Classification of wild mushrooms based on improved ShuffleNetV2

Xingmei Xu, Dawei Yang, Qiqi Wei, Jinying Li, Jian Zhang

Abstract


This study introduced an improved CHE_ShuffleNetV2 model based on ShuffleNetV2 to address the classification challenge of wild mushrooms in a complex environment. The model incorporated a Cross Stage Partial (CSP) structure to simplify its complexity. Furthermore, it adopted Hybrid Dilated Convolution (HDC) to replace conventional convolution, enhancing the model’s recognition accuracy by expanding its receptive field. In addition, the ECA module was integrated to enhance the focus of the model on crucial feature information. The Hardswish activation function was employed instead of the ReLU activation function to reduce the number of parameters. The experimental results demonstrated that the enhanced model achieved improved accuracy, precision, recall, and F1-Score of 95.02%, 95.19%, 94.56%, and 94.00%, respectively, representing improvements of 2.81%, 3.82%, 3.08%, and 3.65%, correspondingly, over the original model. The enhanced model also reduced the parameters and FLOPs to 0.933 M and 104.08 M, respectively, representing reductions of 26.13% and 30.42% over the original model. Compared with commonly used lightweight models such as EfficientNet, DenseNet, and MobileNetV2, the CHE_ShuffleNetV2 model showed superior performance in solving the wild mushroom classification problem in complex environments, exhibiting its suitability for deployment on resource-constrained devices including mobile terminals.
Keywords: classification of wild mushrooms, ShuffleNetV2, deep learning, lightweight
DOI: 10.25165/j.ijabe.20251801.9179

Citation: Xu X M, Yang D W, Wei Q Q, Li J Y, Zhang J. Classification of wild mushrooms based on improved ShuffleNetV2.
Int J Agric & Biol Eng, 2025; 18(1): 208–218.

Keywords


classification of wild mushrooms, ShuffleNetV2, deep learning, lightweight

Full Text:

PDF

References


Li C T, Xu S. Edible mushroom industry in China: Current state and perspectives. Applied microbiology and biotechnology, 2022; 106(11): 3949–3955.

Wu F, Zhou L W, Yang Z L, Bau T, Li T H, Dai Y C. Resource diversity of Chinese macrofungi: edible, medicinal and poisonous species. Fungal diversity, 2019; 98: 1–76.

Zhang X R, Zhao H B, Zhang Y T, Wu K Y. Current situation, challenges, and future development directions of edible fungi industry in China. Horticulture & Seed, 2023; 43(5): 49–54, 97. (in Chinese)

Hamza A, Mylarapu A, Krishna K V, Kumar D S. An insight into the nutritional and medicinal value of edible mushrooms: A natural treasury for human health. Journal of Biotechnology, 2024; 381: 86–99.

Hou X L, Luo C Q, Chen S S, Zhang X W, Jiang J M, Yang Z F, et al. Progress in research on diseases of edible fungi and their detection methods: A review. Crop Protection, 2023; 174: 106420.

Mendoza-Bernal J, González-Vidal A, Skarmeta A F. A convolutional neural network approach for image-based anomaly detection in smart agriculture. Expert Systems with Applications, 2024; 247: 123210.

Qin C Y, Yang Y S, Gu F W, Chen P Y, Qin W C. Application and development of computer vision technology in modern agriculture. Journal of Chinese Agricultural Mechanization, 2023; 44(12): 119–128. (in Chinese)

Kaldarova M, Аkanova A, Nazyrova A, Mukanova A, Tynykulova A. Identification of weeds in fields based on computer vision technology. Eastern-European Journal of Enterprise Technologies, 2023; 124(2): 44–52.

Luan X D. Research on the application of artificial intelligence and computer technology in agricultural modernization. Advances in Computer, Signals and Systems, 2022; 6(2): 6–10.

Tian L, Wang C, Li H L, Sun H T. Recognition method of corn and rice crop growth state based on computer image processing technology. Journal of Food Quality, 2022; 2022(1): 2844757.

Morales A, Villalobos F J. Using machine learning for crop yield prediction in the past or the future. Frontiers in Plant Science, 2023; 14: 1128388.

Thakur P S, Khanna P, Sheorey T, Ojha A. Trends in vision-based machine learning techniques for plant disease identification: A systematic review. Expert Systems with Applications, 2022; 208: 118117.

Yin H, Yi W L, Hu D M. Computer vision and machine learning applied in the mushroom industry: A critical review. Computers and Electronics in Agriculture, 2022; 198: 107015.

Wang Z J, Zhang L L, Li S F, Zhang, Q Y, Fu Q Q, Liu S J. Research on classification and recognition of edible fungi based on machine learning algorithm. Journal of Fuyang Normal University (Natural Science), 2021; 38(4): 42–48. (in Chinese)

Lin N, Wang N, Li Z S, Chen X X, Zhang B, Tian X. Research on feature extraction and recognition of wild edible fungi based on machine vision. Journal of Chinese Agricultural Mechanization, 2020; 41(5): 111–119. (in Chinese)

Pradipkumar V H, Alagu Raja R A. Automatic identification of tree species from UAV images using machine learning approaches. Journal of the Indian Society of Remote Sensing, 2022; 50: 2447–2464.

Chen D G, Guli A, Yin P B, Lu Y N, Li S P. Identification of wild bacteria species based on improved Xception transfer learning. Laser & Optoelectronics Progress, 2021; 58(8): 245–254.

Espinosa-Herrera J M, Macedo-Cruz A, Fernández-Reynoso D S, Flores-Magdaleno H, Fernández-Ordoñez Y M, Soria-Ruíz J. Monitoring and identification of agricultural crops through multitemporal analysis of optical images and machine learning algorithms. Sensors, 2022; 22(16): 6106.

Ma X, Li Y L, Wan L P C, Xu Z X, Song J N, Huang J Q. Classification of seed corn ears based on custom lightweight convolutional neural network and improved training strategies. Engineering Applications of Artificial Intelligence, 2023; 120: 105936.

Xu P, Tan Q, Zhang Y P, Zha X T, Yang S M, Yang R B. Research on maize seed classification and recognition based on machine vision and deep learning. Agriculture, 2022; 12(2): 232.

Ketwongsa W, Boonlue S, Kokaew U. A new deep learning model for the classification of poisonous and edible mushrooms based on improved AlexNet convolutional neural network. Applied Sciences, 2022; 12(7): 3409.

Peng Y J, Xu Y, Shi J, Jiang S Y. Wild mushroom classification based on improved mobilevit deep learning. Applied Sciences, 2023; 13(8): 4680.

Xiao J W, Zhao C B, Li X J, Liu Z Y, Pang B, Yang Y H, et al. Research on mushroom image classification based on deep learning. Software Engineering, 2020; 23(7): 21–26. (in Chinese)

Wan G Q, Yao L. LMFRNet: A lightweight convolutional neural network model for image analysis. Electronics, 2023; 13(1): 129.

Zhu F W, Sun Y, Zhang Y Q, Zhang W J, Qi J. An improved MobileNetV3 mushroom quality classification model using images with complex backgrounds. Agronomy, 2023; 13(12): 2924.

Azadnia R, Noei-Khodabadi F, Moloudzadeh A, Jahanbakhshi A, Omid M. Medicinal and poisonous plants classification from visual characteristics of leaves using computer vision and deep neural networks. Ecological Informatics, 2024; 82: 102683.

Liu H, Hu Q R, Huang D Y. Research on the wild mushroom recognition method based on transformer and the multi-scale feature fusion compact bilinear neural network. Agriculture, 2024; 14(9): 1618.

Zhang F Y, Yin J L, Wu N, Hu X Y, Sun S K, Wang Y B. A dual-path model merging CNN and RNN with attention mechanism for crop classification. European Journal of Agronomy, 2024; 159: 127273.

Iqbal M J, Aasem M, Ahmad I, Alassafi M O, Bakhsh S T, Noreen N, et al. On application of lightweight models for rice variety classification and their potential in edge computing. Foods, 2023; 12(21): 3993.

Liang J, Jiang W P. A ResNet50-DPA model for tomato leaf disease identification. Frontiers in Plant Science, 2023; 14: 1258658.

Zhang X Y, Zhou X Y, Lin M X, Sun J. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA: IEEE, 2018; pp.6848–6856.

Sun W H, Li Y, Feng H L, Weng X, Ruan Y P, Fang K, et al. Lightweight and accurate aphid detection model based on an improved deep-learning network. Ecological Informatics, 2024; 83: 102794.

Jin X, Jiang J X, Li Y, Wang Z. Improved ShuffleNetV2 for action recognition in BPPV treatment. Biomedical Signal Processing and Control, 2024; 88: 105601.

Wang C Y, Liao H Y M, Wu Y H, Chen P Y, Hsieh J W, Yeh I H. CSPNet: A new backbone that can enhance learning capability of CNN. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA: IEEE, 2020; pp.390–391.

Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv: 1511.07122, 2015; In Press. doi: 10.48550/arXiv.1511.07122.

Zhang X, Gao H Y, Wan L. Classification of fine-grained crop disease by dilated convolution and improved channel attention module. Agriculture, 2022; 12(10): 1727.

Wang P Q, Chen P F, Yuan Y, Liu D, Huang Z H, Hou X D, et al. Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA: IEEE, 2018; pp.1451–1460. doi: 10.1109/WACV.2018.00163.

Wang Q L, Wu B G, Zhu P F, Li P H, Zuo W M, Hu Q H. ECA-Net: Efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA: IEEE, 2020; pp.11534–11542. doi: 10.1109/CVPR42600.2020.01155.

Yang L, Xu S, Yu X Y, Long H B, Zhang H H, Zhu Y W. A new model based on improved VGG16 for corn weed identification. Frontiers in Plant Science, 2023; 14: 1205151.

Woo S, Park J, Lee J Y, Kweon I S. CBAM: Convolutional block attention module. In: Computer Vision - ECCV 2018, 2018; pp.3–19. doi: 10.1007/978-3-030-01234-2_1.

Hu J, Shen L, Albanie S, Sun G, Wu E H. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020; 42(8): 2011–2023.

Tan M X, Le Q V. Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv: 1905.11946, 2019; In press. doi: 10.48550/arXiv.1905.11946.

Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017; pp.2261–2269. doi: 10.1109/CVPR.2017.243.

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L C. Mobilenetv2: Inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA: IEEE, 2018; pp.4510–4520. doi: 10.1109/CVPR.2018.00474.

Jawadul K M, Faruq G M O, Md N, Mominul A, Julfikar H, Marcin K. Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM. Scientific Reports, 2024; 14: 16022.

Zhou H M, Chen J G, Niu X L, Dai Z G, Qin L, Ma L S, et al. Identification of leaf diseases in field crops based on improved ShuffleNetV2. Frontiers in Plant Science, 2024; 15: 1342123.

Li X C, Li X H, Zhang S M, Zhang G Y, Zhang M Q, Shang H Y. SLViT: Shuffle-convolution-based lightweight Vision transformer for effective diagnosis of sugarcane leaf diseases. Journal of King Saud University-Computer and Information Sciences, 2023; 35(6): 101401.

Ni H J, Shi Z W, Karungaru S, Lv S, Li X Y, Wang X X, et al. Classification of typical pests and diseases of rice based on the ECA attention mechanism. Agriculture, 2023; 13(5): 1066.

Li N, Xue J M, Wu S B, Qin K D, Liu N. Research on coal and gangue recognition model based on CAM-hardswish with efficientNetV2. Applied Sciences, 2023; 13(15): 8887.




Copyright (c) 2025 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office