Klasifikasi Penyakit Daun Tanaman Menggunakan MobileNetV4 Berbasis Transfer Learning
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DOI:
https://doi.org/10.35718/specta.v10i2.8481436Keywords:
Deep Learning, Convolutional Neural Network (CNN), MobileNetV4, Transfer Learning, Klasifikasi Penyakit Tanaman, Citra DigitalAbstract
Image-based plant leaf disease classification has become an important focus in agriculture to reduce significant losses and improve crop quality. This study proposes the use of Convolutional Neural Network (CNN) with MobileNetV4 architecture for plant leaf disease classification using transfer learning. MobileNetV4 was chosen because of its efficient ability to recognize visual objects with a light number of parameters, making it suitable for implementation on devices with limited resources. The dataset used is PlantVillage, which contains 20,600 labeled images across 15 disease categories. By using MobileNetV4 pre-trained on ImageNet, and modifying the final layers, we achieved a classification accuracy of 99.47%. This approach shows high potential for practical implementation on mobile devices due to its computational efficiency.
Author Biography
Gusti Ahmad Fanshuri Alfarisy, Institut Teknologi Kalimantan
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Copyright (c) 2026 Syifa Maulida, Tito Ariffianto Miftahul Huda, Wafiq Ajriyah, Rizky Amelia, Gusti Ahmad Fanshuri Alfarisy
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-NoDerivs 4.0 Generic(CC BY-ND 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
