Crop Disease Prediction Using Image Processing

Year : 2026 | Volume : 15 | Issue : 02 | Page : 9 16
    By

    Akshita Rajput,

  • Ahsan Rais,

  • Anshika Maurya,

  • Mariyam Fatima,

  1. Student, Department of Computer Science and Technology, Integral University Lucknow, Uttar Pradesh, India
  2. Student, Department of Computer Science and Technology, Integral University Lucknow, Uttar Pradesh, India
  3. Student, Department of Computer Science and Technology, Integral University Lucknow, Uttar Pradesh, India
  4. Professor, Department of Computer Science and Technology, Integral University Lucknow, Uttar Pradesh, India

Abstract

For any country in the world, its livelihood depends on agriculture. However, crop diseases affect the production and food supply of any country because we are unable to detect crop diseases. This paper presents a machine learning CNN (convolutional neural network) model, which uses images of crops to detect diseases. This model detects the diseases in the early stage and provides us with a solution to the crop diseases. It improves the crop yield and production and maintains the food supply. The method of image processing focuses on extracting features from the image, which helps for detection and classification of disease in the early stage only. This enhances the productivity of the crop. In this model, we provide images from the selected dataset, and then image preprocessing is performed. When the preprocessing is done, features of the crop, like shape, size, and color, are extracted. Then we provide the data for the model to train it, and then testing is performed to provide us with the precision, accuracy, and F-1 score. This model focuses on image processing and deep learning, making it an efficient tool. The combination of image processing and artificial intelligence enables fast disease prediction. This does not require expensive laboratory equipment. The main aim of the model is to help farmers with a timely disease diagnosis. Also, automatic systems like this improve crop management and improve productivity. In short, we can say that crop disease prediction using image processing gives us a cost-effective and reliable method for enhancing decision-making.

Keywords: CNN (Convolutional neural network), crop disease prediction, deep learning, image processing, machine learning

[This article belongs to Research and Reviews : Journal of Crop science and Technology ]

How to cite this article:
Akshita Rajput, Ahsan Rais, Anshika Maurya, Mariyam Fatima. Crop Disease Prediction Using Image Processing. Research and Reviews : Journal of Crop science and Technology. 2026; 15(02):9-16.
How to cite this URL:
Akshita Rajput, Ahsan Rais, Anshika Maurya, Mariyam Fatima. Crop Disease Prediction Using Image Processing. Research and Reviews : Journal of Crop science and Technology. 2026; 15(02):9-16. Available from: https://journals.stmjournals.com/rrjocst/article=2026/view=242504


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Regular Issue Subscription Original Research
Volume 15
Issue 02
Received 02/02/2026
Accepted 20/02/2026
Published 01/05/2026
Publication Time 88 Days
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