Insect Classification

Insect classification is undergoing a rapid transformation driven by advancements in machine learning, aiming to improve the accuracy and efficiency of identifying insect species. Current research heavily utilizes deep learning models, particularly convolutional neural networks (CNNs) like ResNet and MobileNet, often enhanced by techniques such as transfer learning and data augmentation to address challenges posed by high intra-species variability and imbalanced datasets. This work is crucial for biodiversity monitoring, agricultural pest management, and ecological studies, as automated, large-scale insect identification is essential for understanding insect population dynamics and their impact on ecosystems. The development of large, multi-modal datasets incorporating image, DNA barcode, and geographic data is also a key focus, enabling more robust and comprehensive analyses.