Machine Learning Approaches for Crop Recommendation

Countries’ competitiveness and economic growth are fueled by innovation. Software is used in sustainable agriculture to provide farmers with data and assistance on crop rotation, harvesting details, and soil management. Sensors are used to measure the soil moisture and temperature. This research contributes to the development of a model that assists farmers by providing crop-related information or crop recommendations based on various attributes such as crop details, soil composition, weather conditions that crop can grow in, temperature, soil PH, and rainfall. This research employs machine learning algorithms such as PART, Decision table, and JRip. Experiments are run on data gathered by the Indian Chamber of Food and Agriculture from the Kaggle repository. The effects of all three algorithms are measured on a variety of scales, including Accuracy, Precision, Recall, and F-Measure. Accuracy is calculated by comparing instances that were correctly and incorrectly predicted. The results show that the PART algorithm performs well, with the highest precision of 98.33% when compared to other methods, and it consumes lesser time to build the model.