Citation

Tanvir, Sadaf and Shabbir, Obaid and Khan, Amjad and Gul, Nasir (2026) Optimizer Performance in Deep Learning for Leaf Disease Classification. Journal of Engineering Technology and Applied Physics, 8 (1). p. 147. ISSN 2682-8383

Text View of Optimizer Performance in Deep Learning for Leaf Disease Classification.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Global food security is at risk due to the spread of plant diseases, particularly in regions where agriculture is a significant economic activity. Early and precise identification of plant diseases helps avoid crop loss and maintain agricultural output. Here, we investigate different deep learning optimizers for the classification of leaf diseases. We employ EfficientNet-B0, a cutting-edge model built on high accuracy and efficiency in image classification tasks intended for agricultural settings with limited resources. The PlantVillage and PlantDoc databases are used to determine the best optimizer. We evaluate the results of five popular optimizers on EfficientNet-B0: Adam, Nadam, Adagrad, RMSprop, and SGD. Empirical findings indicate that Adam produces training, validation, and testing outcomes that outperform other optimizers. Future real-time agricultural application implementations are anticipated to be fueled by this insight.

Item Type:	Article
Uncontrolled Keywords:	Deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	09 Jul 2026 04:13
Last Modified:	09 Jul 2026 04:13
URII:	http://shdl.mmu.edu.my/id/eprint/16355

Downloads

Edit (login required)