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RFID and Drones: The Next Generation of Plant Inventory

Edisto Research and Education Center, 64 Research Road, Blackville, SC 29817, USA
Department of Agricultural Science, Clemson University, 240 McAdams Hall, Clemson, SC 29634, USA
University of Arkansas Div. of Ag. CES, 2301 S. Univ. Ave., Little Rock, AR 72204, USA
Department of Horticulture, Michigan State University, 1066 Bogue St., East Lansing, MI 48824, USA
USDA-ARS Application Technology Research Unit, 1680 Madison Ave., Wooster, OH 44691, USA
Horticulture Department, University of Georgia, 326 Hoke Smith Building, Athens, GA 30602, USA
Author to whom correspondence should be addressed.
Academic Editors: Mohammad Valipour and Pablo Martín-Ramos
AgriEngineering 2021, 3(2), 168-181;
Received: 5 February 2021 / Revised: 30 March 2021 / Accepted: 31 March 2021 / Published: 6 April 2021
Collection of plant inventory (i.e., count, grade, plant size, yield) data is time-consuming, costly, and can be inaccurate. In response to increasing labor costs and shortages, there is an increased need for the adoption of more automated technologies by the nursery industry. Growers, small and large, are beginning to adopt technologies (e.g., plant spacing robots) that automate or augment certain operations, but greater strides must be taken to integrate next-generation technologies into these challenging unstructured agricultural environments. The main objective of this work is to demonstrate merging specific ground and aerial-based technologies (Radio Frequency Identification (RFID), and small Unmanned Aircraft System (sUAS)) into a holistic systems approach to address the specific need of moving toward automated on-demand plant inventory. This preliminary work focuses on evaluating different RFID tags with respect to their distance and orientation to the RFID reader. Fourteen different RFID tags, five distances (1.5 m, 3.0 m, 4.5 m, 6.0 m, and 7.6 m), and four tag orientations (the front of the tag (UP), back of the tag (DN), tag at sideways left (SL), and tag at sideways right (SR)) were assessed. Results showed that the tag upward orientation resulted in the highest scanning total for both the laboratory and field experiments. Two orientations (UP and SR) had significant effect on the scan total of tags. The distance between the reader and the tags at 1.5 m and 6.0 m did not significantly affect the scanning efficiency of the RFID system in horizontally fixed (p-value > 0.05) position regardless of tags. Different tag designs also produced different scan totals. Overall, since most of the tags were scanned at least once (except for Tag 6F), it is a very promising technology for use in nursery inventory data acquisition. This work will create a unique inventory system for agriculture where locations of plants or animals will not present a barrier as the system can easily be mounted on a drone. Although these experiments are focused on inventory in plant nurseries, results for this work has potential for inventory management in other agricultural sectors. View Full-Text
Keywords: RFID; drone; microcontroller; ornamental; precision agriculture; inventory RFID; drone; microcontroller; ornamental; precision agriculture; inventory
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MDPI and ACS Style

Quino, J.; Maja, J.M.; Robbins, J.; Fernandez, R.T.; Owen, J.S., Jr.; Chappell, M. RFID and Drones: The Next Generation of Plant Inventory. AgriEngineering 2021, 3, 168-181.

AMA Style

Quino J, Maja JM, Robbins J, Fernandez RT, Owen JS Jr., Chappell M. RFID and Drones: The Next Generation of Plant Inventory. AgriEngineering. 2021; 3(2):168-181.

Chicago/Turabian Style

Quino, Jannette; Maja, Joe M.; Robbins, James; Fernandez, R. T.; Owen, James S., Jr.; Chappell, Matthew. 2021. "RFID and Drones: The Next Generation of Plant Inventory" AgriEngineering 3, no. 2: 168-181.

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