[1]吴金恺,刘乃森,曹静,等.基于智能移动设备的低成本微型作物光谱反射率测量仪设计与实现[J].江苏农业学报,2024,(03):469-477.[doi:doi:10.3969/j.issn.1000-4440.2024.03.009]
　WU Jin-kai,LIU Nai-sen,CAO Jing,et al.Design and implementation of low-cost miniature crop spectral reflectance meter based on smart mobile devices[J].,2024,(03):469-477.[doi:doi:10.3969/j.issn.1000-4440.2024.03.009]
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基于智能移动设备的低成本微型作物光谱反射率测量仪设计与实现()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2024年03期
页码:
469-477
栏目:
农业信息工程
出版日期:
2024-03-30

文章信息/Info

Title:
Design and implementation of low-cost miniature crop spectral reflectance meter based on smart mobile devices
作者:
吴金恺1; 2; 刘乃森3; 曹静2; 胡佳楠2; 潘嫄嫄2; 毕然2; 郭靖宇3; 王许淇3; 霍丹琪3; 张文宇1; 2; 3
(1.江苏大学农业工程学院,江苏镇江212013；2.江苏省农业科学院无锡分院,江苏无锡214174；3.淮阴师范学院/区域现代农业与环境保护省部共建协同创新中心/江苏省环洪泽湖生态农业生物技术重点实验室/江苏省洪泽湖蓝藻预警与生态修复工程研究中心,江苏淮安223300)
Author(s):
WU Jin-kai1; 2; LIU Nai-sen3; CAO Jing2; HU Jia-nan2; PAN Yuan-yuan2; BI Ran2; GUO Jing-yu3; WANG Xu-qi3; HUO Dan-qi3; ZHANG Wen-yu1; 2; 3
(1. School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;2.Jiangsu Academy of Agricultural Sciences Wuxi Branch, Wuxi 214174, China;3.Huaiyin Normal University/Collaborative Innovation Center of Regional Modern Agriculture ＆ Environmental Protection Co-constructed by the Province and Ministry/Jiangsu Key Laboratory for Eco-Agricultural Biotechnology around Hongze Lake/Jiangsu Engineering Research Center for Cyanophytes Forecast and Ecological Restoration of Hongze Lake, Huaian 223300, China)
关键词:
生长监测; 智能测量仪; 微型化; 智能手机; 光谱反射率
Keywords:
growth monitoring; smart meter; miniaturization; smart phone; spectral reflectance
分类号:
S126
DOI:
doi:10.3969/j.issn.1000-4440.2024.03.009
摘要:
为实现作物生长信息的低成本、实时、无损监测,本研究基于光谱分析技术,选择395 nm和800 nm 2个波段,根据光学系统原理和小型化需求,进行基于智能移动设备的低功耗微型作物光谱反射率测量仪研发,包括封装壳体、光学系统和硬件电路设计及控制系统App开发；并使用积分球和ASD Fieldspec 4 Hi-Res地物光谱仪对测量仪4个通道(395 nm和800 nm太阳光接收通道和作物反射光接收通道)的测量值进行标定,并以反射率为20%、40%、60%、100%的4块标准灰度板为测量对象检验测量仪的准确性。结果表明：4个通道标定方程的决定系数均在0.998 0以上,395 nm和800 nm反射率的均方根误差分别为1.46%和1.07%,平均绝对误差分别为1.17%和0.82%；4块标准反射率灰度板在395 nm和800 nm 2个波段测得反射率的相对误差分别小于2.0%和3.6%,变异系数分别在1.36%～4.17%和0.78%～2.36%。该测量仪体积32 cm3,质量仅20 g,且具有低成本、高精度、易操作、可升级等特性,可实现基于智能移动设备的作物生长实时监测。
Abstract:
In order to achieve low-cost, real-time and non-destructive monitoring of crop growth information, based on spectral analysis technology, this study selected two bands, 395 nm and 800 nm, and carried out the research and development of a low-power miniature crop spectral reflectance measuring instrument based on a smart mobile device according to the principle of the optical system and miniaturisation requirements, including the package housing, optical system and hardware circuit design and control system App development. Measurements of the four channels (395 nm and 800 nm sunlight receiving channels and crop reflected light receiving channels) of the meter were calibrated using an integrating sphere and a spectrometer (ASD), and the accuracy of the meter was checked using four grey scale panels with standard reflectance (20%, 40%, 60%, 100%). The results showed that the coefficients of determination of the calibration equations of the four channels were above 0.998 0, the root mean square errors of 395 nm and 800 nm reflectance were 1.46% and 1.07%, and the average absolute errors were 1.17% and 0.82%, respectively. The relative errors of the reflectance measured by the four standard reflectance greyscale panels in the 395 nm and 800 nm bands were less than 2.0% and 3.6%, respectively, and the coefficients of variation were in the ranges of 1.36%-4.17% and 0.78%-2.36%, respectively. With a volume of 32 cm3 and a weight of only 20 g, the instrument is low-cost, high-precision, easy-to-operate, and scalable, which can realize real-time crop growth monitoring based on smart mobile devices.

参考文献/References:

[1]赵春江,李瑾,冯献. 面向2035年智慧农业发展战略研究[J]. 中国工程科学,2021,23(4):1-9.
[2]曹卫星,姚霞, 程涛,等. 作物生长监测技术的研究与应用[C]// 中国作物学会. 2019年中国作物学会学术年会论文摘要集. 北京：中国作物学会,2019.
[3]周广西. 《沈氏农书》所载水稻施肥技术研究[J]. 南京农业大学学报(社会科学版),2006,6(1):69-72.
[4]陶勤南,方萍,吴良欢,等. 水稻氮素营养的叶色诊断研究[J]. 土壤,1990(4):190-193.
[5]曹绍文. 作物叶色诊断施肥比色卡[J]. 今日科技,1989(7):20.
[6]ZHU Y, YAO X, TIAN Y, et al. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice[J]. International Journal of Applied Earth Observation and Geoinformation,2008,10(1):1-10.
[7]赵春江,黄文江,王纪华,等. 不同品种、肥水条件下冬小麦光谱红边参数研究[J]. 中国农业科学,2002,35(8):980-987.
[8]周启发,王人潮. 水稻氮素营养水平与光谱特性的关系[J]. 浙江农业大学学报,1993,19(增刊1):42-47.
[9]姚霞,朱艳,田永超,等. 小麦叶层氮含量估测的最佳高光谱参数研究[J]. 中国农业科学,2009,42(8):2716-2725.
[10]曹正邓渊,文双雅,高志强. 甘蓝型油菜叶绿素荧光参数的高光谱预测[J]. 江苏农业科学,2023,51(18):185-190.
[11]张筱蕾,刘飞,聂鹏程,等. 高光谱成像技术的油菜叶片氮含量及分布快速检测[J]. 光谱学与光谱分析,2014,34(9):2513-2518.
[12]DELLINGER A E, SCHMIDT J P, BEEGLE D B. Developing nitrogen fertilizer recommendations for corn using an active sensor[J]. Agronomy Journal,2008,100(6):1546-1552.
[13]BORHAN M S, PANIGRAHI S, LORENZEN J H, et al. Multispectral and color imaging techniques for nitrate and chlorophyll determination of potato leaves in a controlled environment[J]. Transactions of the ASAE,2004,47(2):599.
[14]魏永康,杨天聪,臧少龙,等. 基于无人机多光谱影像特征融合的小麦倒伏监测[J]. 中国农业科学,2023,56(9):1670-1685.
[15]王伟康,张嘉懿,汪慧,等. 基于固定翼无人机多光谱影像的水稻长势关键指标无损监测[J]. 中国农业科学,2023,56(21):4175-4191.
[16]凌琪涵,孔发明,宁强,等. 基于无人机多光谱影像的水稻氮营养监测[J]. 农业工程学报,2023,39(13):160-170.
[17]姜友谊,刘博伟,张成健,等. 利用无人机多光谱影像的多品种玉米成熟度监测[J]. 农业工程学报,2023,39(20):84-91.
[18]黄璐,包云轩,郭铭淇,等. 稻纵卷叶螟危害下水稻叶片光谱特征及产量估测[J]. 中国农业气象,2023,44(2):154-164.
[19]陈悦,赵庚星,常春艳,等. 基于Sentinel-2多光谱影像的小麦-玉米轮作耕地粮食产量估测——以曹县为例[J]. 应用生态学报,2023,34(12):3347-3356.
[20]TANG Y L, WANG R C, HUANG J F. Relations between red edge characteristics and agronomic parameters of crops[J]. Pedosphere,2004,14(4):467-474.
[21]PRADHAN S, BANDYOPADHYAY K K, SAHOO R N, et al. Predicting wheat grain and biomass yield using canopy reflectance of booting stage[J]. Journal of the Indian Society of Remote Sensing,2014,42(4):711-718.
[22]SUN J, SHI S, GONG W, et al. Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer[J]. Scientific Reports,2017,7(1):40362.
[23]CHEN P, HABOUDANE D, TREMBLAY N, et al. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat[J]. Remote Sensing of Environment,2010,114(9):1987-1997.
[24]SARLIKIOTI V, MEINEN E, MARCELIS L F M. Crop reflectance as a tool for the online monitoring of LAI and PAR interception in two different greenhouse crops[J]. Biosystems Engineering,2011,108(2):114-120.
[25]段友青,高广智. 精准农业农机装备智能化配置[J]. 现代化农业,2015(3):60-61.
[26]DEBAEKE P, ROUET P, JUSTES E. Relationship between the normalized spad index and the nitrogen nutrition index:application to durum wheat[J]. Journal of Plant Nutrition,2006,29(1):75-92.
[27]SUI R, THOMASSON J A. Ground-based sensing system for cotton nitrogen status determination[J]. Transactions of the ASABE,2006,49(6):1983-1991.
[28]YAO L, WU R, WU S, et al. Design and testing of an active light source apparatus for crop growth monitoring and diagnosis[J]. IEEE Access,2020,8:206474-206490.
[29]MOYA I, CAMENEN L, EVAIN S, et al. A new instrument for passive remote sensing:1. Measurements of sunlight-induced chlorophyll fluorescence[J]. Remote Sensing of Environment,2004,91(2):186-197.
[30]ZHONG Z, LI M, SUN H, et al. Development and application of a smart apparatus for detecting crop nutrition[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(S2):215-219.
[31]NI J, ZHANG J, WU R, et al. Development of an apparatus for crop-growth monitoring and diagnosis[J]. Sensors,2018,18(9):3129.
[32]LIU N, ZHANG W, LIU F, et al. Development of a crop spectral reflectance sensor[J]. Agronomy,2022,12(9):2139.

备注/Memo

备注/Memo:
收稿日期：2024-01-10基金项目：国家重点研发计划项目(2022YFD2001005、2022YFD2001001)；江苏省重点研发计划项目(BE2023323)；江苏省农业科技自主创新基金项目［CX(21)2008］；无锡市财政项目(33212303)作者简介：吴金恺(1999-),男,江苏苏州人,硕士研究生,主要从事智能传感器研究。(E-mail)kevinwujk@163.com通讯作者：张文宇,(E-mail)research@wwery.cn；刘乃森,(E-mail)boomzip@163.com
更新日期/Last Update: 2024-05-20