Browsing by Author "Lyu H"

Now showing 1 - 7 of 7
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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
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    Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models
    (MDPI (Basel, Switzerland), 2023-03-08) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval E; Díaz-Varela RA
    Monitoring grape nutrient status, from flowering to veraison, is important for viticulturists when implementing vineyard management strategies, in order to produce quality wines. However, traditional methods for measuring nutrient elements incur high labour costs. The aim of this study is to explore the potential of predicting grapevine leaf blade nutrient concentration based on hyperspectral data. Leaf blades were collected at two Pinot Noir commercial vineyards at Martinborough, New Zealand. The leaf blade spectral data were obtained with a handheld spectroradiometer, to evaluate surface reflectance and derivative spectra in the spectrum range between 400 and 2400 nm. Afterwards, leaf blades nutrient concentrations (N, P, K, Ca, and Mg) were measured, and their relationships with the hyperspectral data were modelled by machine learning models; partial least squares regression (PLSR), random forest regression (RFR), and support vector regression (SVR) were used. Pearson correlation and recursive feature elimination, based on cross-validation, were used as feature selection methods for RFR and SVR, to improve the model’s performance. The variable importance score of PLSR, and permutation variable importance of RFR and SVR, were used to determine the most sensitive wavelengths, or spectral regions related to each biochemical variable. The results showed that the best predictive performance for leaf blade N concentration was based on PLSR to raw reflectance data (R2 = 0.66; RMSE = 0.15%). The combination of support vector regression with the Pearson correlation selected method and second derivative reflectance provided a high accuracy for K and Ca modelling (R2 = 0.7; RMSE = 0.06%; R2 = 0.62; RMSE = 0.11%, respectively). However, the modelling performance for P and Mg, by different feature groups and variable selection methods, was poor (R2 = 0.15; RMSE = 0.02%; R2 = 0.43; RMSE = 0.43%, respectively). Thus, a larger dataset is needed for improving the prediction of P and Mg. The results indicated that for Pinot Noir leaf blades, raw reflectance data had potential for the prediction of N concentration, while the second-derivative spectra were more suitable to predict K and Ca. This study led to the provision of rapid and non-destructive measurements of grapevine leaf nutrient status.
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    Hyperspectral Imaging Spectroscopy for Non-Destructive Determination of Grape Berry Total Soluble Solids and Titratable Acidity
    (MDPI AG, 2024-05-07) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval E; Krasuki K; Weirzbicki D
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    Non-destructive and on-site estimation of grape total soluble solids by field spectroscopy and stack ensemble learning
    (Elsevier, 2025-02-20) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval E
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    Synthetic hyperspectral reflectance data augmentation by generative adversarial network to enhance grape maturity determination
    (Elsevier B V, 2025-08) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval E
    Non-destructive and rapid grape maturity detection is important for the wine industry. The ongoing development of hyperspectral imaging techniques and deep learning methods has greatly helped in non-destructive assessing of grape quality and maturity, but the performance of deep learning methods depends on the volume and the quality of labeled data for training. Building non-destructive grape quality or maturity testing datasets requires damaging grapes for chemical analysis to produce labels which are time consuming and resource intensive. To solve this problem, this study proposed a conditional Wasserstain Generative Adversarial Network (WGAN) with the gradient penalty data augmentation technique to generate synthetic hyperspectral reflectance data of two grape maturity categories (ripe and unripe) and different Total Soluble Solids (TSS) values. The conditional WGAN with the gradient penalty was trained for a range of epochs: 500, 1000, 2000, 8000, 10,000, and 20,000. After training of 10,000 epochs, synthetic hyperspectral reflectance data were very similar to real spectra for each maturity category and different TSS values. Thereafter, contextual deep three-dimensional CNN (3D-CNN), Spatial Residual Network (SSRN) and Support Vector Machine (SVM) are trained on original training and syn- thetic + original training datasets to classify grape maturity. The synthetic hyperspectral reflectance data, incrementally added to the original training set in steps of 250, 500, 1000, 1500, and 2000 samples, consistently resulted in higher model performance compared to training solely on the original dataset. The best results were achieved by augmenting the training dataset with 2000 synthetic samples and training with a 3D-CNN, yielding a classification accuracy of 91 % on the testing set. To better assess the effectiveness of GAN-based data augmentation methods, two widely used regression models: Partial Least Squares Regression (PLSR) and one-dimensional CNN (1D-CNN) were used based on same data augmentation method. The best result was achieved by adding 250 synthetic samples to the original training set when training 1D-CNN model, yielding an R2 of 0.78, RMSE of 0.63 ◦Brix, and RPIQ of 3.36 on the testing set. This study indicated that deep learning models combined with conditional WGAN with the gradient penalty data augmentation technique had a good application prospect in the grape maturity assessment.
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    Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
    (MDPI AG, 2023-11-19) Lyu H; Grafton M; Ramilan T; Irwin M; Wei H-E; Sandoval E; Zhang C; Liu D
    The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 ◦Brix, and coefficient of determination (R2 ) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 ◦Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 ◦Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.
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    Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality
    (MDPI (Basel, Switzerland), 2023-11-19) Lyu H; Grafton M; Ramilan T; Irwin M; Wei H-E; Sandoval E; Zhang C; Liu D
    The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R2) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.