Forecasting Crop Yields Using Machine Learning Techniques For Sustainable Farming

Crop production plays a crucial role in agriculture planning resource allocation and food security management with the advancement of technology and the availability of vast amounts of agriculture data, machine learning and statistical modelling techniques have emerged as powerful tools for predicting crop yields and identifying potential factors including crop growth. the data set utilized in crop production includes data such as whether data, soil quality is obtained through soil sampling algorithms commonly applied in crop production including Gaussian naive bayes this algorithm leverages the extracted features from the data set to build predictive models that estimate crop production for specific region and the period. Additionally, it highlights the significance of Accurate crop prediction in optimizing agriculture practices mitigating risks associated with climate change, and ensuring sustainable food production. furthermore, the discussion challenges data availability model complexity and interpretability along with the potential Avenue of further research to enhance the accuracy and applicability of the crop production model.