promoting sustainable farming practices. Keywords: Tensor Flow, Deep Learning.