Automated Crop Disease Detection Using Convolutional Neural Networks

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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]27/08/2025 at 10:19 AM[/if 2224] | [if 1553 equals=””] Volume : 03 [else] Volume : 03[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02 | Page : 7 15

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    Kanishka Abhay Sarangdhar, Kanika Debbarma, Enjula Uchoi,

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

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Abstract

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nCrop 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.nn

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

n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Cheminformatics ]

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How to cite this article:
nKanishka Abhay Sarangdhar, Kanika Debbarma, Enjula Uchoi. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Automated Crop Disease Detection Using Convolutional Neural Networks[/if 2584]. International Journal of Cheminformatics. 20/08/2025; 03(02):7-15.

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nKanishka Abhay Sarangdhar, Kanika Debbarma, Enjula Uchoi. [if 2584 equals=”][226 striphtml=1][else]Automated Crop Disease Detection Using Convolutional Neural Networks[/if 2584]. International Journal of Cheminformatics. 20/08/2025; 03(02):7-15. Available from: https://journals.stmjournals.com/ijci/article=20/08/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 03
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02
Received 12/07/2025
Accepted 07/08/2025
Published 20/08/2025
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Publication Time 39 Days

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