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Article

Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models

1
Department of Computer Science & Engineering, Chittagong University of Engineering & Technology (CUET), Chattogram 4349, Bangladesh
2
Department of Computer Science & Engineering, Premier University, Chattogram 4000, Bangladesh
3
Faculty of Education and Integrated Arts and Sciences, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Symmetry 2021, 13(3), 511; https://doi.org/10.3390/sym13030511
Received: 3 March 2021 / Revised: 13 March 2021 / Accepted: 18 March 2021 / Published: 21 March 2021
Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models. View Full-Text
Keywords: plant leaf disease; depth-wise separable convolution; modified adaptive centroid-based segmentation; computational latency; model size plant leaf disease; depth-wise separable convolution; modified adaptive centroid-based segmentation; computational latency; model size
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MDPI and ACS Style

Hossain, S.M.M.; Deb, K.; Dhar, P.K.; Koshiba, T. Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models. Symmetry 2021, 13, 511. https://doi.org/10.3390/sym13030511

AMA Style

Hossain SMM, Deb K, Dhar PK, Koshiba T. Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models. Symmetry. 2021; 13(3):511. https://doi.org/10.3390/sym13030511

Chicago/Turabian Style

Hossain, Syed M.M., Kaushik Deb, Pranab K. Dhar, and Takeshi Koshiba. 2021. "Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models" Symmetry 13, no. 3: 511. https://doi.org/10.3390/sym13030511

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