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All Journal International Journal of Electrical and Computer Engineering Jurnal Mahasiswa Matematika Vidya Karya JRPM (Jurnal Review Pembelajaran Matematika) Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Mathematics and Mathematics Education (JMME) EduLine: Journal of Education and Learning Innovation
Tesdiq Prigel Kaloka
Politeknik Hasnur
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Other

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Journal : International Journal of Electrical and Computer Engineering

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Robusta coffee leaf diseases detection based on MobileNetV2 model Yazid Aufar; Tesdiq Prigel Kaloka
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6675-6683

Abstract

Indonesia is a major exporter and producer of coffee, and coffee cultivation adds to the nation's economy. Despite this, coffee remains vulnerable to several plant diseases that may result in significant financial losses for the agricultural industry. Traditionally, plant diseases are detected by expert observation with the naked eye. Traditional methods for managing such diseases are arduous, time-consuming, and costly, especially when dealing with expansive territories. Using a model based on transfer learning and deep learning model, we present in this study a technique for classifying Robusta coffee leaf disease photos into healthy and unhealthy classes. The MobileNetV2 network serves as the model since its network design is simple. Therefore, it is likely that the suggested approach will be deployed further on mobile devices. In addition, the transfer learning and experimental learning paradigms. Because it is such a lightweight net, the MobileNetV2 system serves as the foundational model. Results on Robusta coffee leaf disease datasets indicate that the suggested technique can achieve a high level of accuracy, up to 99.93%. The accuracy of other architectures besides MobileNetV2 such as DenseNet169 is 99.74%, ResNet50 architecture is 99.41%, and InceptionResNetV2 architecture is 99.09%.
Co-Authors Afli, Febrianto Bustamam, Alhadi Danang Yugo Pratomo Dedi Kuswandi Dwinanda, Indah Gumilang Kadek Ayu Cintya Adelia Olivia Swasti Patuan P Tampubolon Pratiwie, Desy Lutfiani Punaji Setyosari Ridhoni, Wahyu Saida Ulfa Titin Siswantining Widya Fajar Mustika Wilda, Robiatul Witari Yazid Aufar