Implementing Insect Classification Based on Convolutional Neural Networks and Tensorflow
DOI:
https://doi.org/10.54097/hrh97f63Keywords:
Deep Learning Techniques, Insect Image Classification, Convolutional Neural Network Architecture, Tensorflow FrameworkAbstract
Insects are one of the most diverse biological groups on Earth, playing a crucial role in human agricultural production. However, there is a relative shortage of professionals capable of classifying insects. With the rapid advancement of computer vision technology, image classification techniques have been employed to classify insects. Traditional insect image classification requires manual extraction of image features, a process that is both time-consuming and labor-intensive, and the accuracy of the identification results is relatively low. To address these issues, this study adopts deep learning technology and uses Google's TensorFlow framework to build a convolutional neural network (CNN) model for insect classification. The article further analyzes the impact of different optimizers and learning rates on the model's classification performance. Experimental results show that using the Adam optimizer with a learning rate of 0.009 yields the highest recognition accuracy for the CNN model, reaching up to 92%.
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[1] Deng C, Han Y, Zhao B. High-Performance Visual Tracking With Extreme Learning Machine Framework[J]. IEEE Transactions on Cybernetics, 2019, 26(3): 1-12.
[2] Han H.G., Qiao J.F., Bo Y.C. Research on RBF Neural Network Structure Design Based on Information Strength[J]. Acta Automatica Sinica, 2012, 38(7): 1083-1090.
[3] Han H.G., Qiao J.F., Bo Y.C. On Structure Design for RBF Neural Network Based on Information Strength[J]. Acta Automatica Sinica, 2012, 38(7): 1083-1090.
[4] Zhao Z, Yang J, Che H, et al. Application of Artificial Bee Colony Algorithm to Select the Optimal Neural Network Architecture for Predicting Rolling Force in Hot Strip Rolling Process[J]. Journal of Chemical and Pharmaceutical Research, 2013, 5(9): 563-570.
[5] Yan W, Tang D, Lin Y. A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and Its Application[J]. IEEE Transactions on Industrial Electronics, 2017, 64(5): 4237-4245.
[6] Yao L, Ge Z. Deep Learning of Semi-Supervised Process Data with Hierarchical Extreme Learning Machine and Soft Sensor Application[J]. IEEE Transactions on Industrial Electronics, 2017, 65(2): 1490-1498.
[7] Vincent P, Larochelle H, Lajoie I, et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion[J]. Journal of Machine Learning Research, 2010, 11(Dec): 3371-3408.
[8] Zhang H, Zhang S, Yin Y. Online Sequential ELM Algorithm with Forgetting Factor for Real Applications[J]. Neurocomputing, 2017, 261: 144-152.
[9] Lekamalage C.K.L., Song K., Huang G., et al. Multi-Layer Multi-Objective Extreme Learning Machine[A]. In: Dong F. (ed.) 2017 IEEE International Conference on Image Processing [C]. Beijing: IEEE, 2017.
[10] Huang G.B., Zhu Q.Y., Siew C.K. Extreme Learning Machine: Theory and Applications[J]. Neurocomputing, 2006, 70(1-3): 489-501.
[11] He Q, Wang H, Jiang G.Q., et al. Research on Wind Turbine Main Bearing Condition Monitoring Based on Correlation PCA and ELM[J]. Acta Metrologica Sinica, 2018, 39(1): 89-93.
[12] He Q, Wang H, Jiang G.Q., et al. Wind Turbine Main Bearing Condition Monitoring Based on Correlation PCA and ELM[J]. Acta Metrologica Sinica, 2018, 39(1): 89-93.
[13] Toh K. Deterministic Neural Classification[J]. Neural Computation, 2008, 20(6): 1565-1595.
[14] Lu C.B., Mei Y. An Imputation Method for Missing Data Based on an Extreme Learning Machine Auto-Encoder[J]. IEEE Access, 2018, 6: 52930-52935.
[15] Gopakumar V, Tiwari S, Rahman I. A Deep Learning Based Data-Driven Soft Sensor for Bioprocesses[J]. Biochemical Engineering Journal, 2018, 136: 28-39.
[16] Su X, Zhang S, Yin Y, et al. Prediction of Hot Metal Silicon Content for Blast Furnace Based on Multi-Layer Online Sequential Extreme Learning Machine[A]. In: Chen X. (ed.) 37th Chinese Control Conference[C]. Wuhan: IEEE, 2018.
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