Enhancing Yield Estimation and Field Zoning Accuracy in Precision Agriculture Using Solar-Powered Drone-Based Remote Sensing - Abbas Haider Mohammed; Obaid Mohammed Kadhim; Vittalaiah A. (SHS Web of Conferences, 2025)
Precision agriculture is an advanced farming practice that leverages technologies such as GPS, remote sensing, and data analytics to monitor and manage field variability. This approach enhances crop productivity, conserves resources, and improves overall farm efficiency by enabling the precise application of inputs like water, fertilizers, and pesticides based on real-time data. This study proposes a solar-powered, drone-based remote sensing system tailored for precision agriculture to improve yield estimation and field zoning accuracy. Equipped with multispectral and updraft sensors, the drone captures high-resolution images of agricultural fields, providing detailed insights into crop health and soil conditions. The system processes this data using advanced machine learning algorithms to forecast crop yields and generate detailed field zoning maps, enabling optimized resource allocation and improved farm management. The integration of solar power extends the drone's operational time, making it a sustainable solution for large-scale data collection. Field trials validated the system's accuracy, efficiency, and resource-saving capabilities. The income assessment model demonstrated high precision, with data points closely aligning with the 1:1 line, indicating a strong correlation between actual and predicted yields. The field zoning map revealed significant three-dimensional variability, with lower yields in lighter-shaded areas and higher yields in darker-blue regions. Radiation data analysis indicated peak incident radiation at 500 W/m2 and reflected radiation at 200 W/m2 around noon. Drone performance varied, with operational times ranging from 18 to 25 minutes across field segments. The results highlight the effectiveness of the proposed precision agriculture system, showcasing its potential for improving crop yield predictions, optimizing resource distribution, and enhancing overall farm productivity.