OIDS-45: A large-scale benchmark insect dataset for orchard pest monitoring

Abstract

Insects play a crucial role in agricultural production and should not be overlooked. However, there is currently no large-scale dataset available specifically for common insects in orchards. Additionally, datasets for computer vision target detection tasks are limited in the field of insects, which hinders the use of deep learning target detection techniques in orchard insect monitoring. This paper presents the OIDS-45 dataset, which is a large-scale dataset for orchard insect monitoring. The dataset contains 58,585 images of 45 categories of common insects found in orchards. The dataset exhibits a long-tailed distribution, and all images are labeled with borders, making them useful for target detection tasks. The dataset represents the category of orchard insects and has a larger sample size, more categories, and more features in the orchard scenario than previous datasets. We compared our dataset with existing typical insect datasets using advanced target detection algorithms to evaluate its features and quality. The experimental results indicate that current target detection algorithms are not yet capable of accurately identifying and detecting insects in orchards. This is due to the small size of individual insects, the morphological similarities between some species, and the existence of multiple growth stages in some insects. The production and release of this dataset aim to support research in the fields of orchard pest control and insect monitoring in orchards.