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.

通过引入一种名为 “Insect-1M” 的新型数据集，本研究旨在为虫类相关的基础模型训练提供资源，并通过微特征自监督学习方法和相关注意机制来提高昆虫图像建模效果，通过介绍一种描述一致性损失来改善微特征建模，从而在昆虫相关任务的标准基准上实现了最先进的性能，为下一代虫类相关视觉模型赋能，将其功能更接近于精准农业的终极目标。