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Super Resolution-Based Leaf Disease Detection in Potato Plant Using Broad Deep Residual Network (BDRN)

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Abstract

Finding potato plant diseases is still a major challenge, and early disease diagnosis can considerably boost crop yield, which is also beneficial commercially. In this paper, Stationary Wavelet Transform (SWT) is used to decompose the input Low Resolution (LR) and corresponding High-Resolution image pairs into four subbands (LL, LH, HL and HH). The proposed Broad Deep Residual Network (BDRN) utilizes these LR subbands and HR subbands, respectively, as input and output, for training. The given LR test image is processed using this trained BDRN, which further generates the subband residue for the associated Super Resolution (SR) image. Proposed BDRN model is tested for super resolution factors 2, 4, 6 on the publicly available PlantVillage dataset with three classes. Proposed model outperformed the existing ResNet50, AlexNet, GoogleNet models and achieved PSNR 31.4836, 30.8644, 30.5026 and SSIM 0.7706, 0.8791, 0.9456 and classification accuracies 99.61%, 98.05%, 96.09% for super resolution factors 2,4,6.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, India

    Pujari Venkata Yeswanth, Rachit Khandelwal & S. Deivalakshmi

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  1. Pujari Venkata Yeswanth
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This article is part of the topical collection “Enabling Innovative Computational Intelligence Technologies for IOT” guest edited by Omer Rana, Rajiv Misra, Alexander Pfeiffer, Luigi Troiano and Nishtha Kesswani.

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Yeswanth, P.V., Khandelwal, R. & Deivalakshmi, S. Super Resolution-Based Leaf Disease Detection in Potato Plant Using Broad Deep Residual Network (BDRN). SN COMPUT. SCI. 4, 112 (2023). https://doi.org/10.1007/s42979-022-01514-1

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