Comparative Analysis of CNN, EFFICIENTNET and RESNET for Grape Disease Prediction: A Deep Learning Approach
Keywords:
Convolutional Neural Networks, Efficient Net, ResNet, Deep LearningAbstract
Despite being an essential part of the world's agricultural economy, grapes are prone to a number of illnesses that can negatively affect crops quality and productivity. In recent years, application of Deep Learning (DL) techniques in agricultural practices has shown promise in disease prediction and early detection. This study investigates the effectiveness of Convolutional Neural Networks (CNN), Efficient Net, and Residual Networks (ResNet) in predicting diseases in grapevines. The study makes use of a database that includes high-resolution photos of both good and diseased grape leaves, including black rot, leaf blight, and grapevine measles. To standardize and enhance data sets for model training and evaluation, methods for pre-processing are used. Three DL classifiers, namely CNN, Efficient Net, and ResNet, are implemented and fine-tuned using transfer learning. To evaluate the models' performance in disease categorization, a subset of the dataset is used for training, and another subset is used for validation. Assessment criteria includes accuracy, recall, precision and F1-score are utilized to measure the ability to forecast the methods. The outcomes of the experiments demonstrate the comparative performance of CNN, Efficient Net, and ResNet. In this CNN shows the accuracy of 90%, the Efficient Net with an accuracy of 97%, and finally the ResNet with the maximum efficiency of 98%.
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