Pest Detection System for Rice Crop Using Pest-Net Model

DOI

10.2991/978-94-6463-196-8_45How to use a DOI?

Keywords

CNN; Pest-Net; AlexNet; Transfer learning; Pest; Rice crop

Abstract

This paper presents a model for automatic pests identification of rice crops using the CNN approach called the Pest-Net model. This model aims to classify six different major pests affecting rice crops. To establish the novelty and credibility of our work, we have also used the transfer learning approach of CNN i.e. AlexNet model for the classification of the same dataset. It is observed from the experimental results and performance measures that the Pest-Net model performed well and gave good recognition accuracy of 88.6% as compared to the AlexNet model.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sukanya S. Gaikwad
AU  - Mallikarjun Hangarge
PY  - 2023
DA  - 2023/08/10
TI  - Pest Detection System for Rice Crop Using Pest-Net Model
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 590
EP  - 601
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_45
DO  - 10.2991/978-94-6463-196-8_45
ID  - Gaikwad2023
ER  -