Artificial Intelligence and Machine Learning based Farmer’s Friendly Soil Fertilizer Recommendation System Through Expert Analysis

Abstract

One of the key components of the Indian monetary system is agriculture and as is well known, farming supports about 60% of the Indian population and accounts for a sizable portion of the country's GDP. As time goes on, India is becoming one of the world's most significant food exporters. The demand for meals has increased as a result of the growing population. Many educated individuals who work as farmers now also work in agriculture. A promising strategy for increasing crop productivity and maximizing use is precision farming which utilizes machine learning (ML) and Internet of Things (IoT). However, due to a decline in productivity, farmers are still facing issues with significantly lower incomes. Even farmers are making poor crop, fertilizer, and soil choices. This study offers a novel solution to this problem by employing professional analysis and ML and artificial intelligence (AI) techniques to create a farmer-friendly soil fertilizer recommendation system. After analyzing the records, the AI machine uses this suggested technique to provide farmers with answers based on expert review and a thorough historical record set. Therefore, farmers may find the crop suggestion systems to be highly advantageous. Additionally, a number of factors, including pH, nitrogen, phosphorus, potassium, and rainfall, can affect crop output. Therefore, we are offering a method in this paper for using AI and ML algorithms to increase crop yield.