Automated design of Convolutional Neural Network architecture using Gray Wolf Optimization for plant seedlings classification

Convolutional Neural Networks (CNNs) have gained significant popularity in image classification tasks, yet achieving their optimal design remains a challenge due to the vast array of possible layer configurations and associated hyperparameters. Selecting the best CNN model for a given task often demands considerable time investment in training numerous models. To address this issue, we propose an automated method for CNN architecture design, utilizing pretrained models like the Backbone and employing Gray Wolf Optimization. This approach automatically generates and evaluates candidate CNN architectures for classifying plant seedlings. Our objective is to distinguish between weed and crop seedlings. Additionally, we introduce a gray wolf representation to encode CNN architectures and their hyperparameters. Our method combines the strengths of transfer learning from pre-trained models to extract meaningful image features with the optimization capabilities of the Gray Wolf Optimization (GWO) algorithm. By leveraging these techniques, our method achieves exceptional accuracy, surpassing state-of-the-art methods with a validation accuracy of up to 97.83%. This innovative approach offers a transformative tool for enhancing the accuracy of CNN models, tailored specifically to the dataset at hand.