Leaf-StarNet A Lightweight Deep Neural Network for Efficient Plant Leaf Classification

Leaf-StarNet A Lightweight Deep Neural Network for Efficient Plant Leaf Classification | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL The Journal of Engineering This is a preprint and has not been peer reviewed. Data may be preliminary. 20 October 2025 V1 Latest version Share on Leaf-StarNet A Lightweight Deep Neural Network for Efficient Plant Leaf Classification Authors : Mingliang Ge 0009-0009-3108-1644 , Wei Wang [email protected] , and Jun Li Authors Info & Affiliations https://doi.org/10.22541/au.176094199.98409824/v1 Published The Journal of Engineering Version of record Peer review timeline 224 views 140 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Plant leaf classification is a core task in biodiversity monitoring, ecological studies, and smart agriculture. In prac-tice, it often still depends on field identification by botanists, which can be slow, laborious, and limited by the availabil-ity of skilled personnel. To address these issues, we present Leaf-StarNet, a compact convolutional neural network tai-lored for accurate and efficient leaf recognition. The network combines three complementary components: an LS-Conv module for localized and efficient feature extraction, an scSE attention block to refine both spatial and channel infor-mation, and a Frequency-Based Enhancement (FBE) unit to better capture subtle frequency-domain patterns. We evalu-ate the model on the LeafSnap dataset and achieve an accuracy of 96.83%, surpassing lightweight baselines such as MobileNetV2, GhostNetV3, and FasterNet-T1. Ablation experiments confirm the contribution of each module, while Grad-CAM visualizations illustrate how the network’s focus shifts from fine textures in early layers to higher-level se-mantic regions in deeper layers. With its small footprint and low computational demand, Leaf-StarNet is well suited for deployment on mobile or embedded platforms, providing a practical and reliable solution for automated plant leaf clas-sification. Supplementary Material File (figure.docx) Download 4.85 MB File (leaf-starnet a lightweight deep neural network for efficient plant leaf classification.docx) Download 6.04 MB File (table.docx) Download 20.62 KB Information & Authors Information Version history V1 Version 1 20 October 2025 Peer review timeline Published The Journal of Engineering Version of Record 3 Apr 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection The Journal of Engineering Keywords image classification image processing Authors Affiliations Mingliang Ge 0009-0009-3108-1644 University of Shanghai for Science and Technology View all articles by this author Wei Wang [email protected] Naval Center for Distinctive Medicine View all articles by this author Jun Li University of Shanghai for Science and Technology School of Health Science and Engineering View all articles by this author Metrics & Citations Metrics Article Usage 224 views 140 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mingliang Ge, Wei Wang, Jun Li. Leaf-StarNet A Lightweight Deep Neural Network for Efficient Plant Leaf Classification. Authorea . 20 October 2025. DOI: https://doi.org/10.22541/au.176094199.98409824/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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