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Techno.Com: Jurnal Teknologi Informasi
Published by Universitas Dian Nuswantoro
ISSN : 14122693     EISSN : 23562579     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Topik dari jurnal Techno.Com adalah sebagai berikut (namun tidak terbatas pada topik berikut) : Digital Signal Processing, Human Computer Interaction, IT Governance, Networking Technology, Optical Communication Technology, New Media Technology, Information Search Engine, Multimedia, Computer Vision, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Computer Network Security, Natural Language Processing, Business Process, Cognitive Systems, Software Engineering, Programming Methodology and Paradigm, Data Engineering, Information Management, Knowledge Based Management System, Game Technolog
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Articles 821 Documents
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Hybrid MobileNetV2 and Graph Convolutional Networks for Clove Leaf Nutrient Deficiency Classification Cindy Alya Putri; Angga Prasetyo; Fauzan Masykur
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.17463

Abstract

Nutrient deficiency is one of the major factors affecting the productivity of clove (Syzygium aromaticum) plants. Conventional diagnostic methods, including laboratory analysis and visual inspection, are often time-consuming, destructive, or subjective. This study proposes a hybrid deep learning framework that integrates MobileNetV2 and Graph Convolutional Networks (GCNs) for automatic classification of nitrogen, phosphorus, and potassium (NPK) deficiencies in clove leaves. MobileNetV2 was employed as a feature extractor to generate visual embeddings, which were transformed into a graph representation using the K-Nearest Neighbors (KNN) algorithm. The resulting graph was then classified using a Graph Convolutional Network to exploit structural relationships among visually similar leaf samples. Experimental evaluation on four classes (Healthy, Nitrogen deficiency, Phosphorus deficiency, and Potassium deficiency) achieved an overall classification accuracy of 94.57%, with macro-average precision, recall, and F1-score of approximately 95%. These results indicate that integrating graph-based relational learning with convolutional feature extraction effectively improves nutrient deficiency classification in clove leaves and demonstrates the potential of the proposed framework for automated plant health monitoring in precision agriculture.   Keywords - Graph Convolutional Networks, MobileNetV2, NPK deficiency, Graph-based deep learning, Clove leaves.

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