Optimizing Agricultural Output to Predict the Best Possible Productivity and Crop Suitable on the Very Particular Land using Machine Learning Techniques

Increased worldwide agricultural output needs require farmers to use high yield farming practices and evaluate suitable land utilization for crop production. Advanced ML algorithms function in this research to predict suitable yields alongside crop suitability assessments for different land areas. The research adds value to supervised and unsupervised learning principles to establish a ML system which deals effectively with diverse data types including soil chemistry features and climate elements and historical harvest volumes. Investigative efforts reveal modern separated commodities such as ensemble methods and deep learning and hybrid models to boost agricultural field yield outputs. This study adopts frame literature methodologies in feature selection and enhanced predictive models for better crop yield prediction along with advisory functions. The research outcomes create adaptable agricultural information that helps farmers and stakeholders boost their efficient and sustainable modern farming practices.