Plant Disease Detection from Leaf Images Utilizing Ensemble Convolutional Neural Network

For agricultural productivity maximization and economic loss reduction, accurate and rapid identification of plant diseases is essential. This study introduces a state-of-the-art ensemble learning approach to disease classification in plants using images of their leaves. Here, two pre-trained convolutional neural networks (CNNs) serve as the basis for the classifiers, utilizing the benefits of deep CNNs. Using the pictures of the leaves, these base classifiers pull out general characteristics. After that, to get a good picture of the illness patterns, a multilayer perceptron (MLP) combines the features that were retrieved from the two CNNs. Overfitting the training data is less likely to happen with an ensemble design, which improves generalization. The internet application acknowledges user-uploaded pictures, forms them, and provides real-time expectations alongside a visual representation of the input picture. Moreover, the framework guarantees compatibility with different picture resolutions and keeps up a secure record taking care of the instrument. This system serves as a profitable apparatus for ranchers, analysts, and rural specialists, advertising an available arrangement to distinguish plant maladies expeditiously. By moderating the challenges of manual assessment and progressing symptomatic precision, the framework contributes to economical rural homes and improved nourishment generation efficiency. The proposed approach provided 98.05% testing accuracy that showcases the efficacy of the ensemble learning method in identifying plant diseases.