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The Computer Vision and Type-II Fuzzy Logic based Hybrid Intelligent Approach for Quality Measurement of Wheat Crop
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 124 |
| Year of Publication: 2026 |
| Authors: Mandeep Kaur, Harmandeep Singh Gill, Amarinder Singh Riar |
10.5120/ijcae46d13161bfc
|
Mandeep Kaur, Harmandeep Singh Gill, Amarinder Singh Riar . The Computer Vision and Type-II Fuzzy Logic based Hybrid Intelligent Approach for Quality Measurement of Wheat Crop. International Journal of Computer Applications. 187, 124 ( Jul 2026), 43-48. DOI=10.5120/ijcae46d13161bfc
Abstract
In the proposed research, a fuzzy inference system (FIS) coupled with image processing approach has been developed as a decision support system (DSS) for grading the quality of wheat crop. Degree of milling (DDM) and percentage of broken kernels (PBK) quality indices has been graded by the wheat crop experts into five classes. Wheat is one of the most significant cereal crops worldwide and plays a vital role in global food security and agricultural economics. Traditional wheat quality assessment methods are generally manual, time-consuming, subjective, and prone to human error. Recent developments in computer vision, artificial intelligence, and fuzzy logic have enabled automated and intelligent grain quality inspection systems. This research paper proposes a hybrid intelligent framework integrating computer vision techniques with Type-II Fuzzy Logic for efficient and accurate quality measurement of wheat crops. The proposed approach combines image acquisition, preprocessing, feature extraction, classification, and fuzzy inference mechanisms to evaluate wheat grain quality based on parameters such as size, shape, color, texture, impurities, and damage level. The integration of Type-II fuzzy systems helps address uncertainty and ambiguity present in real-world agricultural environments. Experimental analysis demonstrates that the hybrid model significantly improves classification accuracy, robustness, and decision-making capability compared to conventional methods.
References
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