S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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S&M2743 Research Paper of Special Issue
Published in advance: November 5, 2021

EfficientNet: A Low-bandwidth IoT Image Sensor Framework for Cassava Leaf Disease Classification [PDF]

Chih-Cheng Chen, Ju Yan Ba, Tie Jun Li, Christopher Chun Ki Chan, Kun Ching Wang, and Zhen Liu

(Received July 1, 2021; Accepted October 4, 2021)

Keywords: machine vision, leaf diseases, image enhancement, TTA enhancement

Following the cassava leaf disease classification process, we successfully design a novel convolutional neural network (CNN) framework called EfficientNet using low-bandwidth image sensors and a combination of image enhancement and image classification methods. For this study, we have employed low-bandwidth, small-scale IoT image sensors in a farm to capture images of cassava leaves at periodic intervals. We employ data enhancement techniques such as test-time augmentation (TTA) and cutmix, cutout, and k-fold to accurately classify and evaluate the pathology of cassava plants. We carry out multiple simulated experiments to classify and evaluate diseases found in five cassava leaf datasets. Our framework is capable of producing relatively accurate classification results despite small differences between the test set images, and we achieved a classification accuracy for our final test set of 89%, comparable with that in similar studies. Experimental results obtained using a sensor show that EfficientNet significantly outperforms a state-of-the-art cassava leaf disease classification model.

Corresponding author: Christopher Chun Ki Chan, Kun Ching Wang




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