  Insect-pests significantly impact global agricultural productivity and
quality. Effective management involves identifying the full insect community,
including beneficial insects and harmful pests, to develop and implement
integrated pest management strategies. Automated identification of insects
under real-world conditions presents several challenges, including
differentiating similar-looking species, intra-species dissimilarity and
inter-species similarity, several life cycle stages, camouflage, diverse
imaging conditions, and variability in insect orientation. A deep-learning
model, InsectNet, is proposed to address these challenges. InsectNet is endowed
with five key features: (a) utilization of a large dataset of insect images
collected through citizen science; (b) label-free self-supervised learning for
large models; (c) improving prediction accuracy for species with a small sample
size; (d) enhancing model trustworthiness; and (e) democratizing access through
streamlined MLOps. This approach allows accurate identification (>96% accuracy)
of over 2500 insect species, including pollinator (e.g., butterflies, bees),
parasitoid (e.g., some wasps and flies), predator species (e.g., lady beetles,
mantises, dragonflies) and harmful pest species (e.g., armyworms, cutworms,
grasshoppers, stink bugs). InsectNet can identify invasive species, provide
fine-grained insect species identification, and work effectively in challenging
backgrounds. It also can abstain from making predictions when uncertain,
facilitating seamless human intervention and making it a practical and
trustworthy tool. InsectNet can guide citizen science data collection,
especially for invasive species where early detection is crucial. Similar
approaches may transform other agricultural challenges like disease detection
and underscore the importance of data collection, particularly through citizen
science efforts..
