Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification - Kotwal, Jameer Gulab; Kashyap, Ramgopal; Shafi, Pathan Mohd (Multimedia tools and applications, 2024)
Plant disease (PD) detection is a substantial problem that needs to be tackled to develop the economy and improve agricultural production. Using conventional methods to classify plant leaf diseases consumes more time, undergoes vanishing gradients problems, overfitting issues, etc. However, automatic PD detection using deep learning (DL) has attained great significance in detecting PD during the early stages. Therefore, this paper proposes a hybrid strategy based on optimized automatic DL for plant leaf disease classification (PLDC). Initially, the proposed model performs pre-processing using image resizing and Gaussian filtering. Then, the disease infected region is then segmented using the UNet technique to acquire the relevant region and enhance disease classification accuracy. During segmentation, the weight of the UNet model has been tuned by employing the hunter-prey optimization (Hunt-PO) algorithm. Next, feature extraction is accomplished by means of a gray level co-occurrence matrix (GLCM), scale-invariant feature transform (SIFT) and a Gabor filter to extract the crucial features for classification. Further, based on the extracted features, PLDC is performed using artificial driving-EfficientNet (AD-ENet). The proposed PLDC model is implemented in the python platform through the PlantVillage dataset and assessed the performance in terms of different evaluation measures. Moreover, a proposed model’s performance is compared with existing classifiers. The maximum classification accuracy obtained by the proposed PLDC model is 99.91%, superior to the existing classifiers for leaf disease classification.