  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.
