Open Access

ARTICLE

Detection and Classification of Fig Plant Leaf Diseases Using Convolution Neural Network

Rahim Khan¹, Ihsan Rabbi¹, Umar Farooq¹, Jawad Khan^2,*, Fahad Alturise^3,*

1 Institute of Computer Science & IT, University of Science & Technology, Bannu, 28100, Pakistan
2 School of Computing, Gachon University, Seongnam, 13120, Republic of Korea
3 Department of Cybersecurity, College of Computer, Qassim University, Buraydah, Saudi Arabia

* Corresponding Authors: Jawad Khan. Email: ; Fahad Alturise. Email:

(This article belongs to the Special Issue: Deep Neural Networks-based Convergence Technology and Applications)

Computers, Materials & Continua 2025, 84(1), 827-842. https://doi.org/10.32604/cmc.2025.063303

Received 10 January 2025; Accepted 24 March 2025; Issue published 09 June 2025

Abstract

Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a CNN that can identify and categorize diseases in fig tree leaves. The data for this study was collected from gardens in the Amandi and Mamash Khail Bannu districts of the Khyber Pakhtunkhwa region in Pakistan. To minimize the risk of overfitting and enhance the model’s performance, early stopping techniques and data augmentation were employed. As a result, the model achieved a training accuracy of 91.53% and a validation accuracy of 90.12%, which are considered respectable. This comprehensive model assists farmers in the early identification and categorization of fig tree leaf diseases. Our experts believe that CNNs could serve as valuable tools for accurate disease classification and detection in precision agriculture. We recommend further research to explore additional data sources and more advanced neural networks to improve the model’s accuracy and applicability. Future research will focus on expanding the dataset by including new diseases and testing the model in real-world scenarios to enhance sustainable farming practices.

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