Bee vs Wasp Classification Using Advanced Deep Learning Techniques: CNN, VGG 16
Keywords:
CNN, VGG16, VGG19, ResNet34, Mobile-NetAbstract
This paper explores the use of Convolutional Neural Networks (CNNs) and ResNet-34 architecture for grasshopper and grasshopper classification and discrimination from image datasets Using the deep learning capabilities of CNNs and the rest of ResNet-34 learning a, we address image recognition challenges in biological monitoring. Various data sets of bee and wasp images were used to train and validate the ResNet-34 model. The model performed better than traditional methods and achieved high accuracy in discriminating between two groups of insects. This study demonstrates the potential of CNN and ResNet-34 to automatically identify insects, supporting biodiversity research and conservation efforts.
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