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.

翻译：蚜虫侵扰对作物生产、农村社区及全球粮食安全构成重大威胁。化学防治虽对最大化产量至关重要，但全田施用化学药剂既不可持续又成本高昂。因此，精准定位和管理蚜虫对靶向施药至关重要。本文主要研究利用深度学习模型检测蚜虫集群。我们提出一种通过检测蚜虫集群估算侵染水平的新方法。为推进本研究，我们从高粱田间采集了大规模数据集，人工筛选出5,447张含蚜虫图像，并对每张图像中的单个蚜虫集群进行标注。为便于机器学习模型的使用，我们进一步将图像裁剪为图像块，最终构建包含151,380个标注图像块的数据集。随后，我们在蚜虫数据集上实现了四种先进目标检测模型（VFNet、GFLV2、PAA和ATSS）并对比其性能。大量实验结果表明，所有模型在平均精度和召回率方面均表现稳定且性能相近。我们进一步提出合并邻近集群并剔除裁剪产生的微小集群，使检测性能提升约17%。本研究证明了利用机器学习模型自动检测和管理害虫的可行性。标注数据集将向研究社区开放共享。