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Boosted-DEPICT: an effective maize disease categorization framework using deep clustering

  • S.I. : Bio-Inspired Computing for DLA
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Abstract

Clustering of plant disease from digital images is an arduous task due to its dynamic nature and change of appearance under different environmental conditions. In most cases, the image captured in the real-time scenario is subjected to added noise, distortion, poor lighting conditions, and other potential factors that results in poor model performance during the process of discriminating between normal and disease-affected samples. It eventually maximizes the margin of the error rate, thereby leading to misclassification of disease of different varieties of plants in the database with other categories. This paper presents an effective deep clustering-based plant disease categorization algorithm, Boosted-Deep Embedded Regularized Clustering (DEPICT). This model integrates the convolutional autoencoder model with locality-preserving constraints and group sparsity into the network, which improves the embedded learning representation of the images. The PlantVillage and PDD image databases are accessed to develop this model for maize crop. The images are segmented by eliminating the background, cropped, augmented before model training. The performance of the system is evaluated by clustering accuracy and normalized mutual information. The proposed Boosted-DEPICT exhibits better performance, attains promising results with an accuracy of 97.73% and 91.25% on PV and PDD datasets, and outperforms state-of-the-art deep clustering algorithms. This system could be further enhanced by automating the entire process and transforming it into a mobile application for real-time analysis to gain instant results from any region.

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  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    B. V. Gokulnath & G. Usha Devi

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  1. B. V. Gokulnath
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  2. G. Usha Devi
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Correspondence to G. Usha Devi.

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Gokulnath, B.V., Usha Devi, G. Boosted-DEPICT: an effective maize disease categorization framework using deep clustering. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-05303-w

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