IoT-Driven Predictive Analytics for Precision Harvest Timing with LightGBM Models

In agriculture, precise harvest time is essential to maximize production and quality. A predictive analytics system powered by the Internet of Things (IoT) and using LightGBM models for accurate harvest time determination is presented in this study. The framework incorporates IoT sensors to gather environmental data, such as soil moisture, light intensity, temperature, and humidity, which are important markers of crop maturity. The data is analyzed using machine learning models, namely LightGBM, to estimate when to harvest accurately. The proposed framework seeks to improve agricultural efficiency by using real-time data analytics. This would allow farmers to make better crop output, waste, and resource utilization choices. Farmers may dynamically adjust their harvesting plans in response to changing crop conditions due to the IoT-driven strategy that enables constant monitoring of environmental variables. By reducing input waste and environmental effects, this capacity enhances agricultural operations' total productivity while supporting sustainable farming practices. The proposed model can transform harvest management using advanced predictive analytics and IoT technologies, as shown by case studies and simulations confirming its efficacy in several agricultural settings.