土壤硝态氮含量原位检测系统设计  

Design of the detection system for the in-situ measurement of soil nitrate-nitrogen contents

作　　者：赵燕东[1,2,3] 刘宇琦 孙哲 常诚至 赵玥 刘卫平[1,2,3] Zhao Yandong;Liu Yuqi;Sun Zhe;Chang Chengzhi;Zhao Yue;Liu Weiping(School of Engineering,Beijing Forestry University,Beijing 100083,China;Beijing Laboratory of Urban and Rural Ecological Environment,Beijing 100083,China;Forest and Grass Ecological Carbon Neutralization Smart Perception Institute,Beijing 100083,China)

机构地区：[1]北京林业大学工学院,北京100083 [2]城乡生态环境北京实验室,北京100083 [3]林草生态碳中和智慧感知研究院,北京100083

出　　处：《农业工程学报》2022年第15期115-123,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划项目(重要木本花卉轻简高效栽培技术集成与示范/2020YFD1000500);北京市共建项目专项。

摘　　要：针对现阶段土壤硝态氮测量成本较高、无法长期原位测量等问题,该研究提出了一种使用钛烧结滤芯收集土壤溶液,通过近红外光谱法检测土壤溶液中的硝酸根浓度进而得到土壤中硝态氮含量的方法,并设计了相应的检测装置。通过试验对比陶土头与钛烧结滤芯在不同土壤条件下的土壤溶液收集效果,选用钛烧结滤芯作为土壤溶液采集器收集土壤溶液,以近红外LED作为测量光源,采集人工配置土壤溶液的光谱数据,利用BP神经网络进行预训练建立硝态氮含量预测模型。建立的硝态氮含量预测模型其训练集皮尔逊相关系数、测试集皮尔逊相关系数、预测均方根误差分别达到0.997、0.995、3.43。实地测量土壤溶液并与硝酸根离子电极以及土壤养分速测仪进行对比,最大相对偏差为5.9%,可满足实际测量准确性要求。该套检测设备在深度为10~40cm、含水率为15%以上的土壤中有较好的土壤溶液采集效果;检测装置的长期测量标准差为0.006,动态响应时间为1.4 s,具有良好的检测特性。试验结果表明,使用溶液吸光度数据建立的硝态氮预测模型具有较好的预测效果,可以应用于土壤溶液硝态氮原位监测,为长期自动测量土壤硝态氮及水肥一体化系统的搭建提供了一种可行的方案。A long-term in situ measurement cannot fully meet the requirements of soil nitrate measurement in recent years,due to the high cost and the inability.In this study,a measurement method was proposed to collect the soil solution using the titanium sintering filter cartridge,in order to detect the nitrate concentration in the soil solution under near-infrared spectroscopy.Correspondingly,the detection device was also designed in this case.Therefore,it was demonstrated that the nitrate was suitable for the analysis at 1250 to 1860 nm.The commercially available Near-Infrared(NIR)Light-Emitting Diode(LED)products were combined with the 940,1050,1200,1310,1350,1450,and 1550 nm NIR LED.Simulated experiments of soil solution collection were carried out using self-made soil columns.Titanium sintering filter cartridge was selected as the soil solution sampler to collect the soil solution.The results show that the soil solution collection was positively correlated with the water content of the soil,but negatively correlated with the burial depth and the clay content of the soil.It infers that the titanium sintered cartridge was suitable for sandy or clayey soils with low clay content.Once the soil moisture content exceeded 20%,the collection time was selected as 30 min.But the collection time needed to be extended,when the moisture content was low.A systematic evaluation was made to consider the volume of solution required for the test chamber and the vertical distribution characteristics of soil nitrogen.More importantly,the optimal collection time of the soil solution was required over 60 min,where the burial depth was 10 to 40 cm.In addition,the comparative tests confirmed that the titanium sintered cartridge presented a greater capacity for soil solution collection than the LLYQ-P02 soil solution sampler,suitable for lower soil moisture contents.Finally,the BP neural network was trained using a Bayesian regularisation,with the voltage data at different wavelengths as the input layer and solution nitrate-nitrogen content

关 键 词：土壤 氮 溶液 硝态氮 近红外LED BP神经网络 

分 类 号：S153.61[农业科学—土壤学]