Academic Journal of Computing & Information Science, 2024, 7(10); doi: 10.25236/AJCIS.2024.071017.
Mingliang Ge1, Wei Wang2, Jun Li1, Junpeng Pei1, Yousong Wang1
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
2PLA Naval Medical Center, Naval Medical University, Shanghai, China
Plants are the most diverse organisms on Earth and play an irreplaceable role in maintaining ecological balance. Traditional plant identification relies on experience-based classification, which can lead to subjectivity and human error, resulting in instances where identification is either impossible or incorrect. Deep learning enables the automatic recognition and classification of plants by training neural network models, with convolutional neural networks (CNNs) being a significant technology in deep learning that demonstrates strong advantages in enhancing model performance and accuracy. This paper presents an overview of the development history of CNN models, reviews recent research utilizing these models for plant classification, and discusses future trends in the application of CNNs for plant classification.
Plant Identification, Deep Learning, Convolutional Neural Networks, Image Classification
Mingliang Ge, Wei Wang, Jun Li, Junpeng Pei, Yousong Wang. Research Progress on Plant Image Classification Method Based on Convolutional Neural Networks. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 10: 125-132. https://doi.org/10.25236/AJCIS.2024.071017.
[1] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-324.
[2] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks [J]. Advances in neural information processing systems, 2012, 25.
[3] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, F, 2015.
[4] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [J]. arXiv preprint arXiv:14091556, 2014.
[5] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, F, 2016.
[6] Howard A G. Mobilenets: Efficient convolutional neural networks for mobile vision applications [J]. arXiv preprint arXiv:170404861, 2017.
[7] Huang G, Liu Z, Van, et al. Densely connected convolutional networks[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, F, 2017.
[8] Tan M. Efficientnet: Rethinking model scaling for convolutional neural networks [J]. arXiv preprint arXiv:190511946, 2019.
[9] Liu J, Yang S, Cheng Y, et al. Plant leaf classification based on deep learning[C]. Proceedings of the 2018 Chinese Automation Congress (CAC), F, 2018. IEEE.
[10] Huang F, Yu L, Shen T, et al. Chinese herbal medicine leaves classification based on improved AlexNet convolutional neural network[C]. Proceedings of the 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), F, 2019. IEEE.
[11] Wagle S A. Comparison of Plant Leaf Classification Using Modified AlexNet and Support Vector Machine [J]. Traitement du Signal, 2021, 38(1).
[12] Yuesheng F, Jian S, Fuxiang X, et al. Circular fruit and vegetable classification based on optimized GoogLeNet [J]. IEEE Access, 2021, 9: 113599-611.
[13] Siddharth T, Kirar B S, Agrawal D K. Plant species classification using transfer learning by pretrained classifier VGG-19 [J]. arXiv preprint arXiv:220903076, 2022.
[14] Campos-Leal J A, Yee-Rendón A, Vega-López I F. Simplifying vgg-16 for plant species identification [J]. IEEE Latin America Transactions, 2022, 20(11): 2330-8.
[15] Vaidehi M V, Vinod M V. ResNet based classification in CNN for ayurvedic plant categorization using deep learning [J]. Design Engineering, 2021: 1507-16.
[16] Beikmohammadi A, Faez K. Leaf classification for plant recognition with deep transfer learning[C]. Proceedings of the 2018 4th Iranian Conference on Signal Processing and Intelligent Systems, F, 2018.
[17] Lasya. S , Jyothsna. S , Pushpa B R .Optimized Plant Species Classification through MobileNet-Enhanced Hybrid Models[C].2024 5th International Conference for Emerging Technology (INCET).[2024-10-30].DOI:10.1109/INCET61516.2024.10593020.
[18] Wu H, Shi Z, Huang H, et al. Automatic Leaf Recognition Based on Attention DenseNet[C]. proceedings of the Cognitive Systems and Signal Processing: 5th International Conference, ICCSIP 2020, Zhuhai, China, December 25–27, 2020, Revised Selected Papers 5, F, 2021. Springer.
[19] Sathiesh Kumar V, Anubha Pearline S. Real-Time Plant Species Recognition Using Non-averaged DenseNet-169 Deep Learning Paradigm[C]. proceedings of the International Conference on Computer Vision and Image Processing, F, 2022. Springer.
[20] Arun Y, Viknesh G. Leaf classification for plant recognition using EfficientNet architecture[C]. proceedings of the 2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC), F, 2022. IEEE.