大田作物主要病虫害图像数据集综述

doi:10.19788/j.issn.2096-6369.100048

Abstract

Abstract:

Food security is a critical foundation for national stability and economic development, while pest and disease outbreaks in field crops pose a severe threat to grain production, necessitating efficient and precise monitoring and control measures. In recent years, deep learning-based pest and disease image recognition technologies have gained prominence, relying heavily on high-quality image datasets. However, the currently available public datasets face limitations in scale, coverage, and quality, hindering further breakthroughs in research and practical applications. This paper systematically reviews existing image datasets of major field crops, including rice, wheat, maize, and potato, focusing on their sources, key characteristics, and application scenarios. It also analyzes major challenges in data volume, diversity, and standardization. The findings reveal that while datasets exhibit a certain representativeness regarding collection time, location, and pest and disease types, improvements are needed in class balance, diversity, and cross-domain sharing. Summarizing and organizing these datasets provides technical support and theoretical insights to advance precision agriculture and ensure food security.

Key words: field crops, pest and disease images, datasets

ZHAO XiaoDan, HU Lin, LIU TingTing. Review of Image Datasets for Field Crop Pest and Disease Management[J].Journal of Agricultural Big Data, 2026, 8(1): 113-127.

Figures/Tables 9

Table 1

Table 2

Summary of crop disease and pest types"

作物类别	病害类型	虫害类型
水稻	水稻细菌性白叶枯病（Rice Bacterial Blight, RBB）、稻曲病（Rice Sheath Blight, RSB）、稻瘟病（Rice Blast, RB）、胡麻斑病（Sesame Leaf Spot, SLS）、亚麻叶斑病（Flax Leaf Spot, FLS）、纹枯病（Sheath Rot, SR）、细菌性条斑病（Bacterial Leaf Streak, BLS）、窄条斑病（Narrow Brown Leaf Spot, NBLS）、恶苗病（Rice Seedling Blight, RSB）、假黑粉（False Smut, FS）和穗颈瘟（Neck Blast, NB）	稻螟虫（Rice Stem Borer, RSB）、稻瘿蚊（Rice Gall Midge, RGM）、三化螟虫（White-backed Planthopper, WBPH）、稻纵卷叶螟（Rice Leaf Folder, RLF）、稻铁甲虫（Rice Weevil, RW）、飞虱（Planthopper, PH）、褐飞虱（Brown Planthopper, BPH）、灰飞虱（Grey Planthopper, GPH）、稻蓟马（Rice Thrips, RT）、蚜茧蜂（Aphid Parasitoid, AP）、稻蝗（Rice Grasshopper, RG）、稻棘缘蝽（Rice Stink Bug, RSB）
小麦	小麦白粉病（Wheat Powdery Mildew, WPM）、小麦赤霉病（Wheat Fusarium Head Blight, FHB）、小麦梭条斑花叶病（Wheat Stripe Rust, WSR）、小麦雪霉叶枯病（Wheat Snow Mold, WSM）、小麦叶锈病（Wheat Leaf Rust, WLR）、小麦黑星病（Wheat Black Spot, WBS）、小麦黄锈病（Wheat Yellow Rust, WYR）、小麦褐锈病（Wheat Brown Rust, WBR）、小麦黑穗病（Wheat Smut, WS）、小麦秆锈病（Wheat Stem Rust, WSR）、小麦枯萎病（Wheat Wilt Disease, WWD）、卡尔纳尔束穗病（Karnal bunt, KB）、黑杆病（Wheat Black Chaff, WBC）、冠腐病（Wheat Crown Rot, WCR）、根腐病（Wheat Root Rot, WRR）	小麦蚜（Wheat Aphids, WA）
玉米	大斑病（Maize Northern Corn Leaf Blight, NCLB）、南方锈病（Maize Southern Rust, MSR）、小斑病（Maize Southern Leaf Blight, MSLB）、玉米锈病（Maize Rust, MR）、虱锈病（Maize Leaf Rust, MLR）、黑粉病（Maize Smut, MS）、玉米丝黑穗病（Maize Head Smut, MHS）、玉米穗腐病（Maize Ear Rot, MER）、玉米纹枯病（Maize Gray Leaf Spot, GLS）、玉米致死性坏死病（Maize Lethal Necrosis, MLN）、玉米条纹病毒病（Maize Streak Virus, MSV）	玉米象（Maize Weevil, MW）、玉米螟（Maize Borer, MB）、玉米蚜虫（Maize Aphids, MA）、玉米叶螨（Maize Spider Mite, MSM）、玉米黏虫（Maize Armyworm, MAW）
马铃薯	早疫病（Potato Early Blight）；晚疫病（Potato Late Blight）；马铃薯黄化病毒病（Potato Yellowing Virus Disease, PYVD）；马铃薯黑斑病（Potato Black Spot Disease, PBSD）；马铃薯根腐病（Potato Root Rot Disease, PRRD）；马铃薯溃疡病（Potato Ulcer Disease, PUD）	大甲虫（Colorado Potato Beetle, CPB）；金针虫（Wireworm, WW）；温室白粉虱（Greenhouse Whitefly, GHW）、28星瓢虫（28-spotted Lady Beetle, 28SLB）；蝼蛄（Mole Cricket, MC）；马铃薯块茎蛾（Potato Tuber Moth, PTM）；地老虎（Cutworm, CW）；牧草盲蝽（Alfalfa Plant Bug, APB）；猿叶虫（Flea Beetle, FB）；蚜虫（Aphid, AP）
棉花	棉花黄萎病（Cotton Verticillium Wilt, CVW）；棉花枯萎病（Cotton Fusarium Wilt, CFW）；棉花立枯病（Cotton Seedling Damping-off, CSD）；棉花炭疽病（Cotton Anthracnose, CA）	棉铃虫（Cotton Bollworm, CBW）；盲蝽象（Lygus Bug, LB）；棉蚜（Cotton Aphid, CA）；红蜘蛛（Red Spider Mite, RSM）；棉叶螨（Cotton Leaf Mite, CLM）；烟粉虱（Whitefly, WF）

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字段		字段解释
论文	题目	论文的标题，明确研究聚焦的主题
	作者	研究并撰写论文的学者名单
	通讯信息	提供未公开数据的获取渠道以补充数据、代码或方法
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	作物	数据集所涉及的作物类别
	介绍	对数据集的内容和特点进行简要描述，包括数据量、采集方法、主要特征等
	采集时间	数据采集的时间范围
	采集地点	数据采集的地理位置
	服务系统网址/获取网址/DOI	主要是准确定位到数据集，便于数据引用和跨平台检索其中DOI为数据集的永久唯一标识符
	获取/版权协议	数据集是否可获取/使用条款和限制

特征类型	指标	统计内容
空间覆盖	覆盖国家/区域	中国；巴基斯坦；巴西；孟加拉国；印度；土耳其；黎巴嫩；越南；坦桑尼亚；加纳；尼日利亚；埃塞俄比亚；美国
空间覆盖	覆盖洲	亚洲（57.15%）；非洲（28.57%）；北美洲（7.14%）；南美洲（7.14%）
时间覆盖	采集季节	生长季（如水稻拔节-抽穗期、小麦孕穗期）、非生长季
时间覆盖	采集时间	2013年−2023年
数据集生产者	机构类型	高校；科研院所；农业部门；科技企业
数据集生产者	主要来源	数据论文（16.07%）；研究型论文（42.86%）；在线公开数据源（41.07%）

数据集/论文名称	病虫害类型	数据规模	获取地址	图像分辨率	采集时间	采集区域
Bacterial blight of rice dataset	水稻细菌性白叶枯病	数据集大小为2.65 GB，包含975张图像	https://doi.org/10.11922/sciencedb.p00001.00001	300dpi	2014年 9月22日	中国，广西壮族自治区，横州市
Rice pest dataset supports the construction of smart farming systems	亚洲水稻螟、褐飞虱、稻茎蝇、水稻根瘿蚊、水稻叶卷叶蛾、水稻叶蛾、水稻叶蝉、水稻象甲、小褐飞虱和黄水稻螟	数据集大小约为69.6 MB，包含10个类别的3 156张图像	https://zenodo.org/records/8418217	312×312 像素	2022年	图像数据来自各种公开可用的关于不同类型水稻害虫的数据集，由越南湄公河三角洲水稻研究所的专家进行标注和评估
A recognition method of crop diseases and insect pests based on transfer learning and convolution neural network	水稻白叶病、水稻亚麻斑病、稻瘟病	使用的数据集来自农业病虫害研究图像数据库（IDADP）。IDADP包含大量水稻图像资源，论文选取600张图片作为研究对象	http://www.icgroupcas.cn/website_bchtk/fuwu.html	2000 万像素	2018年至今	网络公开数据集；业内同行贡献

数据集/论文名称	病虫害类型	数据规模	获取地址	图像分辨率	采集时间	采集区域
Wheat Leaf Dataset	小麦条锈病、小麦叶斑病	数据集大小为1.41 GB，包含102张健康叶片、208张条锈病、97张斑点病叶片	http://dx.doi.org/10.17632/wgd66f8n6h.1	5760×3840 像素	2021年	埃塞俄比亚，霍莱塔小麦农场
The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop ^[29]	小麦条锈病	NWRD数据集是专为小麦锈病语义分割而构建的小麦锈病的病叶和健康叶片图像真实世界分割数据集。NWRD数据集目前共包含100幅图像	https://github.com/dll-ncai/NUST-Wheat-Rust-Disease-NWRD	6016×4000 像素； 4608×3456 像素	2022年 2−4月	巴基斯坦，伊斯兰堡的农田
Leaf and spike wheat disease detection & classification using an improved deep convolutional architecture ^[106]	卡尔纳尔束穗病、黑谷壳、冠腐病和根腐病、镰刀菌枯萎病、叶锈病、白粉病、棕褐斑病、小麦散黑穗病、小麦条纹花叶	Large Wheat Disease Classification Dataset (LWDCD)小麦病害分类数据集，包含12 000张照片，其中9个病害类别和1个健康类别	https://drive.google.com/drive/folders/1OHKtwD1UrdmhqxrpQEeF_X_pqKotxRGD	96 dpi	2020年	40%为现场收集，其他图像来源为公开可用数据集

数据集/论文名称	病虫害类型	数据规模	获取地址	图像分辨率	采集时间	采集区域
MahindiNet: Maize Leaf Disease Dataset	玉米黏虫	数据集大小为1.79 GB，共包含1 392张图像	https://doi.org/10.57760/sciencedb.12556	72dpi	2023年6月	埃及
Machine Learning Imagery Dataset for Maize Crop: A Case of Tanzania^[22]	玉米致死性坏死病、玉米条纹病	数据集大小为4.26 GB，共有18 148张图像	https://dataverse.harvard.edu/dataset.xhtml persistentId= doi:10.7910/DVN/GDON8Q	96dpi	2021年2月1日至2021年7月1日	南非
一种基于改进神经网络算法ResNet50的玉米病虫害识别模型^[75]	黑粉病、灰斑病、南方锈病、丝黑穗病、穗腐病、纹枯病、小斑病、锈病、叶斑病、玉米螟、玉米蚜虫、玉米叶螨、玉米黏虫	数据集来自百度飞桨中的玉米14类病虫害公开数据集，共6 571张图片。	https://aistudio.baidu.com/datasetdetail/91944； https://aistudio.baidu.com/datasetdetail/181688	28×28像素	2014年	网络公开数据集

Review of Image Datasets for Field Crop Pest and Disease Management

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数据集/论文名称	病虫害类型	数据规模	获取地址	图像分辨率	采集时间	采集区域
Irish Potato Imagery Dataset for Early Detection of Crop Diseases	早疫病、晚疫病	数据集大小为7.1 GB，共包含58 709张图像文件	https://zenodo.org/doi/10.5281/zenodo.8286529	250×240像素	2022年11月22日至2023年4月8日	坦桑尼亚南部高地的姆贝亚地区
2022年内蒙古无人机马铃薯图像数据集^[14]	马铃薯斑块叶片	数据集大小为39 GB，共有 11 438张图像	10.12205/A0007.20220923.12.is.2483	800×560像素	2022年8月13日、16日和18日	中国，内蒙古呼伦贝尔两块成熟期种薯试验田
基于渐进式生成对抗网络的农作物病虫害细粒度分类^[48]	早疫病、晚疫病、马铃薯黄化病毒病、马铃薯黑斑病、马铃薯根腐病、马铃薯溃疡病	采用公开数据集：PlantVillage农作物病虫害数据集。包括小麦、马铃薯等14种作物共38个病害类别共有54 303张健康和病害图片。	https://tianchi.aliyun.com/dataset/160100	256×256像素；224×224像素；1024×1024像素；	2017年	实验室拍摄

数据集/论文名称	病虫害类型	数据规模	获取地址	图像分辨率	采集时间	采集区域
Cotton Crop Diseases	棉花枯萎病、棉花粉蚧病	数据集大小为4.8 GB，共包含999张图像文件	https://zenodo.org/doi/10.5281/zenodo.8383872	72dpi	2021年8月	巴基斯坦南旁遮普地区
新疆棉田主要昆虫图像数据集 CottonInsect^[18]	苜蓿盲蝽、棉铃虫、牧草盲蝽、绿盲蝽、黑食蚜盲蝽、茶翅蝽、中黑盲蝽	包含13种（类）常见的棉田昆虫，其中7类为害虫；共 3 225张图像，原始图像共24 GB	https://doi.org/10.57760/sciencedb.j00001.00866	96dpi	2022年	中国，新疆沙雅县
A comprehensive cotton leaf disease dataset for enhanced detection and classification^[79]	细菌性斑点病、卷叶病毒、除草剂生长损伤、叶蝉危害、叶片变红、叶片斑驳	数据集大小为1.46 GB，包含2 137张原始图像和7 000张增强图像	https://data.mendeley.com/datasets/b3jy2p6k8w/1	231×231像素	2023年10月至2024年1月	孟加拉国家棉花研究所

技术类型	数据集特征	实例
图像处理与特征提取	清晰、细节丰富的图像；较高的分辨率；标注精确；涵盖不同病害、环境条件以及各个生长阶段的图像	孔建磊等依托北京农业智能装备技术研究中心采集图像数据集并提出特征提取的方法^[37]
模型改进与优化	大规模数据集；高标注一致性；适应性强的数据格式	KANG F等研究基于轻量级神经网络的针对马铃薯早疫病和晚疫病叶片的识别模型^[82]
数据增强和迁移学习	有限样本的数据集；高质量标注；包括不同的角度、尺度、光照条件等；适配预训练模型的数据格式	赵立新等以棉花叶部病虫害图像为研究对象，利用迁移学习算法并辅以数据增强技术，实现棉花叶部病虫害图像准确分类^[55]
注意力机制与多模态预训练	多模态数据；高质量的图像和标签；空间和时空信息；丰富的上下文信息	李英辉等提出了一种主成分分析的特征加权融合自适应算法和改进支持向量机相结合的马铃薯病虫害快速检测方法^[31]

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