Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal Medical Informatics Technology
I Indarti
Universitas Nusa Mandiri
Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Classification of Corn Leaf Disease Detection Using ResNet50 and Inception V3 I Indarti; Dewi Laraswati; F Frieyadie
Journal Medical Informatics Technology Volume 4 No. 2, June 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i2.90

Abstract

Corn is an important food crop in Indonesia, requiring accurate classification methods to support agricultural productivity. To evaluate and compare ResNet50 and Inception V3 models with augmentation techniques for corn image classification. Deep learning classification using CNN architectures with rotation, shifting, and flipping augmentation. 3,560 corn images (2,500 training, 700 validation, and 360 testing). ResNet50 achieved 93.05% accuracy, while Inception V3 achieved the highest performance with 94.02% accuracy, 93.04% precision, 93.00% recall, and 93.02% F1-score. Image augmentation significantly improved classification performance, and Inception V3 was identified as the most effective model for corn image classification.
Co-Authors Dewi Laraswati F Frieyadie