A5077049785- Horticultural and Viticultural Research
- Remote Sensing in Agriculture
- Smart Agriculture and AI
- Fermentation and Sensory Analysis
- Irrigation Practices and Water Management
- Plant Water Relations and Carbon Dynamics
- Remote Sensing and LiDAR Applications
- Soil Geostatistics and Mapping
- Wine Industry and Tourism
- Spectroscopy and Chemometric Analyses
- Species Distribution and Climate Change
- Remote Sensing and Land Use
- Imbalanced Data Classification Techniques
- scientometrics and bibliometrics research
- Meta-analysis and systematic reviews
Ernest Gallo Clinic and Research Center
2016-2023
Cummins (United States)
2017-2023
The University of Adelaide
2019-2022
Australian Wine Research Institute
2022
Virginia Tech
2011
Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of growth stages too sparse to fully capture the spatial variability conditions. In addition, traditional yield prediction methods time consuming require large amount samples. Remote sensing data provide detailed temporal information regarding development that is useful for vineyard management. this study, Landsat surface reflectance products from 2013 2014 were...
Abstract Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) crop stress information to ensure long-term sustainability high-value crops. Providing this requires the development tools applicable across continuum from subfield scales improve water management within individual fields up watershed regional assess resources at county state levels. High-value perennial crops (vineyards orchards) are major users,...
A closed loop irrigation system is demonstrated that fully automates the delivery of and calculates water requirement from satellite images. The optimizes for 140 cells located across four hectares land based on two independent objectives (e.g., maximizing yield increasing efficiency) continuously adapting scheduling to local spatial–temporal variability vegetation growing season. Irrigation controlled by a central computer issues commands 693 control nodes start analysis are laid out create...
Soil texture, topographical data, fruit zone light measurements, yield components, and composition data were taken from 125 locations in each of four <i>Vitis vinifera</i> L. cv. Cabernet Sauvignon vineyards the Lodi region California during 2017, 2018, 2019 seasons. Data compared against three sources normalized difference vegetation index (NDVI) with different spatial resolutions: Landsat 8 (LS8<sub>NDVI</sub>; 30 m), Sentinel-2 (S2<sub>NDVI</sub>; 10 manned aircraft (at high resolution,...
<p style="text-align: justify;"><strong>Aims:</strong> Yield monitors are becoming more common in North America. This research evaluates the precision and accuracy of a retro-fitted, commercially available grape yield monitor mid-season, for crop estimation thinning applications, at harvest mapping.</p><p justify;"><strong>Methods Results:</strong> Several were mounted on discharge conveyor belt harvesters both commercial vineyards Sensor response...
Background and Aims Spatial variability in yield fruit composition winegrape vineyards has been demonstrated, but few chemical compounds responsible for impacting wine have analysed at a sample density high enough to compare with remotely sensed imagery. The aims of this project were evaluate spatial grape harvest three seasons canopy vegetation data assess its utility underpinning targeted management. Methods Results samples their aerial imagery products, the normalised difference index...
Crop yield prediction is essential for agricultural planning but remains challenging due to the complex interactions between weather, climate, and management practices. To address these challenges, we introduce a deep learning-based multi-model called Climate-Management Aware Vision Transformer (CMAViT), designed pixel-level vineyard predictions. CMAViT integrates both spatial temporal data by leveraging remote sensing imagery short-term meteorological data, capturing effects of growing...
Background and Aims A large number of fruit samples is required for adequate variogram estimation, making the development prescriptive maps vineyard management cost prohibitive most growers. The project assessed efficacy aggregating from multiple vineyards, over years, to estimate a ‘common’ that could be generated applied more efficiently. Methods Results Fifteen hundred berry were collected 3 years (2017–2019) in four vineyards California analysis composition spatial variability. Maps...