Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding
Authors: Hoang-Quan Nguyen, Thanh-Dat Truong, Xuan Bac Nguyen, Ashley Dowling, Xin Li, Khoa Luu

In precision agriculture the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield. The current machine vision model requires a large volume of data to achieve high performance. However there are approximately 5.5 million different insect species in the world. None of the existing insect datasets can cover even a fraction of them due to varying geographic locations and acquisition costs. In this paper we introduce a novel "Insect-1M" dataset a game-changing resource poised to revolutionize insect-related foundation model training. Covering a vast spectrum of insect species our dataset including 1 million images with dense identification labels of taxonomy hierarchy and insect descriptions offers a panoramic view of entomology enabling foundation models to comprehend visual and semantic information about insects like never before. Then to efficiently establish an Insect Foundation Model we develop a micro-feature self-supervised learning method with a Patch-wise Relevant Attention mechanism capable of discerning the subtle differences among insect images. In addition we introduce Description Consistency loss to improve micro-feature modeling via insect descriptions. Through our experiments we illustrate the effectiveness of our proposed approach in insect modeling and achieve State-of-the-Art performance on standard benchmarks of insect-related tasks. Our Insect Foundation Model and Dataset promise to empower the next generation of insect-related vision models bringing them closer to the ultimate goal of precision agriculture.