Affiliation:
1. MTech, Department of Computer Science Govt. College of Engineering, Amravati, India
2. Assistant Professor, Department of Computer Science Govt. College of Engineering, Amravati, India
Abstract
Agriculture is the most important aspect of survival. Machine learning (ML) could be an important point of view in determining a practical and workable solution to the crop yield problem. Given the current method, which includes manual counting, climate-smart pest management, and satellite photography, the results aren't particularly accurate. The primary goal of this research is to forecast crop and yield yield using various machine learning approaches. SVM, Nave Bayes, and Random Forest are the classifier models used here, with Random Forest providing the highest accuracy. Machine learning algorithms will help farmers choose which crop to cultivate based on variables such as temperature, rainfall, area, and other characteristics This connects the technological and agricultural sectors .