Enhancing agronomical attribute detection through RPAS-based image analysis - A review - Vasumathi, V; Manivannan, V; Ragunath, K P; Sakthivel, N; Balachandar, D; Raja, R; Priya, M V; Kabilan, M; Gogul , S (Plant Science Today, 2025)
The integration of Remotely Piloted Aircraft Systems (RPAS) into agronomical research has revolutionized the collection and analysis of spatial and temporal crop data. RPAS, equipped with high-resolution multispectral, thermal and visible cameras, provide a cost-effective and flexible alternative to traditional laboratory and manual methods. This review synthesizes recent advancements in RPAS-based image analysis for agronomic applications, with a focus on crop monitoring, weed detection, biomass estimation and yield prediction. A critical evaluation of the published information reveals that most studies utilize low-altitude flights with commercial drones integrated with sensors capable of capturing data with high spatial resolution. Image processing techniques, such as vegetation indices, machine learning algorithms and object-based image analysis, are commonly employed to extract biophysical and biochemical parameters. The review of the literature demonstrates a strong correlation between RPAS-derived metrics and ground-based measurements, validating their utility in precision agriculture. However, variability in sensor calibration, flight parameters and environmental conditions presents challenges to reproducibility and scalability. Overall, RPAS-based image analysis offers a promising avenue for enhancing data-driven decision-making in agriculture, contributing to more sustainable and efficient farming practices in the future.