Location: Cotton Production and Processing Research
2024 Annual Report
Objectives
OBJECTIVE 1: Develop commercially viable methods and technologies for use before ginning that reduce harvest costs, preserve fiber/seed quality, enhance the utilization of production/harvest/gin data, and prevent/minimize contamination of upland cotton.
Subobjective 1A: Assessing the influence of seed cotton storage in round modules on lint and seed quality.
Subobjective 1B: Improving the cleanliness and quality of stripper-harvested cotton through improved field cleaning systems.
Subobjective 1C: Development of equipment to detect and remove contaminants from cotton during the harvesting process.
OBJECTIVE 2: Enable commercially preferred technologies/methods/strategies for use in ginning upland cotton that improve cleanliness of seed cotton and lint, detect/remove contamination, preserve fiber quality, and reduce financial costs.
Subobjective 2A: Development of equipment to detect and remove contaminants from cotton in the harvest and ginning processes.
Subobjective 2B: Improving cotton fiber length distribution through novel lint cleaner design.
OBJECTIVE 3: Develop commercially viable post-ginning technologies/techniques that enhance the storage and utilization of upland cotton products/coproducts/byproducts and reduce the environmental footprint of cotton production/processing.
Subobjective 3A: Development of a commercially viable mechanical cottonseed delinting system to remove cotton linters and produce planting quality seed, without the use of chemicals.
Subobjective 3B: Reducing particulate emissions from cotton ginning through improved pollution abatement device design using computational fluid dynamics (CFD) and laboratory testing.
Subobjective 3C: Develop and evaluate the use of cotton plant constituents and other natural fibers in the manufacture of composite materials.
Approach
This five-year project plan addresses critical pre-ginning, ginning and post-ginning issues facing cotton producers and processors in the United States. Our plan of work is based on an interactive research approach which is focused on the development of processes and systems for preserving cotton quality during infield storage and ginning, removing foreign material and contaminants from seed cotton during harvesting and ginning, reducing particulate emissions from ag operations, reducing the environmental impact of acid cottonseed delinting, and increasing the value of cotton byproducts though composite materials. The research plan detailed herein addresses the development of new technologies, methods, and strategies for reducing the economic and environmental costs of cotton harvest, ginning, and post-gin processing of upland cotton and cotton by-products. Commercial viability of the research is a key component of any problem solution.
Progress Report
Objective 1: Field scale experiments were conducted in Mississippi and Texas to investigate the relationship between the change in fiber and seed quality parameters as a function of seed cotton moisture content at harvest and storage period length. The accuracy of several non-reference method devices that measure seed cotton moisture content was evaluated and several devices performed well under field conditions compared to the oven-based reference method. Data on round modules formed from both machine picked and stripped cotton was collected. The modules were ginned at cooperating commercial gins near the growing locations in Texas and Mississippi and quality analyses were conducted on samples of seed and lint collected during the ginning process. Limited data on high moisture content modules were obtained in 2024 and only minor effects on leaf grade and fiber color were observed. Additional experiments to document the change in fiber and seed quality as a function of harvest moisture content and storage duration are ongoing. Scientific presentations to stakeholder groups have been given to report the results of this work.
A new field cleaner was designed and implemented on commercial state-of-the-art cotton strippers. The new machine exhibits improved cleaning efficiency and decreased seed cotton loss compared to prior models. Experiments to optimize the cleaning performance and seed cotton loss of the new machine were carried out under laboratory and field conditions and the resulting saw speed and grid bar settings have been communicated to the research partner through presentations and technical reports for implementation on new year model harvesters. Additional work to further enhance the cleaning performance of the new field cleaner was carried out through the design and testing of an active laydown cylinder. The novel laydown cylinder restrains the flow of cotton to maximize the engagement of seed cotton on the top saw cylinder while actively passing the excess flow of seed cotton directly to the second saw. Experiments were conducted to document the performance of the new laydown cylinder and provide control models for balancing material flow between the top two saw cylinders. Additional experiments are underway to document the effect of the new flow balancing system on cleaning performance and seed cotton loss.
Protocols for evaluating the presence of plastic contamination immediately in front of a harvester have been developed. A design for the mechanical exclusion of plastic contamination has been developed and fabricated. This equipment is designed to be utilized in conjunction with a smart machine-vision system to provide detection, which will then provide the signal to actuate the mechanical exclusion system. The smart machine-vision sensor has been designed and fabricated. Work is ongoing on the development of the machine vision software, which is the heart of the detection system. Traditional machine learning classifiers are being assessed for their potential use in detecting plastic contamination and show promise. In parallel, deep learning models are being assessed for this purpose because of their significant promise for use in uncontrolled lighting environments where traditional machine vision algorithms struggle. Recent developments have produced several deep learning models that appear promising. In particular, a new artificial intelligence paradigm for image classification has been found to be particularly effective: the Vision Transformer (an image classification implementation of the large-language models). This new approach is providing lighting-independent classification of plastic, a critically important milestone for use in outdoor environments such as on harvesters. Plans are underway to test these new models in the upcoming harvest season. Scientific presentations have reported on the new models and algorithms.
Objective 2: Plastic contamination detection and removal systems have been designed and fabricated, and several commercial trials have been conducted. Testing and evaluation of the system were completed in laboratory studies using a cut-down extractor feeder with commercial-scale cross-sectional geometry. Further studies have commenced to explore practical utilization in commercial cotton gins. Initial test results have been successful for several of the primary sources of plastic contamination that the industry struggles with, specifically plastic that comprises more than 85% of contamination found at the USDA - Agricultural Marketing Service classing offices. The research revealed a significant impediment to the adoption of the technology due to a lack of skilled personnel available to run the system. To address this issue, additional work was conducted on the development of an auto-calibration system that will eliminate the need for personnel to monitor and periodically adjust the calibration of the detection system due to changing cotton conditions. The work appears promising when benchmarked against previously obtained commercial field data. Several high-speed AI models have been developed to support the auto-calibration system. Given the promise of this approach, further testing is planned in commercial field trials. Scientific presentations have reported on the new models and algorithms.
Air-type lint cleaners are commonly used after the gin stand to remove heavy foreign matter such as seed coats, seed meats, and other vegetative material from ginned lint before it is processed by more aggressive saw-type lint cleaners. A novel multi-stage air-type lint cleaner was developed and tested for use in processing small samples from breeding and agronomic development research. Elements of the design of the multi-stage air-type lint cleaner have been included in a U.S. patent and the performance of the machine was documented and the results communicated to the research partner through technical presentations and reports. Additional work to scale up the design for use under commercial ginning conditions is underway.
Objective 3: The 8-ft prototype mechanical delinter was installed in a commercial cotton gin in New Mexico and organic Pima cottonseed was processed through the unit over a 4-day period. The processed Pima seed was from one of the cotton gin’s customers who farms both conventional and organic cotton. The delinted organic Pima cottonseed was planted this year (2024) and will be ginned in the 2024-2025 ginning season.
Computational fluid dynamic (CFD) simulation software was developed to study particulate-laden air streams. Experimental testing showed that typical air flows in baffle-type pre-separators exhibit turbulent flow as the dominant flow regime. Consequently, the simulation model was developed to include turbulent flow models with two-way interaction between the particulates and the air stream. However, recent advances in CFD have greatly improved the accuracy of turbulent separating flows via Large-Eddy Simulation (LES) models. Given these significantly improved turbulence models, it was deemed important to redevelop the CFD models to leverage this new technology. While the original turbulence model simulations suggested potential for using an additional skimmer plate in the baffle-type pre-separator to enhance cleaning efficiency, this new, more accurate approach calls this into question. The LES CFD model uncovered previously unappreciated pulse flow mechanisms that suggest significant modifications will be needed to adapt the current physical design to more optimally capture the transition particles, as well as anticipated modifications to the currently accepted guidance for air flow and mass-loadings through the pre-separator. To test the new, improved model, the prototype will be reworked to reflect the insights gained. The next step is to conduct experimental validation tests. Currently, designs are being reviewed, and plans are underway for evaluating the experimental test unit.
Seven lignin coated cellulose nanocrystal composite samples were produced and tested at a university partner’s location. The samples evaluated various treatments of ultrasonic amplitude and duration for dispersion of the ligno-cellulosic nanocrystals (L-CNC) in a polyethylene-based solution to improve morphology and mechanical properties of bio-based materials made from L-CNC. Results showed the highest amplitude with the longest time interval produced a 300% improvement in observed properties compared to the other treatments.
Accomplishments
1. A round cotton module rotating wheel-loader work tool for reducing plastic contamination. Plastic contamination in U.S. grown cotton has increased with the adoption of new harvesters that form cylindrical or “round” modules of seed cotton wrapped in multi-layer plastic film. The increase in plastic contamination is estimated to cost the U.S. cotton industry approximately $750 million annually. Research has shown that much of the plastic contamination originates from segments of the plastic wrap that inadvertently remain in the cotton after the wrap is manually cut and removed from the modules. It is often the case that the wrap is cut at an inappropriate location which causes small pieces of the inner wrap layer to remain in the cotton. To address this issue, ARS researchers at Lubbock, Texas, developed a new hydraulically actuated work tool for use on wheel loaders that rotates round cotton modules into proper position for manual wrap cutting. In automatic positioning mode, the system controls the rotational position of round modules by sensing the location of radio frequency identification tags embedded in the plastic module wrap. Use of this system was shown to reduce the incidence of plastic contamination by 50% at a cooperating commercial ginning facility.
Review Publications
Bajwa, D., Holt, G.A., Stark, N., Bajwa, S., Chanda, S., Quadir, M. 2023. Nano boron oxide and zinc oxide doped lignin containing cellulose nanocrystals improve the thermal, mechanical and flammability properties of high-density poly (ethylene). Polymers. 16(1). https://doi.org/10.3390/polym16010036.
Pelletier, M.G., Wanjura, J.D., Wakefield, J.R., Holt, G.A., Kothari, N. 2023. Cotton gin stand machine-vision inspection and removal system for plastic contamination: Hand intrusion sensor design. AgriEngineering. 6(1). https://doi.org/10.3390/agriengineering6010001.
Armijo, C.B., Delhom, C.D., Whitelock, D.P., Tumuluru, J., Yeater, K.M., Rowe, C., Wanjura, J.D., Sui, R., Holt, G.A., Martin, V.B., Kothari, N. 2023. Evaluation of alternative-design cotton gin lint cleaning machines on fiber length uniformity index. AgriEngineering. 5(4):2123-2138. https://doi.org/10.3390/agriengineering5040130.