1.塔里木大学信息工程学院/塔里木绿洲农业人工智能教育部重点实验室培育基地,阿拉尔 843300;2.塔里木大学南疆特色果树高效优质栽培与深加工技术国家地方联合工程实验室,阿拉尔 843300
李旭，E-mail：lixu2866@126.com
白铁成，E-mail：9550572@qq.com
S562;S127
国家自然科学基金项目（41561088）；新疆生产建设兵团创新创业平台建设项目（2019CB001）；兵团科技创新人才计划（2021CB041）；阿拉尔市科技计划（2021GX02）；南疆特色果树高效优质栽培与深加工技术国家地方联合工程实验室开放课题（FE201805）
- LI Xu 1,2
LI Xu
Institute of Information Engineering， Tarim University/Incubation Base of Ministry of Education Key Laboratory of Agricultural Artificial Intelligence， Tarim Oasis， Alar 843300， China;National Local Joint Engineering Laboratory of High-Efficiency and High-Quality Cultivation and Deep Processing Technology of Southern Xinjiang Special Fruit Trees， Tarim University， Alar 843300， China
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- LIU Wei 1
- BAI Tiecheng 1
- WU Cuiyun 2
- ZHANG Yuyang 2
- WU Jingming 1
1.Institute of Information Engineering， Tarim University/Incubation Base of Ministry of Education Key Laboratory of Agricultural Artificial Intelligence， Tarim Oasis， Alar 843300， China;2.National Local Joint Engineering Laboratory of High-Efficiency and High-Quality Cultivation and Deep Processing Technology of Southern Xinjiang Special Fruit Trees， Tarim University， Alar 843300， China
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为提高枣树种植过程中施用氮肥的精准性，本研究以南疆重要经济作物骏枣
Jujube as one important economic crop in Southern Xinjiang was used to analyze the relationship between raw spectra and first-order differential spectra of jujube leaves and the content of total nitrogen with hyperspectral techniques. A model for predicting the content of nitrogen was established to provide a theoretical basis for nitrogen monitoring and precise fertilization during jujube cultivation. Spectral sensitive variables were used to construct vegetation indices as derivative variables. Multiple linear and nonlinear models for predicting the content of nitrogen were established using derivative variables as variables. The accuracy of models for predicting the content of nitrogen was tested. Results showed that the fitted decision coefficients of models based on the original spectra and first-order differential spectra of jujube trees were greater than 0.75. The overall prediction performance of the original spectral variables was better than that of first-order differential spectra. The best prediction was based on the power function model of the original spectral variables 4： Nit =1.097x0.735， R2=0.821， and RMSE=0.024 5. It is indicated that the model established for predicting the content of nitrogen can achieve good effect of monitoring nitrogen in jujube tree based on hyperspectral reflectance characteristics， and can serve as an important theoretical basis for the nutrient diagnosis of jujube tree.
引用本文
李旭,石子琰,刘伟,白铁成,吴翠云,张宇阳,邬竞明.基于高光谱的枣树叶片氮素表征方法[J].华中农业大学学报,2023,42(3):203-210
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- 收稿日期:2022-10-17
- 在线发布日期: 2023-06-20
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