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All Journal Bulletin of Computer Science Research
Astuti, Wiwik Sri
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Computer Science & IT

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Optimisasi VGG16 dengan Transfer Learning dalam Mendeteksi Penyakit Pada Daun Jagung Ht. Barat, Ade Ismiaty Ramadhona; Astuti, Wiwik Sri; Wanto, Anjar; Solikhun, Solikhun
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.631

Abstract

Corn is one of the major agricultural commodities that plays a strategic role in national food security. However, its productivity often declines due to leaf diseases such as Blight, Common Rust, and Gray Leaf Spot. Manual disease detection is considered inefficient and prone to human error, especially on a large scale. This study aims to develop an automated deep learning-based system for accurate classification of corn leaf diseases. The proposed model utilizes the Convolutional Neural Network (CNN) architecture VGG16 with a transfer learning approach. The dataset comprises 1,200 labeled images of corn leaves categorized into four disease classes, obtained from Kaggle. Image augmentation techniques were applied to improve data diversity and enhance model generalization. The performance of VGG16 was compared with VGG16 Baseline architecture and MobileNetV2. Experimental results show that VGG16 with transfer learning achieved the highest classification accuracy of 96.25%, outperforming the baseline VGG16 (92.92%) and MobileNetV2 (84.58%). These findings demonstrate the effectiveness of VGG16-based transfer learning in automating corn leaf disease detection, supporting the implementation of precision agriculture technology.
Co-Authors Ht. Barat, Ade Ismiaty Ramadhona Solikhun Solikhun, Solikhun Wanto, Anjar