Hybrid Deep Learning Model to Detect Uncertain Diseases in Wheat Leaves

Abstract

To improve the wheat crop’s yield, leaf disease detection has been considered as an important research area. In the digital image processing field, computer vision and deep learning models have gained better performance. Several deep learning-based techniques have also been used previously for wheat leaf disease detection, but the accuracy is still considered a challenging task. The basic convolutional neural networks (CNNs) model of deep learning has poor performance for abnormal image orientation, rotation, etc. Hence, to overcome this issue, a hybrid model that is a combination of VGG-16 and a capsule network is implemented in this paper. VGG-16 is a pre-trained model used for the recognition and categorization of objects. CapsNet is a novel architecture for deep learning design to solve CNN’s limitations for rotation, tillage, or other picture anomalies. Thus, a hybrid model has been developed, integrating VGG-16 and capsule network characteristics. The new hybrid model is termed here as WheCNet. Hence, the VGG-16 and capsule network features are combined into a new hybrid model called WheCNet. Features are first extracted by using the VGG-16 model. When there are misalignment issues with the existing deep learning models, CapsNet layers are used. Finally, sigmoid activation functions and fully connected layers are employed. Overfitting can be avoided by introducing dropouts, which are also used to build more generalized WheCNet models. The performance of WheCNet is compared with ResNet, MobileNet, XceptionNet, and VGG for the same dataset. After the successful implementation, WheCNet exhibits validation accuracy of 98%, while ResNet, MobileNet, XceptionNet and VGG achieve accuracy of 65%, 81%, 52%, and 93%, respectively. Hence, the proposed hybrid model, WheCNet will simplify the detection of uncertain diseases in wheat leaves with higher accuracy.