Designing an Intelligent Fertilizer Recommendation System for Small-scale Farmers

Abstract

The agriculture sector is a vital part of the world's population, as it provides food for everyone. Unfortunately, small-scale farmers often face challenges when it comes to choosing the appropriate amount and type of fertilizer to their crops. This paper proposes an intelligent fertilizer recommendation system (IFRS) that uses machine learning techniques. IFRS is designed to help small-scale farmers make informed decisions when it comes to applying fertilizer. It uses machine learning techniques to analyze and recommend the most effective fertilizer for their crops. This system is built on historical data about the various factors that affect the development and maintenance of a farm's soil and crop yield. It uses machine learning techniques to make informed decisions and improve the efficiency of its operations. The system's dashboard is designed to help farmers easily access and interpret the recommendations it provides. It allows them to input their crop characteristics and location, and it provides personalized fertilizer suggestions based on these data. In addition, it displays the weather conditions and other factors that can affect the application of nutrients. The proposed IFRS would provide small-scale farmers with a reliable and efficient fertilizer recommendation system, which would enhance their income and productivity. The system's user-friendly dashboard and accuracy would make it ideal for use in different countries with varying climates and soil types.