Abstract
Accurate inventory allows for more precise forecasting, including profit projections, easier monitoring, shorter outages, and fewer delivery interruptions. Moreover, the long hours of physical labor involved over such a broad area and the effect of inefficiencies could lead to less accurate inventory. Unreliable data and predictions, unannounced stoppages in operations, production delays and delivery, and a considerable loss of profit can all arise from inaccurate inventory. This paper extends our previous work with drones and RFID by evaluating: the number of flights needed to read all tags deployed in the field, the number of scans per pass, and the optimum drone speed for reading tags. The drone flight plan was divided into eight passes from southwest to northwest and back at a horizontal speed of 2.2, 1.7, and 1.1 m per second (m/s) at a vertically fixed altitude. The results showed that speed did not affect the number of new tags scanned (p-value > 0.05). Results showed that 90% of the tags were scanned in less than four trips (eight passes) at 1.7 m/s. Based on these results, the system can be used for large-scale nursery inventory and other industries that use RFID tags in outdoor environments. We presented two novel measurements on evaluating RFID reader efficiency by measuring how fast the reader can read and the shortest distance traveled by the RFID reader over tag.
Highlights
Introduction published maps and institutional affilAccording to the United States Department of Agriculture’s National AgriculturalStatistics Service [1], 91.1 million acres of land are projected to be used for plant production for 2021
The shows the percentage of new tags read for each pass based on the three drone speeds
The hypothesis was that the speed of the drone and the number of passes has an influence on the reading of unique or new tags
Summary
Specialty crops, including floriculture and nursery products, accounted for. $13.8 billion in sales in 2019; the nursery industry is a multibillion-dollar enterprise that relies on inventory and monitoring to forecast sales, production requirements, and quality improvements [2]. The information collected in an inventory is used for planning that includes labor requirements, space requirements, production timing, and sales and demand trends, including product pricing [3]. Obtaining individual plant information about the location or number of plants in the field is labor intensive and time-consuming. Since this process is done manually, there may be inefficiencies, including missing data, due to human error. It is difficult to avoid mistakes due to a lack of reliable equipment to gather data [4]
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