International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Review on Soyabean Seed Germination Analysis using Image Processing & Various Machine Learning Algorithms |
|---|---|
| ABSTRACT | India is the world's fifth-largest producer of soybeans (Glycine max), one of the most commercially significant oilseed crops. Crop production and agricultural results are strongly impacted by the quality of seed germination. Maintaining seed quality and boosting agricultural productivity depend on accurate assessment of soybean seed germination. Traditional germination assessment methods are often labor-intensive, slow, and prone to subjective judgment, which limits their suitability for large-scale and high-throughput analysis. This paper proposes an automated framework for soybean seed germination assessment using image processing combined with machine learning and deep learning methods. The workflow includes image acquisition, preprocessing, segmentation, feature extraction, and classification of germinated and non-germinated seeds. Key visual descriptors such as morphological, color, and texture characteristics are derived from seed images and used to train predictive models, including Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and YOLO (You Only Look Once) -based detection approaches. The proposed system is intended to enhance the accuracy, consistency, and speed of germination evaluation while reducing dependence on manual inspection. In addition, the integration of computer vision and artificial intelligence can support precision agriculture through dependable and automated seed quality monitoring. The study highlights the potential of image-driven intelligent diagnostic systems for advancing sustainable agriculture and enabling next-generation smart farming applications. |
| AUTHOR | PRIYANKA RAJESH PATIL, DR. AJAY P. THAKARE Research Scholar, Dept. of EXTC, Prof Ram Meghe College of Engineering & Management, Badnera, Amravati, SGBAU, Amravati University, India Professor, Dept. of EXTC., Prof Ram Meghe College of Engineering & Management, Badnera, Amravati, SGBAU, Amravati University, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.14060108 |
| pdf/108_A Review on Soyabean Seed Germination Analysis using Image Processing & Various Machine Learning Algorithms.pdf | |
| KEYWORDS | |
| References | 1. Zanetoni, H.H.R.; Araujo, L.G.; Almeida, R.P.; Cabral, C.E.A. Artificial Intelligence in the Identification of Germinated Soybean Seeds. AgriEngineering 2025, 7, 169. https://doi.org/10.3390/agriengineering7060169 2. Chagas J. T. ;Farias, J.; Souza, R.; Júnior, S. F.; Costa, M. G. Germinação e vigor de sementes criolas de feijão -caupi. Agrar. Acad. 2018,5,488 [CrossRef] 3. Sharma, S., Verma, K., & Hardaha, P. (2023). Implementation of Artificial Intelligence in Agriculture. Journal of Computational and Cognitive Engineering, 2(2), 155-162. https://doi.org/10.47852/bonviewJCCE2202174 4. Patricia L. Suárez, Henry O. Velesaca, Dario Carpio, Angel D. Sappa, Corn kernel classification from few training samples, Artificial Intelligence in Agriculture, Volume 9, 2023, Pages 89-99, ISSN 2589-7217 https://doi.org/10.1016/j.aiia.2023.08.006 5. Gomez, D., Selvaraj, M.G., Casas, J. et al. Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI. Sci Rep 14, 15596 (2024). https://doi.org/10.1038/s41598-024-66281-w 6. Yan, B.; Liu, Y.; Yan, W. A Novel Fusion Perception Algorithm of Tree Branch/Trunk and Apple for Harvesting Robot Based on Improved YOLOv8s. Agronomy 2024, 14, 1895. https://doi.org/10.3390/agronomy14091895 7. Terven, J.; Córdova-Esparza, D.-M.; Romero-González, J.-A. A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Mach. Learn. Knowl. Extr. 2023, 5, 1680-1716. https://doi.org/10.3390/make5040083 8. Sun, W.; Xu, M.; Xu, K.; Chen, D.; Wang, J.; Yang, R.; Chen, Q.; Yang, S. CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n. Agronomy 2025, 15, 128. https://doi.org/10.3390/agronomy15010128 9. S. Liu and Z. Chen, “Rapid detection method of soybean seed germination potential based on the PLSR-MLP fusion model,” Frontiers in Plant Science, vol. 16, Art. no. 1726266, Jan. 2026, doi: 10.3389/fpls.2025.1726266. 10. Oliveira, G.S.d.; Santana, D.C.; Oliveira, I.C.d.; Seron, A.C.d.S.C.; Baio, F.H.R.; Santos, G.A.V.d.; da Silva Junior, C.A.; Teodoro, P.E.; Vaez, R.N.; Alvarez, R.d.C.F.; et al. Leaf and Seed Hyperspectral Signatures Enable Early and Accurate Prediction of Soybean Seed Quality. AgriEngineering 2025, 7, 424. https://doi.org/10.3390/agriengineering7120424 11. Liu, Chuanming Shen, Yifan Mu, Feng Long, Haixia Bilal, AnasYu, Xia Dai, Qi 2025 2025/04/12, “Detection of surface defects in soybean seeds based on improved Yolov9”, Scientific Reports 12631 volume 15 issue1 12. R. B. Hegde, V. Kudva, S. Nayak, N. Sampathila, and A. Thalengala, “Advanced techniques for seed quality assessment and germination monitoring,” Discover Applied Sciences, vol. 7, Art. no. 690, Jul. 2025, doi: 10.1007/s42452-025-07284-8 |
