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United States Department of Agriculture

Agricultural Research Service

Research Project: Pesticide Application Technologies for Spray-drift Management, Maximizing In-field Deposition, and Targeted Spraying

Location: Crop Production Systems Research Unit

Title: Multispectral Imaging Systems for Airborne Remote Sensing to Support Agricultural Production Management

Authors
item Huang, Yanbo
item Thomson, Steven
item Lan, Yubin
item Maas, Stephen -

Submitted to: International Journal of Agricultural and Biological Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 12, 2010
Publication Date: March 31, 2010
Citation: Huang, Y., Thomson, S.J., Lan, Y., Maas, S.J. 2010. Multispectral Imaging Systems for Airborne Remote Sensing to Support Agricultural Production Management. International Journal of Agricultural and Biological Engineering. 3(1):50-62.

Interpretive Summary: Remote sensing imaging systems on agricultural aircraft are a powerful tool for crop production management. Agricultural aircraft carrying remote sensing imaging systems is flexible in setting up for obtaining high quality, high resolution imagery at desired spectral bands, target field location, specific altitude, and time. Multispectral imaging systems, which may cover the wave bands from visible (blue, green, red), near infrared, to thermal infrared, are cost-effective, not data-intensive, and a good source providing data and information of crops, soil, and ground cover for agricultural application and production. For practical applications of aerial multispectral remote sensing imaging system, this work investigated three representative multispectral imaging systems in supporting crop production management. The three systems are from low-cost to relatively high-cost. They can be operated manually or automated. They are either integrated sensor systems or separated camera systems (each for a band). In the investigation, practical issues were described and discussed. The advantages and disadvantages of each system were summarized and compared. The information provided from this study will benefit for developing practical aerial remote sensing systems on fixed-wing aircraft or unmanned autonomous helicopters.

Technical Abstract: Remote sensing has shown promise as a tool for managing agricultural application and production. Earth-observing satellite systems have an advantage for large-scale analysis at regional levels but are limited in spatial resolution. High-resolution satellite systems have been available in recent years, but scheduling these systems for appropriate bands, location of flight, proper altitude, and time of acquisition is difficult. Airborne remote sensing systems offer a flexible, do-it-yourself platform to configure for high quality, high spatial resolution imagery at any desired spectral combination, location, altitude, and time. Use of airborne hyperspectral remote sensing in agriculture has been steadily increasing during the past decade. Compared with hyperspectral systems, multispectral systems are much lower in cost and are less data-intensive. Airborne multispectral techniques are cost-effective and still a good source of crop, soil, or ground cover information for agricultural application and production. This paper investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production. The three systems have been used in agricultural studies. They range from low-cost to relatively high-cost, manually operated to automated, multispectral composite imaging with a single camera and individual imaging with assembly of separate cameras. Practical issues regarding use of the imaging systems were described and discussed. The advantages and disadvantages of each system were summarized and compared in different configurations. The information was provided for developing practical aerial remote sensing systems. System applications are suitable for fixed-wing aircraft and unmanned autonomous helicopter or fixed-wing platforms.

Last Modified: 7/25/2014
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