Aphid Cluster Recognition and Detection in the Wild Using Deep Learning
  Models
Tianxiao Zhang Kaidong Li Xiangyu Chen Cuncong Zhong Bo Luo Ivan Grijalva Brian McCornack Daniel Flippo Ajay Sharda Guanghui Wang
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.