Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.16.8.5
Integrating Squeeze-and-Excitation Network with Pretrained CNN Models for Accurate Plant Disease Detection
Lafta Raheem Ali
; General Directorate of Education of Salahuddin Salahuddin, Iraq
Sabah Abdulazeez Jebur
; Imam Alkadhim University College, Department of Cyber Security Baghdad, Iraq
*
Mothefer Majeed Jahefer
; Imam Alkadhim University College, Department of Computer Science Baghdad, Iraq
Abbas Khalifa Nawar
; Imam Alkadhim University College, Department of Computer Science Baghdad, Iraq
Zaed S. Mahdi
; University of Technology, Information Technology Center Baghdad, Iraq
* Dopisni autor.
Sažetak
The increasing global population and the challenges posed by climate change have intensified the demand for sustainable food production. Traditional agricultural practices are often insufficient, leading to significant crop losses due to diseases and pests, despite the widespread use of pesticides and other chemical interventions. This paper introduces a new approach that integrates deep learning techniques, specifically Convolutional Neural Networks (CNNs) with Squeeze and Excitation (SE) networks, to enhance the accuracy of disease detection in fig leaves. By leveraging three pre-trained CNN models—MobileNetV2, InceptionV3, and Xception—this framework addresses data scarcity issues and improves feature representation while minimizing the risk of overfitting. Data augmentation techniques were employed to counteract data imbalance, and visualization tools like Grad-CAM and t-SNE were utilized for model interpretability. The proposed CNN-SE model was trained and evaluated on a fig leaf dataset comprising 1,196 images of healthy and diseased fig leaves, achieving an accuracy of 92.90% with MobileNet-SE, 91.48% with Inception-SE, and 89.62% with Xception-SE. Our model demonstrates superior performance in detecting fig leaf diseases, presenting a robust solution for sustainable agriculture by providing accurate, efficient, and scalable disease management in crops. The code of the proposed framework is available at https://github.com/lafta/SE-block-with-CNN-Models-for-Plant-Disease-Detection.
Ključne riječi
Deep Learning; Convolutional Neural Network; Squeeze-and-Excitation; Plants diseases detection;
Hrčak ID:
335494
URI
Datum izdavanja:
22.9.2025.
Posjeta: 667 *