Authors

Caroline Nyambura

School of Agriculture and Environmental Sciences (SOAES), Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Abstract

Precise identification of foliar diseases in wheat is crucial for the development of effective crop management strategies. This study presents the Wheat Leaf Convolutional (WLC) model, which enhances the VGG16 architecture, with the objective of detecting and classifying six different types of foliar diseases using deep learning techniques. The model is trained on a dataset comprising images of wheat leaves, which has been augmented through the use of generative adversarial networks (GANs) to improve its generalization capabilities. The WLC model attained an impressive accuracy of 94.88%, significantly exceeding that of traditional CNN models such as ResNet-50, AlexNet, and MobileNet. Performance metrics, including recall, precision, and F1 score, were evaluated across six disease categories: leaf rust, black scale, powdery mildew, wheat streak, Septoria, and healthy plants. The experimental results demonstrate that the WLC model effectively and accurately identifies diseases, establishing it as a valuable tool for real-time applications in precision agriculture. This research contributes to the advancement of wheat disease diagnosis, enabling timely interventions and enhanced agricultural practices.

Keywords

Image classification Image processing Agriculture research WLC Precision agriculture Convolutional neural networks Deep learning Generative Adversial Networks Wheat Leaf Convolutional Data Augmentation Wheat diseases

Citation of this Article

Caroline Nyambura. (2025). Employing Deep Learning Approaches for the Automated Diagnosis and Management of Wheat Plant Diseases. International Current Journal of Engineering and Science (ICJES), 4(9), 14-19. Article DOI: https://doi.org/10.47001/ICJES/2025.409003

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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