Study on key problems for rat hole recognition and count near ground based on deep learning and its application

doi:10.3969/j.issn.1004-1524.20230858

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (9): 2146-2154.DOI: 10.3969/j.issn.1004-1524.20230858

• Biosystems Engineering • Previous Articles Next Articles

Study on key problems for rat hole recognition and count near ground based on deep learning and its application

GUO Xiuming¹(), WANG Dawei²^,³^,⁴^,^*(), LIU Shengping¹^,^*(), ZHU Yeping¹, LIU Xiaohui², LIN Kejian⁴, WANG Jiayu⁵, LI Fei⁵

1. Key Laboratory of Agri-Information Service Technology, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2. State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3. Western Agricultural Research Center, Chinese Academy of Agricultural Sciences, Changji 831100, Xinjiang, China
4. Key Laboratory of Biohazard Monitoring and Green Prevention and Control in Artificial Grassland, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, Inner Mongolia, China
5. Xilingol League Grassland Workstation, Xilinhot 026000, Inner Mongolia, China

Received:2023-07-10 Online:2024-09-25 Published:2024-09-30

Abstract

Abstract:

Rat hole density serves as a significant indicator for assessing the extent of rat damage in grasslands. In the image acquisition for rat hole recognition and count near ground by deep learning, the optimal image input size and shooting angle are crucial. To address these considerations, an image acquisition device equipped with an inclination sensor was designed, and a dataset of 2 325 rat hole images was collected from Xilingol grassland and manually annotated. The recognition performance was compared under 3 image input sizes (416 pixel×416 pixel, 608 pixel×608 pixel, 1 024 pixel×1 024 pixel), 4 shooting angles (21°, 32°, 41°, and 51°), and 2 target recognition models (YOLOv3 and YOLOv4). The findings revealed that YOLOv4 achieved superior performance when the image input size was set as 416 pixel×416 pixel. Furthermore, the recognition performance was optimal at the shooting angle of 41°, while it was the poorest at 32°. The proposed method was validated by comparing it with relevant approaches published within the past three years. These results offered valuable insights to support the development of intelligent monitoring technologies for assessing rat damage in grassland ecosystems.

Key words: object detection, grassland ecosystem, machine vision, rat hole, YOLOv3 model, YOLOv4 model

CLC Number:

S24
TP391.41

GUO Xiuming, WANG Dawei, LIU Shengping, ZHU Yeping, LIU Xiaohui, LIN Kejian, WANG Jiayu, LI Fei. Study on key problems for rat hole recognition and count near ground based on deep learning and its application[J]. Acta Agriculturae Zhejiangensis, 2024, 36(9): 2146-2154.

Figures/Tables 9

References 20

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图片尺寸 Image size	不同模型的精度 Precision of models/%		不同模型的召回率 Recall of models/%		不同模型的推理时间 Time consumption of models/ms
图片尺寸 Image size	YOLOv3	YOLOv4	YOLOv3	YOLOv4	YOLOv3	YOLOv4
416 pixel×416 pixel	89.4	98.2	89.3	91.8	22.8	32.0
608 pixel×608 pixel	90.7	96.0	94.0	90.2	36.4	54.0
1 024 pixel×1 024 pixel	94.6	97.3	98.7	90.6	80.7	120.0

图片尺寸 Image size	不同模型的精度 Precision of models/%		不同模型的召回率 Recall of models/%		不同模型的推理时间 Time consumption of models/ms
图片尺寸 Image size	YOLOv3	YOLOv4	YOLOv3	YOLOv4	YOLOv3	YOLOv4
416 pixel×416 pixel	89.4	98.2	89.3	91.8	22.8	32.0
608 pixel×608 pixel	90.7	96.0	94.0	90.2	36.4	54.0
1 024 pixel×1 024 pixel	94.6	97.3	98.7	90.6	80.7	120.0

拍摄倾角 Shooting angle/(°)	精度 Precision/%	召回率 Recall/%	F1分数 F1 score
21	81.7	81.8	0.896
32	80.1	80.3	0.887
41	91.8	92.1	0.940
51	87.3	89.9	0.920

拍摄倾角 Shooting angle/(°)	精度 Precision/%	召回率 Recall/%	F1分数 F1 score
21	81.7	81.8	0.896
32	80.1	80.3	0.887
41	91.8	92.1	0.940
51	87.3	89.9	0.920

模型 Models	精度 Precision	召回率 Recall
参考文献[7]模型Model in Ref. [7]	91.30	94.08
参考文献[8]方法Model in Ref. [8]	53.32	77.12
参考文献[9]方法Model in Ref. [9]	91.00	77.00
参考文献[10]方法Model in Ref. [10]	91.30	85.90
本文方法Proposed model	98.02	91.80

Study on key problems for rat hole recognition and count near ground based on deep learning and its application

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