Crop Recommendation System Using Machine Learning

Crop recommendation is an important part of precision agriculture for crop productivity improvement. Machine learning techniques have been used many times in this domain to analyze agricultural data and provide recommendations based on various factors such as soil parameters, climate, and other environmental conditions. In this research paper, we recommend a crop recommendation system based on Machine Learning methods that evaluates climatic variables like temperature, humidity, and rainfall as well as soil characteristics like nitrogen, potassium, and pH levels. We have used a dataset consisting of soil parameters and climate data along with corresponding crop yield information we’re going to train and test our models. We have explored and compared the performance of several ML algorithms in providing crop recommendations. Our suggested crop suggestion system, which is based on machine learning, may help farmers and agricultural specialists choose and manage crops intelligently, ultimately improving crop yields and productivity.