PlantDefender: a unified deep learning approach for maize disease and pest detection
- 1Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
- 2Department of Software Engineering, University of Engineering and Technology-Taxila, Taxilia, Pakistan
- 3Faculty of Computing, Riphah International University, Islamabad, Pakistan
Abstract
Introduction: Maize crops face severe threats from various diseases and pests, significantly affecting crop quality and yield. Prompt and accurate diagnosis is essential to minimize agricultural losses. Existing models have demonstrated limitations in handling complex backgrounds, inter- and intra-class variations, and inefficient feature extraction, which complicate the recognition of maize diseases and pests.
Methods: This study proposes a unified deep learning model, termed PlantDefender, for the simultaneous recognition of maize diseases and pests. PlantDefender employs an improved Faster R-CNN architecture with a refined InceptionResNet-V2 as its backbone. The conventional InceptionResNet-V2 is modified by replacing the ReLU activation function with Swish to enhance feature extraction. The extracted features are subsequently processed through the Region Proposal Network (RPN), ROI Align, regression, and classification modules for accurate detection and recognition. The model is evaluated using the IP102 dataset for maize pest recognition and the CD&S dataset for maize leaf disease recognition.
Results: PlantDefender achieves an accuracy of 78.67% on the IP102 dataset and 98.44% on the CD&S dataset. The results demonstrate effective localization and classification performance across both maize pest and disease recognition tasks.
Discussion: The experimental results indicate that the proposed PlantDefender framework can effectively address the challenges associated with the recognition of maize diseases and pests. By integrating the refined InceptionResNet-V2 backbone with Faster R-CNN, the proposed unified model provides a promising approach for automated and accurate maize crop protection.
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BibTeX
@article{albahli2026plantdefender,
title = {{PlantDefender: a unified deep learning approach for maize disease and pest detection}},
author = {Saleh Albahli and Marriam Nawaz and Tahira Nazir},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1960161},
url = {https://doi.org/10.3389/fpls.2026.1960161},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - PlantDefender: a unified deep learning approach for maize disease and pest detection
AU - Saleh Albahli
AU - Marriam Nawaz
AU - Tahira Nazir
PY - 2026
DA - 2026/10/12
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1960161
UR - https://doi.org/10.3389/fpls.2026.1960161
AB - Introduction: Maize crops face severe threats from various diseases and pests, significantly affecting crop quality and yield. Prompt and accurate diagnosis is essential to minimize agricultural losses. Existing models have demonstrated limitations in handling complex backgrounds, inter- and intra-class variations, and inefficient feature extraction, which complicate the recognition of maize diseases and pests. Methods: This study proposes a unified deep learning model, termed PlantDefender, for the simultaneous recognition of maize diseases and pests. PlantDefender employs an improved Faster R-CNN architecture with a refined InceptionResNet-V2 as its backbone. The conventional InceptionResNet-V2 is modified by replacing the ReLU activation function with Swish to enhance feature extraction. The extracted features are subsequently processed through the Region Proposal Network (RPN), ROI Align, regression, and classification modules for accurate detection and recognition. The model is evaluated using the IP102 dataset for maize pest recognition and the CD&S dataset for maize leaf disease recognition. Results: PlantDefender achieves an accuracy of 78.67% on the IP102 dataset and 98.44% on the CD&S dataset. The results demonstrate effective localization and classification performance across both maize pest and disease recognition tasks. Discussion: The experimental results indicate that the proposed PlantDefender framework can effectively address the challenges associated with the recognition of maize diseases and pests. By integrating the refined InceptionResNet-V2 backbone with Faster R-CNN, the proposed unified model provides a promising approach for automated and accurate maize crop protection.
SN - 1664-462X
ER -