Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision

Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision

IntroductionAdvancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. In many fields,...

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Bibliographic Details
Main Authors: Lucas Waltz, Sushma Katari, Chaeun Hong, Adit Anup, Julian Colbert, Anirudh Potlapally, Taylor Dill, Canaan Porter, John Engle, Christopher Stewart, Hari Subramoni, Scott Shearer, Raghu Machiraju, Osler Ortez, Laura Lindsey, Arnab Nandi, Sami Khanal
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Artificial Intelligence
Subjects:
precision agriculture
multimodal data
machine learning
Unmanned Aerial Systems
crop phenotyping
cyberinfrastructure
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2024.1496066/full
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