2016-17 Rapid Ag: Remote Sensing in Agricultural Production

July 1, 2016

Principle Leader

Ian MacRae 

Department

Department of Entomology

Funding Awarded

  • 2016 Fiscal Year: $126,000
  • 2017 Fiscal Year: $56,000

The Problem

The use of Unmanned Aerial Systems (UAS or ‘drones’) as platforms for remotely sensing crop stress holds significant promise for agriculture. However, the ability to differentiate between types of stressors hinders the full potential of these technologies. An increasing number of producers are employing UAS and a number of commercial UAS-based services are offering remote sensing products (a Google search of “agricultural drone service U.S.” will provide over 900K responses). The validity of some of these products and services is unsupported by data; in fact, there are currently few valid research-based recommendations available. The situation is homologous to the marketing of any other pest or nutrient management program for which there is a sparsity of independent evaluation. There is an obvious immediate need for the development and dissemination of recommendations based on independent research.

Background

The use of remote sensing to assess plant health and indicate yield-restricting stressors is not a new concept in agricultural production. However, recent technological advancements increasing the availability and affordability of sensors and UAS as platforms has resulted in unprecedented interest in use of remotely sensing crop health (WAP 2013, Model 2014, Galacher 2015). Unfortunately, neither the analyses nor interpretation of remotely sensed data (especially the identification and differentiation of specific types of plant stressors) are making the same advancements as are sensors and UAS. Several commercial enterprises now offer UAS-based crop scouting services to MN producers, often with highly variable results. In some cases, this variability has resulted in a failure to identify stressors, or long delays in identifying areas of within-field stress, making the data irrelevant to management decisions. 

While remote sensing from UAS platforms has incredible potential to improve agriculture and will eventually change food production practices, the current expectations of producers often exceed what the technology can deliver at this time. Some plant stress can be detected with available technology, but the stress may not be attributable to a specific cause. Induced stress can affect the reflectance of various wavelengths of incident electromagnetic radiation (i.e. spectral reflectance), especially in the higher red, near infrared, and thermal wavelengths (Gates et al 1965, Knipling 1970, Woolley 1971) and has been used to detect insects (e.g. Reisig and Godfrey 2006, 2007, Fraulo et al. 2009), disease (Oerke et al. 2014), and nutrient deficiencies (e.g. Felderhoff et al. 2008, Felderhoff & Gillieson 2011). Considerable research is still needed, to identify specific wavelengths that differentiate between these types of crop stressors (e.g. Kaur et al. 2015, Rocchini et al. 2015, Sahoo et al. 2015). 

Many Minnesota producers and agricultural professionals are already employing this technology and numbers of UAS deployed in agricultural fields is expected to double in the next year. There are currently 700,000 small UAS flying in the United States and the FAA expects an additional 750K - 1M units to be sold by year end, many of which are expected to be deployed in agriculture (US FAA 2015a). In addition, new federal use regulations facilitating agricultural deployment of UAS are expected to be in place by July 2016 (US FAA 2015b) and a simplified registration system was announced on November 20, 2015 (U.S. FAA 2015c). The application of UAS in agriculture is expected to exponentially expand after new FAA regulations are adopted (Eldridge 2014, Freeman & Freeland 2015). Inability to distinguish among stressors can result in inappropriate and ineffective management responses. This, combined with the lack of a current and comprehensive outreach program has resulted in extremely variable understanding of the capabilities of remote sensing technologies. This threatens the smooth adoption and application of remote sensing in Minnesota agriculture and the realization of the benefits these technologies can offer. 

As part of an urgent response to facilitate deployment of these technologies, we propose a project with two primary goals: 1) to advance implementation of UAS-based remote sensing in agriculture through differentiation of stressor-specific spectral responses, thereby addressing an existing knowledge gap in the application of this technology and 2) to disseminate project results and to educate producers and agricultural professionals on the benefits, abilities and limitations of these technologies. 

Results of this project will be of benefit to soybean producers and agricultural professionals with interest in these technologies in the North Central Region and provide a model system for development of UAS-based remote sensing of pests in other field crops. Results will improve the current U of MN College of Food Agriculture and Natural Resource Sciences (CFANS) infrastructure for conducting research on applied remote sensing and UAS deployment in agriculture. 

While new FAA regulations will be implemented, this work will improve understanding of the biology of the systems and utilization of sensors (multispectral and hyperspectral) regardless of regulation of UAS operations. Consequently, the results will also apply to the development of remote sensing for crops using other platforms such as tractor- or implement-mounted, airplane or satellite. Minnesota producers will benefit immediately from research results, allowing them to make educated management decisions on the adoption and practical uses of remote sensing and UAS. These benefits should occur simultaneously with the commercial availability of these technologies. As the project will facilitate the development of the recently created UMN UAS Working Group that addresses remote sensing and UAS issues, continuing research will also provide valuable answers and insights into future agricultural production decisions. 

Objective

  1. Differentiate Stressor Signatures: to evaluate spectral response of within-field crop stressors (insects, disease and nutrient content) and assess the current technologies’ ability to differentiate between these. 
  2. Disseminate Results: develop outreach programming that will disseminate results and educate producers and agricultural professionals on uses and limitations of UAS-based remote sensing. 

Ian MacRae is exploring the value of drones to recognize crop stress in fields.