Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

Abstract

a b s t r a c tPrecision irrigation management of wine grape requires a reliable method to easily quantify and monitor vine water status to allow effective manipulation of plant water stress in response to water demand, cultivar management and producer objective. Mild to moderate water stress is desirable in wine grape in determined phenological periods for controlling vine vigor and optimizing fruit yield and quality according to producer preferences and objectives. The traditional leaf temperature based crop water stress index (CWSI) for monitoring plant water status has not been widely used for irrigated crops in general partly because of the need to know well-watered and non-transpiring leaf temperatures under identical environmental conditions. In this study, leaf temperature of vines irrigated at rates of 35, 70 or 100% of estimated evapotranspiration demand (ET c ) under warm, semiarid field conditions in southwestern Idaho USA was monitored from berry development through fruit harvest in 2013 and 2014. Neural network (NN) models were developed based on meteorological measurements to predict well-watered leaf temperature of wine grape cultivars 'Syrah' and 'Malbec' (Vitis vinifera L.). Input variables for the cultivar specific NN models with lowest mean squared error were 15-min average values for air temperature, relative humidity, solar radiation and wind speed collected within ±90 min of solar noon (13:00 and 15:00 MDT). Correlation coefficients between NN predicted and measured well-watered leaf temperature were 0.93 and 0.89 for 'Syrah' and 'Malbec', respectively. Mean squared error and mean average error for the NN models were 1.07 and 0.82• C for 'Syrah' and 1.30, and 0.98 • C for 'Malbec', respectively. The NN models predicted well-watered leaf temperature with significantly less variability than traditional multiple linear regression using the same input variables. Non-transpiring leaf temperature was estimated as air temperature plus 15• C based on maximum temperatures measured for vines irrigated at 35% (ET c ). Daily mean CWSI calculated using NN estimated well-watered leaf temperatures between 13:00 and 15:00 MDT and air temperature plus 15• C for non-transpiring leaf temperature consistently differentiated between deficit irrigation amounts, irrigation events, and rainfall. The methodology used to calculate a daily CWSI for wine grape in this study provided a daily indicator of vine water status that could be automated for use as a decision-support tool in a precision irrigation system.Published by Elsevier B.V.

Remote Sensing for Irrigation of Horticultural Crops

“…In more stressful conditions (37 • C and very low air humidity), the authors measured a difference of 13 • C in 'Syrah' and 17 • C in 'Malbec' (T nws − T air ). King and Shellie [163] report that a difference between canopy and air temperature of vines of about 17 • C, which is much higher than with T dry values assumed in other studies and slightly less than the energy balance models developed by Jones [111]. T dry for calculation of the CWSI was estimated as T air + 15 • C for both grape cultivars, resulting in a CWSI value between 0 and 1 for irrigation scheduling purposes.…”

“…Stated below King and Shellie [163] obtained CWSI values ranging from 0 to 1, when adopting the T air + 5 • C for estimating the temperature of non-transpiring leaves (T dry ), as proposed by Irmak et al [201] and Möller et al [108] for corn and grape, respectively. The authors found that the maximum temperature difference between the canopy of deficit-irrigated vines and T air was influenced by irrigation frequency.…”

“…As correctly stated by King and Shellie [163], the CWSI has not been widely used for irrigated crops since it is necessary to acquire the temperature difference of both transpiring and non-transpiring leaves under the same environmental conditions. Stated below King and Shellie [163] obtained CWSI values ranging from 0 to 1, when adopting the T air + 5 • C for estimating the temperature of non-transpiring leaves (T dry ), as proposed by Irmak et al [201] and Möller et al [108] for corn and grape, respectively.…”

“…King and Shelliey [163] reported that clouds (thin cirrus), often present during the best two-hour-time for CWSI monitoring, rapidly caused changes in levels of solar radiation and, as a consequence, an effect on T nws . They also provided a detailed description of physiological variations within a canopy; particular attention was given to rapid changes of leaf temperature in response to environmental fluctuations (e.g., air turbulence, changing radiation).…”

“…King and Shellie [163] adopted a neural model to estimate daily mean CWSI starting from well-watered leaf temperatures and air temperature plus 15 • C for a non-transpiring leaf temperature (between 13:00 and 15:00 MDT). They were able to discriminate between deficit irrigation amounts, irrigation events, and rainfall.…”