Integrating Physics-Based Models, Mathematical Optimization, and Statistical Analytics for Advancing Precision Agriculture and Sustainable Farming Practices

The application of physics-based models, mathematical optimization, and statistical analytics is being studied to realize applications in precision agriculture for the sustainable management of affected agroecosystems. Resource efficiency, crop productivity, and environmental sustainability are some targets to improve through multidisciplinary approaches. The datasets on soil moisture, temperature, the health of plants, and daily weather patterns were generated through real-time sensor-based data acquisition, satellite data collection, and historical records. Physically based models were developed to represent soil-water dynamics and plant growth responses under different environmental scenarios. Later, mathematical optimization methods were developed, focusing on resource-efficient strategies for irrigation, fertilization, and pest management, where statistical analyses such as regressions, geostatistics, and time-series analyses were applied to models to predict crop yields and resource needs accurately. The developed models became an input to the decision support system for real-time decision-making according to the recommended resource allocation for better farming scenarios. Pilots have reported a 15% crop yield increase with considerable reductions in water and fertilizer use, contributing to cost savings and environmental conservation. The results prove that these methods, when combined, deliver increased productivity and reduced resource consumption while minimizing the environmental footprint, providing a sustainable approach to modern agriculture. This technique has good prospects for profitability in farming operations and enhancing sustainability in agriculture in the long term, especially as it relates to climate change and resource depletion.