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VGG-ICNN: A Lightweight CNN model for crop disease identification

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Abstract

Crop diseases cause a substantial loss in the quantum and quality of agricultural production. Regular monitoring may help in early stage disease detection an d thereby reduction in crop loss. An automatic plant disease identification system based on visual symptoms can provide a smart agriculture solution to such problems. Various solutions for plant disease identification have been provided by researchers using image processing, machine learning and deep learning techniques. In this paper a lightweight Convolutional Neural Network ‘VGG-ICNN’ is introduced for the identification of crop diseases using plant-leaf images. VGG-ICNN consists of around 6 million parameters that are substantially fewer than most of the available high performing deep learning models. The performance of the model is evaluated on five different public datasets covering a large number of crop varieties. These include multiple crop species datasets: PlantVillage and Embrapa with 38 and 93 categories, respectively, and single crop datasets: Apple, Maize, and Rice, each with four, four, and five categories, respectively. Experimental results demonstrate that the method outperforms some of the recent deep learning approaches on crop disease identification, with 99.16% accuracy on the PlantVillage dataset. The model is also shown to perform consistently well on all the five datasets, as compared with some recent lightweight CNN models.

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Authors and Affiliations

  1. PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482001, India

    Poornima Singh Thakur, Tanuja Sheorey & Aparajita Ojha

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  1. Poornima Singh Thakur
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  2. Tanuja Sheorey
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Thakur, P.S., Sheorey, T. & Ojha, A. VGG-ICNN: A Lightweight CNN model for crop disease identification. Multimed Tools Appl 82, 497–520 (2023). https://doi.org/10.1007/s11042-022-13144-z

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