ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
This article is part of the Research TopicSmart Plant Pest and Disease Detection Machinery and Technology: Innovations for Sustainable AgricultureView all 18 articles
PalmNeXt: A ConvNeXt-Based Deep Learning Model for Pest Detection in Date Palm Leaves
Provisionally accepted
- 1University of Jeddah, Jeddah, Saudi Arabia
- 2Sir Syed CASE Institute of Technology, Islamabad, Pakistan
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Automated pest detection is essential for timely and accurate crop monitoring, yet many existing approaches rely on manual inspection or computationally heavy models that struggle with small and variable datasets. To address these challenges, we introduce an enhanced ConvNeXt-Tiny–based framework that incorporates a tailored preprocessing pipeline to improve feature quality and overall performance. The model is evaluated on an RGB image dataset of 3,000 date palm leaf samples across four classes (Bug, Dubas, Healthy, Honey). Its performance is compared against two custom baselines, CNN-Attention and ResNet13-Attention, as well as state-of-the-art models including ViT, ECA-Net, and the standard ConvNeXt-Tiny. Experimental results show that our preprocessing-augmented ConvNeXt-Tiny achieves the highest accuracy, precision, recall, and F1-score, outperforming both custom and state-of-the-art baselines. These findings demonstrate the effectiveness of the proposed lightweight solution for scalable and high-accuracy pest detection in precision agriculture.
Keywords: automated pest detection, ConvNeXt-Tiny, Data preprocesng, Date palm leaves, Transfer Learning
Received: 03 Nov 2025; Accepted: 16 Dec 2025.
Copyright: © 2025 Ashraf, Aslam, Saeed and Hussain. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Muhammad Zeeshan Aslam
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