Abstract
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.
Publisher
National Academy of Sciences of Ukraine (Co. LTD Ukrinformnauka) (Publications)
Reference20 articles.
1. Dreyfus, G. (2005) Neural networks: methodology and applications. Springer-Verlag, Berlin. 498 p.
2. A neural network-based synchronized computed torque controller for three degree-of- freedom planar parallel manipulators with uncertainties compensation;Doan;International Journal of Advanced Robotic Systems 15(2),2018
3. Hardware implementation of radial- basis neural networks with Gaussian activa- tion functions on FPGA;Shymkovych;Neural Comput- ing and Applications 33,2021
4. 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. https://doi.org/10.1007/s00521-019-04299-2
5. A deep learning classifier for sentence classification in bio- medical and computer science abstracts;Goncalves;Neural Computing and Applications 32,2020
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