Scopus Indexed Publications

Paper Details


Title
A Crop Pest Classification Model Using Deep Learning Techniques
Author
Md. Abdul Malek, Md Zahid Hasan, Sanjida Sultana Reya, Shakhawat Hossain,
Email
Abstract
This paper provides a pest identification system to classify crops' beneficial and harmful pests. For that purpose, the paper first provides a detailed description of the available pests-identification techniques along with their pros and cons. Based on the investigation, a novel classification technique is proposed in this paper. The proposed pests-identification and classification model has been developed using the Convolutional Neural Network (CNN). The model has been trained with a dataset of 9,500 images of 20 different pests. The system has been tested with a huge amount of data and validated across other traditional classification models. The classification accuracy of the proposed system is measured by 90% that is far more superior to other conventional methods.
Keywords
pest-identification , deep learning , CNN , crops , transfer learning , beneficial pest , harmful pest
Journal or Conference Name
2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)
Publication Year
2021
Indexing
scopus