Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification
Dublin Core
Title
Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification
Subject
chili; comparison; CNN; mobilenetV2
Description
Chili is an important agricultural commodity in Indonesia and plays a significant role in the nation's economic growth. Its
demand by households and industries reaches up to 61%. However, this high demand also means that monitoring efforts need
to be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not promptly
addressed, they can lead to a decrease in production levels, which can negatively affect the economy. With technological
advancements, automatic monitoring using image processing is now highly feasible, making monitoring more efficient and
effective. Common chili plant diseases include Chili leaf yellowing disease, Chili leaf curling disease, and cercospora leaf
spots and Magnesium Deficiency with symptoms that can be observed through the shape and color of the leaves. This research
aims to classify chili plant diseases by comparing the CNN algorithm and the pre-trained MobileNetV2 based model
performance using Confussion Matrix. The study shows that the MobileNetV2 model, trained with a learning rate of 0.001,
produces a more optimal model with an accuracy of 90% and based on the calculation of the confusion matrix, the average
percentage values for recall, precision, and F1 score are 92%. These findings highlight the potential of image processing and
pre-trained models to support efforts to monitor plant diseases and improve chili production.
demand by households and industries reaches up to 61%. However, this high demand also means that monitoring efforts need
to be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not promptly
addressed, they can lead to a decrease in production levels, which can negatively affect the economy. With technological
advancements, automatic monitoring using image processing is now highly feasible, making monitoring more efficient and
effective. Common chili plant diseases include Chili leaf yellowing disease, Chili leaf curling disease, and cercospora leaf
spots and Magnesium Deficiency with symptoms that can be observed through the shape and color of the leaves. This research
aims to classify chili plant diseases by comparing the CNN algorithm and the pre-trained MobileNetV2 based model
performance using Confussion Matrix. The study shows that the MobileNetV2 model, trained with a learning rate of 0.001,
produces a more optimal model with an accuracy of 90% and based on the calculation of the confusion matrix, the average
percentage values for recall, precision, and F1 score are 92%. These findings highlight the potential of image processing and
pre-trained models to support efforts to monitor plant diseases and improve chili production.
Creator
Achmad Naila Muna Ramadhani, Galuh Wilujeng Saraswati, Rama Tri Agung, Heru Agus Santoso
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
August 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Achmad Naila Muna Ramadhani, Galuh Wilujeng Saraswati, Rama Tri Agung, Heru Agus Santoso, “Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10017.