土壤氮磷钾含量传感测定技术研究进展

doi:10.13733/j.jcam.issn.20955553.2024.12.042

摘要/Abstract

摘要：

化肥价格上涨和化学物质污染饮用水源的生态担忧促使精准农业和基于场地的管理成为焦点，农业界正通过优化作物产量和减少化肥使用来应对化肥生产成本上升，但土壤氮磷钾含量的准确测定依然主要依赖费时费力的人工采样和实验室分析，限制智能化施肥的发展。为此，国内外学者致力于研究土壤氮磷钾含量的传感测定技术，以实现快速测定土壤养分含量。通过分析现有基于光谱技术和电化学技术的研究，发现这些技术在特定条件下能够准确测定土壤中的氮磷钾含量，但在复杂田间环境中仍存在难以克服的挑战，尚无法实现自主测定。未来需要在土壤条件、光照补偿、自动化作业等方面进行深入研究，以开发实时、原位的NPK检测系统。

关键词: 土壤氮磷钾, 配方施肥, 传感测定, 光谱

Abstract:

The rising fertilizer prices and ecological concerns over chemical pollutants in drinking water sources have pushed precision agriculture and site-specific management to the forefront. The agricultural sector is seeking ways to optimize crop yields while minimizing fertilizer use in response to the increasing costs of fertilizer production. However, accurate determination of soil nitrogen (N), phosphorus (P), and potassium (K) content still relies primarily on labor-intensive manual sampling and laboratory analysis, hindering the advancement of intelligent fertilization. To address this, researchers both domestically and internationally are focusing on sensor technologies for rapid soil nutrient assessment. This paper reviews current studies on spectroscopic and electrochemical techniques, finding that while these methods can accurately measure soil NPK content under controlled conditions, they face significant challenges in complex field environments and cannot yet achieve autonomous measurement. Future research needs to delve into soil conditions, light compensation, and automated operations to develop real-time, in situ NPK detection systems.

Key words: soil nitrogen, phosphorus and potassium, formulated fertilizer application, sensing measurement, spectroscopy

中图分类号:

S158.2

刘恒, , , 袁全春, , 吕晓兰, . 土壤氮磷钾含量传感测定技术研究进展[J]. 中国农机化学报, 2024, 45(12): 289-296.

Liu Heng, , , Yuan Quanchun, , Lü Xiaolan, . Research progress of sensing measurement technology for nitrogen, phosphorus and potassium content in soil[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(12): 289-296.

参考文献

［1］　Adamchuk V I, Hummel J W, Morgan M T, et al. On-the-go soil sensors for precision agriculture ［J］. Computers and electronics in agriculture, 2004, 44(1): 71-91.

［2］　Sinfield J V, Fagerman D, Colic O. Evaluation of sensing technologies for on-the-go detection of macro-nutrients in cultivated soils ［J］. Computers and Electronics in Agriculture, 2010, 70(1): 1-18.

［3］　汪洪涛, 李耀翔. 基于NIR-PLS的土壤碳含量预测模型研究［J］. 森林工程, 2014, 30(1): 5-8.

Wang Hongtao, Li Yaoxiang. Predicting soil carbon content based on the near infrared spectroscopy and partial least squares ［J］. Forest Engineering, 2014, 30(1): 5-8.

［4］　卢艳丽, 白由路, 王磊, 等. 黑土土壤中全氮含量的高光谱预测分析［J］. 农业工程学报, 2010, 26(1): 256-261.

Lu Yanli, Bai Youlu, Wang Lei, et al. Determination for total nitrogen content in black soil using hyperspectral data ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(1): 256-261.

［5］　张瑶, 李民赞, 郑立华, 等. 基于近红外光谱分析的土壤分层氮素含量预测［J］. 农业工程学报, 2015, 31(9): 121-126.

Zhang Yao, Li Minzan, Zheng Lihua, et al.Prediction of soil total nitrogen content in different layers based on near infrared spectral analysis ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(9): 121-126.

［6］　赵燕东, 刘宇琦, 孙哲, 等. 土壤硝态氮含量原位检测系统设计［J］. 农业工程学报, 2022, 38(15): 115-123.

Zhao Yandong, Liu Yuqi, Sun Zhe, et al. Design of the detection system for the insitu measurement of soil nitrate-nitrogen contents ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(15): 115-123.

［7］　Shibusawa S. Online real time soil sensor ［C］. IEEE/ASME International Conference on Advanced Intelligent Mechatronics. IEEE, 2003.

［8］　Christy C D, Drummond P, Laird D A. An onthego spectral reflectance sensor for soil ［C］. 2003 ASAE Annual Meeting, 2003.

［9］　Linker R, Kenny A, Shaviv A, et al. Fourier transform infraredattenuated total reflection nitrate determination of soil pastes using principal component regression, partial least squares, and crosscorrelation ［J］. Applied spectroscopy, 2004, 58(5): 516-520.

［10］　Jahn B, Linker R, Upadhyaya S, et al. Midinfrared spectroscopic determination of soil nitrate content ［J］. Biosystems Engineering, 2006, 94(4): 505-515.

［11］　Adamchuk V I, Dobermann A, Morgan M T, et al. Feasibility of onthego mapping of soil nitrate and potassium using ionselective electrodes ［C］. 2002 ASAE Annual Meeting, 2002.

［12］　Adamchuk V I, Lund E, Sethuramasamyraja B, et al. Direct measurement of soil chemical properties onthego using ionselective electrodes ［J］. Computers and electronics in agriculture, 2005, 48(3): 272-294.

［13］　Sethuramasamyraja B, Adamchuk V I, Dobermann A, et al. Agitated soil measurement method for integrated onthego mapping of soil pH, potassium and nitrate contents ［J］. Computers and electronics in agriculture, 2008, 60(2): 212-225.

［14］　孔盼, 张淼, 任海燕, 等. 土壤硝态氮电极法测定的快速前处理工艺研究［J］. 农业机械学报, 2015, 46(S1): 102-107.

Kong Pan, Zhang Miao, Ren Haiyan, et al.Rapid pretreament method for soil nitrate nitrogen detection based on ionselective electrode ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(S1): 102-107.

［15］　李雁华, 张淼, 郑杰, 等. 基于ISE的湿土硝态氮检测方法研究［J］. 农业机械学报, 2016, 47(S1): 285-290.

Li Yanhua, Zhang Miao, Zheng Jie, et al.ISE-based sensor fusion method for wet soil nitratenitrogen detection ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(S1): 285-290.

［16］　杜尚丰, 潘奇, 曹淑姝. 基于ISE的土壤硝态氮多参数检测仪研究［J］. 农业机械学报, 2017, 48(S1): 277-283, 301.

Du Shangfeng, Pan Qi, Cao Shushu.Development of soil nitratenitrogen detection device with multiple parameters based on ISE ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(S1): 277-283, 301.

［17］　路逍, 潘林沛, 李雁华, 等. 基于ISE的土壤硝态氮原位检测模型比较［J］. 农业机械学报, 2021, 52(S1): 297-303.

Lu Xiao, Pan Linpei, Li Yanhua, et al.Comprison of detection models for soil nitrate concentration based on ISE ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(S1): 297-303.

［18］　李希灿, 王静, 李玉环, 等. 基于模糊集分析的土壤质量指标高光谱反演［J］. 地理与地理信息科学, 2008, 24(4): 25-28.

Li Xican, Wang Jing, Li Yuhuan, et al.High spectral retrieved deduction of soil quality index based on fuzzy sets analysis ［J］. Geography and GeoInformation Science, 2008, 24(4): 25-28.

［19］　郭志新, 梁亮, 何见. 一种林地土壤氮磷钾含量快速测定的新方法［J］. 中国农学通报, 2011, 27(2): 61-65.

Guo Zhixin, Liang Liang,He Jian. A new method for rapid measurement of N, P, K contents of forest soil ［J］. Chinese Agricultural Science Bulletin, 2011, 27(2): 61-65.

［20］　Mouazen A M, Kuang B. Online visible and near infrared spectroscopy for infield phosphorous management ［J］. Soil and Tillage Research, 2016, 155: 471-477.

［21］　侯广利, 李雪莹, 邱慧敏, 等. 土壤磷形态的反射光谱特征及其光谱建模［J］. 山东科学, 2021, 34(1): 82-88.

Hou Guangli, Li Xueying, Qiu Huimin, et al.The reflectance spectra characteristics of soil phosphorus fractions and their spectral model ［J］. Shandong Science, 2021, 34(1): 82-88.

［22］　Cohen M, Mylavarapu R S, Bogrekci I, et al. Reflectance spectroscopy for routine agronomic soil analyses ［J］. Soil Science, 2007, 172(6): 469-485.

［23］　Maleki M, Van Holm L, Ramon H, et al. Phosphorus sensing for fresh soils using visible and near infrared spectroscopy ［J］. Biosystems Engineering, 2006, 95(3): 425-436.

［24］　Mouazen A M, Maleki M, De Baerdemaeker J, et al. Online measurement of some selected soil properties using a VISNIR sensor ［J］. Soil and Tillage Research, 2007, 93(1): 13-27.

［25］　Pungor E, Toth K, Havas J. Theorie und Anwendung der heterogenen Gummimembranelektroden für die Bestimmung einiger Ionen ［J］. Microchimica Acta, 1966, 54: 689-698.

［26］　Lemos S, Menezes E, Chaves F, et al. In situ soil phosphorus monitoring probe compared with conventional extraction procedures ［J］. Communications in Soil Science and Plant Analysis, 2009, 40(7-8): 1282-1294.

［27］　Satoh H, Miyazaki Y, Taniuchi S, et al. Improvement of a phosphate ionselective microsensor using Bis (dibromophenylstannyl) methane as a carrier ［J］. Analytical Sciences, 2017, 33(7): 825-830.

［28］　Topcu C, Caglar B, Onder A, et al. Structural characterization of chitosansmectite nanocomposite and its application in the development of a novel potentiometric monohydrogen phosphateselective sensor ［J］. Materials Research Bulletin, 2018, 98: 288-299.

［29］　Abbas M N, Radwan A L A, Nooredeen N M, et al. Selective phosphate sensing using copper monoaminophthalocyanine functionalized acrylate polymerbased solidstate electrode for FIA of environmental waters ［J］. Journal of Solid State Electrochemistry, 2016, 20: 1599-1612.

［30］　Barhoumi L, Baraket A, Nooredeen N M, et al. Silicon nitride capacitive chemical sensor for phosphate ion detection based on copper phthalocyanineacrylatepolymer ［J］. Electroanalysis, 2017, 29(6): 1586-1595.

［31］　Xiao D, Yuan H Y, Li J, et al. Surfacemodified cobaltbased sensor as a phosphatesensitive electrode ［J］. Analytical Chemistry, 1995, 67(2): 288-291.

［32］　董陶, 张军军, 杨慧中. 磷酸盐离子选择电极的制作及其性能特性［J］. 自动化仪表, 2011, 32(9): 60-63.

Dong Tao, Zhang Junjun, Yang Huizhong.Manufacture and performance characteristic of the phosphate ion selective electrode ［J］. Process Automation Instrumentation, 2011, 32(9): 60-63.

［33］　张军军, 杨慧中. 一种磷酸根离子选择电极的测量与补偿［J］. 传感器与微系统, 2011, 30(4): 124-126, 130.

Zhang Junjun, Yang Huizhong.Measurement and compensation of a phosphate ionselective electrode ［J］. Transducer and Microsystem Technologies, 2011, 30(4): 124-126, 130.

［34］　吴世杰, 张海云, 逯文晶, 等. 基于钴的磷酸盐离子选择性电极研究［J］. 环境科学与技术, 2012, 35(S1): 234-238.

Wu Shijie, Zhang Haiyun, Lu Wenjing, et al.A new phosphate ion sensitive electrode based on cobalt ［J］. Environmental Science & Technology, 2012, 35(S1): 234-238.

［35］　Arvas M B, Gorduk O, Gencten M, et al. Preparation of a novel electrochemical sensor for phosphate detection based on a molybdenum blue modified poly (vinyl chloride) coated pencil graphite electrode ［J］. Analytical Methods, 2019, 11(30): 3874-3881.

［36］　Cinti S, Talarico D, Palleschi G, et al. Novel reagentless paperbased screenprinted electrochemical sensor to detect phosphate ［J］. Analytica Chimica Acta, 2016, 919:78-84.

［37］　Li Y, Jiang T, Yu X, et al. Phosphate sensor using molybdenum ［J］. Journal of the Electrochemical Society, 2016, 163(9): B479.

［38］　徐永明, 蔺启忠, 王璐, 等. 基于高分辨率反射光谱的土壤营养元素估算模型［J］. 土壤学报, 2006, 43(5): 709-716.

Xu Yongming, Lin Qizhong, Wang Lu, et al. Model for estimating soil nutrient elements based on high resolution reflectance spectra ［J］. Acta Pedologica Sinica, 2006, 43(5): 709-716.

［39］　李伟, 张书慧, 张倩, 等. 近红外光谱法快速测定土壤碱解氮、速效磷和速效钾含量［J］. 农业工程学报, 2007, 23(1): 55-59.

Li Wei, Zhang Shuhui, Zhang Qian, et al. Rapid prediction of available N, P and K content in soil usingnear infrared reflectance spectroscopy ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2007, 23(1): 55-59.

［40］　Detar W R, Chesson J H, Penner J V, et al. Detection of soil properties with airborne hyperspectral measurements of bare fields ［J］. Transactions of the ASABE, 2008, 51(2): 463-470.

［41］　张淑娟, 王凤花, 张海红, 等. 基于主成分分析和BP神经网络的土壤养分近红外光谱检测［J］. 山西农业大学学报(自然科学版), 2009, 29(6): 483-487.

Zhang Shujuan, Wang Fenghua, Zhang Haihong, et al. Nearinfrared determination of soil nutrients based on principal component analysis and BP neural network ［J］. Journal of Shanxi Agricultural University (Natural Science Edition), 2009, 29(6): 483-487.

［42］　Hu G, Sudduth K A, He D, et al. Soil phosphorus and potassium estimation by reflectance spectroscopy ［J］. Transactions of the ASABE, 2016, 59(1): 97-105.

［43］　He Y, Huang M, García A, et al. Prediction of soil macronutrients content using near-infrared spectroscopy ［J］. Computers and Electronics in Agriculture, 2007, 58(2): 144-153.

［44］　Christy C D. Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy ［J］. Computers and Electronics in Agriculture, 2008, 61(1): 10-19.

［45］　Cies＇la J, Ryz＇ak M, Bieganowski A, et al. Use of ionselective electrodes for determination of content of potassium in EgnerRhiem soil extracts ［J］. Research in Agricultural Engineering, 2007, 53(1): 29-33.

［46］　Jaworska E, Lewandowski W, Mieczkowski J, et al. Critical assessment of graphene as iontoelectron transducer for allsolidstate potentiometric sensors ［J］. Talanta, 2012, 97: 414-419.

［47］　Fakih I, Centeno A, Zurutuza A, et al. High resolution potassium sensing with largearea graphene fieldeffect transistors ［J］. Sensors and Actuators B: Chemical, 2019, 291: 89-95.

［48］　Ning J, Lin X, Su F, et al. Development of a molecular K+ probe for colorimetric/fluorescent/photoacoustic detection of K+ ［J］. Analytical and Bioanalytical Chemistry, 2020, 412: 6947-6957.

［49］　Naderi M, Hosseini M, Ganjali M R. Naked-eye detection of potassium ions in a novel gold nanoparticle aggregationbased aptasensor ［J］. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 195: 75-83.

［50］　Ping J, Wang Y, Wu J, et al. Development of an all-solid-state potassium ion-selective electrode using graphene as the solidcontact transducer ［J］. Electrochemistry Communications, 2011, 13(12): 1529-1532.

［51］　Lin Jianhan, Wang Maohua, Zhang Mao, et al. Development and modelling of a soil nitrate and potassium simultaneous rapid detection system based on ion selective electrodes ［J］. New Zealand Journal of Agricultural Research, 2007, 50(5): 635-640.

［52］　Kim H J, Hummel J W, Sudduth K A, et al. Simultaneous analysis of soil macronutrients using ion-selective electrodes ［J］. Soil Science Society of America Journal, 2007, 71(6): 1867-7187.

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