Ground-based hyperspectral remote sensing for weed management in crop production

Authors: Huang, Yanbo; LEE, MATTHEW - Mississippi State University; Thomson, Steven; Reddy, Krishna

Submitted to: International Journal of Agricultural and Biological Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/9/2016
Publication Date: 4/1/2016
Citation: Huang, Y., Lee, M.A., Thomson, S.J., Reddy, K.N. 2016. Ground-based hyperspectral remote sensing for weed management in crop production. International Journal of Agricultural and Biological Engineering. 9(2):98-109.

Interpretive Summary: Remote sensing monitors soil, crop growth, weed infestation, insects, diseases, and water status in crop fields for precision crop production management. Scientists in USDA-ARS Crop Production Systems Research Unit, Stoneville, Mississippi and Mississippi State University have collaboratively developed various remote sensing systems and methods to acquire and analyze spectral data and images of crop leaves and canopy. Ground-based remote sensing systems offer portability, flexibility and controllability for crop sensing in lab and field. This paper presents our studies in developing and applying ground-based hyperspectral remote sensing techniques in weed management for detection of crop injury from dicamba and differentiation between glyphosate resistant and sensitive weeds. Results indicate the potential of ground-based hyperspectral remote sensing for precision weed management.

Technical Abstract: Agricultural remote sensing has been developed and applied in monitoring soil, crop growth, weed infestation, insects, diseases, and water status in farm fields to provide data and information to guide agricultural management practices. Precision agriculture has been implemented through prescription mapping of crop fields at different scales with the data remotely sensed from space-borne, airborne and ground-based platforms. Ground-based remote sensing techniques offer portability, flexibility and controllability in applications for precision agriculture. In weed management, crop injury from off-target herbicide spray drift and herbicide resistance in weeds are two important issues. For precision weed management, we have developed ground-based hyperspectral remote sensing techniques for detection of crop injury from dicamba and differentiation between glyphosate resistant and sensitive weeds. This paper presents our techniques for ground-based hyperspectral remote sensing for these two applications. Results illustrate the advantages of ground-based hyperspectral remote sensing for precision weed management.