  Aphids are efficient vectors to transmit virus yellows in sugar beet fields.
Timely monitoring and control of their populations are thus critical to prevent
the large-scale outbreak of virus yellows. However, the manual counting of
aphids, which is the most common practice, is labor-intensive and
time-consuming. Additionally, two of the biggest challenges in aphid counting
are that aphids are small objects and their density distributions are varied in
different areas of the field. To address these challenges, we proposed a hybrid
automatic aphid counting network architecture which integrates the detection
network and the density map estimation network. When the distribution density
of 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 count
aphids. To the best of our knowledge, this is the first framework integrating
the detection network and the density map estimation network for counting
tasks. Through comparison experiments of counting aphids, it verified that our
proposed approach outperforms all other methods in counting aphids. It achieved
the lowest MAE and RMSE values for both the standard and high-density aphid
datasets: 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 of
the original Yolov5. Especially for extremely small aphids and densely
distributed aphids, the detection performance of the improved Yolov5 is
significantly better than the original Yolov5. This work provides an effective
early warning for the virus yellows risk caused by aphids in sugar beet fields,
offering protection for sugar beet growth and ensuring sugar beet yield. The
datasets and project code are released at:
https://github.com/JunfengGaolab/Counting-Aphids.
