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Author: search.filters.author.Irwin MSubject: search.filters.subject.0203 Classical Physics

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    Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation
    (MDPI AG, 12/08/2021) Wei H-E; Grafton M; Bretherton M; Irwin M; Sandoval E
    Monitoring and management of plant water status over the critical period between flower-ing and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.
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    Evaluation of the use of UAV-derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring based on Machine Learning Algorithms and SHAP Analysis
    (1/06/2022) Wei H-E; Grafton MC; Bretherton M; Irwin M; Sandoval E; Horne, D; Singh, R
    Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. The spatial and temporal variation of GWS, is conventionally characterized by using a pressure bomb. This is laborious and time-consuming, which limits the number of samples. Although unmanned aerial vehicles (UAVs) can provide mapping across the entire vineyards efficiently, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band which makes the monitoring of GWS problematic. As GWS is an integrated response to vegetation characteristics, temporal trends, weather conditions, and soil/terrain data, it is assumed that these ancillary variables have the potential to enhance the capability of UAVs for GWS monitoring. The goal of this study is to explore whether and which of these ancillary variables may improve the accuracy of GWS estimation using UAV, and provide insights into the contribution that each selected variable contributes to the variation in GWS. A UAV was flown over two Pinot Noir vineyards in New Zealand to generate aerial images with 4.3 cm resolution, and 18 vegetation indices (VIs) were computed for every sampled grapevine. The strongly correlated VIs were used as the core input for later GWS modeling. Ancillary data included soil/terrain, weather, and temporal variables. Slope and elevation were extracted from a digital elevation model, and apparent electrical conductivity (ECa) was obtained from an EM38 survey. A local weather station provided continuous air temperature, humidity, rainfall, wind speed, and irradiance data, which were computed as variables at weekly and daily intervals. Day of the year (DOY) was used to represent the temporal trend along the growing season. Three machine learning algorithms (elastic net, random forest regression, and support vector regression) were used to regress the predictors against stem water potential (Ψstem), measured by a pressure bomb and used as a proxy for GWS. Shapley Additive exPlanations (SHAP) analysis (a statistical tool that weighs the importance of each variable in a model) was used to assess the relationship between selected variables and Ψstem. The results show that Transformed Chlorophyll Absorption Reflectance Index (TCARI) and Excess Green Index (ExG) are the best correlated VIs, but their correlation with Ψstem is poor (rTCARI = 0.6; rExG = 0.58). The coefficient of determination (R2) of the TCARI-based model increased from 0.35 to 0.7 when DOY and elevation were included as ancillary inputs. R2 of the ExG-based model increased from 0.3 to 0.74 when DOY, elevation, slope, ECa, and daily average windspeed, were included as ancillary inputs. Support vector regression was the best model to describe the relationship between Ψstem and selected predictors. This study has provided proof of the concept of developing GWS estimation models that potentially enhance the monitoring capacities of UAVs for GWS, as well as provide individual GWS mapping at the vineyard scale. This may enable growers to improve irrigation management, leading to controlled vegetative growth and optimized berry quality.
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    Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models
    (MDPI AG, 8/03/2023) Lyu H; Grafton MC; Ramilan T; Irwin M; Sandoval - Cruz E; Díaz-Varela, RA