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

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-b...

Full description

Saved in:
Bibliographic Details
Published in:Sensors and materials 2021-01, Vol.33 (11), p.4031
Main Authors: Chen, Chih-Cheng, Ba, Ju Yan, Li, Tie Jun, Chan, Christopher Chun Ki, Wang, Kun Ching, Liu, Zhen
Format: Article
Language:English
Subjects:
Artificial neural networks
Bandwidths
Cassava
Classification
Disease
Evaluation
Image classification
Image enhancement
Medical imaging
Plant diseases
Sensors
Test sets
Testing time
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0914-4935
2435-0869
DOI:10.18494/SAM.2021.3526