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Article

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

1
Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya 793022, India
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Department of Electrical Engineering Fundamentals, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
3
Faculty of Law, Administration and Economics, University of Wroclaw, 50-145 Wroclaw, Poland
*
Authors to whom correspondence should be addressed.
Academic Editor: Juan M. Corchado
Electronics 2021, 10(12), 1388; https://doi.org/10.3390/electronics10121388
Received: 24 May 2021 / Revised: 6 June 2021 / Accepted: 8 June 2021 / Published: 9 June 2021
(This article belongs to the Special Issue New Technological Advancements and Applications of Deep Learning)
The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems. View Full-Text
Keywords: artificial intelligence; convolutional neural network; deep learning; machine learning; transfer learning artificial intelligence; convolutional neural network; deep learning; machine learning; transfer learning
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MDPI and ACS Style

Hassan, S.M.; Maji, A.K.; Jasiński, M.; Leonowicz, Z.; Jasińska, E. Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics 2021, 10, 1388. https://doi.org/10.3390/electronics10121388

AMA Style

Hassan SM, Maji AK, Jasiński M, Leonowicz Z, Jasińska E. Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics. 2021; 10(12):1388. https://doi.org/10.3390/electronics10121388

Chicago/Turabian Style

Hassan, Sk M., Arnab K. Maji, Michał Jasiński, Zbigniew Leonowicz, and Elżbieta Jasińska. 2021. "Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach" Electronics 10, no. 12: 1388. https://doi.org/10.3390/electronics10121388

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