Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops

Aqeel Iftikhar Jajja, Assad Abbas, Hasan Ali Khattak (), Gniewko Niedbała (), Abbas Khalid, Hafiz Tayyab Rauf and Sebastian Kujawa
Additional contact information
Aqeel Iftikhar Jajja: Department of Computer Science, COMSATS University Islamabad, Islamabad 45500, Pakistan
Assad Abbas: Department of Computer Science, COMSATS University Islamabad, Islamabad 45500, Pakistan
Hasan Ali Khattak: School of Electrical Engineering & Computer Science (SEECS), National University of Sciences & Technology (NUST), H12, Islamabad 44000, Pakistan
Gniewko Niedbała: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Abbas Khalid: Department of Computer Science and IT, The University of Lahore, Lahore 54590, Pakistan
Hafiz Tayyab Rauf: Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST18 0YB, UK
Sebastian Kujawa: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland

Agriculture, 2022, vol. 12, issue 10, 1-17

Abstract: Cotton is one of the world’s most economically significant agricultural products; however, it is susceptible to numerous pest and virus attacks during the growing season. Pests (whitefly) can significantly affect a cotton crop, but timely disease detection can help pest control. Deep learning models are best suited for plant disease classification. However, data scarcity remains a critical bottleneck for rapidly growing computer vision applications. Several deep learning models have demonstrated remarkable results in disease classification. However, these models have been trained on small datasets that are not reliable due to model generalization issues. In this study, we first developed a dataset on whitefly attacked leaves containing 5135 images that are divided into two main classes, namely, (i) healthy and (ii) unhealthy. Subsequently, we proposed a Compact Convolutional Transformer (CCT)-based approach to classify the image dataset. Experimental results demonstrate the proposed CCT-based approach’s effectiveness compared to the state-of-the-art approaches. Our proposed model achieved an accuracy of 97.2%, whereas Mobile Net, ResNet152v2, and VGG-16 achieved accuracies of 95%, 92%, and 90%, respectively.

Keywords: computer vision; CCT; cotton pest attack; whitefly attack; deep learning; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/10/1529/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/10/1529/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:10:p:1529-:d:922799

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().