ODHEPDC: Optimal Trained Deep Hybrid Ensemble of Classifier for Plant Disease Classification With Improved Deep Fuzzy Clustering

Abstract

Plant diseases are the major factors that affects the quality production as it affects or interrupts the plant's vital functions. The early detection of crop disease could assist farmers in implementing the right preventative measures at the right moment to eradicate it. The main goal of the ODHEPDC (Optimal Trained Deep Hybrid Ensemble of Classifier for Classification of Plant Disease) model is the classification of diseases leaf images. The primary step is to improve the input image by using the MF model to remove the noise. This is considered as the preprocessing step. Improved fuzzy clustering algorithm, leading to the identification of the regions, ROI as well as non-ROI. Next to this, the appropriate features are extracted to define the feature set that includes MPPT feature, PHOG feature, and MTP features as well. However, the curse of dimensionality is the greatest crisis in the classification problem, hence, improved feature level fusion is progressed, which is the simple concatenation of the extracted features. In this, the improved calculation of information gain ensures the reduction and fusion of feature set. The fused features are the inputs to ensemble classification model with the classifiers like CNN, RNN, and DBN classifiers, which gives the classified results. To boost up the performance of classification model, the Deep Maxout model in the ensemble is optimally trained by a new Bald Eagle Search Updated Pelican Optimization (BESUPO) Algorithm via optimal weights tuning as the model determines the final classification outcome. The validation results prove the disease classification performance via the given architecture than extant schemes.