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Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Oyewola, David Opeoluwa;Dada, Emmanuel Gbenga;Misra, Sanjay;Damaševičius, Robertas
Title: Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing
Is part of: PeerJ computer science. London : PeerJ Inc., 2021, article no. e352
Extent: p. 1-15
Date: 2021
Note: Article no. e352
Keywords: Cassava disease;Pattern recognition;Image processing;Deep learning;Convolutional neural networks;Distinct block processing;Data augmentation
Abstract: 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
Affiliation(s): Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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