气候变化领域集成服务门户(CCPortal): Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

CCPortal

DOI	10.1109/ACCESS.2024.3373000
	Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification
	Altabaji, Wassem I. A. E.; Umair, Muhammad; Tan, Wooi-Haw; Foo, Yee-Loo; Ooi, Chee-Pun
发表日期	2024
ISSN	2169-3536
起始页码	12
卷号	12
英文摘要	Early detection of plant diseases is essential for effective crop disease management to prevent yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models, namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet model involved updates to LeafNet's architectural parameters, whereas transfer learning techniques were applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training were determined by considering several factors such as batch size, data augmentation, learning rate, and optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test accuracies on the same datasets, respectively. This study contributes to the development of automated disease classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.
英文关键词	Plant diseases; Detection algorithms; Deep learning; Convolutional neural networks; Agriculture; Classification algorithms; Crop yield; Transfer learning; Hyperparameter optimization; convolutional neural networks; transfer learning; image classification
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001184766800001
来源期刊	IEEE ACCESS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/294277
作者单位	Multimedia University
推荐引用方式 GB/T 7714	Altabaji, Wassem I. A. E.,Umair, Muhammad,Tan, Wooi-Haw,et al. Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification[J],2024,12.
APA	Altabaji, Wassem I. A. E.,Umair, Muhammad,Tan, Wooi-Haw,Foo, Yee-Loo,&Ooi, Chee-Pun.(2024).Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification.IEEE ACCESS,12.
MLA	Altabaji, Wassem I. A. E.,et al."Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification".IEEE ACCESS 12(2024).