Identification of Maize Leaf Diseases Based On AlexNet and ResNet50 Convolutional Neural Networks
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Date
2023-08-30
Authors
Micheni, Maurice
Birithia, Rael
Mugambi, Cyrus
Too, Boaz
Kinyua, Margaret K
Journal Title
Journal ISSN
Volume Title
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Abstract
Maize crop protection is crucial for global food security, requiring accurate disease
identification. In Kenya, farmers rely on subjective visual analysis of symptomatic leaves,
which is time-consuming and prone to errors. Computer vision technologies, like deep
learning and machine learning, offer promising solutions for disease identification. This
study applies Convolutional Neural Networks (CNNs), specifically AlexNet and ResNet-50,
to automatically learn image features and enhance speed and accuracy in maize leaf
disease identification. A dataset of 3200 digital maize leaf disease images from Embu
County is used for training and testing. AlexNet achieved the highest average accuracy of
98.3%, followed by ResNet-50 at 96.6%. The machine learning, support vector machine
(SVM) exhibited the lowest average accuracy of 85.5%. These findings highlight the
significance of utilizing AlexNet and ResNet-50 in maize leaf disease identification and
classification.
Description
Abstract on Identification of Maize Leaf Diseases
Keywords
Maize, Convolutional Neural Network (CNN), AlexNet, ResNet-50, Support Vector machine (SVM)
Citation
Indonesian Journal of Computer Science (IJCS) Volume 12 Number 4