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Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Saud Yonbawi1, Sultan Alahmari2, T. Satyanarayana murthy3, Ravuri Daniel4, E. Laxmi Lydia5, Mohamad Khairi Ishak6, Hend Khalid Alkahtani7,*, Ayman Aljarbouh8, Samih M. Mostafa9

1 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
2 King Abdul Aziz City for Science and Technology, Riyadh, Kingdom of Saudi Arabia
3 Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India
4 Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, India
5 Department of Computer Science and Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam, India
6 School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Nibong Tebal, Penang, Malaysia
7 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia
8 Department of Computer Science, University of Central Asia, Naryn, Kyrgyzstan
9 Faculty of Computers and Information, South Valley University, Qena, Egypt

* Corresponding Author: Hend Khalid Alkahtani. Email:

Computer Systems Science and Engineering 2023, 46(3), 3847-3864.


Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops (MMTL-IPCAC) technique. The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization (CLAHE) approach for image enhancement. The neural architectural search network (NASNet) model is applied for feature extraction, and a modified grey wolf optimization (MGWO) algorithm is employed for the hyperparameter tuning process, showing the novelty of the work. At last, the extreme gradient boosting (XGBoost) model is utilized to carry out the insect classification procedure. The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.


Cite This Article

S. Yonbawi, S. Alahmari, T. S. Murthy, R. Daniel, E. L. Lydia et al., "Modified metaheuristics with transfer learning based insect pest classification for agricultural crops," Computer Systems Science and Engineering, vol. 46, no.3, pp. 3847–3864, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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