Evaluating the Performance of Different Machine Learning Algorithms for Crop Recommendation

A significant portion of Indians view agriculture as their main source of revenue. Crop production is a significant industry in our nation. Applications of machine learning are changing the data processing and decisioning process that have a significant influence on the world economy. The natural nutrients or physiochemical characteristics of the soil aid in the understanding of the dynamics of the soil ecosystem by the researchers and are essential in assisting farmers in making daily choices. In order to forecast and evaluate soil properties and make better decisions on agricultural soil health management, our work focuses on closely examining and assessing machine learning (ML) methodologies.Machine learning has entered the picture with the introduction of big data technologies and high-performance computers, creating new opportunities for data-intensive research in the interdisciplinary field of agricultural technology. On the other hand, single-component soil property predictions were not as accurate. In the long run, combining these technologies can have revolutionized contemporary agriculture by raising crop yields and reducing waste. Helping someone cultivate crops successfully to obtain a high yield is the aim of this activity. Estimating enhanced crop output through the application of machine learning algorithms, which are utilized in crop yield estimates, is one of the challenging issues facing the agricultural sector. We have implemented the five supervised learning models. Random Forest give the best accuracy score of 97%.