本文已被:浏览 1282次 下载 833次 |
![本文二维码信息](/download_upload_file.aspx?file_name=/uploadfile/two_dimensional_code_img/zgnydxxb/2020/8/20200803.png) 码上扫一扫! |
基于RGB颜色空间的早稻氮素营养监测研究 |
叶春1,2,刘莹1*,李艳大2,曹中盛2,张丽娜3,刘继忠1
|
|
(1.南昌大学 机电工程学院, 南昌 330031;2.江西省农业科学院 农业工程研究所/江西省智能农机装备工程研究中心/江西省农业信息化工程技术研究中心, 南昌 330200;3.中国农业机械化科学研究院 产业创新中心, 北京 100083) |
|
摘要: |
针对双季稻区水稻过量施肥带来环境污染和成本提高问题,设计不同品种氮肥梯度大田试验,应用数码相机获取早稻冠层数字图像,研究不同色彩参数及早稻氮素营养指标的时空变化特征,以期确立双季早稻氮素营养预测模型。结果表明:不同品种同一氮肥处理下图像色彩参数差异不大;拔节期数字图像参数对氮素营养指标敏感;模型构建结果显示,图像参数INT与水稻氮素营养指标构建的模型决定系数(R2)最大,模型预测效果最佳,R2分别为0.895 7和0.924 7;进一步采用多元回归分析和BP神经网络分析法进行预测,预测效果均较好。对预测结果进行检验,发现品种对于模型的构建影响不大,以BP神经网络分析法构建的叶片氮浓度(LNC)模型和以INT为敏感色彩参数构建的叶片氮积累量(LNA)回归模型效果最优,而多元回归分析方法则效果不佳。早稻冠层RGB颜色空间敏感参数与氮素营养指标间相关性较好,可以实现氮素营养的无损监测诊断。 |
关键词: 早稻 图像 氮营养 RGB颜色空间 监测 |
DOI:10.11841/j.issn.1007-4333.2020.08.03 |
投稿时间:2020-01-12 |
基金项目:国家重点研发计划项目(2016YFD0300608);国家青年拔尖人才支持计划项目;江西省科技计划项目(20182BCB22015,20161BBI90012);江西省“双千计划”项目联合资助 |
|
Monitoring the nitrogen nutrition of early rice based on RGB color space |
YE Chun1,2,LIU Ying1*,LI Yanda2,CAO Zhongsheng2,ZHANG Lina3,LIU Jizhong1
|
(1.School of Mechanical and Electrical Engineering, Nanchang University, Nanchang 330031, China;2.Institute of Agricultural Engineering/Jiangxi Province Engineering Research Center of Intelligent Agricultural MachineryEquipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China;3.Industrial Innovation Center, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China) |
Abstract: |
In view of the problem of environmental pollution and cost increase caused by excessive nitrogen fertilization application of double-cropping rice, field experiments on nitrogen gradients of different varieties were conducted in Jiangxi in 2019. Digital camera was used to measure images of early rice canopy, and different color parameters and characteristics of the nitrogen nutrition indexes of early rice were investigated. A monitoring model of nitrogen nutrition for early rice was established. The results showed that the differences of color parameters of images under the same nitrogen fertilizer treatment of different varieties were small. The digital image parameters at jointing stage were sensitive to nitrogen nutrition indexes. The model construction results showed that the model constructed between INT and the nitrogen nutrition indexes(R2)had the largest coefficient of determination, and the model displayed the best prediction effect with R2 of 0. 895 7 and 0. 924 7, respectively. At the same time, the results of multiple regression analysis and BP neural network analysis confirmed that the prediction results were good. The results obtained from prediction tests showed that the variety has little effect on the construction of the model. The leaf nitrogen content(LNC)model constructed with BP neural network analysis and the leaf nitrogen accumulation(LNA)regression model constructed with INT as the sensitive color parameter had the best effect, while the multiple regression analysis method had poor result. In conclusion, the early rice canopy RGB color space sensitive parameters have a good correlation with nitrogen nutrition indicators, which meets the needs of nitrogen nutrition non-destructive monitoring and diagnosis. |
Key words: image early rice nitrogen nutrition RGB color space monitoring |