Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea

• Published in: Insects (Basel, Switzerland) Vol. 14; no. 6; p. 526
• Main Authors: Lee, Sangjun, Kim, Hangi, Cho, Byoung-Kwan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.06.2023; MDPI
• Subjects: Accuracy; artificial intelligence; Artificial neural networks; Biological monitoring; Biological research; Biology, Experimental; Cameras; Classification; Color; Counting methods; Culicidae; Deep learning; Dengue fever; Embedded systems; Encephalitis; Error analysis; Fluorescence; Human error; Identification and classification; Identification methods; Image acquisition; Image analysis; Image classification; Image enhancement; image identification; Image processing; Imaging systems; Imaging systems in biology; Infections; Infectious diseases; Insects; Labor; Machine learning; Malaria; Methods; Monitoring; mosquito; Mosquitoes; Neural networks; Object recognition; Pest control; Populations; Preempting; Species; Tropical diseases; Vector-borne diseases; Vectors (Biology); West Nile virus; Zika virus; South Korea; United States > US; deep learning; image identification; artificial intelligence; mosquito

Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In...