Boosted-DEPICT: an effective maize disease categorization framework using deep clustering
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S.I. : BIO-INSPIRED COMPUTING FOR DLA
Boosted-DEPICT: an effective maize disease categorization framework using deep clustering B. V. Gokulnath1 • G. Usha Devi1 Received: 5 May 2020 / Accepted: 18 August 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Autoencoders Boosted-DEPICT Deep clustering Image processing Plant disease
1 Introduction Agriculture is an essential activity and plays a key role in the development of human life and its welfare. Plant diseases are turning out to be a greater threat to food production [1]. It directly affects the livelihood of the smallholder farmers as they completely depend on more crop yield. These smallholder farmers all around the globe satisfy the global food consumption at the rate of 80%, where nearby 50% of the loss is observed mainly due to diseases and pests as common reasons [2–4]. Due to the lack of potential infrastructure, the early diagnosis of plant & G. Usha Devi [email protected] B. V. Gokulnath [email protected] 1
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
pests and disease becomes difficult, also challenging to restrict it all over the crop field. In modern agricultural practices, crop stability exhibits the strength of the production [5]. Substantial growth can be attained through effective c
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