Principal Investigator(s): Carlos Zúñiga Espinoza, Lav R. Khot, Sindhuja Sankaran, and Pete W. Jacoby
Organizations: Department of Biological System Engineering, Washington State University, Pullman, WA 99164-6120; Center for Precision and Automated Agricultural Systems, Washington State University, Prosser,WA 99350-8694; Department of Crop and Soil Science, Washington State University, Pullman, WA 99164-6420
Emails: carlos.zuniga@wsu.edu (C.Z.E); sindhuja.sankaran@wsu.edu (S.S.); jacoby@wsu.edu (P.J.)
Correspondence: lav.khot@wsu.edu; Tel.: 509-786-9302
Received: 8 June 2017; Accepted: 5 September 2017; Published: 16 September 2017
Abstract:
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.
Keywords: deficit irrigation; normalized difference vegetation index; green normalized difference vegetation index; canopy temperature; stomatal conductance
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