Implementing Sentinel-2 Data and Machine Learning to Detect Plant Stress in Olive Groves

Abstract

Olives are an essential crop for Greece and constitute a major economic and agricultural factor. Diseases, pests, and environmental conditions are all factors that can deteriorate the health status of olive crops by causing plant stress. Researchers can utilize remote sensing to assist their actions in detecting these sources of stress and act accordingly. In this experiment, Sentinel-2 data were used to create vegetation indices for commercial olive fields in Halkidiki, Northern Greece. Twelve machine learning algorithms were tested to determine which type would be the most efficient to detect plant stress in olive trees. In parallel, a test was conducted by testing 26 thresholds to determine how setting different thresholds for stress incidence affects model performance and which threshold constitutes the best choice for more accurate classification. The results show that among all tested classification algorithms, the quadratic discriminant analysis provided the best performance of 0.99. The stress incidence threshold used in the current case to generate the best-performing model was 6%, but the results suggest that setting customized thresholds relevant to specific cases would provide optimal results. The best-performing model was used in a one-vs.-rest multiclass classification task to determine the source of the stress between four possible classes: “healthy”, “verticillium”, “spilocaea”, and “unidentified”. The multiclass model was more accurate in detection for the “healthy” class (0.99); the “verticillium” and “unidentified” classes were less accurate (0.76); and “spilocaea” had the lowest score (0.72). Findings from this research can be used by experts as a service to enhance their decision-making and support the application of efficient strategies in the field of precision crop protection.