Zobrazeno 1 - 10
of 2 149
pro vyhledávání: '"Earles, A."'
Rapid detection of foodborne bacteria is critical for food safety and quality, yet traditional culture-based methods require extended incubation and specialized sample preparation. This study addresses these challenges by i) enhancing the generalizab
Externí odkaz:
http://arxiv.org/abs/2411.19514
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 calle
Externí odkaz:
http://arxiv.org/abs/2411.16989
Thermal cameras are an important tool for agricultural research because they allow for non-invasive measurement of plant temperature, which relates to important photochemical, hydraulic, and agronomic traits. Utilizing low-cost thermal cameras can lo
Externí odkaz:
http://arxiv.org/abs/2405.19413
Autor:
Choi, Taeyeong, Guevara, Dario, Cheng, Zifei, Bandodkar, Grisha, Wang, Chonghan, Bailey, Brian N., Earles, Mason, Liu, Xin
In agricultural environments, viewpoint planning can be a critical functionality for a robot with visual sensors to obtain informative observations of objects of interest (e.g., fruits) from complex structures of plant with random occlusions. Althoug
Externí odkaz:
http://arxiv.org/abs/2303.05764
In recent years, deep learning models have become the standard for agricultural computer vision. Such models are typically fine-tuned to agricultural tasks using model weights that were originally fit to more general, non-agricultural datasets. This
Externí odkaz:
http://arxiv.org/abs/2208.02707
Autor:
Olenskyj, Alexander G., Sams, Brent S., Fei, Zhenghao, Singh, Vishal, Raja, Pranav V., Bornhorst, Gail M., Earles, J. Mason
Publikováno v:
Comput. Electron. Agric. 198 (2022)
Yield estimation is a powerful tool in vineyard management, as it allows growers to fine-tune practices to optimize yield and quality. However, yield estimation is currently performed using manual sampling, which is time-consuming and imprecise. This
Externí odkaz:
http://arxiv.org/abs/2208.02394
Autor:
Shailja C. Shah, Rohan Gupta, Ranier Bustamante, Mark Lamm, Hanin Yassin, Ashley Earles, Adriana Hung, Alese Halvorson, Robert Greevy, Samir Gupta, Joshua Demb, Lin Liu, Christianne L. Roumie
Publikováno v:
Gastro Hep Advances, Vol 3, Iss 1, Pp 78-83 (2024)
Background and Aims: There are limited contemporary population-based data on Helicobacter pylori epidemiology and outcomes in the United States. Our primary aim was to create a validated cohort of veterans with H pylori testing or treatment using Vet
Externí odkaz:
https://doaj.org/article/abb35ed0286b4b78bff7ad214faa3f62
Autor:
Rippner, Devin A., Raja, Pranav, Earles, J. Mason, Buchko, Alexander, Momayyezi, Mina, Duong, Fiona, Parkinson, Dilworth, Forrestel, Elizabeth, Shackel, Ken, Neyhart, Jeffrey, McElrone, Andrew J.
X-ray micro-computed tomography (X-ray microCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both
Externí odkaz:
http://arxiv.org/abs/2203.09674
Trait measurement is critical for the plant breeding and agricultural production pipeline. Typically, a suite of plant traits is measured using laborious manual measurements and then used to train and/or validate higher throughput trait estimation te
Externí odkaz:
http://arxiv.org/abs/2112.03205
Publikováno v:
Plant Phenomics, Vol 6 (2024)
Deep learning and multimodal remote and proximal sensing are widely used for analyzing plant and crop traits, but many of these deep learning models are supervised and necessitate reference datasets with image annotations. Acquiring these datasets of
Externí odkaz:
https://doaj.org/article/281b13726fdd4650bbdebc86d003add7