[1]冉颖杭,谢天铧,霍连飞,等.农田背景噪声下的土壤结构体数字图像信息特征[J].江苏农业学报,2019,(02):313-320.[doi:doi:10.3969/j.issn.1000-4440.2019.02.011]
　RAN Ying-hang,XIE Tian-hua,HUO Lian-fei,et al.Information characteristics of digital images of in-situ soil structure under the background noise in the farmland[J].,2019,(02):313-320.[doi:doi:10.3969/j.issn.1000-4440.2019.02.011]
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农田背景噪声下的土壤结构体数字图像信息特征()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年02期
页码:
313-320
栏目:
耕作栽培·资源环境
出版日期:
2019-04-30

文章信息/Info

Title:
Information characteristics of digital images of in-situ soil structure under the background noise in the farmland
作者:
冉颖杭1; 谢天铧1; 霍连飞1; 孙克润2; 丁启朔1; 何瑞银1; 汪小旵1
（1.南京农业大学工学院/江苏省智能化农业装备重点实验室，江苏南京210031；2.银华春翔有限公司，江苏连云港222200）
Author(s):
RAN Ying-hang1; XIE Tian-hua1; HUO Lian-fei1; SUN Ke-run2; DING Qi-shuo1; HE Rui-yin1; WANG Xiao-chan1
(1.College of Engineering, Nanjing Agricultural University/Key Laboratory of Intelligent Agricultural Equipment in Jiangsu Province, Nanjing 210031, China;2.Yinhuachunxiang Co.,Ltd., Lianyungang 222200, China)
关键词:
数字图像; 原位分析; 土壤尺度分布; 线段法; 体视概率法
Keywords:
digital image; in-situ analysis; size distribution; line section method; volume probability method
分类号:
TP751
DOI:
doi:10.3969/j.issn.1000-4440.2019.02.011
文献标志码:
A
摘要:
智慧农业的技术基础是基于机器主体的农情信息获取、加工与分析。土壤结构管理是智慧农业的重要子模块，但目前尚不清楚农田背景噪声下土壤结构体的图像信息特征以及目标信息的信噪分离、纯化和加工方法。本研究逐级递进界定田间土壤结构体的数字图像信息特征，并针对自然光源、秸秆及有机质混杂、土壤结构体孔隙等噪声源逐一处理，同时引入了线段法、体视概率法定量土壤结构体信息。筛分法所得结果用于对照。结果表明农田背景下土壤结构体信息受多重环境背景噪声影响，为此需要进行系统补光、噪声过滤、土壤孔隙形态修补等技术处理才能够实现基于机器主体的土壤结构体目标信息获取。运用线段法和体视概率法均能较好地定量土壤结构体的尺度分布信息，线段法与体视概率法所得到的土壤结构体的累积分布数据与筛分数据的相关性（R2）大于0.96、均方根误差（RMSE）小于 0.1 mm。
Abstract:
The technological basis of intelligent agriculture is concerned with machinery-based system for information collection, processing and analysis. Management of soil structure is a key sub-model for intelligent agriculture. However, little has been reported on the image characteristics of soil structural information in-situ, not to mention the separation of target information from background noise, signal purification and processing. Step-by-step definition was made to illustrate the information characteristics of soil structural images. Technological solutions were made for each specific background noise such as. supplementary lighting, filtering of straw noises, and soil pores and clods boundary patching etc. Line section method and volume probability method were applied for soil structural information statistics. In addition, sieving method was used to collect soil structural information as a referencing basis. It was found that, under field conditions, soil structural information was intensely affected or even merged in the background noises. It was thus necessary to implement artificial lighting, straw noise filtering, patching or fixing on the soil pores. Both line section method and volume probability method quantified soil structural information satisfactorily. As compared with the sieving method (R2>0.96, RMSE<0.1 mm), both methods provided higher results.

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备注/Memo

备注/Memo:
收稿日期：2018-07-18 基金项目：国家大学生创新性实验计划项目(201710307100）;国家重点研发计划“粮食丰产增效科技创新”重点专项（2016YFD0300900）;江苏省苏北科技专项（ZL-LYG2017008）;江苏省农业科技自主创新基金项目[CX（17）1002] 作者简介：冉颖杭（1997-）,女，重庆人，本科,主要从事土壤物理力学的研究。（E-mail） r.yinghang@outlook.com 通讯作者：丁启朔，（E-mail）qsding@njau.edu.cn
更新日期/Last Update: 2019-05-05