Insect Detection

Insect detection research focuses on developing automated systems for identifying and classifying insects, primarily using computer vision and machine learning techniques. Current efforts concentrate on improving the accuracy and robustness of deep learning models, such as convolutional neural networks (CNNs) including variations like ResNet and MobileNet, often incorporating transfer learning and novel architectures to enhance feature extraction and handle challenges like image variability and out-of-distribution samples. These advancements are crucial for applications in agriculture (pest management), biodiversity monitoring (assessing population trends and habitat use), and public health (controlling disease vectors like mosquitoes), enabling more efficient and effective interventions.