Enhancing Crop Yield Prediction with IoT and Machine Learning in Precision Agriculture

To improve agricultural production prediction in the context of precision agriculture, this research investigates the combination of machine learning algorithms with IoT technologies. Using cutting-edge technology, precision agriculture provides creative ways to meet the rising need for food production to feed a growing global population. IoT devices, such as sensors and drones, gather large volumes of data on crop health, temperature, humidity, and soil moisture. Accurate crop production projections are produced by processing this data using machine learning techniques. These algorithms can find patterns and connections in historical and current data, which helps farmers make well-informed choices about pest control, fertilization, and irrigation. The uses of machine learning methods, including neural networks, decision trees, and regression models, in agricultural production prediction are covered in this research. The potential for revolutionizing agricultural methods via the combination of IoT and machine learning is enormous, as it might enhance sustainability, efficiency, and crop yields to satisfy the needs of an expanding population.