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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Deep learning for automatic identification of plants through leaf

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pp. 709–716Vol. 44Issue 4May 2023DOI: 10.47974/JIOS-1269XML
Received:
02 Sep 2022
Published Online:
28 Aug 2023
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1269
Pages:
709–716

Abstract

Automatic identification of plants, has been a widely explored field for the conservation of environment. Deep Learning has been extensively used in image recognition tasks due to its powerful ability to extract features from the given set of images. In this paper, we have trained Convolutional neural Network models from scratch by first pre-processing the images using MobileNet’s pre-processing input function to identify the plant species using leaf images. Four CNN models are discussed at different depths to understand how the accuracy of identification can be improved and the impact of hyperparameters namely batch size and number of epochs have on the accuracy of identification. The four models have been evaluated on two freely available leaf datasets: Flavia and Swedish. To reduce overfitting, data-augmentation and Early Stopping callback has been applied. The performance of the proposed CNN model was also compared to SVM, Random Forest and K-Nearest Neighbors classifiers on both datasets. Maximum accuracies were reported to be 95.35 % and 95.24% on Flavia and Swedish respectively. 

Keywords

Deep learningMachine learningMobileNetConvolutional neural networksPattern recognitionComputer vision

Subject Classifications

68T07

References

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