Prediction of Crops Based on Characteristics Using Feature Selection Algorithm

Research in agriculture is expanding. Crop forecasting is critical in agriculture and strongly dependent on the soil and environmental elements such as temperature, humidity, and rainfall. Crop prediction based on characteristics is an essential task in agriculture for optimizing yields and resource allocation. By selecting the most relevant features, the accuracy and efficiency of crop prediction models can be enhanced. The process involves data collection, preprocessing, feature selection, model development, evaluation, and prediction. It involves gathering of a comprehensive dataset encompassing diverse characteristics such as soil type, climate conditions, precipitation, temperature, sunlight duration, pH level, and nutrient content. Additionally, historical crop yield data for various crops are included to establish a relationship between characteristics and outcomes. The current research provides the better results in terms of distinct parameters with different machine learning algorithms.