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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Veröffentlicht in:	PeerJ. Computer science Jg. 7; S. e352
Hauptverfasser:	Oyewola, David Opeoluwa, Dada, Emmanuel Gbenga, Misra, Sanjay, Damaševičius, Robertas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States PeerJ. Ltd 02.03.2021 PeerJ, Inc PeerJ Inc
Schlagworte:	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-Zugang:	Volltext
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