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All Journal The Indonesian Journal of Computer Science
Tin Tin Wai
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparative Evaluation of Inception V3 and YOLOv8 for Strawberry Plant Diseases Classification Using Deep Learning Models Tin Tin Wai; Aye, Maung; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4750

Abstract

Plant diseases and pests threaten agricultural productivity, with leaf diseases causing major crop losses. Early detection is essential to mitigate these impacts. This study presents a system for detecting strawberry leaf diseases using deep learning-based Convolutional Neural Networks (CNNs) by utilizing two pre-trained models, Inception V3 and YOLOv8, to classify leaves as healthy or diseased. A custom dataset of 5,192 images, comprising one healthy class and four disease-infected categories (leaf blight, blotch, scorch, and spot), is used. Inception V3 achieved 93.8% accuracy, while YOLOv8 outperformed it with 95.4% accuracy, a mAP of 78.6%, and precision, recall, and F1-scores of 89%, 88%, and 89%, respectively. With a compact size of 12 MB and a rapid inference time of 10 ms per image, YOLOv8 is highly suitable for real-time applications. These findings highlight YOLOv8's potential to improve agricultural productivity and food security through precise and efficient disease detection.
Co-Authors Aye, Maung Hla, Tin Tin