Automated Crop Disease Detection Using Convolutional Neural Networks

Year : 2025 | Volume : 03 | Issue : 02 | Page : 7 15
    By

    Kanishka Abhay Sarangdhar,

  • Kanika Debbarma,

  • Enjula Uchoi,

  1. Scholar, Computer Science and Engineering, Lovely Professional University National Institute of Technology Jalandhar,, Punjab, India
  2. Professor, Computer Science and Engineering, Lovely Professional University National Institute of Technology, Arunachal Pradesh, India
  3. Professor, Computer Science and Engineering, Lovely Professional University National Institute of Technology Jalandhar,, Punjab, India

Abstract

Crop diseases contribute to major losses in agricultural production worldwide generating enormous economic costs. This study investigates the possibility of Convolutional Neural Networks (CNN) imaging techniques to auto-detect diseases associated with plants through image processing. A model was developed and trained on a publicly available plant disease dataset containing labeled images of several diseases. The CNN could classify various plant diseases with accuracy of 95%, precision of 92%, and recall of 93%. This study also shows a hybrid CNN model that uses the latest preprocessing methods, such as data augmenttion, normalization, and image resizing, to make it better at finding crop diseases. The model finds a good balance between high accuracy, precision, and recall by using a dataset that is available to the public and adding a variety of crop disease images to it. The core discovery in this research was that CNNs, when combined with effective preprocessing, represent a relatively reliable method for detecting diseased plants and soil with less reliance on manual inspection for identifying disease, or with expert knowledge of diseased plants. Also, more research needs to be collected (e.g., and additional careful curation of the existing dataset, more crop types, more plant diseases, and unique/real-world crop grown data) or possibly and more diversification of datasets will be needed for deeper application, including transition from the dataset to in-field, realtime applications.

Keywords: Crop disease detection, convolutional neural networks, image processing, deep learning, agricultural technology, automated diagnostics, plant pathology, sustainable agriculture, dataset preprocessing, real-time deployment

[This article belongs to International Journal of Cheminformatics ]

How to cite this article:
Kanishka Abhay Sarangdhar, Kanika Debbarma, Enjula Uchoi. Automated Crop Disease Detection Using Convolutional Neural Networks. International Journal of Cheminformatics. 2025; 03(02):7-15.
How to cite this URL:
Kanishka Abhay Sarangdhar, Kanika Debbarma, Enjula Uchoi. Automated Crop Disease Detection Using Convolutional Neural Networks. International Journal of Cheminformatics. 2025; 03(02):7-15. Available from: https://journals.stmjournals.com/ijci/article=2025/view=224944


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Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 12/07/2025
Accepted 07/08/2025
Published 20/08/2025
Publication Time 39 Days
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