CNN-Based Plant Disease Detection from Leaf Images

  • Authors

    • R. Manivannan Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu Nadu, India
    • A. Amsaram PG Scholar, Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, ‎India
    • V. Vivekanandhan Department of CSE(AI&ML)، Malla Reddy College of Engineering, Secunderabad, India
    • Ravikanth Garladinne Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur,‎Andhra Pradesh, India
    • Gobinathan N Department of Information Technology, V.R.S. College of Engineering and Technology, Tamil Nadu, India
    • Veeramanikandan K Department of Computer Science and Engineering, Panimalar Engineering College, Tamil Nadu, India
    https://doi.org/10.14419/mbt9cc39

    Received date: July 18, 2025

    Accepted date: August 26, 2025

    Published date: September 7, 2025

  • CNN; Classification; Disease Detection; Leaf Images; Prediction

  • Abstract

    Crop diseases pose a significant threat to global food security, and accurately identifying these ‎diseases remains a challenging task. Various diseases—such as gray leaf spot, common rust, ‎northern leaf blight, and others—affect different parts of the plant, including leaves, stems, and ‎roots, ultimately leading to substantial yield losses. Given the wide range of disease types and ‎their visual similarities, manual classification by human experts is both time-consuming and ‎prone to high error rates. To address these challenges, there is a growing need for automated ‎systems capable of detecting and classifying plant leaf diseases with high accuracy. This ‎technical review paper presents a comprehensive overview of common plant diseases, their ‎classifications, and the techniques employed to manage them. Additionally, it explores existing ‎literature and recent advancements in deep learning-based approaches, particularly those using ‎publicly available benchmark datasets for automated plant leaf disease detection and ‎classification‎.

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  • How to Cite

    Manivannan , R. ., Amsaram , A. ., Vivekanandhan , V. ., Garladinne , R. ., N, G. ., & K , V. . (2025). CNN-Based Plant Disease Detection from Leaf Images. International Journal of Basic and Applied Sciences, 14(5), 209-215. https://doi.org/10.14419/mbt9cc39