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All Journal Journal of Intelligent Systems Technology and Informatics
Nur Sahid, Ahmad
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Computer Science & IT Control & Systems Engineering Engineering

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Journal : journal of intelligent systems technology and informatics

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Image Classification Using MobileNet Based on CNN Architecture for Grape Leaf Disease Detection Nur Sahid, Ahmad; Cahyadi, Deden Ruli
Journal of Intelligent Systems Technology and Informatics Vol 1 No 1 (2025): JISTICS, March 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i1.7

Abstract

Grape cultivation, while economically important, is often challenged by various leaf diseases that can significantly impact yield and quality, underscoring the need for rapid and accurate detection methods. Traditional diagnostic approaches can be time-consuming and require expert knowledge, whereas advanced image classification techniques offer a promising avenue for automated disease identification. This research aimed to develop and rigorously evaluate a Convolutional Neural Network (CNN) model, specifically leveraging the MobileNetV2 architecture, for the precise classification of four common grape leaf diseases: healthy, Black Rot, Esca (also known as Black Measles), and Leaf Blight. The methodology encompassed dataset acquisition and pre-processing, data augmentation to increase training data diversity, and applying transfer learning using pre-trained MobileNetV2 weights, followed by a fine-tuning stage to adapt the model to the specific task. A comprehensive evaluation on 1,805 previously unseen test images demonstrated the model's exceptional performance, achieving an overall accuracy of 99.89%. Ultimately, the proposed approach significantly outperforms previous methods, demonstrating the feasibility of applying lightweight CNN architectures to real-world detection scenarios. The main contribution of this research is showing that high computational efficiency can be achieved without sacrificing accuracy, paving the way for implementation in digital detection systems with limited resources, particularly for mobile devices or edge systems.
A Deep Learning Method for Forest Fire Classification Using Convolutional Kolmogorov-Arnold Network Nur Sahid, Ahmad; Fauzi, Dhika Restu
Journal of Intelligent Systems Technology and Informatics Vol 2 No 1 (2026): JISTICS, March 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i1.48

Abstract

Forest fires pose a significant threat, requiring advanced detection systems. Conventional deep learning models, such as Convolutional Neural Networks (CNNs), are often limited by fixed activation functions that struggle to model the complex, irregular visual patterns of fire. This architectural rigidity presents a research gap for more adaptive neural architectures. This study addresses this gap by proposing and evaluating a novel method for forest fire classification using a Convolutional Kolmogorov-Arnold Network (CKAN), an architecture featuring learnable activation functions to improve detection accuracy and flexibility. Following a systematic machine learning lifecycle, this research utilized a public Kaggle dataset of 14,063 'Fire' and 'Nofire' images. Extensive data augmentation was applied to enhance model robustness. We designed a hybrid CKAN model combining a CNN feature extractor with a KAN module that uses learnable B-spline activation functions for classification. The model was trained for 30 epochs with the AdamW optimizer and Binary Cross-Entropy loss, followed by a rigorous evaluation on an unseen test set. The proposed CKAN model demonstrated exceptional performance, achieving 98.04% accuracy and an AUC-ROC of 0.9955, significantly outperforming conventional architectures. Grad-CAM analysis confirmed that the model focused on relevant visual features of fire and smoke, thereby validating its decision-making process. The findings establish the CKAN architecture as a highly effective and computationally efficient approach for forest fire classification, making it a powerful and promising solution for deployment in real-world, resource-constrained environmental monitoring systems.
Co-Authors Cahyadi, Deden Ruli Fauzi, Dhika Restu