Detection of citrus leaf diseases using a deep learning technique

The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be complete...

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Veröffentlicht in:International journal of electrical and computer engineering (Malacca, Malacca) Jg. 11; H. 2; S. 1719
Hauptverfasser: Luaibi, Ahmed R., Salman, Tariq M., Miry, Abbas Hussein
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Yogyakarta IAES Institute of Advanced Engineering and Science 01.04.2021
Schlagworte:
Computer vision
Data augmentation
Data points
Datasets
Deep learning
Image manipulation
Machine learning
Neural networks
Plant diseases
Recognition
ISSN:2088-8708, 2722-2578, 2088-8708
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Zusammenfassung:The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.
Bibliographie:ObjectType-Article-1
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ISSN:2088-8708
2722-2578
2088-8708
DOI:10.11591/ijece.v11i2.pp1719-1727