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A novel deep neural network model using network deconvolution with attention based activation for crop disease classification

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Abstract

Accurate classification of crop diseases in its initial phase can help farmers to take necessary actions against the damage to their crops. The paper presents a Deep Learning (DL)-based crop disease classification approach that uses network deconvolution operation and attention-based activation function in each feature extraction layer. The existence of channel-wise and pixel-wise correlations in real-world images makes model training challenging. There is hardly any method available in the current literature that proposes the image correlation removal technique. The network deconvolution operation can efficiently remove both the correlations layer-wise. Hence, it is used in the proposed model. An attention-based activation function called AReLU is adopted in the model. The significance of AReLU activation function is to facilitate faster training. It can deal with gradient vanishing issue. The study considered Plant Village (PV), Tomato, and Grape datasets for performance evaluation. 80: 20 train-test split of the dataset was considered. The proposed model delivered significant results in comparison to other existing models, offering 100%, 99.27% and 99.10% classification accuracies and 99.88%, 99.06% and 99.01% F1-scores on Grape, PV and Tomato datasets respectively.

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Data availability

The authors provide references to all materials used in this work. All datasets are freely available in the internet. 1. (https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset), 2. (https://github.com/spMohanty/PlantVillage-Dataset).

Code availability

Custom code is available.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, North Eastern Regional Institute of Science and Technology, Arunachal, Pradesh, India

    Nayan Kumar Sarkar & Moirangthem Marjit Singh

  2. Department of Computer Science, Vidyasagar University, West Bengal, India

    Utpal Nandi

Authors
  1. Nayan Kumar Sarkar
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  2. Moirangthem Marjit Singh
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Contributions

Nayan Kumar Sarkar: Conceptualization, Implementation and Drafting; Moirangthem Marjit Singh: Investigation, Methodology, Analysis and Supervision; Utpal Nandi: Review and Editing.

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Correspondence to Moirangthem Marjit Singh.

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Sarkar, N.K., Singh, M.M. & Nandi, U. A novel deep neural network model using network deconvolution with attention based activation for crop disease classification. Multimed Tools Appl 83, 17025–17045 (2024). https://doi.org/10.1007/s11042-023-16125-y

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