Location: Cotton Ginning Research
2021 Annual Report
Objectives
1. Develop methods and devices to enable the reduction of plastic contaminants in commercially harvested cotton.
1.1. Develop a UAV-based intelligent system to identify and remove plastic particles in cotton field.
1.2. Develop a sensor and control system to remove plastic contamination in ginning process.
2. Develop and evaluate tools and methods to enable the commercial preservation of cotton fiber quality and increase ginning efficiency.
2.1. Develop and evaluate sensors for cotton moisture measurement in real time in situ.
2.2. Detect moisture in cotton module using UAV-based platform.
2.3. Develop and evaluate air-bar lint cleaner to increase the turnout and preserve fiber quality.
2.4. Develop a sensing and control system to automatically adjust ginning process for optimal ginning efficiency.
3. Develop methods to enable the use of commercial cotton gin trash and seeds for bio-products and bio-energy.
3.1. Develop new methods to process gin trash for bio-products and energy.
3.2. Investigate moisture dynamics in cotton seeds.
Approach
The Cotton Ginning Research Unit seeks to develop cotton ginning technologies to maximize fiber quality, increase ginning efficiency, and minimize the environmental impact of ginning. Plastic contaminants in U.S. cotton are rapidly increasing in recent years and have become a serious threat to U.S. cotton industry by reducing marketable quality.
New sensing and control systems and ginning machinery are needed to clean the contaminants, improve fiber quality and ginning efficiency, and increase cotton producers’ profitability. Researchers will develop and evaluate sensing and control systems to remove plastic contaminants from cotton and develop new tools for accurate cotton moisture measurements. UAV (unmanned aerial vehicle) remote sensing will be used as a platform to find and remove the plastics from cotton fields and to detect moisture in cotton modules.
Optical sensors, data processing, automatic controls and the like will be designed and built to detect and remove the plastic materials during gin processing. Moisture sensors, coupled with improved measurement of mass-flow rate and new models, will be developed and tested to accurately determine moisture of seed cotton, cotton lint, and cotton seeds in real time. Using the data gathered, an improved control system will be designed and fabricated to optimize ginning efficiency.
Additional research includes developing and evaluating new lint cleaning technology to better preserve fiber quality and increase the ginning turnout. Studies on new methods to use gin trash for bio-energy will also be conducted in this project.
Progress Report
Test results of two camera and drone systems proved not to be high enough resolution for future computer-based object detection. Lower UAV flight heights show promise for appropriate target ranges. A new camera system with higher resolution will enhance target resolution. Additional testing on the depth of the fields containing plastic contamination will determine the amount of the field to be surveyed. A tool for annotating machine vision data is being developed in house and is in the early stages of testing. This tool allows a user to view a photo and draw bounding boxes around objects of interest, an enhancement unavailable with most existing open-source and cloud-based tools. The research is partially funded by Cotton Incorporated.
Hardware and software to capture and process images of the cotton in an air stream has been procured and configured, and preliminary testing has been on-going. The system uses an AI (Artificial Intelligence) based methodology for optical plastic detection. Several potential processing platforms are being considered along with cameras, AI accelerators, and software models. Cost is being considered during the initial design stage in order to increase the potential for future adoption by stakeholders. Special attention is being devoted to the issues that arise from capturing images of relatively fast-moving objects.
A seed cotton capacitive moisture sensor was developed and tested under static conditions as research to advance development of a real-time model for predicting moisture content of seed cotton as it flows through a pipeline. Preliminary results justify a next phase of testing in the Micro Gin at the Stoneville CGRU once Covid restrictions have been lifted.
Conceptual design work focused on modifying a saw type lint cleaner to address seed coat fragments (SCF) is underway and will be incorporated into a prototype lint cleaner to be installed and tested in the Micro Gin. The concept is unique in that the premise is to separate and recover good fiber from the seed coat fragment at the lint cleaner. This research is being supported by the purchase of a Rieter UNIclean B 15 pre-cleaner to be installed in the big gin for its evaluation as both a lint cleaner and seed cotton pre-cleaner. This research is of immediate interest to our stakeholder cotton producers in the Southeast, the National Cotton Council of America and Cotton Incorporated.
Research on gin stand energy is resulting in a low-cost system using mostly standard components with some customized electronics. Work is ongoing to select the most suitable components for collecting data from multiple inputs. The focus of the research is on breeder gin stands used for cultivar selection. Cultivar influence on power consumption has not traditionally been a selection criterion. The research is partially funded by Cotton Incorporated.
Accomplishments
1. Plastic contamination in U.S. cotton. Marketability of U.S. cotton is being threatened by the presence of plastic contamination. Plastic from the field is ending up in the bale at the cotton gin. It has become the responsibility of the cotton gin to remove the contaminant before it reaches the textile mill. Industry wide research is currently underway to address the issue. ARS researchers in Stoneville, Mississippi, are party to the effort. ARS researchers are using an artificial intelligence-based methodology for optical delineation of high-speed lint cotton in an air duct behind the gin stand. It is at this interval only lint is present. The technology addresses issues that arise from capturing images of relatively fast-moving objects such as latency and rolling shutter distortion. Low-cost processing platforms are being considered to facilitate stakeholder support and investment.
Review Publications
Delhom, C.D., Knowlton, J., Martin, V.B., Blake, C.D. 2020. The classification of cotton. Journal of Cotton Science. 24:189–196.
