An Efficient CNN Model for Automated Cotton Leaf

Year : 2025 | Volume : 14 | Issue : 03 | Page : 01 10
    By

    Sonali Kamra,

  • Vijay Laxmi,

  1. Research Scholar, Department of Computer Applications, Guru Kashi University, Talwandi Sabo, India
  2. Professor, Department of Computer Applications, Guru Kashi University, Talwandi Sabo, India

Abstract

Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse dataset containing multiple categories of cotton leaf images, including both healthy and diseased samples. The CNN architecture is designed to learn important features from the input images and classify them into predefined disease classes. Training is conducted over 100 epochs, allowing the model to achieve a strong balance between high accuracy and generalization. Performance is evaluated using several key metrics, including accuracy, precision, recall, and confusion matrix analysis. The results confirm the effectiveness of the model in correctly identifying different types of cotton leaf diseases, with minimal misclassification observed across classes. This study highlights the potential of deep learning techniques, especially CNNs, in the domain of precision agriculture. The proposed model enables early disease diagnosis, which is critical for timely intervention and effective disease management. In the future, improvements such as expanding the dataset, applying advanced data augmentation methods, and combining CNN with other machine learning algorithms may further enhance model performance and adaptability. Additionally, deploying this model in a real-time environment, such as a mobile or web application, can significantly increase its accessibility and usability for farmers and agricultural experts. Such tools can assist in on-field disease monitoring, leading to better-informed decision-making and more efficient crop management. Overall, this approach supports the development of smart agricultural solutions and contributes to sustainable farming practices. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse dataset containing multiple categories of cotton leaf images, including both healthy and diseased samples. The CNN architecture is designed to learn important features from the input images and classify them into predefined disease classes. Training is conducted over 100 epochs, allowing the model to achieve a strong balance between high accuracy and generalization. Performance is evaluated using several key metrics, including accuracy, precision, recall, and confusion matrix analysis. The results confirm the effectiveness of the model in correctly identifying different types of cotton leaf diseases, with minimal misclassification observed across classes. This study highlights the potential of deep learning techniques, especially CNNs, in the domain of precision agriculture. The proposed model enables early disease diagnosis, which is critical for timely intervention and effective disease management. In the future, improvements such as expanding the dataset, applying advanced data augmentation methods, and combining CNN with other machine learning algorithms may further enhance model performance and adaptability. Additionally, deploying this model in a real-time environment, such as a mobile or web application, can significantly increase its accessibility and usability for farmers and agricultural experts. Such tools can assist in on-field disease monitoring, leading to better-informed decision-making and more efficient crop management. Overall, this approach supports the development of smart agricultural solutions and contributes to sustainable farming practices.

Keywords: Agriculture, Cotton leaf, Machine learning, Deep learning, Convolutional Neural Network

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

How to cite this article:
Sonali Kamra, Vijay Laxmi. An Efficient CNN Model for Automated Cotton Leaf. Research and Reviews : Journal of Crop science and Technology. 2025; 14(03):01-10.
How to cite this URL:
Sonali Kamra, Vijay Laxmi. An Efficient CNN Model for Automated Cotton Leaf. Research and Reviews : Journal of Crop science and Technology. 2025; 14(03):01-10. Available from: https://journals.stmjournals.com/rrjocst/article=2025/view=229392


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Regular Issue Subscription Original Research
Volume 14
Issue 03
Received 29/05/2025
Accepted 07/07/2025
Published 21/07/2025
Publication Time 53 Days
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