Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing

For people in developing countries, cassava is a major source of calories and carbohydrates. However, Cassava Mosaic Disease (CMD) has become a major cause of concern among farmers in sub-Saharan Africa countries, which rely on cassava for both business and local consumption. The article proposes a...

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Bibliographic Details
Published in:PeerJ. Computer science Vol. 7; p. e352
Main Authors: Oyewola, David Opeoluwa, Dada, Emmanuel Gbenga, Misra, Sanjay, Damaševičius, Robertas
Format: Journal Article
Language:English
Published: United States PeerJ. Ltd 02.03.2021
PeerJ, Inc
PeerJ Inc
Subjects:
Accuracy
Algorithms
Algorithms and Analysis of Algorithms
Artificial Intelligence
Artificial neural networks
Business metrics
Cable television broadcasting industry
Carbohydrates
Cassava
Cassava disease
Classification
Color separations
Computer Vision
Convolution
Convolutional neural networks
Data Mining and Machine Learning
Datasets
Deep learning
Developing countries
Distinct block processing
Farmers
Flowers & plants
Image classification
Image contrast
Image enhancement
Image processing
LDCs
Machine learning
Medical imaging
Model accuracy
Mosaics
Neural networks
Pattern recognition
Performance evaluation
Plant diseases
United States
Sub-Saharan Africa
Asia
Africa
Latin America
Deep learning
Image processing
Data augmentation
Pattern recognition
Convolutional neural networks
Distinct block processing
Cassava disease
ISSN:2376-5992, 2376-5992
Online Access:Get full text
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Summary:For people in developing countries, cassava is a major source of calories and carbohydrates. However, Cassava Mosaic Disease (CMD) has become a major cause of concern among farmers in sub-Saharan Africa countries, which rely on cassava for both business and local consumption. The article proposes a novel deep residual convolution neural network (DRNN) for CMD detection in cassava leaf images. With the aid of distinct block processing, we can counterbalance the imbalanced image dataset of the cassava diseases and increase the number of images available for training and testing. Moreover, we adjust low contrast using Gamma correction and decorrelation stretching to enhance the color separation of an image with significant band-to-band correlation. Experimental results demonstrate that using a balanced dataset of images increases the accuracy of classification. The proposed DRNN model outperforms the plain convolutional neural network (PCNN) by a significant margin of 9.25% on the Cassava Disease Dataset from Kaggle.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.352