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Submission Received: March 3, 2020
Submission Accepted: July 30, 2020
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Latest submission: September 16, 2018
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Added to review: May 29, 2020
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Home > User > Author > Submissions > #3104 > Summary
#3104 Summary
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Submission
Authors	Hendri - Darmawan, Mike Yuliana, Mochammad Zen Samsono Hadi, Arun Kumar Sangaiah
Title	Deep Metric Learning with Augmented Latent Fusion and Response-Based Knowledge Distillation on Edge Device for Paddy Pests and Disease Identification
Original file	3104-7969-1-SM.docx  2024-09-10
Supp. files	None	Add a Supplementary File
Submitter	Dr. Mike - Yuliana Mail
Date submitted	September 10, 2024 - 03:42 PM
Section	AETECH-24
Editor	Alde Alanda Mail
Status
Status	In Review
Initiated	2024-10-19
Last modified	2024-10-19
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Authors
Name	Hendri - Darmawan Mail
Affiliation	Politeknik Elektronika Negeri Surabaya
Country	Indonesia
Bio Statement	—
 
Name	Mike Yuliana Mail
Affiliation	Politeknik Elektronika Negeri Surabaya
Country	Indonesia
Bio Statement	—
Principal contact for editorial correspondence.
 
Name	Mochammad Zen Samsono Hadi Mail
Affiliation	Politeknik Elektronika Negeri Surabaya
Country	Indonesia
Bio Statement	—
 
Name	Arun Kumar Sangaiah Mail
Affiliation	National Yunlin University of Science and Technology
Country	Taiwan, Province of China
Bio Statement	—
Title and Abstract
Title	Deep Metric Learning with Augmented Latent Fusion and Response-Based Knowledge Distillation on Edge Device for Paddy Pests and Disease Identification
 
Abstract	The health of paddy fields significantly impacts rice yields and the economic stability of farmers. Limited number of experts available to monitor these issues poses a challenge. Consequently, a reliable diagnostic system is necessary to identify pests and diseases in rice crops. In this study, we propose deep metric learning with augmented latent fusion (FADMAKA) combined with a response-based knowledge distillation (KD) approach. The student model, which processes single RGB input images, is trained using soft latent labels derived from four augmented input from the teacher model. Our method delivers a high validation accuracy of 0.973, maintains an accuracy of 0.782 on the unseen data, and with rapid inference time of 38.911 milliseconds. This approach’s accuracy outperforms softmax deep learning classification with fine-tuning, which only has a maximum accuracy of 0.739 on the unseen data with computation time of 36.224 ms, and the DML with augmented latent fusion with k-NN classifier on the same base model, which achieves an accuracy of 0.78 with computation time of 124.977 ms. Our proposed model has 0.12 giga floating point operations per second (GFLOPs) that is suitable for edge devices with low computational resources. Following the modeling phase, we deployed the highest-accuracy student model to a Raspberry Pi 4B device equipped with a camera. This system is capable of providing biological agent-based recommendations for identified pest and disease threats in rice fields. Our approach not only improved accuracy but also demonstrated efficiency, enabling farmers to identify pest and disease without relying on internet connectivity. 
Indexing
Keywords	Knowledge distillation; Deep metric learning; Model compression; Paddy plant pests and disease
 
Language	en
Supporting Agencies
Agencies	Direktorat Jenderal Pendidikan Vokasi under the 2024 Penelitian Tesis Magister (PTM) Scheme
References
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