Reliable assessment of crop water stress is essential for improving irrigation efficiency for upland crop and orchard fruit production. Thermal sensing combined with the crop water stress index (CWSI) provides a non-destructive approach for characterizing plant water status; however, its performance depends strongly on baseline determination, sensing platform, canopy architecture, environmental conditions, image-processing procedures, and validation strategy. This paper aimed to review thermal imaging and point-based infrared thermometry (IRT) approaches for CWSI measurement and mapping, with particular emphasis on sensing requirements of upland field crops and orchard fruit tree canopies. A PRISMA-aligned literature search was conducted using major scientific databases and complementary citation tracking, resulting in 68 primary studies included in the structured thematic synthesis. Empirical, theoretical, and artificial-reference CWSI formulations were compared together with handheld, ground-based, unmanned aerial vehicle (UAV)-mounted, fixed camera, and point-based IRT sensing configurations. Radiometric calibration, canopy segmentation, temperature extraction, spatial interpolation, and physiological validation methods were critically evaluated. The reviewed evidence indicates that image-based approaches provide valuable spatial information but remain sensitive to radiometric drift, mixed canopy–background pixels, shadowing, and baseline uncertainty, whereas point-based IRT offers high temporal resolution but depends strongly on representative sensor placement and field-of-view geometry. These limitations differed according to canopy architecture, with soil–background contamination being particularly important in incomplete field canopies and radiation-driven within-canopy heterogeneity being more important in orchards. Combining periodic spatial thermal mapping with continuous point-based monitoring provided a promising framework for precision irrigation. Future research should prioritize transferable baseline parameterization, standardized uncertainty reporting, multi-season commercial validation, benchmark datasets, and crop-specific irrigation thresholds.

Accurate, non-destructive estimation of potato volume can support conveyor-based yield monitoring, but individual sensing modalities provide incomplete geometric information. This study developed and evaluated a low-cost sensing approach combining top-view RGB imaging and ultrasonic height measurement under controlled potato-harvester conveyor conditions. One hundred potatoes were evaluated repeatedly under 20 combinations of five conveyor speeds (0.10–0.50 ms⁻¹ and four inclination angles (15–30°). Two RGB cameras provided projected-area measurements across the conveyor width, while an eight-sensor ultrasonic array provided complementary potato-height measurements. Four linear volume-estimation formulations were compared: height only, projected area only, area and height as separate predictors (A+H), and an area–height fusion feature (A×H). The combined models substantially outperformed the single-sensor approaches. The (A+H) model achieved a mean (R²) of 0.88, RMSE of 3.27 cm³, and MAE of 2.54 cm³, while the (A×H) fusion model achieved a mean (R²) of 0.88, RMSE of 3.33 cm³, and MAE of 2.60 cm³. Mixed-effects analysis showed that model type significantly affected estimation error (p < 0.001), confirming the benefit of combining RGB-derived area and ultrasonic height. Conveyor speed had the dominant effect on accuracy (p < 0.001), whereas inclination angle produced a statistically significant but comparatively small effect, and the speed × angle interaction was non-significant. The highest accuracy was obtained at 0.10 ms⁻¹, while performance declined with increasing speed, consistent with reduced RGB and ultrasonic sampling opportunities. These results demonstrate the feasibility of RGB–ultrasonic fusion for potato volume estimation under controlled dynamic conveyor conditions; field-scale robustness remains to be validated under commercial harvesting environments.