The Key Issues and Evaluation Methods for Constructing Agricultural Pest and Disease Image Datasets: A Review

doi:10.12133/j.smartag.SA202306012

Abstract

Abstract:

[Significance] The scientific dataset of agricultural pests and diseases is the foundation for monitoring and warning of agricultural pests and diseases. It is of great significance for the development of agricultural pest control, and is an important component of developing smart agriculture. The quality of the dataset affecting the effectiveness of image recognition algorithms, with the discovery of the importance of deep learning technology in intelligent monitoring of agricultural pests and diseases. The construction of high-quality agricultural pest and disease datasets is gradually attracting attention from scholars in this field. In the task of image recognition, on one hand, the recognition effect depends on the improvement strategy of the algorithm, and on the other hand, it depends on the quality of the dataset. The same recognition algorithm learns different features in different quality datasets, so its recognition performance also varies. In order to propose a dataset evaluation index to measure the quality of agricultural pest and disease datasets, this article analyzes the existing datasets and takes the challenges faced in constructing agricultural pest and disease image datasets as the starting point to review the construction of agricultural pest and disease datasets. [Progress] Firstly, disease and pest datasets are divided into two categories: private datasets and public datasets. Private datasets have the characteristics of high annotation quality, high image quality, and a large number of inter class samples that are not publicly available. Public datasets have the characteristics of multiple types, low image quality, and poor annotation quality. Secondly, the problems faced in the construction process of datasets are summarized, including imbalanced categories at the dataset level, difficulty in feature extraction at the dataset sample level, and difficulty in measuring the dataset size at the usage level. These include imbalanced inter class and intra class samples, selection bias, multi-scale targets, dense targets, uneven data distribution, uneven image quality, insufficient dataset size, and dataset availability. The main reasons for the problem are analyzed by two key aspects of image acquisition and annotation methods in dataset construction, and the improvement strategies and suggestions for the algorithm to address the above issues are summarized. The collection devices of the dataset can be divided into handheld devices, drone platforms, and fixed collection devices. The collection method of handheld devices is flexible and convenient, but it is inefficient and requires high photography skills. The drone platform acquisition method is suitable for data collection in contiguous areas, but the detailed features captured are not clear enough. The fixed device acquisition method has higher efficiency, but the shooting scene is often relatively fixed. The annotation of image data is divided into rectangular annotation and polygonal annotation. In image recognition and detection, rectangular annotation is generally used more frequently. It is difficult to label images that are difficult to separate the target and background. Improper annotation can lead to the introduction of more noise or incomplete algorithm feature extraction. In response to the problems in the above three aspects, the evaluation methods are summarized for data distribution consistency, dataset size, and image annotation quality at the end of the article. [Conclusions and Prospects] The future research and development suggestions for constructing high-quality agricultural pest and disease image datasets based are proposed on the actual needs of agricultural pest and disease image recognition:(1) Construct agricultural pest and disease datasets combined with practical usage scenarios. In order to enable the algorithm to extract richer target features, image data can be collected from multiple perspectives and environments to construct a dataset. According to actual needs, data categories can be scientifically and reasonably divided from the perspective of algorithm feature extraction, avoiding unreasonable inter class and intra class distances, and thus constructing a dataset that meets task requirements for classification and balanced feature distribution. (2) Balancing the relationship between datasets and algorithms. When improving algorithms, consider the more sufficient distribution of categories and features in the dataset, as well as the size of the dataset that matches the model, to improve algorithm accuracy, robustness, and practicality. It ensures that comparative experiments are conducted on algorithm improvement under the same evaluation standard dataset, and improved the pest and disease image recognition algorithm. Research the correlation between the scale of agricultural pest and disease image data and algorithm performance, study the relationship between data annotation methods and algorithms that are difficult to annotate pest and disease images, integrate recognition algorithms for fuzzy, dense, occluded targets, and propose evaluation indicators for agricultural pest and disease datasets. (3) Enhancing the use value of datasets. Datasets can not only be used for research on image recognition, but also for research on other business needs. The identification, collection, and annotation of target images is a challenging task in the construction process of pest and disease datasets. In the process of collecting image data, in addition to collecting images, attention can be paid to the collection of surrounding environmental information and host information. This method is used to construct a multimodal agricultural pest and disease dataset, fully leveraging the value of the dataset. In order to focus researchers on business innovation research, it is necessary to innovate the organizational form of data collection, develop a big data platform for agricultural diseases and pests, explore the correlation between multimodal data, improve the accessibility and convenience of data, and provide efficient services for application implementation and business innovation.

Key words: agricultural pests, data set, deep learning, monitoring and warning, data acquisition, data annotations, data set evaluation

GUAN Bolun, ZHANG Liping, ZHU Jingbo, LI Runmei, KONG Juanjuan, WANG Yan, DONG Wei. The Key Issues and Evaluation Methods for Constructing Agricultural Pest and Disease Image Datasets: A Review[J]. Smart Agriculture, 2023, 5(3): 17-34.

Figures/Tables 14

Table 1

Comparison of different agricultural pest and disease datasets

序号	数据集名称	类别数量/个	描述	来源
1	Plant Leaves^［16］	22	覆盖12种植物，包括芒果，阿琼，雪桐，番石榴，白耳，贾蒙，麻风树，蓬蓬，罗勒，石榴，柠檬和中国芹等植物，共4503张图像，2278张健康的叶片和2225张患病的叶片	https：//www.kaggle.com/datasets/csafrit2/plant-leaves-for-image-classification
2	Plant Village^［17］	38	利用互联网图像对健康和病害的作物叶片进行标注，一共38个类别，涵盖了苹果、蓝莓、玉米、葡萄、橘子等作物以及作物的17种真菌疾病、4种细菌疾病、2种霉菌疾病、2种病毒性疾病、1种由螨引起的疾病共54，303张健康和病害图片	https：//github.com/spMohanty/PlantVillage-Dataset
3	IP102^［18］	102	主要在互联网上搜集图片并进行标注形成的数据集，含有幼虫、成虫等不同的形态的102个害虫类别。共75,222张图像，训练集45,095张，验证集7508张，测试集22,619张	https：//github.com/xpwu95/IP102
4	Rice Leaf Disease Images^［19］	4	作者自行拍摄构建的患病水稻叶片图像数据集，使用尼康DSLR-D5600拍摄，部分样本来自网络图像，单张图像像素大小为300×300。包含细菌性枯病、稻瘟病、褐斑和苔斑4种，共5932张图片，其中测试集800张，5132张被增强用作训练集	https：//doi.org/10.1016/j.compag. 2020.105527
5	大田作物病害识别研究图像数据集^［20］	15	以图像数据库的形式存储，包含小麦、水稻、玉米3种大田作物的15种病害，共17,625张样本	http：//www.doi.org/10.11922/sciencedb.745
6	葡萄病害识别图像数据集^［21］	7	包含葡萄白粉病、葡萄花叶病毒病、葡萄黑霉病、葡萄灰霉病、葡萄溃疡病、葡萄霜霉病和葡萄酸腐病7种病害，共3622张样本	http：//www.doi.org/10.11922/sciencedb.j00001.00311
7	AgriPest ^［22］	14	共49,707张图像样本，大概按照9：1的方式划分为44,716张训练数据集和4991张验证数据集，包含4种作物的14类害虫	https：//www.mdpi.com/1424-822 0/21/5/1601
8	苹果叶片病害数据集^［23］	5	包含斑点叶落病、褐斑病、花叶病、灰斑病和锈病5种病害，原始图片2029张，其中411张落叶病、435张褐斑病、375张花叶病、370张灰斑病和438张锈病。数据增强后共24,348张样本，图像像素大小统一为512×512	http：//www.agridata.cn/data.htm l#/paperdetail？id=4363
9	桉树害虫数据集^［24］	3	桉树红胶木虱（Eucalyptus redgum lerp psyllid， Glycaspis brimblecombei）、桉树榈蝽科害虫（haumastocoris peregrinus）和一种寄生虫，共748张样本，图像像素为500×500	https：//www.frontiersin.org/articl es/10.3389/fevo.2021.600931/full
10	Rustia2021^［25］	4	包含苍蝇、蓟马、粉虱、蠓类4种虫害，共990张样本	https：//onlinelibrary.wiley.com/do i/10.1111/jen.12834
11	Pest24^［26］	24	包含24种害虫，共25,378张样本。该数据集包含大尺度多目标图像、小尺度对象图像、高相似度对象图像和密集分布对象	https：//doi.org/10.1016/j.compag. 2020.105585
12	西红柿害虫数据集^［27］	8	互联网中收集到的8种常见的害虫，原始图片609张，数据增强后共4263张	https：//data.mendeley.com/datasets/s62zm6djd2/1
13	桔小实蝇等六种常见果园害虫图像数据集^［28］	6	包含桔小实蝇、金龟子、梨小食心虫、青叶蝉、星天牛、柑桔大实蝇6个种类。原始图像1613张，具有显著性特征图片799张，增强后2412张样本	https：//www.agridata.cn/data.html#/datadetail？id=286640

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序号	数据集名称	类别数	样本容量/张	母体标准差	偏度系数	峰态系数	分辨率类型	标注信息
1	IP102	102	75,222	966.59	3.73	14.45	小	有
2	Pest	7	4639	357.04	0.70	-0.89	小	无
3	Plant Village	38	54,303	1158.27	2.73	5.70	小	无
4	西红柿虫害	8	609	32.52	0.05	-0.94	小	无
5	果园害虫	6	1568	169.27	-0.02	-1.50	小	无
6	Rice Leaf Disease Images	4	5932	118.70	-0.72	-1.44	小	无
7	苹果叶片病害	5	24,348	374.38	-0.20	-1.73	小	无
8	Wheat Leaf Dataset	3	407	51.18	1.71	-1.49	大	无

环境	特点
实验环境	周围环境可控、病虫害种类较少、便于采集图像
大田自然环境	周围环境多变、病虫害受季节、温湿度等影响明显
自然光源	光线不可控、受位置、季节、时间等影响较大
人工光源	光线颜色和强弱相对可控、利于拍摄细节特征

序号	设备类型	优点	缺点	适用场景
1	智能手机	灵活方便、简单易操作	图像清晰度不够	室内、户外
2	单反相机	灵活方便、图像清晰、能凸显较多的细节	需要掌握较专业的拍摄技术	室内、户外
3	无人机平台	能更多地拍摄病虫害群体特征	拍摄不到病虫害个体特征	户外
4	自动采集装备	自动化操作、效率高	价格昂贵、一旦安装完毕，只能拍摄特定的种类	室内、户外
5	显微镜	更多凸显图像细节特征	携带不方便、拍摄过程较为麻烦、拍摄背景单一	室内

预测值/实际值	实际正样本	实际负样本
预测正样本	TP	FP
预测负样本	FN	TN

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