Insects make up a large proportion of living organisms. Research shows that their numbers are declining and many species are becoming extinct. However, monitoring insect ecosystems is difficult as existing methods are labor-intensive and inefficient.
But thanks to advances in artificial intelligence, entomologists are now actively using deep learning models to classify insects. Converged neural networks are showing great potential for automatic detection and classification of insects from videos and images.
Image collection has also become easier with improved sensors. Microcomputers use unique processors for real-time object detection. Radar sensors also allow insects to be studied at scale.
Monitoring using AI models can map insect biodiversity, identify endangered species and develop solutions to save them from extinction. Combining image data with acoustic data can help identify insects that are harder to detect. AI can even analyze insect movement data in microclimatic variations to construct thermal performance curves for species.
To verify results based on images that cannot definitively identify insects, and to collect new data for AI training, hands-on human involvement is needed. To this end, it is possible to collect independent data on insect DNA that is left on flowers or other objects, or targeted capture of insects visible to the camera. Updating insect datasets, public accessibility for learning with proper hardware infrastructure, metadata, and classification is very important for going beyond the possible.
Artificial intelligence is thus a great way to process data from cameras and other insect sensors that operate continuously during diurnal and seasonal cycles. The analysis of this data makes it possible to estimate insect abundance, diversity, and biomass. And in the future, deep learning models can quantify variations in phenotypic traits, behavior, and interactions, which will revolutionize entomology.
