RGB UAV Imagery for Crop Monitoring and Decision Support in Precision Agriculture

Digitalization and data-driven decision-making are transforming modern agriculture, where rapid and high-resolution assessment of crop condition is essential for optimizing field interventions. Multispectral approaches such as satellite-derived normalized difference vegetation index are widely used; however, their spatial resolution and temporal availability may limit the detection of fine-scale variability within agricultural fields. This study investigates the capability of unmanned aerial vehicle (UAV)-acquired red, green, and blue (RGB) imagery to provide relevant information regarding crop condition. Two autumn crops, wheat and rapeseed, located in Olt County, Romania, were analyzed. The acquired images were processed photogrammetrically to generate orthomosaics, followed by the extraction of vegetation indices derived from the visible spectrum, namely Excess Green Index and Visible Atmospherically Resistant Index. Both indices provided meaningful information on vegetation condition, with the latter exhibiting enhanced sensitivity to intra-field variability. The UAV-derived Excess Green Index and Visible Atmospherically Resistant Index maps revealed spatial variability that was not equally distinguishable in the satellite Normalized Difference Vegetation Index data, particularly for the wheat crop. Individually, the two RGB vegetation indices classified approximately 16% of each analyzed field as vulnerable. These vulnerable regions represent relative, image-based spectral anomalies rather than independently validated indicators of physiological crop stress, nutrient deficiency, or yield loss. Following rule-based fusion and the removal of isolated detections, 21.55% of the wheat field and 19.75% of the rapeseed field were identified as spatially coherent vulnerable areas. The resulting maps were subsequently converted into machinery-oriented candidate intervention zones, supporting an illustrative simulation of how spatially differentiated field information could be integrated into conventional agricultural equipment. These findings demonstrate that UAV-based RGB imagery can provide spatially explicit information for crop monitoring and can support the identification of candidate areas for subsequent field verification and precision-agriculture decision-making.