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.

翻译：蚜虫是威胁农作物、农村家庭及全球粮食安全的主要因素之一。化学害虫防治是作物生产中提高产量的必要环节，然而考虑到环境污染和成本问题，无需将化学方法应用于整个农田。因此，准确定位蚜虫并评估侵染程度对于精确局部施用杀虫剂至关重要。蚜虫检测极具挑战性，因为单个蚜虫体型极小且常以集群形式密集聚集。本文提出通过检测蚜虫集群来评估侵染程度。我们在高粱田采集了数百万张图像，手动筛选出包含蚜虫的5,447张图像，并对图像中的每个蚜虫集群进行了标注。为将这些图像用于机器学习模型，我们将图像裁剪为补丁，创建了一个包含超过151,000个图像补丁的标注数据集。随后，我们实现并比较了四种最先进的目标检测模型的性能。