Image-Based Analysis for Identification of Plant Leaf Pathologics Using Deep Learning

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@article{1717463,
      author = {R. Deepak Kesav, Dr. S. Prakasam},
      title = {Image-Based Analysis for Identification of Plant Leaf Pathologics Using Deep Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {427-435},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717463.pdf},
      abstract = {This project introduces a Convolutional Neural Network (CNN) as the proposed system for plant disease prediction, with a comparative analysis conducted against two existing models: Recurrent Neural Networks (RNN_GRU) and Artificial Neural Networks (ANN_MLP). The proposed CNN model is specifically designed to address the limitations of traditional approaches, such as lower accuracy and slower prediction times, particularly when handling complex image data. The system allows users to upload images of plant leaves, select the type of plant (e.g., potato, tomato, grape), and choose between RNN_GRU, ANN_MLP, or the newly developed CNN model for disease prediction. Additionally, users can run all three models simultaneously to compare their outputs, enabling a comprehensive evaluation of performance. Predictions are securely stored in a SQLite database, along with metadata such as confidence scores, prediction times, timestamps, and a unique group ID for efficient retrieval and management. Built using Flask, the application provides a professional-grade user interface with features like secure authentication, prediction history tracking, and deletion of past predictions. Comparative analysis demonstrates that the proposed CNN model significantly outperforms RNN_GRU and ANN_MLP in terms of accuracy, prediction speed, and overall reliability, making it a more effective tool for real-time agricultural applications. This advancement highlights the potential of CNNs in transforming agricultural practices by providing faster, more accurate, and reliable disease predictions, thereby contributing to improved crop health, reduced losses, and increased agricultural productivity.},
      keywords = {Plant Disease Detection, Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Flask, SQLite, Image Processing, Machine Learning, Prediction Pipeline.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717463}
  }