A New Dataset and Comparative Study for Aphid Cluster Detection

TL;DR

This paper introduces a new dataset of annotated aphid images and compares four object detection models to improve the localization of aphid clusters for targeted pest control.

Contribution

It provides a large, annotated dataset for aphid detection and evaluates multiple models to advance automated pest monitoring techniques.

Findings

Four state-of-the-art detection models were compared.

The dataset contains over 151,000 labeled image patches.

Aphid cluster detection accuracy was improved.

Abstract

Aphids are one of the main threats to crops, rural families, and global food security. Chemical pest control is a necessary component of crop production for maximizing yields, however, it is unnecessary to apply the chemical approaches to the entire fields in consideration of the environmental pollution and the cost. Thus, accurately localizing the aphid and estimating the infestation level is crucial to the precise local application of pesticides. Aphid detection is very challenging as each individual aphid is really small and all aphids are crowded together as clusters. In this paper, we propose to estimate the infection level by detecting aphid clusters. We have taken millions of images in the sorghum fields, manually selected 5,447 images that contain aphids, and annotated each aphid cluster in the image. To use these images for machine learning models, we crop the images into patches and created a labeled dataset with over 151,000 image patches. Then, we implement and compare the performance of four state-of-the-art object detection models.