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Plant disease detection using a depth-wise separable-based adaptive deep neural network

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Abstract

The study of agriculture has grown significantly, and the detection of plant diseases is a significant area of concern. Early detection reduces the overall impact of disease on the plant. Farmers worldwide struggle to stop various types of damage from germs and micro-organisms, including viruses, fungi, worms, protozoa, and insects. Numerous approaches have recently been put up for the naming and categorization of diseases. Identifying plant illnesses early and treating them to avoid a severe reduction in crop production is critical. Researchers have presented diverse classification algorithms; however, they still need to be improved by noise, redundant and irrelevant features, and feature extraction approaches. Potato, a starchy food crop widely consumed by the population, is easily infected by diseases spreading on the crop, resulting in low crop productivity. Therefore, this research proposed a novel technique for detecting plant diseases using a depth-wise separable-based adaptive deep neural network (DSDNN). The PlantVillage dataset of healthy and unhealthy leaves is taken for model training purposes. Initially, a Gaussian filter is used for pre-processing and the normalization process; after that, the segmentation of plants is done using an Enthalpy-based graph clustering method followed by feature extraction techniques. Finally, DSDNN is used for the identification of plant disorders. The proposed method attained an accuracy of 99%, which is superior to the existing methods.

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Authors and Affiliations

  1. University School of ICT, GGSIP University, Delhi, India

    Ila Kaushik & Anurag Jain

  2. The NorthCap University, Gurugram, India

    Nupur Prakash

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  1. Ila Kaushik
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Kaushik, I., Prakash, N. & Jain, A. Plant disease detection using a depth-wise separable-based adaptive deep neural network. Multimed Tools Appl 84, 887–915 (2025). https://doi.org/10.1007/s11042-024-19047-5

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