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Image-Based Plant Disease Identification by Deep Learning Meta-Architectures

Saleem, Muhammad Hammad; Khanchi, Sapna; Potgieter, Johan; Arif, Khalid Mahmood

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Authors

Sapna Khanchi

Johan Potgieter

Khalid Mahmood Arif



Abstract

The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment.

Citation

Saleem, M. H., Khanchi, S., Potgieter, J., & Arif, K. M. (2020). Image-Based Plant Disease Identification by Deep Learning Meta-Architectures. Plants, 9(11), 1451. https://doi.org/10.3390/plants9111451

Journal Article Type Article
Acceptance Date Oct 25, 2020
Online Publication Date Oct 27, 2020
Publication Date Oct 27, 2020
Deposit Date Feb 17, 2024
Publicly Available Date Feb 21, 2024
Journal Plants
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 9
Issue 11
Pages 1451
DOI https://doi.org/10.3390/plants9111451
Keywords Plant Science; Ecology; Ecology, Evolution, Behavior and Systematics

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