Journal of Agricultural Big Data >
Aphid Image Dataset Based on Natural Background
Received date: 2023-08-15
Accepted date: 2023-08-30
Online published: 2023-11-14
Agricultural pests are important reasons affecting crop yield and quality. Aphid is an important group of agricultural pest. Detecting and counting aphids is an important link for early detection and management of this pest. With the development of information technology, many experts and scholars have conducted extensive research on the identification of agricultural pests using computer vision, and have made certain progress. High-quality and large-scale basic data often play a decisive role in the development of computer vision, but the lack of this kind of image data is one of the challenges faced by pest identification. Aphids have features such as small size, dense distribution, inter insect shelter, and multiple forms of same species. These features also pose a serious challenge for the detection and counting of aphids. This article provides a total of 6287 high-definition original images, including a dataset of 13 agricultural pests (aphids) including peach aphid, cotton aphid, and grain constrictor aphid, etc. These aphid images were collected using DSLR cameras in a natural field environment. In order to ensure the high quality and reliability of the data, these images are cleaned and organized by professional personnel, and identified and classified by experts in the field of plant protection. This dataset can provide a data foundation for recognition, detection, counting and classification of aphids.
Data summary:
| Items | Description |
|---|---|
| Dataset name | Aphid Image Dataset Based on Natural Background |
| Specific subject area | Plant protection |
| Research topic | Aphid |
| Time range | 2013-2023 |
| Geographical scope | China |
| Data types and technical formats | Data type: image; Technical formats:*.jpg |
| Dataset structure | The dataset contains a total of 6287 images of 13 types of aphids, including Hyalopterus amygdali, Myzus persicae, Aphis gossypii, Rhopalosiphum padi, Aphis spiraecola, Aphis craccivora, Uroleucon formosanum, Sitobion miscanthi, Brevicoryne brassicae, Lipaphis erysimi, Rhopalosiphum maidis, Panaphis juglandis, and Nippolachnus piri. |
| Volume of data | 16.8 GB |
| Data accessibility | CSTR: DOI: |
| Financial support | General Program of National Natural Science Foundation of China “Research on Few-shot Pest Recognition Inspired by Knowledge Transfer and Causal Reasoning”(32171888) Anhui Academy of Agricultural Sciences Research Platform Project “Agricultural Intelligent Technology Research and Development Center”(2023YL1014) |
Key words: aphid; computer vision; image data
Wei DONG , JingBo ZHU , BoLun GUAN , JuanJuan KONG , RunMei LI , Meng ZHANG , LiPing ZHANG . Aphid Image Dataset Based on Natural Background[J]. Journal of Agricultural Big Data, 2023 , 5(3) : 112 -117 . DOI: 10.19788/j.issn.2096-6369.230315
| [1] | Jun Liu, Xuewei Wang. Plant diseases and pests detection based on deep learning: a review[J]. Plant Methods, 2021, 24, 17(1):22. doi: 10.1186/s13007-021-00722-9. |
| [2] | 瞿肇裕, 曹益飞, 徐焕良, 等. 农作物病虫害识别关键技术研究综述. 农业机械学报, 2021, 52(7):1-18. |
| [3] | 康飞龙, 李佳, 刘涛, 等. 多类农作物病虫害的图像识别应用技术研究综述. 江苏农业科学, 2020, 48(22): 22-27. |
| [4] | 蒋心璐, 陈天恩, 王聪, 等. 农业害虫检测的深度学习算法综述. 计算机工程与应用, 2023, 59(6): 30-44. |
| [5] | Weiguang Ding, Graham Taylor. Automatic moth detection from trap images for pest management[J]. Computers and Electronics in Agriculture, 2016, 123: 17-28. |
| [6] | DU J, LIU L, LI R, et al. Towards densely clustered tiny pest detection in the wild environment[J]. Neurocomputing, 2022, 490: 400-412. |
| [7] | 刘奎, 聂博文, 王广军, 等. 改进Yolov5的玉米叶部蚜虫检测方法[J]. 合肥学院学报(综合版), 2023, 40(2):81-89. |
| [8] | ZHA M, QIAN W, YI W, et al. A lightweight YOLOv4-based forestry pest detection method using coordinate attention and feature fusion[J]. Entropy, 2021, 23(12): 1587. |
| [9] | 安徽省农业科学院农业经济与信息研究所农业大数据团队. 农业病虫害图像数据库(PDPP)//植物保护数据库[DB/OL]. |
| [10] | 裴浩然. 基于深度学习和关键点的蚜虫检测方法研究[D]. 安庆市: 安庆师范大学, 2022. |
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