University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Biological Systems Engineering: Papers and Publications Biological Systems Engineering 2020 Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges Santosh Pitla University of Nebraska-Lincoln, [email protected] Sreekala Bajwa Montana State University, [email protected] Santosh Bhusal Washington State University Tom Brumm Iowa State University, [email protected] Tami M. Brown-Brandl University of Nebraska-Lincoln, [email protected] FSeeollow next this page and for additional additional works authors at: https:/ /digitalcommons.unl.edu/biosysengfacpub Part of the Bioresource and Agricultural Engineering Commons, Environmental Engineering Commons, and the Other Civil and Environmental Engineering Commons Pitla, Santosh; Bajwa, Sreekala; Bhusal, Santosh; Brumm, Tom; Brown-Brandl, Tami M.; Buckmaster, Dennis R.; Condotta, Isabella; Fulton, John; Janzen, Todd J.; Karkee, Manoj; Lopez, Mario; Moorhead, Robert; Sama, Michael; Schumacher, Leon; Shearer, Scott; and Thomasson, Alex, "Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges" (2020). Biological Systems Engineering: Papers and Publications. 727. https://digitalcommons.unl.edu/biosysengfacpub/727 This Article is brought to you for free and open access by the Biological Systems Engineering at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Biological Systems Engineering: Papers and Publications by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Authors Santosh Pitla, Sreekala Bajwa, Santosh Bhusal, Tom Brumm, Tami M. Brown-Brandl, Dennis R. Buckmaster, Isabella Condotta, John Fulton, Todd J. Janzen, Manoj Karkee, Mario Lopez, Robert Moorhead, Michael Sama, Leon Schumacher, Scott Shearer, and Alex Thomasson This article is available at DigitalCommons@University of Nebraska - Lincoln: https://digitalcommons.unl.edu/ biosysengfacpub/727 Number 70 Issue Paper November 2020 Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges Ground and aerial robots combined with artificial intelligence (AI) techniques have potential to tackle the rising food, fiber, and fuel demands of the rapidly growing population that is slated to be around 10 billion by the year 2050. (Photo illustration by Megan Wickham with images from Santosh Pitla and Shutterstock.) ABSTRACT which subsequently laid foundation for in crop and animal agriculture to in- mechanized agriculture. While preci- crease input efficiency and agricultural Crop and animal production tech- sion agriculture has enabled site-specific output with reduced adverse impact on niques have changed significantly over management of crop inputs for improved the environment. Ground and aerial ro- the last century. In the early 1900s, yields and quality, precision livestock bots combined with artificial intelligence animal power was replaced by trac- farming has boosted efficiencies in (AI) techniques have potential to tackle tor power that resulted in tremendous animal and dairy industries. By 2020, the rising food, fiber, and fuel demands improvements in field productivity, highly automated systems are employed of the rapidly growing population that is This publication was made possible through funding provided by the United Soybean Board (“USB”). As stipulated in the Soybean Promotion, Research, and Consumer Information Act, USDA’s Agricultural Marketing Service (“AMS”) has oversight responsibilities for USB. AMS prohibits the use of USB’s funds to influence legislation and/or to influence governmental policy or action. Any opinions,fi ndings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of USB, USDA, and/or AMS. CAST Issue Paper 70 Task Force Members Authors Dr. John Fulton, Associate Professor, Department Head, Mississippi State Ohio State University, Columbus, Ohio University, Mississippi State, Missis- Dr. Santosh Pitla, Chair, Associate Todd J. Janzen, President, Janzen Agri- sippi Professor, University of Nebraska, cultural Law, LLC, Indianapolis, Indiana Lincoln, Nebraska Dr. Manoj Karkee, Associate Professor, Reviewers Dr. Sreekala Bajwa, Vice President, Washington State University, Prosser, Dean & Director, Montana State Uni- Dr. Srinivasulu Ale, Associate Profes- Washington versity, Bozeman, Montana sor, Texas A&M AgriLife Research Dr. Mario Lopez, Technical Develop- Santosh Bhusal, Ph.D. Candidate, Dr. Ed Barnes, Senior Director of ment Manager MQ&OFSS, DeLaval, Washington State University, Pullman, Agricultural and Environmental Kansas City, Missouri Washington Research, Cotton Incorporated, Cary, Dr. Robert Moorhead, Director, Geo- Dr. Tom Brumm, Associate Profes- North Carolina systems Research Institute, Mississippi sor, Iowa State University, Ames, Dr. Brian Steward, Professor, Iowa State University, Mississippi State, Iowa State University, Ames, Iowa Mississippi Dr. Tami Brown-Brandl, Profes- Dr. Michael Sama, Associate Profes- sor, University of Nebraska-Lincoln, CAST Liaison sor, University of Kentucky, Lexington, Lincoln, Nebraska Kentucky Dr. Carroll Moseley, Syngenta Dr. Dennis R. Buckmaster, Profes- Dr. Leon Schumacher, Professor, Uni- Crop Protection, Greensboro, North sor, Purdue University, West versity of Missouri, Columbia, Missouri Carolina Lafayette, Indiana Dr. Scott Shearer, Professor and Chair, Dr. Juan Ticarico, Innovation Center Dr. Isabella Condotta, Assistant Ohio State University, Columbus, Ohio for U.S. Dairy, Rosemont, Illinois Professor, University of Illinois, Champaign, Illinois Dr. Alex Thomasson, Professor and slated to be around 10 billion by the year 2050. This Issue Paper presents oppor- tunities provided by ground and aerial robots for improved crop and animal production, and the challenges that could potentially limit their progress and adop- tion. A summary of enabling factors that could drive the deployment and adoption of robots in agriculture is also presented along with some insights into the training Figure 1. Classification and Important Application Domains of Agricultural Robots. needs of the workforce who will be in- volved in the next-generation agriculture. address these impending local and global cient crop varieties in research plots, and INTRODUCTION demands (Tilman et al. 2001). Efficiently be more efficient in managing livestock managing crop inputs such as seeds, (Chen 2018). Motivated by these op- AND BACKGROUND chemicals, fertilizers, and labor to boost portunities, numerous teams around the The increase in demand for food, feed, yields and quality while minimizing world have been working on research and fiber, and energy is inevitable, given environmental impacts is important to development of various types of robotic the projected population increase in the support the on-going biotechnology ef- solutions for crop and animal farming. coming decades as well as increasing forts. Agricultural robots combined with Figure 1 provides a broad classifica- affluence in developing economies of the digital agriculture techniques are seen tion of such agricultural robots. Ground world. Food production has to increase as an effective method for monitoring robots or unmanned ground vehicles by approximately 70% to feed 9.7 bil- large acreages to assess crop conditions, (UGVs) and aerial robots or Unmanned lion people by the year 2050 (Bruinsma increase precise management of crop Aerial Systems (UASs) are used in both 2009). Advancements in biotechnology inputs in row and specialty crops, allow row-crops and specialty crops. Robotic are seen as one of the potential solu- efficient harvesting in specialty crops, manipulators (robotic arms) on the other tions for increasing food production to evaluate high yielding and resource effi- hand are primarily used in dairy, specialty 2 COUNCIL FOR AGRICULTURAL SCIENCE AND TECHNOLOGY crop, and greenhouse applications. Each physically entering the field, or provides Producers interested in using aerial of these robot categories along with some a more efficient way to count or analyze robots on their farms will need to decide examples are discussed in detail in the livestock. UASs can quickly determine whether to operate their own UAS or hire next section. soil moisture and crop health over the a UAS service provider. Service provid- entirety of the field with great preci- ers can standardize operational variables Ground Robots sion. Similarly, UAS-based systems can to meet data quality and application needs be used to measure animal temperature and amortize the cost of the technology Changing weather patterns and the quickly and non-invasively, and thereby over more users per unit time, whereas need to boost agricultural productiv- identify which animal might be in heat or self-operation provides greaterfl exibility ity require a paradigm shift in how have an illness. As miniaturized sensors, in use and access. Similarly, there are field operations are accomplished. For such as microwave radiometers, short- trade-offs in running analyses locally or example, UGVs could perform repetitive wave infrared sensors, and LiDARs (light using cloud computing. operations with great precision for up to detection and ranging), are integrated 22 hours per day (with a small portion of with UASs, opportunities to measure soil Robotic Manipulators that time for service and maintenance) moisture and crop health precisely, map Robotic