Remote Sensing of Nematode Stress in Coffee: UAV-Based Multispectral and Thermal Imaging Approaches

Early and non-destructive detection of plant-parasitic nematodes is critical for implementing site-specific management in coffee production systems. This study evaluated the potential of unmanned aerial vehicle (UAV) multispectral and thermal imaging, combined with textural analysis, to detect Meloidogyne exigua infestation in Coffea arabica (Topázio variety). Field surveys were conducted in two contrasting seasons (dry and rainy), and nematode incidence was identified and quantified by counting root galls. Vegetation indices (NDVI, GNDVI, NGRDI, NDRE, OSAVI), individual spectral bands, canopy temperature, and Haralick texture features were extracted from UAV-derived imagery and correlated with gall counts. Under the conditions of this experiment, strong correlations were observed between gall number and the red spectral band in both seasons (R > 0.60), while GNDVI (dry season) and NGRDI (rainy season) showed strong negative correlations with gall density. Thermal imaging revealed moderate positive correlations with infestation levels during the dry season, indicating potential for early stress detection when foliar symptoms were absent. Texture metrics from the red and green bands further improved detection capacity, particularly with a 3 × 3 pixel window at 135°. These results demonstrate that UAV-based multispectral and thermal imaging, enhanced by texture analysis, can provide reliable early indicators of nematode infestation in coffee.