Aphid infestation poses a significant threat to crop production, rural
communities, and global food security. While chemical pest control is crucial
for maximizing yields, applying chemicals across entire fields is both
environmentally unsustainable and costly. Hence, precise localization and
management of aphids are essential for targeted pesticide application. The
paper primarily focuses on using deep learning models for detecting aphid
clusters. We propose a novel approach for estimating infection levels by
detecting aphid clusters. To facilitate this research, we have captured a
large-scale dataset from sorghum fields, manually selected 5,447 images
containing aphids, and annotated each individual aphid cluster within these
images. To facilitate the use of machine learning models, we further process
the images by cropping them into patches, resulting in a labeled dataset
comprising 151,380 image patches. Then, we implemented and compared the
performance of four state-of-the-art object detection models (VFNet, GFLV2,
PAA, and ATSS) on the aphid dataset. Extensive experimental results show that
all models yield stable similar performance in terms of average precision and
recall. We then propose to merge close neighboring clusters and remove tiny
clusters caused by cropping, and the performance is further boosted by around
17%. The study demonstrates the feasibility of automatically detecting and
managing insects using machine learning models. The labeled dataset will be
made openly available to the research community.

使用深度学习模型检测蚜虫簇集，我们提出了一种新的方法来估计感染水平。通过对多麦科田野捕捉的 5,447 张图像中的蚜虫集群进行手动选择并进行注释，我们构建了一个庞大的数据集。然后，我们在该数据集上实施和比较了四种最先进的目标检测模型（VFNet、GFLV2、PAA 和 ATSS）的性能。结果表明，所有模型在平均精度和召回率方面都表现出稳定的相似性能。我们进一步提出合并紧邻簇集和去除由裁剪引起的小簇集的方法，使性能进一步提升约 17%。该研究证明了使用机器学习模型自动检测和管理昆虫的可行性，并将该标注数据集公开提供给研究界。