Hyperfidelis: A Software Toolkit to Empower Precision Agriculture with GeoAI

Autor: Vasit Sagan, Roberto Coral, Sourav Bhadra, Haireti Alifu, Omar Al Akkad, Aviskar Giri, Flavio Esposito
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Remote Sensing, Vol 16, Iss 9, p 1584 (2024)
Druh dokumentu: article
ISSN: 2072-4292
DOI: 10.3390/rs16091584
Popis: The potential of artificial intelligence (AI) and machine learning (ML) in agriculture for improving crop yields and reducing the use of water, fertilizers, and pesticides remains a challenge. The goal of this work was to introduce Hyperfidelis, a geospatial software package that provides a comprehensive workflow that includes imagery visualization, feature extraction, zonal statistics, and modeling of key agricultural traits including chlorophyll content, yield, and leaf area index in a ML framework that can be used to improve food security. The platform combines a user-friendly graphical user interface with cutting-edge machine learning techniques, bridging the gap between plant science, agronomy, remote sensing, and data science without requiring users to possess any coding knowledge. Hyperfidelis offers several data engineering and machine learning algorithms that can be employed without scripting, which will prove essential in the plant science community.
Databáze: Directory of Open Access Journals
Nepřihlášeným uživatelům se plný text nezobrazuje