TELKOMNIKA Telecommunication, Computing, Electronics and Control
A comparative study of mango fruit pest and disease recognition
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
A comparative study of mango fruit pest and disease recognition
A comparative study of mango fruit pest and disease recognition
Subject
CNN, InceptionResNet-V2, Inception-V3, Mango pest and disease, ResNet50, VGG16
Description
Mango is a popular fruit for local consumption and export commodity.
Currently, Indonesian mango export at 37.8 M accounted for 0.115% of
world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export
destinations such as gamma-ray in Australia, or hot water treatment in
Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images.
Currently, Indonesian mango export at 37.8 M accounted for 0.115% of
world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export
destinations such as gamma-ray in Australia, or hot water treatment in
Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images.
Creator
Kusrini, Suputa, Arief Setyanto, I Made Artha Agastya, Herlambang Priantoro, Sofyan Pariyasto
Source
DOI: 10.12928/TELKOMNIKA.v20i6.21783
Publisher
Universitas Ahmad Dahlan
Date
December 2022
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Kusrini, Suputa, Arief Setyanto, I Made Artha Agastya, Herlambang Priantoro, Sofyan Pariyasto, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
A comparative study of mango fruit pest and disease recognition,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4459.
A comparative study of mango fruit pest and disease recognition,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4459.