AI‑Powered Deep Learning Web Application for Automated Plant Disease Diagnosis with Rich Visual Analytics

Intelligent Agriculture

Research Article

AI‑Powered Deep Learning Web Application for Automated Plant Disease Diagnosis with Rich Visual Analytics

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https://doi.org/10.54963/ia.v1i2.1665
Volume 1 Issue 2 (2025): In Progress
Copyright (c) 2025 Jaydish John kennedy, Gurusamy Chelladurai
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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Intelligent Agriculture (IA) publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by Intelligent Agriculture (IA) agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.

Authors

  • Jaydish John kennedy

    Department of Botany, St. Joseph’s College (Autonomous), Trichirappalli, Tamil Nadu 620002, India
  • Gurusamy Chelladurai

    Department of Botany, St. Joseph’s College (Autonomous), Trichirappalli, Tamil Nadu 620002, India

Received: 23 July 2025 | Revised: 12 September 2025 | Accepted: 18 September 2025 | Published Online: 2 October 2025

This work presents an AI system powered by artificial intelligence and based on deep learning for diagnosing and detecting plant diseases. Using a CNN that has been trained and optimized on the Plant Village dataset, major crops such as tomatoes, potatoes, and bell peppers can have their illnesses properly classified. The method provides comprehensive diagnostic data, including taxonomy, organisms responsible for the disease, nutritional deficit mimics, and external symptoms, in addition to illness class predictions. Innovatively, the system incorporates the Rich Python library, which enables a graphical, colour-coded command-line interface. Because of this, users can receive detailed, interactive feedback within the terminal itself. The programme was designed with easy use in mind and is intended for use by researchers, educators, and farmers in real-world agricultural settings. Facilitating the detection and understanding of plant health issues in real time aids in learning and practical decision-making. This study demonstrates how integrating AI with agricultural diagnostics can enhance interpretability, usefulness, and overall impact. Finally, it stresses how technology based on deep learning could revolutionize crop health monitoring and agricultural education.

Keywords:

Plant Disease Detection Deep Learning Image Classification Convolutional Neural Network (CNN)

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