International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 11 (November 2025), Pages: 57-71

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 Original Research Paper

A garlic disease identification model based on near-infrared spectroscopy and an optimized ResNet

 Author(s): 

 Rongfeng Zhang 1, *, Tang Ye 1, Zexi Li 1, Ting Deng 2

 Affiliation(s):

  1College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou, China
  2Network and Information Technology Office, South China University of Technology, Guangzhou, China

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0003-3711-176X

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.11.007

 Abstract

Early detection of garlic diseases is essential for improving agricultural quality and productivity. This study presents a novel garlic disease identification model based on near-infrared (NIR) spectroscopy and a convolutional neural network, named ST-1DResNet (One-dimensional Residual Networks with Squeeze-and-Excitation and tanh activation). The model overcomes the vanishing gradient problem, adaptively adjusts channel weights, and efficiently extracts spectral features without requiring preprocessing or manual feature extraction. Experimental results show that ST-1DResNet achieves a classification accuracy of 97.75%, outperforming the original ResNet and four classical deep learning models by an average of 6.40%. Compared with traditional machine learning methods and optimized SVM models, it improves accuracy by 11.63% and 2.67%, respectively. The model is compact, computationally efficient, and supports fast training, making it suitable for deployment in resource-limited environments. Its strong generalization performance, validated using an external mango dataset, highlights its scalability. Overall, ST-1DResNet provides a practical, accurate, and non-destructive approach for crop disease detection, contributing to quality control and intelligent diagnosis in modern agriculture.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Garlic disease, Near-infrared spectroscopy, Convolutional neural network, Deep learning, Agricultural diagnosis

 Article history

 Received 21 May 2025, Received in revised form 28 September 2025, Accepted 14 October 2025

 Funding

This work was supported in part by the Science and Technology Commissioner Project of Guangdong Provincial Department of Science and Technology under Grant KTP20240366 and KTP20210242. 

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Zhang R, Ye T, Li Z, and Deng T (2025). A garlic disease identification model based on near-infrared spectroscopy and an optimized ResNet. International Journal of Advanced and Applied Sciences, 12(11): 57-71

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 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5  Fig. 6  Fig. 7  Fig. 8  Fig. 9  Fig. 10  Fig. 11  Fig. 12  Fig. 13 

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5  Table 6 

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 References (41)

  1. Anderson NT, Walsh KB, Subedi PP, and Hayes CH (2020). Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content. Postharvest Biology and Technology, 168: 111202.  https://doi.org/10.1016/j.postharvbio.2020.111202    [Google Scholar]
  2. Anum H, Tong Y, and Cheng R (2024). Different preharvest diseases in garlic and their eco-friendly management strategies. Plants, 13(2): 267.  https://doi.org/10.3390/plants13020267    [Google Scholar] PMid:38256820 PMCid:PMC10818302
  3. Archana R and Jeevaraj PE (2024). Deep learning models for digital image processing: A review. Artificial Intelligence Review, 57: 11.  https://doi.org/10.1007/s10462-023-10631-z    [Google Scholar]
  4. Chen F, Li S, Han J, Ren F, and Yang Z (2024). Review of lightweight deep convolutional neural networks. Archives of Computational Methods in Engineering, 31(4): 1915-1937.  https://doi.org/10.1007/s11831-023-10032-z    [Google Scholar]
  5. Chollet F (2017). Xception: Deep learning with depthwise separable convolutions. In the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA: 1251-1258.  https://doi.org/10.1109/CVPR.2017.195    [Google Scholar]
  6. Chouhan SS, Singh UP, Sharma U, and Jain S (2024). Classification of different plant species using deep learning and machine learning algorithms. Wireless Personal Communications, 136(4): 2275-2298.  https://doi.org/10.1007/s11277-024-11374-y    [Google Scholar]
  7. Cui Y, Zhang S, Dong X, Li Q, Huang Y, Meng X, Wang Y, and Ye Y (2024). Garlic-specific fertilizer improves economic and environmental outcomes in China. HortScience, 59(5): 605-612.  https://doi.org/10.21273/HORTSCI17677-23    [Google Scholar]
  8. Dedecan O, Talapov T, Demral M, Sarpkaya K, Ceyhan Dİ, and Can C (2022). Molecular and pathogenic characterization of Fusarium oxysporum and Fusarium proliferatum causing basal root rot in garlic in Turkey. Australasian Plant Disease Notes, 17: 37.  https://doi.org/10.1007/s13314-022-00484-w    [Google Scholar]
  9. Feng J, Zhang S, Zhai Z, Yu H, and Xu H (2024). DC2Net: An Asian soybean rust detection model based on hyperspectral imaging and deep learning. Plant Phenomics, 6: 0163.  https://doi.org/10.34133/plantphenomics.0163    [Google Scholar] PMid:38586218 PMCid:PMC10997487
  10. Feng L, Wu B, Zhu S, He Y, and Zhang C (2021). Application of visible/infrared spectroscopy and hyperspectral imaging with machine learning techniques for identifying food varieties and geographical origins. Frontiers in Nutrition, 8: 680357.  https://doi.org/10.3389/fnut.2021.680357    [Google Scholar] PMid:34222304 PMCid:PMC8247466
  11. Gálvez L and Palmero D (2021). Incidence and etiology of postharvest fungal diseases associated with bulb rot in garlic ( Alllium sativum) in Spain. Foods, 10(5): 1063.  https://doi.org/10.3390/foods10051063    [Google Scholar] PMid:34065850 PMCid:PMC8151520
  12. He K, Zhang X, Ren S, and Sun J (2016). Deep residual learning for image recognition. In the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA: 770-778.  https://doi.org/10.1109/CVPR.2016.90    [Google Scholar] PMid:26180094
  13. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, and Adam H (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. Arxiv Preprint Arxiv:1704.04861.  https://doi.org/10.48550/arXiv.1704.04861    [Google Scholar]
  14. Hu J, Shen L, and Sun G (2018). Squeeze-and-excitation networks. In the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA: 7132-7141.  https://doi.org/10.1109/CVPR.2018.00745    [Google Scholar]
  15. Huang G, Liu Z, Van Der Maaten L, and Weinberger KQ (2017). Densely connected convolutional networks. In the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA: 4700-4708.  https://doi.org/10.1109/CVPR.2017.243    [Google Scholar] PMCid:PMC5598342
  16. Li H and Ni J (2024). Proportionate affine projection tanh algorithm and its step-size optimization. Signal Processing, 223: 109553.  https://doi.org/10.1016/j.sigpro.2024.109553    [Google Scholar]
  17. Liu B, Wang J, and Li C (2024). Application of PLS–NN model based on mid-infrared spectroscopy in the origin identification of Cornus officinalis. RSC Advances, 14(22): 15209-15219.  https://doi.org/10.1039/D4RA00953C    [Google Scholar] PMid:38737973 PMCid:PMC11082643
  18. Liu L, Wang B, Xu X, and Xu J (2025). A tea classification method based on near infrared spectroscopy (NIRS) and transfer learning. Infrared Physics and Technology, 145: 105713.  https://doi.org/10.1016/j.infrared.2025.105713    [Google Scholar]
  19. Lundberg SM and Lee SI (2017). A unified approach to interpreting model predictions. In the 31st Conference on Neural Information Processing Systems, Long Beach, USA: 1-10.    [Google Scholar]
  20. Ma S, He K, and Peng X (2023). Online coal identification based on one-dimensional convolution and its industrial applications. Applied Sciences, 13(17): 9867.  https://doi.org/10.3390/app13179867    [Google Scholar]
  21. Martín Andrés A and Álvarez Hernández M (2025). Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements. Advances in Data Analysis and Classification, 19(1): 177-207.  https://doi.org/10.1007/s11634-024-00581-x    [Google Scholar]
  22. Muhammad W, Bhutto Z, Shah SAR, Shah J, Shaikh MH, Hussain A, Thaheem I, and Ali S (2022). Deep transfer learning CNN based approach for COVID-19 detection. International Journal of Advanced and Applied Sciences, 9(4): 44-52.  https://doi.org/10.21833/ijaas.2022.04.006   [Google Scholar]
  23. Neupane D, Kim Y, Seok J, and Hong J (2021). CNN-based fault detection for smart manufacturing. Applied Sciences, 11(24): 11732.  https://doi.org/10.3390/app112411732    [Google Scholar]
  24. Ninh DK, Phan KD, Nguyen TTA, Dang MN, Le Thanh N, and Ferrero F (2024). Classification of urea content in fish using absorbance near-infrared spectroscopy and machine learning. Applied Sciences, 14(19): 8586.  https://doi.org/10.3390/app14198586    [Google Scholar]
  25. Ong P, Jian J, Li X, Zou C, Yin J, and Ma G (2025). Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 324: 125001.  https://doi.org/10.1016/j.saa.2024.125001    [Google Scholar] PMid:39180971
  26. Promboonruang S and Boonrod T (2023). Deep transfer learning CNN based for classification quality of organic vegetables. International Journal of Advanced and Applied Sciences, 10(12): 203-210.  https://doi.org/10.21833/ijaas.2023.12.022    [Google Scholar]
  27. Sen Y, Zhenmin W, Houqing Z, and Wenlong S (2024). Rice variety classification based on optimized near-infrared spectral classification model. Rice Science, 31(1): 6-9.  https://doi.org/10.1016/j.rsci.2023.11.003    [Google Scholar]
  28. Shagun S, Bains A, Sridhar K, Dhull SB, Patil S, Gupta VK, Chawla P, and Sharma M (2024). A comprehensive review on impact of post-harvest management and treatment practices on the quality of garlic ( Allium sativum L) during storage. Scientia Horticulturae, 337: 113586.  https://doi.org/10.1016/j.scienta.2024.113586    [Google Scholar]
  29. Shao W, Li Y, Diao S, Jiang J, and Dong R (2017). Rapid classification of Chinese quince ( Chaenomeles speciosa Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration. Analytical and Bioanalytical Chemistry, 409: 115-120.  https://doi.org/10.1007/s00216-016-9944-7    [Google Scholar] PMid:27796451
  30. Sharifi K, Sheykhi S, and Magami E (2021). First report of garlic leaf blight caused by Stemphylium vesicarium in Iran. Journal of Plant Pathology, 103: 1007-1007.  https://doi.org/10.1007/s42161-021-00822-4    [Google Scholar]
  31. Shen Y, Yang Z, Khan Z, Liu H, Chen W, and Duan S (2025). Optimization of improved YOLOv8 for precision tomato leaf disease detection in sustainable agriculture. Sensors, 25(5): 1398.  https://doi.org/10.3390/s25051398    [Google Scholar] PMid:40096213 PMCid:PMC11902794
  32. Simonyan K and Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. In the 3rd International Conference on Learning Representations, San Diego, USA: 1-14.    [Google Scholar]
  33. Tian F, Wang L, and Xia M (2022). Signals recognition by CNN based on attention mechanism. Electronics, 11(13): 2100.  https://doi.org/10.3390/electronics11132100    [Google Scholar]
  34. Tudu CK, Dutta T, Ghorai M et al. (2022). Traditional uses, phytochemistry, pharmacology and toxicology of garlic ( Allium sativum), a storehouse of diverse phytochemicals: A review of research from the last decade focusing on health and nutritional implications. Frontiers in Nutrition, 9: 949554.  https://doi.org/10.3389/fnut.2022.929554    [Google Scholar] PMid:36386956 PMCid:PMC9650110
  35. Vijaykumar KN, Kulkarni S, Hiremath SM et al. (2023). Field screening of garlic genotypes for identification of resistant sources against purple blotch disease. International Journal of Plant and Soil Science, 35(14): 365-370.  https://doi.org/10.9734/ijpss/2023/v35i143059    [Google Scholar]
  36. Workneh YY, Legesse NH, Shiferaw HK, and Ashenafi BD (2024). Management of garlic white rot ( Stromatinia cepivora) with fungicides and host resistance in North Shewa, central highland of Ethiopia. Journal of Plant Pathology, 106(3): 1335-1345.  https://doi.org/10.1007/s42161-024-01690-4    [Google Scholar]
  37. Yeboah PN and Baz Musah HB (2022). NLP technique for malware detection using 1D CNN fusion model. Security and Communication Networks, 2022: 2957203.  https://doi.org/10.1155/2022/2957203    [Google Scholar]
  38. Zewde T, Fininsa C, Sakhuja PK, and Ahmed S (2007). Association of white rot ( Sclerotium cepivorum) of garlic with environmental factors and cultural practices in the North Shewa highlands of Ethiopia. Crop Protection, 26(10): 1566-1573.  https://doi.org/10.1016/j.cropro.2007.01.007    [Google Scholar]
  39. Zhai C, Wang W, Gao M, Feng X, Zhang S, and Qian C (2024). Rapid classification of rice according to storage duration via near-infrared spectroscopy and machine learning. Talanta Open, 10: 100343.  https://doi.org/10.1016/j.talo.2024.100343    [Google Scholar]
  40. Zhang H, Hu L, Liang W, Li Z, Yuan M, Ye Y, Wang Z, Ren Y, and Li X (2024). BCT-OFD: Bridging CNN and transformer via online feature distillation for COVID-19 image recognition. International Journal of Machine Learning and Cybernetics, 15: 2347-2366.  https://doi.org/10.1007/s13042-023-02034-x    [Google Scholar]
  41. Zou X, Wang Q, Chen Y et al. (2025). Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy. Food Chemistry, 463: 141053.  https://doi.org/10.1016/j.foodchem.2024.141053    [Google Scholar] PMid:39241414