Precision irrigation management is based on the accuracy and feasibility of sensor data assessing the plant water status. Multispectral and thermal infrared images acquired from an unmanned aerial vehicle (UAV) were analyzed to evaluate the applicability of the data in the assessment of variants of subsurface irrigation configurations. The study was carried out in a Cabernet Sauvignon orchard located near Benton City, Washington. Plants were subsurface irrigated at a 30, 60, and 90 cm depth, with 15%, 30%, and 60% irrigation of the standard irrigation level as determined by the grower in commercial production management. Half of the plots were irrigated using pulse irrigation and the other half using continuous irrigation techniques. The treatments were compared to the control plots that received standard surface irrigation at a continuous rate. The results showed differences in fruit yield when the control was compared to deficit irrigated treatments (15%, 30%, 60% of standard irrigation), while no differences were found for comparisons of the techniques (pulse, continuous) or depths of irrigation (30, 60, 90 cm). Leaf stomatal conductance of control and 60% irrigation treatments were statistically different compared to treatments receiving 30% and 15% irrigation. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and canopy temperature were correlated to fruit yield and leaf stomatal conductance. Significant correlations (p < 0.01) were observed between NDVI, GNDVI, and canopy temperature with fruit yield (Pearson’s correlation coefficient, r = 0.68, 0.73, and −0.83, respectively), and with leaf stomatal conductance (r = 0.56, 0.65, and −0.63, respectively) at 44 days before harvest. This study demonstrates the potential of using low-altitude multispectral and thermal imagery data in the assessment of irrigation techniques and relative degree of plant water stress. In addition, results provide a feasibility analysis of our hypothesis that thermal infrared images can be used as a rapid tool to estimate leaf stomatal conductance, indicative of the spatial variation in the vineyard. This is critically important, as such data will provide a near real-time crop stress assessment for better irrigation management/scheduling in wine grape production.

Small unmanned aerial systems (UAS) are gaining global attention for rapid image-based decision making in agricultural production. In this study, the aim was to evaluate UAS-based imagery for rapid assessment of wheat winter survival and spring stand in winter wheat production and crop necrosis in potato production. Both are critical aspects of field (arid) and row (irrigated) crop farming practices. Aerial images from 97 hard and 352 soft single nucleotide polymorphism winter wheat plots, and 32 potato field plots (with 1 and 2 years of green manure applications) were acquired using a multi-band imaging sensor integrated with UAS. The UAS-based imagery was useful in evaluating winter wheat plant winter survival and spring stand, with Pearson correlation coefficient (r) in the range 0.60–0.82 between imagery and ground reference data. Similarly, the image-based potato field necrosis assessment showed a strong relationship with ground reference data (r = 0.93 and 0.88 for 1 and 2 years of green manure application, respectively). Overall, UAS imagery provided quantifiable, timely, and unbiased field data with high spatial resolution (about 2.3 cm/pixel for images acquired at 100 m altitude) that can aid in field and row crop production decision making.

Geospatial crop water use mapping is critical for field-scale site-specific irrigation management. Landsat 7/8 satellite imagery with a widely adopted METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) energy balance model (LM approach) estimates accurate evapotranspiration (ET) but limits field-scale spatiotemporal (30 m pixel⁻¹, ~16 days) mapping. A study was therefore conducted to map actual ET of commercially grown irrigated-field crops (spearmint, potato, and alfalfa) at very high-resolution (7 cm pixel⁻¹). Six small unmanned aerial system (UAS)-based multispectral and thermal infrared imagery campaigns were conducted (two for each crop) at the same time as the Landsat 7/8 overpass. Three variants of METRIC model were used to process the UAS imagery; UAS-METRIC-1, -2, and -3 (UASM-1, -2, and -3) and outputs were compared with the standard LM approach. ET root mean square differences (RMSD) between LM-UASM-1, LM-UASM-2, and LM-UASM-3 were in the ranges of 0.2–2.9, 0.5–0.9, and 0.5–2.7 mm day⁻¹, respectively. Internal calibrations and sensible heat fluxes majorly resulted in such differences. UASM-2 had the highest similarity with the LM approach (RMSD: 0.5–0.9, ET_dep,abs (daily ET departures): 2–14%, r (Pearson correlation coefficient) = 0.91). Strong ET correlations between UASM and LM approaches (0.7–0.8, 0.7–0.8, and 0.8–0.9 for spearmint, potato, and alfalfa crops) suggest equal suitability of UASM approaches as LM to map ET for a range of similar crops. UASM approaches (Coefficient of variation, CV: 6.7–24.3%) however outperformed the LM approach (CV: 2.1–11.2%) in mapping spatial ET variations due to large number of pixels. On-demand UAS imagery may thus help in deriving high resolution site-specific ET maps, for growers to aid in timely crop water management.

Site-specific irrigation management for perennial crops such as grape requires water use assessments at high spatiotemporal resolution. In this study, small unmanned-aerial-system (UAS)-based imaging was used with a modified mapping evapotranspiration at high resolution with internalized calibration (METRIC) energy balance model to map water use (UASM-ET approach) of a commercial, surface, and direct-root-zone (DRZ) drip-irrigated vineyard. Four irrigation treatments, 100%, 80%, 60%, and 40%, of commercial rate (CR) were also applied, with the CR estimated using soil moisture data and a non-stressed average crop coefficient of 0.5. Fourteen campaigns were conducted in the 2018 and 2019 seasons to collect multispectral (ground sampling distance (GSD): 7 cm/pixel) and thermal imaging (GSD: 13 cm/pixel) data. Six of those campaigns were near Landsat 7/8 satellite overpass of the field site. Weather inputs were obtained from a nearby WSU-AgWeatherNet station (1 km). First, UASM-ET estimates were compared to those derived from soil water balance (SWB) and conventional Landsat-METRIC (LM) approaches. Overall, UASM-ET (2.70 ± 1.03 mm day⁻¹ [mean ± std. dev.]) was higher than SWB-ET (1.80 ± 0.98 mm day⁻¹). However, both estimates had a significant linear correlation (r = 0.64–0.81, p < 0.01). For the days of satellite overpass, UASM-ET was statistically similar to LM-ET, with mean absolute normalized ET departures (ET_d,MAN) of 4.30% and a mean r of 0.83 (p < 0.01). The study also extracted spatial canopy transpiration (UASM-T) maps by segmenting the soil background from the UASM-ET, which had strong correlation with the estimates derived by the standard basal crop coefficient approach (T_d,MAN = 14%, r = 0.95, p < 0.01). The UASM-T maps were then used to quantify water use differences in the DRZ-irrigated grapevines. Canopy transpiration (T) was statistically significant among the irrigation treatments and was highest for grapevines irrigated at 100% or 80% of the CR, followed by 60% and 40% of the CR (p < 0.01). Reference T fraction (T_rF) curves established from the UASM-T maps showed a notable effect of irrigation treatment rates. The total water use of grapevines estimated using interpolated T_rF curves was highest for treatments of 100% (425 and 320 mm for the 2018 and 2019 seasons, respectively), followed by 80% (420 and 317 mm), 60% (391 and 318 mm), and 40% (370 and 304 mm) of the CR. Such estimates were within 5% to 11% of the SWB-based water use calculations. The UASM-T-estimated water use was not the same as the actual amount of water applied in the two seasons, probably because DRZ-irrigated vines might have developed deeper or lateral roots to fulfill water requirements outside the irrigated soil volume. Overall, results highlight the usefulness of high-resolution imagery toward site-specific water use management of grapevines.

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.