This review evaluates the use of unmanned aerial vehicles (UAVs) in detecting and managing water stress in specialty crops through thermal, multispectral, and hyperspectral imaging. Based on 104 scholarly articles from 2012 to 2024, the review highlights the advantages, limitations, and evolution of these imaging systems. Vineyards are the most studied crops for precision irrigation compared to other crops. The paper traces the shift from standalone imaging to multi-sensor fusion approaches, integrating vegetation indices and machine learning models for improved accuracy, resolution, and real-time stress assessment. It also addresses knowledge gaps such as scalability, payload constraints, and computational demands. Issues like flight altitude, sensor angle, and lighting conditions can lead to data inconsistencies, affecting water stress detection and decision-making. Emerging technologies like LiDAR, AI, and machine learning are proposed to enhance UAV data processing and stress detection. Future research should focus on developing automated data correction, multi-sensor fusion, and AI-driven real-time analysis to address sensor calibration and environmental factors. The review also advocates for integrating UAV data with satellite and ground sensors into smart irrigation systems to create a multi-scale monitoring framework, thereby advancing precision agriculture and water resource management.

Efficient irrigation management is essential for optimizing yield and quality in specialty crops like hot peppers (Capsicum chinense), particularly under controlled greenhouse environments. This study employed a novel sensor-based system integrating soil moisture and sap flux monitoring to evaluate water use dynamics in Capsicum chinense, a species for which such applications have not been widely reported. Three cultivars—Habanero, Helios, and Lantern—were grown under three volumetric soil moisture contents: low (15%), medium (18%), and high (21%). Water uptake was measured at leaf (transpiration, stomatal conductance) and plant levels (sap flux via heat balance sensors). Photosynthesis, fruit yield, and capsaicinoid concentrations were assessed. Compared to high irrigation, medium and low irrigation increased photosynthesis by 16.6% and 22.2%, respectively, whereas high irrigation favored greater sap flux and vegetative growth. Helios exhibited an approximately 8.5% higher sap flux as compared to Habanero and about 10% higher as compared to Lantern. Helios produced over 30% higher fruits than Habanero and Lantern under high irrigation. Habanero recorded the highest pungency, with a capsaicinoid level of 187,292 SHU—exceeding Lantern and Helios by 56% and 76%, respectively. Similarly, nordihydrocapsaicin and dihydrocapsaicin accumulation were more cultivar-dependent than irrigation-dependent. No significant interaction between cultivar and irrigation was observed, indicating genotype-driven water use strategies. Our study contributes to precision horticulture by integrating soil moisture and sap flux sensors to reveal cultivar-specific water use strategies in Capsicum chinense, thereby demonstrating the potential of an integrated sensor-based irrigation system for efficient irrigation management under increasing water scarcity in protected environments. As a preliminary greenhouse study aimed at maintaining consistent irrigation throughout the growing season across three volumetric soil moisture levels, these findings provide a foundation for subsequent validation and exploration under diverse soil moisture conditions including variations in stress duration, stress frequency, and stress application at different phenological stages.