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Performance and Applicability of Transfer Learners for Cocoa Swollen Shoot Detection

Performance and Applicability of Transfer Learners for Cocoa Swollen Shoot Detection

Justice Kwame Appati
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 10
ISSN: 1947-9301|EISSN: 1947-931X|EISBN13: 9781799861010|DOI: 10.4018/IJTD.2021040105
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MLA

Appati, Justice Kwame. "Performance and Applicability of Transfer Learners for Cocoa Swollen Shoot Detection." IJTD vol.12, no.2 2021: pp.68-77. http://doi.org/10.4018/IJTD.2021040105

APA

Appati, J. K. (2021). Performance and Applicability of Transfer Learners for Cocoa Swollen Shoot Detection. International Journal of Technology Diffusion (IJTD), 12(2), 68-77. http://doi.org/10.4018/IJTD.2021040105

Chicago

Appati, Justice Kwame. "Performance and Applicability of Transfer Learners for Cocoa Swollen Shoot Detection," International Journal of Technology Diffusion (IJTD) 12, no.2: 68-77. http://doi.org/10.4018/IJTD.2021040105

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Abstract

An accurate and reliable cocoa swollen shoot disease diagnosis is the desire of traditional farmers with low-resolution smart devices. In this study, an efficient cocoa swollen shoot disease identification method base on transfer learners using pre-trained VGG16 and ResNet was proposed. These pre-trained models were trained using 456 samples and validated with 114 samples. The dataset constitutes low-resolution images, VGG16 and ResNet, and achieved an accuracy of 98.25 and 94.73%, respectively. With the objective of proposing a more reliable and accurate model, VGG16 is noted to scale better in terms of performance for implementation.

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