FUZZY LOGIC AND ANFIS IN THE CONTROL OF COTTON PRIMARY PROCESSING OPERATIONS: A REVIEW OF APPLICATIONS, DESIGN CHOICES AND PROSPECTS FOR SEED DELINTING
- Authors
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Navruzbek Kurbonov
PhD student, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, Uzbekistan -
Mirshod Abdullayev
Assistant, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, Uzbekistan
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- Keywords:
- fuzzy logic control ANFIS cotton ginning seed cotton drying cotton grading cottonseed delinting process control
- Abstract
- Cotton primary processing turns seed cotton into baled lint and planting or crushing seed through drying, cleaning, ginning, lint cleaning and seed delinting. Each stage works on a raw material whose moisture, trash content and fuzz level change from lot to lot, while product quality is usually measured in the laboratory long after the material has passed the machine. Fuzzy logic and adaptive neuro-fuzzy inference systems (ANFIS) are often suggested for such poorly modelled processes, but their use along the cotton processing chain has not been reviewed as a whole. This paper reviews [N] studies published between 1990 and 2026 that apply fuzzy, neuro-fuzzy or ANFIS methods to cotton primary processing or to closely related drying and oilseed operations. The studies were classified by processing stage, task (modelling, classification or control) and level of validation (simulation, laboratory or industrial). Most fuzzy and ANFIS work in the cotton sector deals with fibre grading and quality prediction, whereas closed-loop control is reported mainly for dryers and, rarely, for gin automation. Design choices such as the inference type, the number and shape of membership functions and the source of the rule base are seldom justified. Saw delinting of cottonseed is almost absent from this literature. The review summarises transferable design lessons and proposes a control architecture for saw delinting that combines an ANFIS quality model, a fuzzy supervisory layer and adaptation to saw wear.
- References
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