农业大数据学报2024，Vol.6Issue(3)：400-411,12.DOI:10.19788/j.issn.2096-6369.000023
数据驱动的农业深度学习方法计量分析
Metrological Analysis of Data-driven Deep Learning Methods for Agriculture
摘要
Abstract
With the development and application of artificial intelligence,computer vision,deep learning and other science and technology in the field of agriculture,the data-driven deep learning model for agriculture has become a new research paradigm for agricultural information extraction,and agricultural datasets are the basis for deep learning model training,and high-quality,large-scale,and diverse datasets can effectively improve the model performance,thus boosting the application of deep learning in the field of smart agriculture.To help researchers in related fields better understand the driving force of data for deep learning and give full play to the application of deep learning in the field of agriculture,this paper analyzes the datasets through metrology and summarizes the basic qualities of agricultural datasets such as type,scale,and source,which are divided into four categories according to the deep learning methods,such as target detection,image segmentation,and image recognition,and into seven categories according to the application areas,such as visual navigation,feature recognition,non-destructive testing and other 7 categories.The results show that the type of dataset is dominated by image data,and the data volume of images is concentrated in the range of 500 to 1500,and due to the specificity of agricultural data collection,most of the dataset is constructed by individuals and some of them are from public datasets,and the dataset is mainly utilized to carry out feature recognition.In the future,as the scale of the model becomes larger and larger,the requirements for the dataset are also upgraded,and it is necessary to continuously construct large-scale,balanced distribution,and accurately labeled datasets.In this paper,we provide a theoretical basis for data to promote deep learning agricultural applications by emphasizing the driving force and the importance of data to the deep learning model.关键词
数字农业/深度学习/数据集/计量分析Key words
Digital agriculture/deep learning/datasets/metrological analysis引用本文复制引用
李佳乐,张建华,王健,周国民..数据驱动的农业深度学习方法计量分析[J].农业大数据学报,2024,6(3):400-411,12.基金项目
国家重点研发计划(2022YFF0711805),国家自然科学基金(31971792,32160421),中国农业科学院创新工程(CAAS-ASTIP-2023-AII,ZDXM23011),三亚中国农业科学院国家南繁研究院南繁专项(YBXM2312,YDLH01,YDLH07,YBXM10),中央级公益性科研院所基本科研业务费专项(JBYW-AII-2023-06),三亚崖州湾科技城科技专项(SCKJ-JYRC-2023-45). （2022YFF0711805）
