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Deep transfer learning enabled DenseNet model for content based image retrieval in agricultural plant disease images

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Abstract

Content based image retrieval (CBIR) is an effective method to retrieve the images depending upon the visual contents such as color, shape, texture, etc. Recently, the CBIR models can be employed in the agricultural sector for plant disease detection. Though several CBIR models are existed in the literature, only few works have few works have focused on the design of CBIR for plant diseases. In this aspect, this study introduces a Deep Transfer Learning Enabled DenseNet Model for Content Based Image Retrieval in Agricultural Plant Disease Images, named DTLDN-CBIRA model. Since limited number of samples exist in the dataset, data augmentation is carried out using two processes namely rotation and flipping. In addition, the DTLDN-CBIRA model uses densely connected networks (DenseNet-201) model as a feature extractor. At the same time, the hyperparameters of the deep learning (DL) models considerably influence the retrieval performance and the stochastic gradient descent (SGD) optimizer is used for hyperparameter tuning of the DenseNet-201 model. Finally, Manhattan distance metric is used to measure the similarity between the images and the images with high similarity will be retrieved from the database. The design of DTLDN-CBIRA technique for plant disease image retrieval process shows the novelty of the work. The performance validation of the DTLDN-CBIRA model takes place using benchmark dataset and the results reported the supremacy of the DTLDN-CBIRA model over the recent methods with maximum precision of 100%, recall of 81.90%, and F-score of 89.90%.

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Authors and Affiliations

  1. Department of Computer and Information Science, Faculty of Science, Annamalai University, Chidambaram, Tamil Nadu, India

    M. Karthikeyan

  2. Department of Computer Science, D.G.G.Arts College for Women, Mayiladuthurai, Tamil Nadu, India

    D. Raja

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  1. M. Karthikeyan
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  2. D. Raja
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Correspondence to D. Raja.

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Appendices

Appendix I-Test Image (Apple Rust)

figure a

Appendix II-Test Image (Healthy)

figure b

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Karthikeyan, M., Raja, D. Deep transfer learning enabled DenseNet model for content based image retrieval in agricultural plant disease images. Multimed Tools Appl 82, 36067–36090 (2023). https://doi.org/10.1007/s11042-023-14992-z

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  • DOI: https://doi.org/10.1007/s11042-023-14992-z

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