Academic Journal
Optimized wheat seed classification using YOLO with morphological image feature enhancement.
| Τίτλος: | Optimized wheat seed classification using YOLO with morphological image feature enhancement. |
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| Συγγραφείς: | Deepika B; Department of Computer Science and Engineering, Dhanalakshmi Srinivasan University, Samayapuram, Tiruchirappalli, 621 112, Tamil Nadu, India., Shanmugapriya N; Department of Artificial Intelligence & Data Science, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram, Tiruchirappalli, 621 112, Tamil Nadu, India. shanmugapriyan.set@dsuniversity.ac.in., Gopi R; Department of Computer Science and Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur, 621 212, Tamil Nadu, India. |
| Πηγή: | Scientific reports [Sci Rep] 2026 Feb 28; Vol. 16 (1). Date of Electronic Publication: 2026 Feb 28. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Ιατρικοί όροι (MeSH): | Triticum*/classification , Triticum*/anatomy & histology , Seeds*/classification , Seeds*/anatomy & histology , Image Processing, Computer-Assisted*/methods, Detection Algorithms ; Algorithms ; Deep Learning |
| Περίληψη: | The article presents a superior computer vision system to detect and grade wheat seeds. It is concerned about the integration of deep-learning detection and conventional image processing methods to enhance the overall classification accuracy. The current methods of seed classification in wheat seeds have been found to lack proper visibility of the features particularly in low contrast images, small defects, or overlapping of seeds, and also, irregular lighting situations. The conventional feature extractors are weak, and the deep models by itself fail in cases where the morphological features like grooves, cracks, and shriveling are not well pronounced. To overcome these constraints, the proposed YOLO-Integrated Morphological Feature Enhancement Pipeline (Y-MFEP) uses dilation, erosion, opening, closing and top-hat transformations to enhance structural features and detect them using YOLO. The fused images are the improved feature maps and the original images, which allows the YOLO to identify finer seed variations more accurately. Such a hybrid pipeline enhances the visibility of the edges, defects, and texture uniformity without sacrificing the real-time detection performance. The given method is used to grade the quality of wheat at agricultural processing and procurement centers automatically. It guarantees quick, stable, and high-scaling classification of fit, broken, shriveled, and infected seeds. The results demonstrate that Y-MFEP has a major advantage of improving the accuracy, mAP and defect-detection sensitivity, which creates a more dependable and automated wheat quality measurement. The classification accuracy (85–95%), defect sensitivity index (0.775), edge clarity score (78–85), intersection over union (75–82%), and small object detection rate (75–90%) is reached in the proposed method. |
| Competing Interests: | Declarations. Competing interests: The authors declare no competing interests. |
| References: | MethodsX. 2024 Dec 17;14:103114. (PMID: 39811616) Sci Rep. 2025 Nov 18;15(1):40436. (PMID: 41253897) Sci Rep. 2024 Sep 30;14(1):22728. (PMID: 39349934) Front Plant Sci. 2022 Mar 21;13:828454. (PMID: 35386677) Sci Rep. 2022 Jul 14;12(1):11997. (PMID: 35835850) Plant Methods. 2025 May 30;21(1):75. (PMID: 40448208) Plants (Basel). 2025 Oct 03;14(19):. (PMID: 41095199) J Sci Food Agric. 2024 Dec;104(15):9093-9101. (PMID: 38817147) Front Nutr. 2023 Feb 24;10:1053196. (PMID: 36908903) |
| Contributed Indexing: | Keywords: Feature fusion; Image processing; Morphological enhancement; Seed quality assessment; Wheat classification; YOLO |
| Entry Date(s): | Date Created: 20260301 Date Completed: 20260627 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC13057161 |
| DOI: | 10.1038/s41598-026-41846-z |
| PMID: | 41764335 |
| Βάση Δεδομένων: | MEDLINE |
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