Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network



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DOI: 
https://doi.org/10.1016/j.aiia.2021.05.002
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Licensing of resource: 
Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)
Type: 
journal article
Journal: 
Artificial Intelligence in Agriculture
Pages: 
90-101
Volume: 
5
Year: 
2021
Publisher(s): 
Description: 

Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies. This paper proposes a novel hybrid model based on Convolutional Autoencoder (CAE) network andConvolutional Neural Network (CNN) for automatic plant disease detection. To the best of our knowledge, a hy-brid system based on CAE and CNN to detect plant diseases automatically has not been proposed in any state-of-the-art systems present in the literature. In this work, the proposed hybrid model is applied to detect BacterialSpot disease present in peach plants using their leaf images, however, it can be used for any plant disease detec-tion. The experiments performed in this paper use a publicly available dataset named PlantVillage to get the leafimages of peach plants. The proposed system achieves 99.35% training accuracy and 98.38% testing accuracyusing only 9,914 training parameters. The proposed hybrid model requires lesser number of training parametersas compared to other approaches existing in the literature. This, in turn, significantly decreases the time requiredto train the model for automatic plant disease detection and the time required to identify the disease in plantsusing the trained model.

Publication year: 
2021
Keywords: 
Plant disease detection
Convolutional autoencoder
Convolutional neural network
Deep learning in agriculture