Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: 4/27/2020
Publication Date: 4/27/2020
Citation: Wanjura, J.D., Holt, G.A., Pelletier, M.G., Barnes, E.M. 2020. Advances in managing cotton modules using rfid technology - system development update. National Cotton Council Beltwide Cotton Conference. 588-609.
Interpretive Summary: The use of RFID technology to identify cotton modules has enabled new methods for tracking and managing seed cotton from the field to the gin. This technology creates new possibilities for logistical management, asset tracking, product traceability, and precision agriculture regarding fiber quality mapping. While some in the cotton ginning industry have used pieces of the system to create module inventory lists or pickup reports, no system exists that compiles all module specific identification, position, and harvest related data into one management system. The overall goal of this research is to develop an electronic module management system for use by gins which utilizes RFID technology and other associated systems (i.e. John Deere’s HID Cotton Pro) to provide useful information to ginners and producers. The specific objectives of this work in 2019 were to expand the system developed in 2018 to include new stationary RFID scanning bridge utilities for use at truck scales and module feeders, develop a permanent bale identification (PBI) scanning system for logging lint bales as they are weighed on the bale scale, and provide system updates and improvements to the tools developed in 2018 (RFID Module Scan, RFID Truck Scan, and RFID Gin Data Management). The goal is for this electronic module management system to be used to demonstrate the utility of RFID technology in managing modules and help producers and ginners identify new sources of value through the enhanced use of module location and harvest information. The new and updated systems were field tested at a gin in Louisiana in 2019. The systems performed as designed and opportunities for system improvement with regard to scanning performance and reliability were noted. The system as tested in 2019 is the basis of a new module management system that can function without the need for paper tags or other means of manual module identification.
Technical Abstract: An electronic module management (EMM) system was developed and field tested in 2018 that makes use of radio frequency identification (RFID) tags contained on each portion of round cotton module wrap. The system developed in 2018 consisted of a hand-held mobile scanning application for scanning modules in the field or gin yard (RFID Module Scan), a scanning system for use on module trucks (RFID Truck Scan) that automates the process of scanning modules and recording position and cotton ownership information, and a data management utility (RFID Gin Data Management) that compiles all module scan information from the various scanning systems and serves as a central data hub for generating ginner and grower reports. The EMM system was expanded in 2019 to include stationary scanning bridges at the truck scale (RFID Scale Bridge) and module feeder (RFID Feeder Bridge), and a lint bale logging utility (PBI Logger) at the bale scale. The new bridge scanning tools provide alternative system options for collecting module specific data necessary for tracking and managing modules from the field to the gin (i.e. serial number, ownership information, load number, weight, etc.) The PBI Logger utility provides data useful in associating lint bale weight and quality to the round module from which it was ginned; thus, enabling quality and yield mapping at the module scale. The expanded EMM system was field tested at Tanner and Co. Gin in Frogmore, LA and the system operated as designed. System performance and opportunities improvement were noted and are discussed herein. The expanded EMM system developed in 2019 provides the basis for a module management system that does not rely on the use of paper tags or other means of manual module identification.