基于近红外漫反射测量的车载式原位土壤参数检测仪开发

doi:10.3964/j.issn.1000-0593(2020)09-2856-06

摘要
参考文献
相关文章 (15)

全文: PDF (4572 KB)
输出: BibTeX | EndNote (RIS)

摘要：农田变量施肥作业需要对农田土壤养分信息进行高精度的快速原位采集，已有的设备不能满足精细农业田间测量的需要，为此基于近红外漫反射测量开发了一款新型车载式原位土壤参数检测仪。检测仪采用光照稳定性更好的卤钨光源代替太阳光进行土壤光谱检测，以提高仪器对工作条件的适应性。由7个敏感波长(1 070, 1 130, 1 245, 1 375, 1 450, 1 550, 1 680 nm)构成的土壤氮素测量极限学习机模型提高了仪器的测量实时性和精度。检测仪由机械部分、光学部分及控制部分组成。机械部分为检测仪提供平台支撑，光学部分为检测仪提供检测光源，主要由卤钨光源、光源转接法兰、近红外导光光纤、检测总成（含入射光出口端、InGaAS光电探测器及7个敏感波长的滤光片）等组成，控制部分实现对土壤测量信号的采集及处理。检测仪工作时，卤钨光源通过近红外导光光纤、检测总成中的入射光出口端将检测光源传输到待测土壤表面，通过测量土壤表面漫反射光的光谱反射率检测土壤养分参数。在卤钨光源和近红外导光光纤连接处设计光源转接法兰，最大限度的减小检测光源在传输过程中的损失。漫反射光经过检测总成中的滤光片滤波后，由相应的InGaAS光电探测器实现光电转换，再经信号处理单元计算出各个敏感波长处的反射率。检测仪采用灰度标准板进行光学标定测试，测试结果显示，检测仪在7个敏感波长处的反射率与MATRIX-I型傅里叶光谱分析仪反射率相关系数最高为0.997 8，平均值为0.927 8，表明检测仪有较高的检测精度。为进一步对检测仪农田土壤养分的检测精度进行评估，进行了检测仪的农田应用试验，检测结果表明检测仪检测值与实验室标准检测方法检测值的相关系数都在0.90以上。试验结果表明，车载式原位土壤参数检测仪能够实现对农田土壤养分信息的快速原位高精度检测。

关键词：近红外光谱学；漫反射；土壤养分；车载式原位检测仪

Abstract：Variable fertilizing requires the rapid and in-situ high-accuracy collection of farmland soil nutrients information. However, existing equipment could not meet the needs of field measurement in precision agriculture. Hence, a vehicle-mounted in-situ soil parameters detector was developed based on near-infrared (NIR) diffuse reflection. The detector used a tungsten halogen light source with better illumination stability instead of sunlight to perform soil spectrum detection to improve the adaptability of the instrument to working conditions. A soil total nitrogen measurement extreme learning machine model consisting of seven sensitive wavelengths (1 070, 1 130, 1 245, 1 375, 1 450, 1 550, 1 680 nm) was developed to improve the real-time measurement accuracy. The detector consisted of a mechanical part, optical part and control part. The mechanical part provided platform support for the detector, The optical part was composed of a halogen tungsten light source, a light source adapter flange, a NIR guiding fiber, and a set of detection assembly including an incident light exit end, seven InGaAS photodetectors, and seven single-band filters. The control part realized the collection and processing of the soil measurement signal with a MSP430F149 main control chip module When the detector performed farmland soil nutrients detection, and the tungsten halogen light source transmitted the detection light source to the surface of the detection soil through the NIR guiding fiber and incident light exit end of the detection assembly. The diffuse light from the surface of the detection soil was used to detect soil nutrient parameters. A light source adapter flange at the junction of the tungsten halogen light source and the NIR guiding fiber was designed to minimize the loss of the detection source during transmission. The filter of detection assembly filtered the diffuse light to become a single-band detection light, and the InGaAS photodetector realized photoelectric conversion of the single-band detection light, and the signal processing unit calculated the reflectance at each sensitive wavelength. After the development of the detector was completed, a standard gray board was used as the measurement object to conduct the optical calibration test. The test results showed that the correlation coefficient (R) between the reflectance value of the detector at seven sensitive wavelengths and the reflectance value of the MATRIX-I type Fourier spectrum analyzer had a maximum of 0.997 8 and an average of 0.927 8, which indicated that the detector had higher detection accuracy. In order to further evaluate the detection accuracy of farmland nutrients content using the detector, and the farmland application test of the detector was carried out at the Tongzhou Experimental Station of China Agricultural University. The test results showed that the correlation coefficient (R) between the measured value of soil nutrients content using the detector and the laboratory standard test method were all above 0.90. The test results showed that the vehicle-mounted in-situ soil parameters detector could realize rapid in-situ high-accuracy collection of farmland nutrients information.

Key words：Near infrared spectroscopy; Diffuse reflection; Soil nutrients; Vehicle-mounted in-situ detector

收稿日期: 2019-08-10 修订日期: 2019-12-22

中图分类号:

O433.4

基金资助: 国家重点研发计划项目(2017YFD0201500-2017YFD0201501，2016YFD0700300-2016YFD0700304)，国家自然科学基金项目（31801265）资助

通讯作者: 李民赞 E-mail: limz@cau.edu.cn

作者简介: 周鹏，1990年生，中国农业大学信息与电气工程学院博士研究生 e-mail: zhoupeng@cau.edu.cn

引用本文:

周鹏，李民赞，杨玮，冀荣华，孟超. 基于近红外漫反射测量的车载式原位土壤参数检测仪开发[J]. 光谱学与光谱分析, 2020, 40(09): 2856-2861.
ZHOU Peng, LI Min-zan, YANG Wei, JI Rong-hua, MENG Chao. Development of Vehicle-Mounted in-situ Soil Parameters Detector Based on NIR Diffuse Reflection. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2856-2861.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2020)09-2856-06 或 https://www.gpxygpfx.com/CN/Y2020/V40/I09/2856

[1] Kweon G, Maxton C. Biosystems Engineering, 2013, 115(1)：66.
[2] Evangelou S, Stamatiadis J S, Schepers E, et al. Precision Agriculture, 2018, 19(3)：570.
[3] Luo Y Q, Zhao X Y, Wang T, et al. Acta Prataculturae Sinica, 2017, 26(8)：200.
[4] Song X D, Yang F, Ju B, et al. CATENA, 2018, 171：588.
[5] ZHOU Peng, YANG Wei, LI Min-zan, et al(周鹏，杨玮，李民赞，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2017, 48(S1)：271.
[6] Yao X Q, Yang W, Li M Z, et al. IFAC-PapersOnLine，2018, 51(17)：660.
[7] Morellos A, Pantazi X E, Moshou D, et al. Biosystems Engineering, 2016, 152：104.
[8] Zhang S, Shao M, Li D. Geoderma, 2017, 295：119.
[9] Zhang Y, Li M Z, Zheng L H, et al. Computers and Electronics in Agriculture, 2016, 124(C)：29.
[10] AN Xiao-fei, LI Min-zan, ZHENG Li-hua, et al(安晓飞, 李民赞, 郑立华, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(s1)：283.
[11] Zhou P, Zhang Y, Yang W, et al. Computers and Electronics in Agriculture, 2019, 160：51.
[12] Kodaira M, Shibusawa S. Geoderma, 2013, 199：64.
[13] Maleki M R, Mouazen A M, Ramon H, et al. Soil and Tillage Research, 2007, 94(1)：239.
[14] Maleki M R, Mouazen A M, Ketelaere B D, et al. Biosystems Engineering, 2008, 99(1)：35.
[15] An X, LiM Z, Zheng L H, et al. Precision Agriculture, 2014, 15(1)：3.
[16] Zhou P, Yang W, Li M Z, et al. IFAC-PapersOnLine, 2018, 51(17)：51.
[17] CHENG Meng, ZHANG Jun-yi, LI Min-zan, et al(程萌, 张俊逸, 李民赞, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(s1)：157.
[18] ZHANG Yao, LI Min-zan, ZHENG Li-hua, et al(张瑶, 李民赞, 郑立华, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(9)：121.