Cargando…
CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones
As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat’s yield performance effectively. Nevertheless, it is stil...
Autores principales: | Chen, Jiawei, Zhou, Jie, Li, Qing, Li, Hanghang, Xia, Yunpeng, Jackson, Robert, Sun, Gang, Zhou, Guodong, Deakin, Greg, Jiang, Dong, Zhou, Ji |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10316027/ https://www.ncbi.nlm.nih.gov/pubmed/37404534 http://dx.doi.org/10.3389/fpls.2023.1219983 |
Ejemplares similares
-
Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat
por: Zhu, Yulei, et al.
Publicado: (2021) -
Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice
por: Teng, Zixuan, et al.
Publicado: (2023) -
Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics
por: Araujo, Raquel Fernandes, et al.
Publicado: (2020) -
Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery
por: Chandel, Narendra S., et al.
Publicado: (2022) -
Optimal settings and advantages of drones as a tool for canopy arthropod collection
por: Madden, Jamie C., et al.
Publicado: (2022)