A New Dataset and Comparative Study for Aphid Cluster Detection
This work addresses the challenge of pest control for farmers and agricultural stakeholders, but it is incremental as it focuses on dataset creation and model comparison without introducing new methods.
The paper tackles the problem of detecting aphid clusters in crop fields to enable precise pesticide application, by creating a new dataset of 151,000 image patches and comparing four state-of-the-art object detection models, though no specific performance numbers are provided.
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.