Abstract
With the increase in world population, decreased farmland, and global climate changes, the search for intelligent systems has become important to maintain product quality and productivity. For the continuous, quality, and clean production of wheat, which is a basic macronutrient for human health, it is necessary to use high-yield, high-quality, and unmixed clean seeds. The target of this review is to provide computer-aided identification of wheat species in the food industry and to offer taxonomists an opportunity to overcome classification difficulties. This research also highlights a systematic and functional wheat identification approach using scanning electron microscope (SEM) imaging techniques. The review found that not only accurately classifies the SEM images of wheat species of deep learning methods but also yields them being distinguished under different environmental conditions (irrigated and non-irrigated). Recently, the EfficientNet models are noteworthy as providing higher training speed and better parameter efficiency compared to the previous models. Therefore, the EfficientNet-B4 and EfficientNetV2-M were utilized on the top of an effective pre-processing task. Findings confirm that SEM imaging is outstanding when it comes to diagnosis and classification in the agricultural industry. The experimental results show that the proposed technique provides significantly better quantitative results and higher accuracy rates than the state-of-the-art Convolutional Neural Networks (CNN) algorithms.
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Data availability
The datasets generated and analyzed during the current study are not publicly available due created by the SEM device belonging to the Eskisehir Osmangazi University but are available from the corresponding author on reasonable request.
References
Aversa R, Coronica P, De Nobili C, Cozzini S (2020) Deep learning, feature learning, and clustering analysis for sem image classification. Data Intell 2:513–528
Banerjee A, Mittra B (2018) Morphological modification in wheat seedlings infected by Fusarium oxysporum. Eur J Plant Pathol 152:521–524
Buades A, Coll B, Morel J 2005 A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). 60–65.
Cui C, Hu Q, Ren J, Zhao H, You L, Zhao M (2013) Effect of the structural features of hydrochloric acid-deamidated wheat gluten on its susceptibility to enzymatic hydrolysis. J Agric Food Chem 61:5706–5714
Fahmy BFG, Ghadir NMFA, Manaa SH, Ghadir MF (2015) Occurrence of entomopathogenic fungi in grain aphids in upper egypt, with reference to certain pathogenic tests using scanning electron microscope. Egyption J Biolog Pest Cont 25:177–181
Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874
Genaev MA, Skolotneva ES, Gultyaeva EI, Orlova EA, Bechtold NP, Afonnikov DA (2021) Image-based wheat fungi diseases identification by deep learning. Plants (Basel). 10(8):1500
Gong Z, Chen BK, Liu J, Zhou C, Anchel D, Li X, Ge J, Bazett-Jones DP, Sun Y (2014) Fluorescence and SEM correlative microscopy for nanomanipulation of subcellular structures. Light Sci Appl. 3:e224–e224
Işık Ş, Özkan K (2014) A comparative evaluation of well-known feature detectors and descriptors. Intern J Appl Mathemat Electron Compt. 3(1):1–6
Kavuran G (2021) SEM-net: deep features selections with binary particle swarm optimization method for classification of scanning electron microscope images. Mater Today Commun 27:102198
Kitahara AR, Holm EA (2018) Microstructure cluster analysis with transfer learning and unsupervised learning. Integrat Mater Manufact Innov 7:148–156
Koga D, Kusumi S, Shibata M, Watanabe T (2021) Applications of scanning electron microscopy using secondary and backscattered electron signals in neural structure. Front Neuroanat 15:759804
Kundu S, Jana P, De D, Roy M 2015 SEM image processing of polymer nanocomposites to estimate filler content. 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). 1–5.
Le Gouis J, Oury F-X, Charmet G (2020) How changes in climate and agricultural practices influenced wheat production in Western Europe. J Cereal Sci 93:102960
Lee CY, Yan L, Wang T, Lee SR, Park CW (2011) Intelligent classification methods of grain kernels using computer vision analysis. Meas Sci Technol 22:064006
Modarres MH, Aversa R, Cozzini S, Ciancio R, Leto A, Brandino GP (2017) Neural network for nanoscience scanning electron microscope image recognition. Sci Rep 7:13282
Morgounov A, Zykin V, Belan I, Roseeva L, Zelenskiy Y, Gomez-Becerra HF, Budak H, Bekes F (2010) Genetic gains for grain yield in high latitude spring wheat grown in Western Siberia in 1900–2008. Field Crop Res 117:101–112
Na J, Kim G, Kang S-H, Kim S-J, Lee S (2021) Deep learning-based discriminative refocusing of scanning electron microscopy images for materials science. Acta Mater 214:116987
Özkan K, Işık Ş, Yavuz BT (2019) Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples. J Sci Food Agric 99(11):4977–4984
Phankokkruad M, Wacharawichanant S 2012 Identification, counting, and sizing of dispersed phase droplet of scanning electron microscopy micrograph using digital image processing. In: 2012 5th International Congress on Image and Signal Processing. 510–514.
Popielarska-Konieczna M, Kozieradzka-Kiszkurno M, Tuleja M, Ślesak H, Kapusta P, Marcińska I, Bohdanowicz J (2013) Genotype-dependent efficiency of endosperm development in culture of selected cereals: histological and ultrastructural studies. Protoplasma 250:361–369
Safari H, Balcom BJ, Afrough A (2021) Characterization of pore and grain size distributions in porous geological samples-an image processing workflow. Comput Geosci 156:104895
Sunani SK, Bashyal BM, Kharayat BS, Prakash G, Krishnan SG, Aggarwal R (2020) Identification of rice seed infection routes of Fusarium fujikuroi inciting bakanae disease of rice. J Plant Pathol 102:113–121
Szegedy C, Ioffe S, Vanhoucke V, Alemi A 2016 Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. In: AAAI Conference on Artificial Intelligence.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z 2016 Rethinking the Inception Architecture for Computer Vision, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2818–2826.
Szegedy C, Wei L, Yangqing J, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A 2015 Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1–9.
Tan M, Le QV (2019) Efficientnet: rethinking model scaling for convolutional neural networks. ArXiv abs. 1905:11946
Tan M, Le QV (2021) EfficientnetV2: smaller models and faster training. ArXiv abs. 2104:00298
Too J, Abdullah AR, Mohd Saad N, Tee W (2019) EMG feature selection and classification using a Pbest-guide binary particle swarm optimization. Compt 7:12
Tsutsui K, Terasaki H, Uto K, Maemura T, Hiramatsu S, Hayashi K, Moriguchi K, Morito S (2020) A methodology of steel microstructure recognition using SEM images by machine learning based on textural analysis. Mater Today Commun 25:101514
Zhang Y, Gu J, Tan H, Di M, Zhu L, Weng X (2011) Straw based particleboard bonded with composite adhesives. BioResources 6:464–476
Zheng Z, Fang H, Liu D, Tan Z, Gao X, Hu W, Peng H, Tong L, Hu W, Zhang J (2017) Nonlocal response in infrared detector with semiconducting carbon nanotubes and graphdiyne. Adv Sci (Weinh) 4:1700472
Zhou T, Shi X, YanYan Li C, Chen S, Zhao Y, Zhou W, Zhou K, Zeng X 2020 An effective method of contour extraction for SEM image based on DCNN. In: 2020 International Workshop on Advanced Patterning Solutions (IWAPS). 1–4.
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Anagun, Y., Isik, S., Olgun, M. et al. The classification of wheat species based on deep convolutional neural networks using scanning electron microscope (SEM) imaging. Eur Food Res Technol 249, 1023–1034 (2023). https://doi.org/10.1007/s00217-022-04192-8
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DOI: https://doi.org/10.1007/s00217-022-04192-8