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The appropriate selection of rootstock-scion combinations to improve yield and fully realize grafting benefits requires an in-depth understanding of rootstock-scion synergy. Toward this end, we grafted two determinate-type scions [grape tomato (‘BHN 1022') and beefsteak tomato (‘Skyway')] onto four rootstocks with different characteristics to examine plant growth, yield performance, biomass production, and fruit mineral nutrient composition. The study was conducted during two growing seasons (spring and fall plantings in Florida) under organic production in high tunnels with the non-grafted scions as controls. Rootstocks had previously been designated as either “generative” (‘Estamino') or “vegetative” (‘DR0141TX') by some commercial suppliers or had not been characterized [‘RST-04-106-T' and ‘SHIELD RZ F1 (61-802)']. Also, ‘Estamino', ‘DR0141TX', and ‘RST-04-106-T' had been described as more vigorous than ‘SHIELD RZ F1 (61-802)'. In both planting seasons (with low levels of soilborne disease pressure), the “vegetative” and “generative” rootstocks increased marketable and total fruit yields for both scions except for the beefsteak tomato grafted with the “vegetative” rootstock in fall planting. Positive effects of ‘RST-04-106-T' on fruit yield varied with scions and planting seasons, and were most manifested when grafted with the beefsteak tomato scion in fall planting. ‘SHIELD RZ F1 (61-802)' led to similar yields as the non-grafted controls except for grafting with the grape tomato scion in fall planting. For vegetative and fruit biomass, both the “vegetative” and “generative” rootstocks had positive impacts except for the beefsteak tomato in fall planting. For fruit mineral composition, the “vegetative” and “generative” rootstocks, both highly vigorous, consistently elevated fruit P, K, Ca, Zn, and Fe contents on a dry weight basis, whereas the other rootstocks did not. Overall, although the more vigorous rootstocks enhanced tomato plant productivity and fruit minerals, the evidence presented here does not support the suggestion that the so-called “vegetative” and “generative” rootstocks have different impacts on tomato scion yield, biomass production, or fruit mineral contents. More studies with different production systems and environmental conditions as well as contrasting scion genotypes are needed to further categorize the impacts of rootstocks with different vigor and other characteristics on plant biomass production and their implications on fruit yield development.
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Predicting Grapevine Physiological Parameters Using Hyperspectral Remote Sensing Integrated with Hybrid Convolutional Neural Network and Ensemble Stacked Regression
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.
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- PAR ID:
- 10542268
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 16
- Issue:
- 14
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2626
- Subject(s) / Keyword(s):
- aerial hyperspectral remote sensing deep learning ensemble learning stacking regressor UAV high throughput phenotyping grapevine rootstock carbon assimilation stomatal conductance transpiration
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs16142626
