Pest24: A large-scale very small object data set of agricultural pests for multi-target detection.

• We have established a multi-target data set of agricultural pests, Pest24, for precision agriculture. • We analyzed the data properties of Pest24 finding that three factors mainly affect pest detection. • Experiments suggest the practical values of several STOA detection methods for real-time monitoring pests. • Pest24 can be used as gold standard for DL-based detection algorithm evaluation in the community. Precision agriculture poses new challenges for real-time monitoring pest population in field based on new-generation AI technology. In order to provide a big data resource for training pest detection deep learning models, this paper establishes a large-scale multi-target standardized data set of agricultural pests, named Pest24. Specifically, the data set currently consists of 25,378 field pest annotated images collected from our automatic pest trap & imaging device. Totally, 24 categories of typical pests are involved in Pest24, which dominantly destroy field crops in China every year. We apply several state-of-the-art deep learning detection methods, Faster RCNN, SSD, YOLOv3, Cascade R-CNN to detect the pests in the data set, and obtain encouraging results for real-time monitoring field crop pests. To explore the factors that affect the detection accuracy of pests, we analyze the data set in a variety of aspects, finding that three factors, i.e. relative scale, number of instances and object adhesion, mainly influence the pest detection performance. Overall, Pest24 is featured typically with large scale multi-pest image data, very small object scales, high object similarity and dense pest distribution. We hope that Pest24 promotes accurate multi-pest monitoring for precision agriculture and also benefits the machine vision community by providing a new specialized object detection benchmark. [ABSTRACT FROM AUTHOR]