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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)
Nugroho, Hermawan
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Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering

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Enzyme dosage detection to degrade feathers in edible bird’s nests: A comparative convolutional neural networks study Liana, Verianti; Arifiandika, Rizal; Rohmatulloh, Bagas; Nafi’ah, Riris Waladatun; Hidayat, Arif; Hendrawan, Yusuf; Al-Riza, Dimas Firmanda; Mahatmanto, Tunjung; Nugroho, Hermawan
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Vol 6, No 4 (2023)
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.afssaae.2023.006.04.6

Abstract

Edible Bird’s Nest (EBN), a costly food product made from swiftlet’s saliva, has encountered a longstanding problem of plucking the swiftlet’s feather from the nests. The destructive and inefficient manual process of plucking the feathers can be substituted with a serine protease enzyme alternative. Accurate detection of enzyme dosage is crucial for ensuring efficient feather degradation with cost-effective enzyme usage. This research employed the transfer learning method using pretrained Convolutional Neural Networks (Pt-CNN) to detect enzyme dosage based on EBN’s images. This study aimed to compare the image classification mechanisms, architectures, and performance of three Pt-CNN: Resnet50, InceptionResnetV2, and EfficientNetV2S. InceptionResnetV2, using parallel convolutions and residual networks, significantly contributes to learning rich informative features. Consequently, the InceptionResnetV2 model achieved the highest accuracy of 96.18%, while Resnet50 and EfficientNetV2S attained only 30.44% and 17.82%, respectively. The differences in architecture complexity, parameter count, dataset characteristics, and image resolution also play a role in the performance disparities among the models. The study’s findings aid future researchers in streamlining model selection when facing limited datasets by understanding the reasons for the model’s performance and contributing to a non-destructive and quick solution for EBN’s cleaning process.  
Plant Disease Identification Using Image Processing: A Systematic Literature Review Minarni, Minarni; Rusydi, Muhammad Ilhamdi; Darwison, Darwison; Nugroho, Hermawan; Sunaryo, Budi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7171

Abstract

This article is a literature review focusing on plant disease identification using image processing techniques. This review aims to provide a comprehensive analysis of dataset sources, preprocessing methodologies, segmentation techniques, feature extraction processes, and various classification methods, along with their associated accuracies. It also discusses challenges encountered and potential future research directions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a literature search was conducted in the Scopus database to obtain primary studies. The search covered Scopus-indexed journals and proceedings published by IEEE, Elsevier, Springer, MDPI, and ACM between 2019 and 2025. The initial identification phase yielded 9,286 studies screened. Further screening was performed based on specific eligibility criteria, including relevance to the topic, year of publication, subject area, document type, and articles written in English, resulting in the selection of 82 studies for the review. The findings indicate that the most commonly used dataset is PlantVillage, followed by field data. The dominant preprocessing techniques include image enhancement and augmentation. For segmentation and feature extraction, the most frequently used methods were k-means and CNN, respectively. Sixty-one studies achieved an accuracy exceeding 90%. However, several key challenges remain: data limitations, methodological issues, and practical constraints. Future research should focus on developing more representative datasets, hybrid approaches that integrate classical and deep learning methods, and lightweight, adaptive decision support systems suitable for real-world agricultural applications. This review supports continued progress in this field by providing valuable insights for researchers developing image-based methods for identifying plant diseases.
Co-Authors Al-Riza, Dimas Firmanda Arif Hidayat Arifiandika, Rizal Budi Sunaryo, Budi Darwison Darwison Liana, Verianti Minarni Minarni Muhammad Rusydi H. Nafi’ah, Riris Waladatun Rohmatulloh, Bagas Tunjung Mahatmanto Yusuf Hendrawan