Predicting Crop Yield Using Machine Learning Algorithms for Precision Agriculture and Optimization Techniques

The aim is to predict crop yields using machine learning techniques to help farmers and agricultural planners make informed decisions. The data used for this study were collected from multiple sources such as weather reports, soil quality measurements, satellite imagery, and historical crop yield records. The data is cleaned and normalized using minmax scaling to bring all values to the same range. Next, feature selection technique such as Principal Component Analysis and recursive feature extraction (RFE) is used to identify the most important factors affecting crop yield, such as rainfall, temperature, soil pH, and fertilizer application. In order to forecast yield categories (low, medium, and high), a selection of characteristics was then utilized to train and assess classification algorithms like Random Forest, Decision Tree, and Support Vector Machine (SVM). Metrics, including accuracy, precision, recall, and F1-score, were used to evaluate the model and each classifier's performance.