Convolutional neural network model and software for classification of typical pests

Y.S. Bezliudnyi, V.M. Shymkovysh, A.Yu. Doroshenko


A model of a convolutional neural network, a dataset for neural network training, and a software tool for the classification of typical insect pests have been developed, which allows recognizing the class of insect pests from an image. The structure of the neural network model was optimized to improve the classification results. In addition, the user interface, authentication, and authorization, data personalization, the presence of user roles and the appropriate distribution of functionality by role, the ability to view statistics on classified insects in a certain period of time were developed. Functional testing of the developed software application on a heterogeneous set of images of insects of 20 different classes was performed.

Prombles in programming 2021; 4: 95-102


image classification; convolutional neural networks; modular architecture; Python; Keras

Full Text:



Dreyfus, G. (2005) Neural networks: methodology and applications. Springer-Verlag, Berlin. 498 p.

Doan, Q. V., Le, T. D., Le, Q. D., & Kang, H.J. (2018) A neural network-based synchronized computed torque controller for three degree-of- freedom planar parallel manipulators with uncertainties compensation. International Journal of Advanced Robotic Systems. 15(2). pp. 1-13.

Shymkovych, V., Telenyk, S., Kravets, P. (2021) Hardware implementation of radial- basis neural networks with Gaussian activa- tion functions on FPGA. Neural Comput- ing and Applications. 33. pp. 9467-9479.

Melchert, F., Bani, G., Seiffert, U., Biehl, M. (2020) Adaptive basis functions for prototype-based classification of functional data. Neural Computing and Applications. 32. pp. 18213-18223.

Goncalves, S., Cortez, P., Moro, S. (2020) A deep learning classifier for sentence classification in bio- medical and computer science abstracts. Neural Computing and Applications. 32. pp. 6793-6807.

Kravets, P., Shymkovych, V. (2020) Hardware Implementation Neural Network Controller on FPGA for Stability Ball on the Platform. In: Hu Z., Petoukhov S., Dychka I., He M. (eds) Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing. 938. pp. 247-256.

P., Kravets, V., Nevolko, V., Shymkovych, L., Shy- mkovych (2020) Synthesis of High-Speed Neuro- Fuzzy-ControllersBased on FPGA. 2020 IEEE 2nd International Conference onAdvancedTrends in Information Theory (ATIT). pp. 291-295.

Passalis, N., Tefas, A. (2020) Continuous drone control using deepreinforcement learning for frontal view person shooting. Neural Computing and Applications. 32. pp. 4227-4238.

Zhao, Z., Zheng, P., Xu, S., Wu, X. (2019) Object detection with deep learning: a review. IEEE Transactions on Neural Networks and Learning Systems. 30. pp. 3212-3232.

Kravets, P.I., Shimkovich, V.N., Ferens, D.A. (2015) Method and algorithms of implementation on PLIS the activation function for artificial neuron chains. Elektronnoe Modelirovanie. 37(4). pp. 63-74. (in Russian)

Hong, Q., Li, Y., Wang, X. (2020) Memristive continuous Hopfield neural network circuit for image restoration. Neural Computing and Applications. 32. pp. 8175-8185.

V., Shymkovych, V., Samotyy, S., Telenyk, P., Kravets, T., Posvistak (2018) A real time control system for balancing a ball on a platform with FPGA parallel implementation. Technical Transactions. Poland. 5. pp. 109-117.

Shao, L., Zhu, F., Li, X. (2015) Transfer learning for visual categorization: a survey. IEEE Transactions on Neural Networks and Learning Systems. 26. pp. 1019-1034.

P.I., Kravets, T.I., Lukina, V.M., Shymkovych, I.I., Tkach (2012) Development and research the technology of evaluation neural network models MIMO-objects of control. Visnyk NTUU "KPI" Informatics operation and computer systems. 57. pp. 144-149. (in Ukrainian)

Y., LeCun, B., Boser, J.S., Denker, D., Henderson, R.E., Howard, W., Hubbard, L.D., Jackel (1989) Backpropagation applied to handwritten zip code recognition. Neural computation. 1. pp. 541-551.

Asifullah Khan, Anabia Sohail, Umme Zahoora, Aqsa Saeed Qureshi (2020) A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review. 53. pp. 5455-5516.

Kingma, D.P., Ba, J. (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

H., Yessou, G., Sumbul, B., Demir (2020) A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification. IGARSS2020-2020IEEEInternationalGeoscience and Remote Sensing Symposium. pp. 1349-1352. 2020.9323583

S., Ioffe, C., Szegedy (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, PMLR. 37. pp. 448-456.

T.N., Mundhenk, B.Y., Chen, G., Friedland (2020) Efficient Saliency Maps for Explainable AI. European Conference on Computer Vision (ECCV).



  • There are currently no refbacks.