Abstract
Accurate crop and yield prediction is crucial for optimizing agricultural productivity, managing food security, and supporting sustainable farming practices. This study presents a machine learning-based approach to predict crop yields by analyzing various environmental, soil, and weather-related factors. Using data from agricultural regions, the model incorporates variables such as rainfall, temperature, soil properties, and crop type to enhance the accuracy of yield predictions. Several machine learning algorithms, including decision trees, random forests, and neural networks, are evaluated for performance, with a focus on predictive accuracy, computational efficiency, and scalability. The results demonstrate that machine learning can significantly improve the precision of crop yield forecasts compared to traditional statistical methods. This model has the potential to assist farmers, policymakers, and agricultural businesses in making informed decisions related to crop management, resource allocation, and market planning. Ultimately, the study highlights the transformative role of machine learning in advancing precision agriculture and ensuring sustainable agricultural growth.