Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks

• Published in: Engineering applications of artificial intelligence 2024-07, Vol.133, p.108307, Article 108307
• Main Authors: Catal Reis, Hatice, Turk, Veysel
• Format: Article
• Language: English
• Subjects: Convolutional neural network; Depthwise separable convolution; Image enhancement techniques; Integrated deep learning; Multi-head attention mechanism; Multi-head attention mechanism depthwise separable convolution inception reduction network; Potato leaf disease; Vision transformer; Voting ensemble learning
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2024.108307

Early diagnosis of plant diseases is essential in reducing economic losses for farmers and increasing production efficiency. Therefore, Computer-Aided Diagnosis (CAD) systems supported by artificial intelligence technologies can be developed to help diagnose diseases quickly and accurately by examining the symptoms and signs in plant leaves. In this study, Multi-head Attention Mechanism Depthwise Separable Convolution Inception Reduction Network (MDSCIRNet) architecture, an image-based deep convolutional neural network, is proposed for classifying potato leaf diseases. The main components of the MDSCIRNet architecture are depthwise separable convolution (DSC) and a multi-head attention mechanism. The proposed architecture has been compared with modern algorithms developed with DSC technology, such as Xception, MobileNet, and deep learning algorithms, such as ResNet101, InceptionV3, and EfficientNetB2, to evaluate its performance in the classification process. In addition, hybrid methods developed with the classical machine learning algorithms Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Adaptive Boosting (AdaBoost), and MDSCIRNet model, integrated deep learning model, hard voting ensemble learning model. Suggested methods such as these were also used in the experimental process. Moreover, techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), and Hypercolumn were used to improve the image quality of the data set. In the experimental process, the MDSCIRNet deep learning architecture achieved 99.24% accuracy in the study using the original dataset. While a 99.11% accuracy rate was achieved with the integrated deep learning model and hard voting ensemble learning model, the highest success rate of 99.33% was performed in the study conducted with the MDSCIRNet + SVM method. This study contributes to developing new and effective strategies in the agricultural industry for the early diagnosis and control of potato plant diseases. Machine learning-based approaches offer the potential to minimize economic losses and increase productivity in production by allowing farmers to intervene early. •A novel deep learning model (MDSCIRNet) based on transformer architecture and depthwise separable convolutions is proposed to classify potato leaf disease.•A new method is proposed combining the integrated deep learning and ensemble learning models.•Hybrid m