Cascaded CNN For Early Detection of Plant Diseases Machine ‎Vision Techniques in Smart Agriculture

  • Authors

    • Sujatha Kesavan Department of Computer Science Engineering, Lincoln University College, Petaling Jaya, Malaysia
    • Ganesh Khekare School of Computer Science & Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India https://orcid.org/0000-0002-1687-4699
    • Divya Midhunchakkaravarthy Centre of Postgraduate Studies, Lincoln University College, Petaling Jaya, Malaysia https://orcid.org/0000-0002-8044-1863
    • N.P.G. Bhavani Department of ECE, Saveetha Institute of Medical and Technical Sciences, Chennai, India
    • M. Malathi Department of ECE, Rajalakshmi Institute of Technology, Chennai, India
    https://doi.org/10.14419/b9c87e95

    Received date: June 19, 2025

    Accepted date: July 30, 2025

    Published date: August 4, 2025

  • Plant Disease Detection; Deep Learning Neural Networks; Artificial Intelligence; Plant Stress; Smart Agriculture; Cascaded Convolutional ‎Neural Networks.

  • Abstract

    Plant disease detection is important because plants absorb the greenhouse gases emitted by industries and vehicles and liberate oxygen, ‎to extend human life in the Universe. The biodiversity chain is fully reliant on plant growth and mainly purifies the air and makes it suitable for ‎respiration. Various diseases like Bacterial Spot (BS), Early Blight (EB), Late Blight, Mold (LBM), Leaf Spot (LS), Spider Mites (SM), ‎Target Spot (TS), and Yellow Leaf Curl (YLC) attack the plants, causing retardation in plant growth, thereby offering stress to the plants. ‎Plant stress reduces crop productivity and creates a deadly impact on the economy. Several studies have exhibited that the quality of ‎agricultural products is seriously affected due to plant stress. Plants are stressed due to many reasons like extended use of synthetic fertilizers-‎ers, soil nutrients, plant diseases, and the various physiological parameters like atmospheric temperature, sunlight, soil pH, and soil moisture ‎content, which may vary seasonally. The benchmark images of the leaves under normal conditions are gathered, and pre-processing is per-‎formed. Further, the feature extraction is done by various types of Convolutional Neural Network (CNN) models like Faster Region-based CNN (FR-CNN), R-CNN, Cascaded CNN, and compared with the standardized CNN model. An optimal solution is offered using ‎Artificial Intelligence (AI) for early-stage detection of chlorophyll content and as well as plant disease prediction. The important advantage ‎of AI AI-based detection approach is to identify the leaf area turning yellow or brown at the start with optimal accuracy‎.

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    Kesavan, S., Khekare, G., Midhunchakkaravarthy, D., Bhavani , N. ., & Malathi , M. . (2025). Cascaded CNN For Early Detection of Plant Diseases Machine ‎Vision Techniques in Smart Agriculture. International Journal of Basic and Applied Sciences, 14(4), 55-66. https://doi.org/10.14419/b9c87e95