https://doi.org/10.4081/jae.2024.1593

Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network

Vol. 55 No. 3 (2024)
Published: 17 July 2024
Attention mechanism, lychee classification, residual network, transfer learning
Abstract Views: 305
PDF: 111
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Authors

Yiming Xiao
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Jianhua Wang
jhw655@scau.edu.cn
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou; Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou, China.
Hongyi Xiong
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Fangjun Xiao
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Renhuan Huang
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Licong Hong
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Bofei Wu
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Jinfeng Zhou
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Yongbin Long
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou; Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou, China.
Yubin Lan
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou; Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou, China.

Lychee, a key economic crop in southern China, has numerous similar-looking varieties. Classifying these can aid farmers in understanding each variety's growth and market demand, enhancing agricultural efficiency. However, existing classification techniques are subjective, complex, and costly. This paper proposes a lychee classification method using an improved ResNet-34 residual network for six common varieties. We enhance the CBAM attention mechanism by replacing the large receptive field in the SAM module with a smaller one. Attention mechanisms are added at key network stages, focusing on crucial image information. Transfer learning is employed to apply ImageNet-trained model weights to this task. Test set evaluations demonstrate that our improved ResNet-34 network surpasses the original, achieving a recognition accuracy of 95.8442%, a 5.58 percentage point improvement.

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Akkem, Y., Biswas, S.K., Varanasi, A., 2023. Smart farming using artificial intelligence: a review. Eng. Appl. Artif. Intell. 120, 105899. DOI: https://doi.org/10.1016/j.engappai.2023.105899
Alfonso, G., Delfina, M., Rocco, Z., Carmine, C., Nicola, L., 2023. Touchscreen gestures as images. A transfer learning approach for soft biometric traits recognition. Expert Syst. Appl. 219. DOI: https://doi.org/10.1016/j.eswa.2023.119614
Amin, S.M., Adam, K., Marek, K., Józef, K., 2023. Compatible-domain transfer learning for breast cancer classification with limited annotated data. Comput. Biol. Med. 154. DOI: https://doi.org/10.1016/j.compbiomed.2023.106575
Aradhya, M.K., Zee, F.T., Manshardt, R.M., 1995. Isozyme variation in lychee (litchi chinensis sonn.). Sci. Hortic. 63(1-2), 21-35. DOI: https://doi.org/10.1016/0304-4238(95)00788-U
B. Zhou., A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, 2016. Learning deep features for discriminative localization. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921-2929. DOI: https://doi.org/10.1109/CVPR.2016.319
Chang, C.C., 1961. The lychee growing in taiwan. J. Agric. Assoc. China 33, 51-63.
Chang, J., Chen, P., Chen, I., 2017. Litchi breeding and plant management in taiwan. The Lychee Biotechnology, 31-58. DOI: https://doi.org/10.1007/978-981-10-3644-6_2
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., Sutskever, I., 2021. MPViT: Multi-Scale Pyramid Vision Transformer for Dense Prediction Tasks. arXiv preprint arXiv:2112.11150.
Hong-hai, Y., Xiao-peng, Y., Shao-kun, L., Ping, L., Xin-hong, H., 2022. Radar emitter multi-label recognition based on residual network. Defence Technology 18(3), 410-417. DOI: https://doi.org/10.1016/j.dt.2021.02.005
Jiang, H., Diao, Z., Shi, T., Zhou, Y., Wang, F., Hu, W., Zhu, X., Luo, S., Tong, G., Yao, Y., 2023. A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. Comput. Biol. Med. 157, 106726. DOI: https://doi.org/10.1016/j.compbiomed.2023.106726
Jiang, N., Zhu, H., Liu, W., Fan, C., Jin, F., Xiang, X., 2021. Metabolite differences of polyphenols in different litchi cultivars (litchi chinensis sonn.) Based on extensive targeted metabonomics. Molecules 26(4), 1181. DOI: https://doi.org/10.3390/molecules26041181
Khurshid, S., Ahmad, I., Anjum, M.A., 2004. Genetic diversity in different morphological characteristics of litchi (litchi chinensis sonn.). Int J Agri Biol 6, 1062-1065.
Lee, Y., Kim, J., Willette, J., Hwang, S.J., 2022. MPViT: Multi-Path Vision Transformer for Dense Prediction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7287-7296. DOI: https://doi.org/10.1109/CVPR52688.2022.00714
Lin, K., Zhao, Y., Wang, L., Shi, W., Cui, F., Zhou, T., 2023. Mswnet: a visual deep machine learning method adopting transfer learning based upon resnet 50 for municipal solid waste sorting. Front. Env. Sci. Eng. 17(776). DOI: https://doi.org/10.1007/s11783-023-1677-1
Li, X., Huang, H., Zhao, H., Wang, Y., Hu, M., 2020. Learning a convolutional neural network for propagation-based stereo image segmentation. The Visual Computer 36, 39-52. DOI: https://doi.org/10.1007/s00371-018-1582-y
Liu, Y., Wu, Z., 2018. An improved threshold multi-level image recovery scheme. Journal of information security and applications 40, 166-172. DOI: https://doi.org/10.1016/j.jisa.2018.03.009
Liu, D., Wang, L., Sun, D., Zeng, X., Qu, J., Ma, J., 2014. Lychee variety discrimination by hyperspectral imaging coupled with multivariate classification. Food Anal. Meth. 7(9), 1848-1857. DOI: https://doi.org/10.1007/s12161-014-9826-6
Liu, W., Xiao, Z., Bao, X., Yang, X., Fang, J., Xiang, X., 2015. Identifying litchi (litchi chinensis sonn.) Cultivars and their genetic relationships using single nucleotide polymorphism (snp) markers. Plos One 10(e01353908). DOI: https://doi.org/10.1371/journal.pone.0135390
Madhou, M., Normand, F., Bahorun, T., Hormaza, J.I., 2013. Fingerprinting and analysis of genetic diversity of litchi (litchi chinensis sonn.) Accessions from different germplasm collections using microsatellite markers. Tree Genet. Genomes 9(2), 387-396. DOI: https://doi.org/10.1007/s11295-012-0560-1
Menzel, C.M., Huang XuMing, H.X., Liu ChengMing, L.C., 2005. Cultivars and plant improvement. Litchi and longan: botany, production and uses. CABI Publishing Wallingford UK, pp. 59-86. DOI: https://doi.org/10.1079/9780851996967.0059
Mitra, S.K., Pathak, P.K., 2008. Litchi production in the asia-pacific region. III International Symposium on Longan, Lychee, and other Fruit Trees in Sapindaceae Family 863, pp. 29-36. DOI: https://doi.org/10.17660/ActaHortic.2010.863.1
Osako, Y., Yamane, H., Lin, S., Chen, P., Tao, R., 2020. Cultivar discrimination of litchi fruit images using deep learning. Sci. Hortic. 269(109360). DOI: https://doi.org/10.1016/j.scienta.2020.109360
S. J. Pan, Q. Yang, 2010. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345-1359. DOI: https://doi.org/10.1109/TKDE.2009.191
Sennan, S., Pandey, D., Alotaibi, Y., Alghamdi, S., 2022. A novel convolutional neural networks based spinach classification and recognition system. Computers, Materials & Continua 73(1). DOI: https://doi.org/10.32604/cmc.2022.028334
Shaikh, T.A., Rasool, T., Lone, F.R., 2022. Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Comput. Electron. Agric. 198, 107119. DOI: https://doi.org/10.1016/j.compag.2022.107119
Stephen, A., Punitha, A., Chandrasekar, A., 2023. Designing self attention-based resnet architecture for rice leaf disease classification. Neural Computing and Applications 35(9), 6737-6751. DOI: https://doi.org/10.1007/s00521-022-07793-2
Taghizadeh, A.A.A., Hossein, M., 2023. A novel application of deep transfer learning with audio pre-trained models in pump audio fault detection. Comput. Ind. 147. DOI: https://doi.org/10.1016/j.compind.2023.103872
WOO, S., PARK, J., LEE J-Y., Kweon, S., 2018. CBAM: Convolutional Block Attention Module. The European Conference on Computer Vision. 3-19. DOI: https://doi.org/10.1007/978-3-030-01234-2_1
Wang, P., Luo, F., Wang, L., Li, C., Niu, Q., Li, H., 2022. S-resnet: an improved resnet neural model capable of the identification of small insects. Front. Plant Sci. 13, 5241. DOI: https://doi.org/10.3389/fpls.2022.1066115
Wang, T., Zhao, L., Huang, P., Zhang, X., Xu, J., 2021. Haze concentration adaptive network for image dehazing. Neurocomputing 439, 75-85. DOI: https://doi.org/10.1016/j.neucom.2021.01.042
Wu, D., Ying, Y., Zhou, M., Pan, J., Cui, D., 2023. Improved resnet-50 deep learning algorithm for identifying chicken gender. Comput. Electron. Agric. 205, 107622. DOI: https://doi.org/10.1016/j.compag.2023.107622
Wu, S.X., 1998. Encyclopedia of china fruits: litchi. China Forestry Press, Beijing.
Xuanjie, Q., Fang, Y., Haihong, L., 2023. A difference attention resnet-lstm network for epileptic seizure detection using eeg signal. Biomed. Signal Process. Control 83. DOI: https://doi.org/10.1016/j.bspc.2023.104652
Xuanyu, W., Yixiong, F., Shanhe, L., Hao, Z., Bingtao, H., Zhaoxi, H., Jianrong, T., 2023. Improving neucube spiking neural network for eeg-based pattern recognition using transfer learning. Neurocomputing 529. DOI: https://doi.org/10.1016/j.neucom.2023.01.087
Yao, P., Gao, Y., Simal-Gandara, J., Farag, M.A., Chen, W., Yao, D., Delmas, D., Chen, Z., Liu, K., Hu, H., 2021. Litchi (litchi chinensis sonn.): A comprehensive review of phytochemistry, medicinal properties, and product development. Food Funct. 12(20), 9527-9548. DOI: https://doi.org/10.1039/D1FO01148K
Yu, H., Liu, J., Chen, C., Heidari, A.A., Zhang, Q., Chen, H., 2022. Optimized deep residual network system for diagnosing tomato pests. Comput. Electron. Agric. 195, 106805. DOI: https://doi.org/10.1016/j.compag.2022.106805
Yu, H., Sun, H., Tao, J., Qin, C., Xiao, D., Jin, Y., Liu, C., 2023. A multi-stage data augmentation and ad-resnet-based method for epb utilization factor prediction. Autom. Constr. 147(104734). DOI: https://doi.org/10.1016/j.autcon.2022.104734
Zhang, R., Zeng, Q., Deng, Y., Zhang, M., Wei, Z., Zhang, Y., Tang, X., 2013. Phenolic profiles and antioxidant activity of litchi pulp of different cultivars cultivated in southern china. Food Chem. 136(3-4), 1169-1176. DOI: https://doi.org/10.1016/j.foodchem.2012.09.085
patch matching for image editing applications. Neurocomputing 305, 39-50.
Zhao, Y., Wang, X., Che, T., Bao, G., Li, S., 2023. Multi-task deep learning for medical image computing and analysis: a review. Comput. Biol. Med. 153, 106496. DOI: https://doi.org/10.1016/j.compbiomed.2022.106496
Zichuan, N., Biao, L., Ying, Y., 2023. Deep domain adaptation network for transfer learning of state of charge estimation among batteries. J. Energy Storage 61. DOI: https://doi.org/10.1016/j.est.2023.106812

How to Cite

Xiao, Y. (2024) “Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network”, Journal of Agricultural Engineering, 55(3). doi: 10.4081/jae.2024.1593.
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