# Lightweight Deep Convolutional Models for Jackfruit Leaf Disease Identification
> Arifuddin N.A.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105034947241
Jurnal / Konferensi: Proceedings 7th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/ICIMCIS68501.2025.11327324
Citations: 0

## Authors
- Arifuddin N.A.

## Abstract
This work evaluates three lightweight convolutional neural network (CNN) architectures MobileNetV2, EfficientNet, and NASNetMobile for automatic recognition of six jackfruit leaf conditions: Healthy, Algal Spot, Anthracnose, Leaf Spot, Phyllosticta, and Sooty Mold. A transfer-learning approach was applied, where pretrained ImageNet weights were retained and only the classifier layers were trained. Among the three architectures, MobileNetV2 achieved the highest accuracy at 97.11%, followed by EfficientNet at 96.15%. NASNetMobile yielded a lower result of 77.00%, indicating limitations in its ability to distinguish certain disease categories under the given configuration. The evaluation further showed strong per-class scores for MobileNetV2, including perfect F1-scores for the Healthy and Sooty Mold classes. The findings confirm that compact CNN models, especially MobileNetV2, are well-suited for agricultural disease monitoring and can be integrated into mobile or edge-based decision-support systems for field use.

## Keywords
- Convolutional neural network
- Artificial intelligence
- Classifier (UML)
- Pattern recognition (psychology)
- Computer science
- Identification (biology)
- Deep learning
- Plant disease
- Artificial neural network
- Feature extraction
- Field (mathematics)
- Machine learning
- Backpropagation

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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