Predicting Grapevine Physiological Parameters Using Hyperspectral Remote Sensing Integrated with Hybrid Convolutional Neural Network and Ensemble Stacked Regression

Author:

Sharma Prakriti1,Villegas-Diaz Roberto2ORCID,Fennell Anne1ORCID

Affiliation:

1. Agronomy, Horticulture and Plant Science Department, South Dakota State University, Brookings, SD 57007, USA

2. Department of Public Health, Policy and Systems, University of Liverpool, Liverpool L69 3GL, UK

Abstract

Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, quantum yield of PSII, and transpiration can illuminate the rootstock effect on scion physiology. However, these measures are time-consuming and limited to leaf-level analysis. This study used different rootstocks to investigate the potential application of aerial hyperspectral imagery in the estimation of canopy level measurements. A statistical framework was developed as an ensemble stacked regression (REGST) that aggregated five different individual machine learning algorithms: Least absolute shrinkage and selection operator (Lasso), Partial least squares regression (PLSR), Ridge regression (RR), Elastic net (ENET), and Principal component regression (PCR) to optimize high-throughput assessment of vine physiology. In addition, a Convolutional Neural Network (CNN) algorithm was integrated into an existing REGST, forming a hybrid CNN-REGST model with the aim of capturing patterns from the hyperspectral signal. Based on the findings, the performance of individual base models exhibited variable prediction accuracies. In most cases, Ridge Regression (RR) demonstrated the lowest test Root Mean Squared Error (RMSE). The ensemble stacked regression model (REGST) outperformed the individual machine learning algorithms with an increase in R2 by (0.03 to 0.1). The performances of CNN-REGST and REGST were similar in estimating the four different traits. Overall, these models were able to explain approximately 55–67% of the variation in the actual ground-truth data. This study suggests that hyperspectral features integrated with powerful AI approaches show great potential in tracing functional traits in grapevines.

Funder

National Science Foundation

South Dakota Agricultural Experiment Station

Publisher

MDPI AG

Reference85 articles.

1. Delaying berry ripening through manipulating leaf area to fruit ratio;Balda;J. Grapevine Res.,2013

2. Reducing the sugar and pH of the grape (Vitis vinifera L. cvs. ’Grenache’ and ’Tempranillo’) through a single shoot trimming;Sancha;S. Afr. J. Enol. Vitic.,2013

3. Spring temperatures alter reproductive development in grapevines;Keller;Aust. J. Grape Wine Res.,2010

4. General phenological model to characterise the timing of flowering and veraison of Vitis vinifera L;Parker;Aust. J. Grape Wine Res.,2011

5. Response of grapevine phenology to recent temperature change and variability in the wine-producing area of Sremski Karlovci, Serbia;Ruml;J. Agric. Sci.,2016

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