Insect Classification Using Pretrained Deep Neural Networks and Transfer Learning

Ecosystems cannot function without the presence of insects, yet due to their enormous diversity and minute variances, recognising and categorising them may be difficult. It is possible to reliably categorise insect species using deep learning algorithms. Deep learning-based insect categorization may be used for a variety of purposes, such as advancing scientific research, enhancing agricultural pest management, and assisting environmental conservation. The development of a deep learning model allows us to rapidly and precisely identify insects, even when they are in different life stages or seem identical to other species. By doing so, their actions and effects on the environment may be better understood. Considering the advancements in the fields of AI, especially in the area of deep learning, we have proposed to use transfer learning mechanism to classify the insects on IP102 dataset. Various standard models like VGG, Inception, Xception, MobileNet, DenseNet, ResNet and EfficientNet are used as a base model and then fine tuned to learn the representations. The best training set and validation set accuracy are found to be 71.34% and 64.95% respectively for the EfficientNetB7 model.