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Automated Plant Disease Detection Using Efficient Deep Ensemble Learning Model for Smart Agriculture
Abstract
Early diagnosis of plant diseases is essential for successful plant disease prevention and control, as well as agricultural production management and decision-making. In this research, an efficient weighted average deep ensemble learning (EWADEL) model is used to detect plant diseases automatically. Transfer learning (TL) is a technique used to enhance existing algorithms. The performances of several pre-trained neural networks with DL such as ResNet152 DenseNet201, and InceptionV3, in addition to the usefulness of a weighted average ensemble models, are demonstrated for disease linked with leaf identification. To that aim, a EWADEL methodology is being researched in order to construct a robust network capable of predicting 12 different diseases of apple, Pomegranate, and tomato crops. Several convolutional neural network architectures were examined and ensemble to increase predictive performance using the EWADEL. In addition, the proposed approach included an examination of several deep learning models and developed EWADEL models.
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