Abstract
Monitoring and management of plant water status over the critical period between flowering 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.
Highlights
Grapevine (Vitis spp.) is considered one of the most important berry crops in the world, due to its commercial derivative—wine
Note: “No” refers to the assigned number of each pipeline, “1D” refers to the first derivative, “2D” refers to the second derivative, “Continuum Removal (CR)” refers to continuum removal, “SI” refers to simple ratio indices, “Vegetation Indices (VIs)” refers to vegetation indices, “Recursive Feature Elimination Based on Cross-Validation (RFECV)” refers to recursive feature elimination based on cross-validation, “Partial least squares regression (PLSR)” refers to partial least squares regression, “RFR” refers to random forest regression, “SVR” refers to support vector regression, and “LR”
Previous studies have stated that the NIR-shortwave infrared (SWIR) spectrum is more suitable for water status estimation [58,80], this paper suggests that statistically significant wavelengths correlated with Ψstem variation span several spectral regions over the entire spectrum, when different transformed datasets are used as inputs, in the modeling pipelines employed by this study
Summary
Grapevine (Vitis spp.) is considered one of the most important berry crops in the world, due to its commercial derivative—wine. The market price of this product is defined by the quality of harvested berries, and water management applied during the growing season has a significant effect on this quality [1]. Inadequate water inputs can harm berry quality as the production of some quality-specific flavor precursors is compromised [2]. Excessive irrigation can result in high vigor and strong vegetative growth, further delaying ripening and generating undesirable flavors in the wine [3]. Maintaining grapevine water status (GWS) within a specific range is critical to quality management, and the growers’ profit. Studies have shown vines in a single block exhibit a significant variation of GWS even if they receive the same amount of irrigation [4].
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