Assessing Olive Diseases in Albania for UAV- and AI-Based Monitoring

Olive cultivation is an important agricultural sector in Albania, where disease monitoring remains challenging due to fragmented orchards, heterogeneous management practices, and limited adoption of advanced sensing technologies. Recent studies have shown the potential of unmanned aerial vehicles (UAVs), sensor systems, and artificial intelligence (AI) for monitoring specific olive diseases in different olive-growing regions. However, the suitability of olive diseases reported in Albania for monitoring with UAVs and AI has not yet been systematically assessed. This paper examines the main olive diseases relevant to Albania using a semi-quantitative, literature-based multicriteria framework in which five monitoring criteria are scored from 1 to 3 and combined using equal weights. It also formalizes a UAV-first screening workflow that links image acquisition, AI-based canopy segmentation, feature extraction, anomaly scoring, decision thresholds, and targeted field or laboratory confirmation. Based on this assessment, the study identifies the most promising disease targets for future research and outlines key considerations for sensor selection and validation. The paper provides a context-specific foundation for future UAV- and AI-supported disease monitoring in Albanian olive groves. The revised analysis also distinguishes indicative acquisition targets from experimentally validated detection limits and specifies practical requirements for ground truth, radiometric calibration, dataset design, and geospatial validation.