Precision agriculture addresses modern farming challenges like climate change, soil degradation, and water scarcity by using advanced technologies. This study focuses on two key areas: soil moisture prediction and crop recommendation. Machine learning models like LSTM and CNN-LSTM efficiently predict soil moisture, enabling precise irrigation scheduling. For crop selection, Random Forest outperformed other methods, providing reliable recommendations based on soil and climate factors. Integrating these models optimizes water use, boosts crop yields, and supports sustainable farming. Future research aims to incorporate real-time data and implement these solutions in farm management systems, revolutionizing agricultural practices for better resource efficiency and productivity.
