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Aphids are efficient vectors to transmit virus yellows in sugar beet fields.Timely monitoring and control of their populations are thus critical to preventthe large-scale outbreak of virus yellows. However, the manual counting ofaphids, which is the most common practice, is labor-intensive andtime-consuming. Additionally, two of the biggest challenges in aphid countingare that aphids are small objects and their density distributions are varied indifferent areas of the field. To address these challenges, we proposed a hybridautomatic aphid counting network architecture which integrates the detectionnetwork and the density map estimation network. When the distribution densityof aphids is low, it utilizes an improved Yolov5 to count aphids. Conversely,when the distribution density of aphids is high, its witches to CSRNet to countaphids. To the best of our knowledge, this is the first framework integratingthe detection network and the density map estimation network for countingtasks. Through comparison experiments of counting aphids, it verified that ourproposed approach outperforms all other methods in counting aphids. It achievedthe lowest MAE and RMSE values for both the standard and high-density aphiddatasets: 2.93 and 4.01 (standard), and 34.19 and 38.66 (high-density),respectively. Moreover, the AP of the improved Yolov5 is 5% higher than that ofthe original Yolov5. Especially for extremely small aphids and denselydistributed aphids, the detection performance of the improved Yolov5 issignificantly better than the original Yolov5. This work provides an effectiveearly warning for the virus yellows risk caused by aphids in sugar beet fields,offering protection for sugar beet growth and ensuring sugar beet yield. Thedatasets and project code are released at:https://github.com/JunfengGaolab/Counting-Aphids.

Advancing Early Detection of Virus Yellows: Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields