Location: Cotton Ginning Research2022 Annual Report
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.
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.
Research using UAV (Unmanned Arial Vehicle) drones with mounted visual image capture technology has yielded useful data in the Stoneville, Mississippi, Cotton Ginning Research Unit’s (CGRU) contribution to plastic contamination mitigation. Established UAV flight heights show promise for appropriate target ranges and new camera systems with higher resolution have enhanced target resolution. Additional testing on the depth of the fields containing plastic contamination provided the solution for determining the optimum amount of the field to be surveyed. A tool for annotating machine vision data has been developed and tested by CGRU researchers. 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. A prototype power meter for tabletop gins has been developed by CGRU researchers. The power meter serves as a testbed for development of a low-cost system usable by cotton variety breeders. The power meter uses raw voltage and current data to create an algorithm for automatic detection of active ginning. This is vital for extracting actionable information from the raw data. Low cost and ease of use is key to adoption of the technology. This research is partially funded by Cotton Incorporated. A prototype seed cotton capacitive moisture sensor has been developed by Mississippi State University in collaboration with the CGRU under NACA 58-6066-9-038. The objective of the research is to advance development of a real-time model for predicting moisture content of seed cotton as it flows through a pipeline. In this second year of development, the prototype unit has been installed in the Micro Gin at the Stoneville CGRU where testing and evaluation are in process. Modification of a saw type lint cleaner to address seed coat fragments (SCF) has progressed to the point a modified grid bar with potential patentable features has been installed and preliminary feasibility tests have been conducted. 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 of immediate interest to our stakeholder cotton producers in the Southeast, the National Cotton Council of America and Cotton Incorporated. Implications of the research touch the entire cotton fiber value chain.
1. Co-pelletization of cotton gin trash with complementary bio-waste. Cotton gin trash (CGT) is the vegetable by-product of the cotton harvesting process. CGT comprises from 10% to 40% of the harvested material by weight per bale of cotton depending on the harvesting method. By volume it may constitute as much as 50%. CGT has little if any market value with minimal amounts used as a supplement for cattle farmers, the remainder left to compost or returned to fields as a minimal nutrient supplement. When stored or windrowed, run-off from CGT can lead to waterway issues from herbicides and pesticides, and on occasion CGT may take a heat due to high moisture content resulting in spontaneous combustion. ARS researchers in Stoneville, Mississippi, are studying the feasibility of utilizing CGT as a source or co-source for bioenergy and as a bio-nutrient. The feasibility study parallels research efforts on pelletization of CGT. The long-term impact of the study is to support the disposition of CGT using environmentally sustainable methods to generate additional revenue for stakeholders.
2. 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. ARS researchers in Stoneville, Mississippi, are developing technology to address the issue. Artificial intelligence-based methodology is being applied for visual recognition of plastic and other contaminants in cotton fields and high-speed air ducts in cotton gins. The technology applied to air ducts addresses issues that arise from capturing images of relatively fast-moving objects. Low-cost processing platforms are being considered to facilitate stakeholder support and investment. This long-term goal is in support of an industry wide effort to eliminate plastic contamination in U.S. cotton.
3. Tabletop ginning energy: variable effects. Cotton ginning equipment consumes energy whether processing cotton or running idle. The research community has long discussed the ladder as the idle energy. Several studies have subtracted the idle energy from the total measured energy to obtain the energy going to processing lint fiber. One study expressed the power used as a ratio between the idle power and running power for various gin functions. This study uses the term baseline energy to denote the portion of the energy that the equipment consumes even when not actively ginning. Current work looks at subtracting the baseline energy from the total energy to get the energy that does work on the cotton. This type of energy is the active energy. The objective of this work is to look at the energy consumed by small-scale tabletop gins while testing a variety of variables. The variables considered include sample size, operator, and cotton cultivar. Progress of studies by ARS researchers in Stoneville, Mississippi, has resulted in development of best practices for collecting data and guidelines on what data individuals can safely compare.
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