Integrated deep learning and ensemble learning model for deep feature-based wheat disease detection

Abstract

Early detection of plant diseases is critical to prevent disease spread and assist farmers. Thanks to their high discrimination ability, Convolutional Neural Network (CNN)-based architectures can offer practical solutions in identifying different plant diseases. This study proposes a combined method to classify wheat plant diseases using the Integrated Deep Learning Framework (IDLF) and ensemble learning (EL) model. The proposed method uses pre-trained deep neural networks (ImageNet -based model (w/pre-training)). In addition, in the classification process, the performance of hybrid methods consisting of 13 deep learning architectures (DLA) trained from scratch, pre-trained DLA, deep, and-machine learning models were analyzed. Moreover, the use of hypercolumn, contrast stretching, and “Contrast Limited Adaptive Histogram Equalization (CLAHE)” techniques are used to improve the image quality of the dataset. It is aimed to obtain a strong classification performance by combining these methods. In the experimental process, in the study using the original dataset, the RegNetY080 model trained from scratch had an accuracy value of 97.64%; in the study with the optimized dataset with CLAHE, an accuracy value of 98.33%; in the study performed with the CLAHE-improved dataset and transfer learning method, an accuracy value of 99.58% was obtained. An accuracy of 99.58% was achieved in the proposed hybrid study with the pre-trained RegNetY080 model and Random Forest (RF). The proposed method using IDLF and EL model provided 99.72% accuracy. When the proposed method was applied to the Wheat Leaf Dataset and Wheat Leaf Disease dataset, 97.56% accuracy was obtained for the Wheat Leaf Dataset and 99.43% for Wheat Leaf Disease. The results of this study can be helpful to experts and farmers in the detection of plant diseases. © 2023 Elsevier B.V.