Intelligent Crop Selection Using Predictive Analytics in a Soil and Climate-Aware Machine Learning Model

Abstract

Crop selection can be a base of sustainable agriculture. It includes in-depth knowledge about all the parameters and conditions of soils and climatic conditions. It covers the prediction analytics-based machine learning model for smart crop selection integration with indicators of soil fertility, weather, and historical data on crop yields. The recommended crops are based on decision trees, gradient-boosting machines, and deep-learning algorithms. Thus, the validity of the suggested framework is justified in terms of agricultural datasets with high prediction accuracy. The results illustrate that predictive analytics helps farmers make better decisions while minimizing the risk of crop failure and maximizing agricultural production output. Scalable and adaptable, the approach can be expanded to other areas and soil types. This work contributes to the development of precision farming by using AI-based decision-support tools for sustainable and productive use. Further improvement with refinement in its predictive capability will be accomplished by integrating information from remote sensing and real-time climate monitoring into future work.