Using Machine Learning and Hyperspectral Images to Assess Damages to Corn Plant Caused by Glyphosate and to Evaluate Recoverability
Glyphosate is the most widely used herbicide in crop production due to the widespread adoption of glyphosate-resistant (GR) crops. However, the spray of glyphosate onto non-target crops from ground or aerial applications can cause severe injury to non-GR corn plants. To evaluate the crop damage of t...
Main Authors: | Ting Zhang, Yanbo Huang, Krishna N. Reddy, Pingting Yang, Xiaohu Zhao, Jingcheng Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-03-01
|
Series: | Agronomy |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4395/11/3/583 |
Similar Items
-
Exploring Groundwater Recoverability in Texas: Maximum Economically Recoverable Storage
by: Justin C. Thompson, et al.
Published: (2020-12-01) -
Foliar levels of macro and micronutrients in glyphosate-tolerant corn submitted to herbicides
by: Núbia Maria Correia, et al.
Published: (2013-12-01) -
Key Recoverability in Wireless Sensor Networks
by: Filippo Gandino, et al.
Published: (2019-01-01) -
Some Aspects Concerning Progrmmes Targeting the Utilisation of the Recoverable Power in Caras Severin County
by: Nadia Potoceanu, et al.
Published: (2008-10-01) -
A highly efficient and recoverable bi-cinchona alkaloid ligand for the catalytic asymmetric aminohydroxylation of olefins
by: Sun Xiaoli, et al.
Published: (2006-01-01)