Machine Learning Based Novel Hybrid Approach for PlantLeaf Disease Detection and Feature Extraction Alongwith Statistical Analysis Using Big Data

  • Authors

    • Mrs. P. Valarmathi Research scholar (Part Time), Department of Computer Science, Periyar University, Salem -636 011. Assistant Professor, Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women (Autonomous), Affiliated to ‎Periyar University, Tiruchengode, Namakkal DT, India-637 205‎
    • Dr. I. Laurence Aroquiaraj Professor, Department of Computer Science, Periyar University, Salem, India- 636 011‎
    https://doi.org/10.14419/0726ga64

    Received date: November 3, 2025

    Accepted date: December 11, 2025

    Published date: December 31, 2025

  • Canny Edge Detection; Long Short Term Memory (LSTM); Plant Disease Detection; Colour Feature Extraction

  • Abstract

    Plant diseases pose a serious threat to global food security and agricultural productivity [29]. They are commonly caused by pathogens such ‎as viruses, bacteria, fungi, insects, rusts, and nematodes, with most infections affecting the stem and leaves of plants [2]. To address these ‎challenges, researchers have applied various approaches including deep learning methods, image processing techniques, and machine learn‎ing algorithms. This study focuses on two main techniques: Canny Edge Detection (CED) and color-based feature extraction, selected after ‎reviewing 43 research articles published in reputed journals. Additionally, Long Short-Term Memory (LSTM) networks were integrated ‎with Canny Edge Detection to enable disease detection in videos and time-series data. For experimental evaluation, leaf samples from lady’s ‎finger, brinjal, and tomato were tested. The proposed model demonstrated high accuracy: 99.55%, 99.10%, and 98.7% using Canny Edge ‎Detection; 99.62%, 99.23%, and 99.13% with color-based feature extraction; and 99.48%, 99.50%, and 99.39% using the hybrid model. ‎This digital plant disease detection framework can provide significant support to farmers and cultivators by enabling timely and accurate ‎identification of crop diseases‎.

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  • How to Cite

    Valarmathi, M. P. (2025). Machine Learning Based Novel Hybrid Approach for PlantLeaf Disease Detection and Feature Extraction Alongwith Statistical Analysis Using Big Data (D. I. L. . Aroquiaraj , Trans.). International Journal of Basic and Applied Sciences, 14(8), 633-642. https://doi.org/10.14419/0726ga64