Deep CNN-Based Planthopper Classification Using a High-Density Image Dataset

Mohd Firdaus Ibrahim, Siti Khairunniza-Bejo (), Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad and Mohammad Aufa Mhd Bookeri
Additional contact information
Mohd Firdaus Ibrahim: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Siti Khairunniza-Bejo: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Marsyita Hanafi: Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Mahirah Jahari: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Fathinul Syahir Ahmad Saad: Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis, Arau 02600, Malaysia
Mohammad Aufa Mhd Bookeri: Engineering Research Centre, Malaysian Agriculture Research and Development Institute, Seberang Perai 13200, Malaysia

Agriculture, 2023, vol. 13, issue 6, 1-17

Abstract: Rice serves as the primary food source for nearly half of the global population, with Asia accounting for approximately 90% of rice production worldwide. However, rice farming faces significant losses due to pest attacks. To prevent pest infestations, it is crucial to apply appropriate pesticides specific to the type of pest in the field. Traditionally, pest identification and counting have been performed manually using sticky light traps, but this process is time-consuming. In this study, a machine vision system was developed using a dataset of 7328 high-density images (1229 pixels per centimetre) of planthoppers collected in the field using sticky light traps. The dataset included four planthopper classes: brown planthopper (BPH), green leafhopper (GLH), white-backed planthopper (WBPH), and zigzag leafhopper (ZIGZAG). Five deep CNN models—ResNet-50, ResNet-101, ResNet-152, VGG-16, and VGG-19—were applied and tuned to classify the planthopper species. The experimental results indicated that the ResNet-50 model performed the best overall, achieving average values of 97.28% for accuracy, 92.05% for precision, 94.47% for recall, and 93.07% for the F1-score. In conclusion, this study successfully classified planthopper classes with excellent performance by utilising deep CNN architectures on a high-density image dataset. This capability has the potential to serve as a tool for classifying and counting planthopper samples collected using light traps.

Keywords: planthoppers; convolutional neural network; machine vision; paddy cultivation (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: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/6/1155/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/6/1155/ (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:13:y:2023:i:6:p:1155-:d:1159509

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 ().