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Mathematical and Visual Understanding of a Deep Learning Model Towards m-Agriculture for Disease Diagnosis

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Abstract

Disease detection and classification based on the disease spot found on the leaves is of great importance in improving the agricultural productivity. This paper provides a comprehensive overview of the prevailing applications of computer vision and deep learning techniques in the field of agriculture highlighting the necessity of disease identification and classification using leaf image dataset. A novel classification framework is proposed explaining its working principle. The proposed framework is applied on the multispace image reconstruction inputs. The multispace image reconstruction inputs are used to generate a new set of images containing its gradient images. Then high level semantic features are extracted from the original and reconstructed images, via. convolutional and depthwise separable convolutional layers. Finally, softmax classifier is used for classification. The hyperparameters and computational cost are computed mathematically which provides an insight of creativeness to the researchers. The framework performance is evaluated and compared with the related works on publicly available apple leaf image dataset.

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Authors and Affiliations

  1. Department of CSE, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

    Mercelin Francis

  2. Department of IT, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

    C. Deisy

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  1. Mercelin Francis
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  2. C. Deisy
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Correspondence to Mercelin Francis.

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On behalf of all authors, I (Mercelin Francis) hereby state that there is no conflict of interest.

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Francis, M., Deisy, C. Mathematical and Visual Understanding of a Deep Learning Model Towards m-Agriculture for Disease Diagnosis. Arch Computat Methods Eng 28, 1129–1145 (2021). https://doi.org/10.1007/s11831-020-09407-3

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