A5094137747- Horticultural and Viticultural Research
- Leaf Properties and Growth Measurement
- Fermentation and Sensory Analysis
- Botanical Research and Applications
- Plant Physiology and Cultivation Studies
- Nuts composition and effects
University of California, Davis
2024
Grape cluster compactness is a key trait that influence fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this essential for optimizing vineyard management improving grapevine cultivars. In study, we performed quantitative locus (QTL) mapping to identify genomic regions associated with architecture yield components in bi-parental population derived from Vitis vinifera cv. Riesling crossed Cabernet Sauvignon. A total 138 full-sibling progeny were evaluated...
Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, yield. Evaluation methods for these include visual scoring, manual methodologies, computer vision, with the latter being most scalable approach. Most of existing vision approaches processing images often rely on conventional segmentation or machine learning extensive training limited generalization. The Segment Anything Model (SAM), a novel foundation model trained massive image...
Summary Grapevine leaves are a model morphometric system. Sampling over ten thousand using dozens of landmarks, the genetic, developmental, and environmental basis leaf shape has been studied morphospace for genus Vitis predicted. Yet, these representations fail to capture exquisite features at high resolution. We measure shapes 139 grapevine 1672 pseudo-landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces vasculature blade, we have method...
Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, yield. Evaluation methods for these include visual scoring, manual methodologies, computer vision, with the latter being most scalable approach. Most of existing vision approaches processing images often rely on conventional segmentation or machine learning extensive training limited generalization. The Segment Anything Model (SAM), a novel foundation model trained massive image...
Societal Impact Statement Grapevine leaves are emblematic of the strong visual associations people make with plants. Leaf shape is immediately recognizable at a glance, and therefore, this used to distinguish grape varieties. In an era computationally enabled machine learning‐derived representations reality, we can revisit how view use shapes forms that plants display understand our relationship them. Using computational approaches combined time‐honored methods, predict theoretical possible,...