IMPLEMENTATION OF TRANSFER LEARNING MOBILENETV2 ARCHITECTURE FOR IDENTIFICATION OF POTATO LEAF DISEASE

Adilah M, Tika and Kristiyanti, Dinar Ajeng (2023) IMPLEMENTATION OF TRANSFER LEARNING MOBILENETV2 ARCHITECTURE FOR IDENTIFICATION OF POTATO LEAF DISEASE. Journal of Theoretical and Applied Information Technology, 101 (16). 6273 -6285. ISSN 1817-3195

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Abstract

Potatoes are one of the third most important food crops in the world. Potato farming has problems in the form of diseases that attack the leaves. These diseases can affect the quality of potato plants, resulting in crop failure. Digital image processing is a method that can be used to assist farmers in identifying potato leaf diseases. The development of digital image processing has been carried out, one of which is by using the Convolutional Neural Network (CNN) algorithm. CNN requires big data. CNN architecture will experience overfitting if it uses little data, where the classification model has high accuracy on training data but poor accuracy on test data. This research utilizes Transfer Learning and Augmentation methods to avoid overfitting on too little data. Transfer Learning method used in this research is MobileNetV2. The results of the trials in this study indicate that the MobileNetV2 Transfer Learning method has good classification performance results and produces a high accuracy value of 99.6%.

Item Type:	Article
Keywords:	Convolutional Neural Network, Leaf Disease Classification, MobileNetV2, Potato Leaf Disease, Transfer Learning
Subjects:	000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 004 Computer Science, Data Processing, Hardware 300 Social Sciences > 330 Economics > 338 Production (Agriculture, Business Enterprise, Extraction of Minerals, General Production)
Divisions:	Faculty of Engineering & Informatics > Information System
Depositing User:	Administrator UMN Library
Date Deposited:	04 Sep 2024 09:29
Last Modified:	04 Sep 2024 09:29
URI:	https://kc.umn.ac.id/id/eprint/31215

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