Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization

One of the world's most widely grown crops is corn. Crop loss due to diseases has a major economic effect, putting the food supply in jeopardy. In many parts of the world, lack of infrastructure still slows disease diagnosis. In this context, effective detection of corn leaf diseases is necessary to limit any unfavorable impacts on the yield. This research has been carried out on the corn leaf images, having three classes of diseases and one healthy class, collected from web resources by using the densely connected convolutional neural networks (CNNs). In this work, VGG16, a variant of CNN, is investigated to classify the infected and healthy leaves. We conduct four different sets of experiments using pretrained VGG16 as a classifier, feature extractor, and fine-tuner. To improve our results, Bayesian optimization is used to choose optimal values for hyperparameters, and transfer learning is explored to fine-tune and reduce the training time of the proposed models. In comparison with earlier proven methods, transfer learning on VGG16 produced better results by leveraging a test accuracy of more than 97% while requiring less training time.