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.

Highlights

  • Water management is an important aspect in the wine industry

  • The results showed the normalized difference vegetation index (NDVI), and the ratio between the transformed chlorophyll absorption in reflectance (TCARI) and the optimized soil-adjusted vegetation index (OSAVI) as indices highly correlated to stomatal conductance and stem water potential (Ψstem, R2 = 0.68, p < 0.05)

  • In this study, a unmanned aerial vehicle (UAV) integrated sensing system was tested to assess the grapevine responses under different subsurface irrigation regimes and sensing data was compared with crop responses

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Summary

Introduction

Water management is an important aspect in the wine industry. Grapevine water status can affect berry parameters critical to production of premium quality red wines such as the phenolic composition [1]. Bellvert et al [8] assessed the spatial variability in water stress Pinot Noir grape vines using CWSI This index was highly correlated to leaf water potential (ΨL ) where leaf-level temperature data was acquired using an infrared thermometer (R2 = 0.83, p < 0.0001) and a thermal infrared sensor integrated with a small UAV (R2 = 0.71, p < 0.0001). The use of optical sensors mounted on UAVs allows data collection with high temporal and spatial resolution to evaluate the crop water status. In this sense, different studies have been carried out in grapevines for water status characterization [4,8,9]. In this study, a UAV integrated sensing system was tested to assess the grapevine responses under different subsurface irrigation regimes and sensing (multispectral and thermal infrared sensors) data was compared with crop responses (stomatal conductance and fruit yield)

Site Location and Experimental Design
Data Acquisition and Processing
Response Variables
Image Processing and Statistical Analysis
80 DBH showed thatcontrol the control had a NDVI compared to the other
Thermal
Findings
Conclusions
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