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All Journal Bulletin of Electrical Engineering and Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Communication in Biomathematical Sciences International Journal of Global Operations Research
Devi Munandar, Devi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : Bulletin of Electrical Engineering and Informatics

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Chili leaf segmentation using meta-learning for improved model accuracy Suwarningsih, Wiwin; Kirana, Rinda; Husnul Khotimah, Purnomo; Riswantini, Dianadewi; Fachrur Rozie, Andri; Nugraheni, Ekasari; Munandar, Devi; Arisal, Andria; Roufiq Ahmadi, Noor
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.7929

Abstract

Recognizing chili plant varieties through chili leaf image samples automatically at low costs represents an intriguing area of study. While maintaining and protecting the quality of chili plants is a priority, classifying leaf images captured randomly requires considerable effort. The quality of the captured leaf images significantly impacts the development of the model. This study applies a meta-learning approach to chili leaf image data, creating a dataset and classifying leaf images captured using mobile devices with varying camera specifications. The images were organized into 14 experimental groups to assess accuracy. The approach included 2-way and 3-way classification tasks, with 3-shot, 5-shot, and 10-shot learning scenarios, to analyze the influence of various chili leaf image factors and optimize the classification and segmentation model's accuracy. The findings demonstrate that a minimum of 10 shots from the meta-test dataset is sufficient to achieve an accuracy of 84.87% using 2-way classification meta-learning combined with the mix-up augmentation technique.
Co-Authors Andria Arisal, Andria Ashgi, Rizky Atje Setiawan Abdullah Budi Nurani Ruchjana Diah Chaerani Endang Rusyaman Fachrur Rozie, Andri Habsy, Muhammad Yusuf Al Husnul Khotimah, Purnomo Izzaturrahim, Muh. Hafizh Johar, Dwindi Agryanti Khotimah, Purnomo Husnul Natari, Rifani Bhakti Nugraheni, Ekasari Rachmawati, Ro'fah Nur Rinda Kirana Riswantini, Dianadewi Roufiq Ahmadi, Noor Wiwin Suwarningsih