InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline.

ABSTRACT: Insect pests significantly impact global agricultural productivity and crop quality. Effective integrated pest management strategies require the identification of insects, including beneficial and harmful insects. Automated identification of insects under real-world conditions presents several challenges, including the need to handle intraspecies dissimilarity and interspecies similarity, life-cycle stages, camouflage, diverse imaging conditions, and variability in insect orientation. An end-to-end approach for training deep-learning models, InsectNet, is proposed to address these challenges. Our approach has the following key features: (i) uses a large dataset of insect images collected through citizen science along with label-free self-supervised learning to train a global model, (ii) fi

SUBMITTER: Chiranjeevi S 

PROVIDER: S-EPMC11783291 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature