摘要
蚜虫的发生是影响农作物产量和质量的重要原因之一。对蚜虫进行检测和计数是对虫害早发现、早治理的重要环节。随着信息技术的发展,已经有专家学者利用计算机视觉感知技术对农业害虫进行识别研究,并取得了一定的进展。高质量、大规模的基础数据对计算机视觉的发展往往能够起到决定性作用,缺少高质量、大规模的基础图像数据是蚜虫精准识别研究面临的难题。蚜虫是一类重要的农业害虫,具有尺寸微小、密集分布、虫间遮挡和同种多形态等特征,这些特征对于蚜虫的检测与计数又是一项严峻的挑战。本文提供了包括桃粉蚜、桃蚜、棉蚜、禾谷缢管蚜等13种农业蚜虫数据集,共6287张高清原始图像。这些蚜虫图像是利用单反相机在自然大田环境中采集、以文件夹形式进行存储、经过从事图像数据管理的专业人员清洗和整理、并由植保专家对其进行鉴定和分类的,保障了数据的高质量和可靠性。该数据集可为蚜虫的识别、检测计数和分类提供数据基础。
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.
作者
董伟
朱静波
管博伦
孔娟娟
李闰枚
张萌
张立平
DONG Wei;ZHU JingBo;GUAN BoLun;KONG JuanJuan;LI RunMei;ZHANG Meng;ZHANG LiPing(Institute of Agricultural Economics and Information,Anhui Academy of Agricultural Sciences,Hefei 230001,China)
出处
《农业大数据学报》
2023年第3期112-117,共6页
Journal of Agricultural Big Data
基金
国家自然基金面上项目“知识迁移与因果推理启发的小样本害虫图像识别研究”(项目编号:32171888)
安徽省农业科学院科研计划项目“农业智能化技术研发中心”(2023YL014)。