Crop Selection and Yield Prediction using Intelligent Algorithms

The selection of a crop and the prediction of its yield are essential steps in successful agriculture, assisting farmers in decision-making and enhancing crop production. This process involves determining the crop yield per unit area, considering factors such as soil type, weather conditions, and crop management practices. Yield prediction plays a crucial role in modern agriculture, allowing farmers to optimize resource allocation, prepare for potential pest and disease outbreaks, and improve overall farm efficiency. While existing crop yield prediction models provide reasonable estimations, further improvement can be achieved with state-of-the-art algorithms. Various machine learning algorithms, including Random Forest, Linear Regression, Deep Learning, and Convolutional Neural Networks (CNN), have been applied to support the proposed research on crop yield prediction. These predictions contribute to achieving higher yields and enhancing the quality of crops.