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Journal of Zhejiang University (Agriculture and Life Sciences)  2021, Vol. 47 Issue (2): 135-146    DOI: 10.3785/j.issn.1008-9209.2020.08.171
Reviews     
Current situation and development trend of agricultural Internet of Things technology
Pengcheng NIE1,2(),Hui ZHANG1,2,Hongliang GENG3,Zheng WANG3,Yong HE1,2,4()
1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2.Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs (Zhejiang University), Hangzhou 310058, China
3.West Electronic Business Co. , Ltd. , Yinchuan 750000, China
4.Huanan Industrial Technology Research Institute of Zhejiang University, Guangzhou 510530, China
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Abstract  

Agricultural Internet of Things (IoT) technology is an emerging technology to promote the intelligent development of modern agriculture, which has been widely used in every link of agricultural production. With the rapid development of information technologies such as new perception technology, information transmission technology, artificial intelligence, blockchain and other information technologies, the application of China’s agricultural IoT is facing new opportunities. New sensing technologies such as spectroscopy, spectral imaging, and machine vision provide new ideas for realizing fast, real-time, non-destructive sensing. The new communication technology represented by 5G, combined with information processing technologies such as multi-source information fusion, artificial intelligence, blockchain, edge computing, etc., makes the transmission and processing of information faster, safer and more reliable. This article deeply analyzed agricultural IoT technology from four core levels of perception, transmission, processing, and application. Based on the new information technology system, the frontier trends of future research were explored, in order to provide some enlightenment for the innovation and development of agricultural IoT technology in China in the future.



Key wordsagricultural Internet of Things      information perception      information transmission      information processing      system application     
Received: 17 August 2020      Published: 25 April 2021
CLC:  S-1  
Corresponding Authors: Yong HE     E-mail: pcn@zju.edu.cn;yhe@zju.edu.cn
Cite this article:

Pengcheng NIE,Hui ZHANG,Hongliang GENG,Zheng WANG,Yong HE. Current situation and development trend of agricultural Internet of Things technology. Journal of Zhejiang University (Agriculture and Life Sciences), 2021, 47(2): 135-146.

URL:

http://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2020.08.171     OR     http://www.zjujournals.com/agr/Y2021/V47/I2/135


农业物联网技术现状与发展趋势

农业物联网技术是推动现代农业智能化发展的新兴技术,已广泛应用于农业生产的各个环节。随着新型感知技术、信息传输技术、人工智能、区块链等信息技术的快速发展,我国农业物联网应用面临新的机遇。光谱及光谱成像、机器视觉等新型传感技术,为实现快速实时无损感知提供了新思路。以5G为代表的新型通信技术,结合多源信息融合、人工智能、区块链、边缘计算等信息处理技术,使信息的传输与处理更加快速和安全可靠。本文从农业物联网的感知、传输、处理和应用4个核心层面,对农业物联网技术展开了较深入的分析,并结合新型信息技术体系,探索农业物联网的未来发展趋势,以期为我国未来农业物联网技术的创新和产业发展提供一些启示。


关键词: 农业物联网,  信息感知,  信息传输,  信息处理,  系统应用 
Fig. 1 Detection of pesticide residues in soil based on Raman spectroscopySERS: Surface-enhanced Raman spectroscopy; TERS: Tip-enhanced Raman spectroscopy; SHINERS: Shell-isolated nanoparticle-enhanced Raman spectroscopy.
Fig. 2 Rapid and nondestructive detection of pesticide residues on the surface of fruits and vegetables based on SERS
Fig. 3 Agricultural Internet of Things application systemIoT: Internet of Things; PAN: Personal area network; LAN: Local area network; WLAN: Wireless local area network.
[1]   宋韬,鲍一丹,何勇.利用光谱数据快速检测土壤含水量的方法研究.光谱学与光谱分析,2009,29(3):675-677. DOI:10.3964/j.issn.1000-0593(2009)03-0675-03
SONG T, BAO Y D, HE Y. Research on the method for rapid detection of soil moisture content using spectral data. Spectroscopy and Spectral Analysis, 2009,29(3):675-677. (in Chinese with English abstract)
doi: 10.3964/j.issn.1000-0593(2009)03-0675-03
[2]   夏佳欣,范成发,王可嘉,等.基于太赫兹透射谱的土壤含水量测量.激光与光电子学进展,2011,48(2):97-102. DOI:10.3788/LOP48.023001
XIA J X, FAN C F, WANG K J, et al. Soil moisture measurement based on terahertz transmission spectrum. Laser Optoelectronics Progress, 2011,48(2):97-102. (in Chinese with English abstract)
doi: 10.3788/LOP48.023001
[3]   宋海燕.土壤近红外光谱检测.北京:化学工业出版社,2013.
SONG H Y. Soil Near Infrared Spectroscopy. Beijing: Chemical Industry Press, 2013. (in Chinese)
[4]   JIA X L, CHEN S C, YANG Y Y, et al. Organic carbon prediction in soil cores using VNIR and MIR techniques in an alpine landscape. Scientific Reports, 2017,7:2144. DOI:10.1038/s41598-017-02061-z
doi: 10.1038/s41598-017-02061-z
[5]   DWORAK V, AUGUSTIN S, GEBBERS R. Application of terahertz radiation to soil measurements: initial results. Sensors, 2011,11(10):9973-9988. DOI:10.3390/s111009973
doi: 10.3390/s111009973
[6]   NIAZI N K, SINGH B, MINASNY B. Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site. International Journal of Environmental Science and Technology, 2015,12(6):1965-1974. DOI:10.1007/s13762-014-0580-5
doi: 10.1007/s13762-014-0580-5
[7]   余克强,赵艳茹,刘飞,等.激光诱导击穿光谱技术检测土壤中的铅和镉含量.农业工程学报,2016,32(15):197-203. DOI:10.11975/j.issn.1002-6819.2016.15.027
YU K Q, ZHAO Y R, LIU F, et al. Laser-induced breakdown spectroscopy for determining content of Pb and Cd in soil. Transactions of the CSAE, 2016,32(15):197-203. (in Chinese with English abstract)
doi: 10.11975/j.issn.1002-6819.2016.15.027
[8]   李斌,赵春江.基于太赫兹光谱的土壤重金属铅含量检测初步研究.农业机械学报,2016,47():291-296. DOI:10.6041/j.issn.1000-1298.2016.S0.045
LI B, ZHAO C J. Preliminary research on heavy metal Pb detection in soil based on terahertz spectroscopy. Transactions of the Chinese Society of Agricultural Machinery, 2016,47():291-296. (in Chinese with English abstract)
doi: 10.6041/j.issn.1000-1298.2016.S0.045
[9]   PENG Y, KHEIR R B, ADHIKARI K, et al. Digital mapping of toxic metals in Qatari soils using remote sensing and ancillary data. Remote Sensing, 2016,8(12):1003. DOI:10.3390/rs8121003
doi: 10.3390/rs8121003
[10]   NIE P C, DONG T, XIAO S P, et al. Quantitative determination of thiabendazole in soil extracts by surface-enhanced Raman spectroscopy. Molecules, 2018,23(8):1949. DOI:10.3390/molecules23081949
doi: 10.3390/molecules23081949
[11]   HE Y, XIAO S P, DONG T, et al. Gold nanoparticles with different particle sizes for the quantitative determination of chlorpyrifos residues in soil by SERS. International Journal of Molecular Sciences, 2019,20(11):2817. DOI:10.3390/ijms20112817
doi: 10.3390/ijms20112817
[12]   赵春喜.土壤中有机污染物的太赫兹时域光谱检测分析.科技信息,2010(8):498. DOI:10.3969/j.issn.1001-9960.2010.08.422
ZHAO C X. Terahertz time domain spectrum analysis of organic pollutants in soil. Scientific and Technological Information, 2010(8):498. (in Chinese)
doi: 10.3969/j.issn.1001-9960.2010.08.422
[13]   朱文静.基于偏振-高光谱多维光信息的西红柿氮磷钾及交互作用检测研究.江苏,镇江:江苏大学,2014.
ZHU W J. Research on the detection of facility crop nutrition information based on reflectance spectrum image. Zhenjiang, Jiangsu: Jiangsu University, 2014. (in Chinese with English abstract)
[14]   孔汶汶,刘飞,邹强,等.基于近红外光谱技术的油菜叶片丙二醛含量快速检测方法研究.光谱学与光谱分析,2011,31(4):988-991. DOI:10.3964/j.issn.1000-0593(2011)04-0988-04
KONG W W, LIU F, ZOU Q, et al. Fast determination of malondialdehyde in oilseed rape leaves using near infrared spectroscopy. Spectroscopy and Spectral Analysis, 2011,31(4):988-991. (in Chinese with English abstract)
doi: 10.3964/j.issn.1000-0593(2011)04-0988-04
[15]   苏辰.基于反射光谱图像的设施作物营养信息探测研究.江苏,镇江:江苏大学,2017.
SU C. Research on detection of facility crop nutrition information based on reflectance spectrum image. Zhenjiang, Jiangsu: Jiangsu University, 2017. (in Chinese with English abstract)
[16]   HE Y, ZHANG C, LIU F, et al. Determination of pigments concentration of oilseed rape (Brassica napus L.) leaves using hyperspectral imaging. Applied Engineering in Agriculture, 2015,31(1):23-30. DOI:10.13031/aea.31.10690
doi: 10.13031/aea.31.10690
[17]   杨菲菲,李世娟,刘升平,等.作物环境胁迫高光谱遥感监测研究进展.中国农业科技导报,2020,22(4):85-93. DOI:10.13304/j.nykjdb.2019.0489
YANG F F, LI S J, LIU S P, et al. Research progress on hyperspectral remote sensing monitoring of crop environmental stress. Journal of Agricultural Science and Technology, 2020,22(4):85-93. (in Chinese with English abstract)
doi: 10.13304/j.nykjdb.2019.0489
[18]   秦彩杰,李勇.作物病虫害自动识别技术研究:基于视频监控和无人机平台.农机化研究,2021,43(10):42-45, 50. DOI:10.13427/j.cnki.njyi.2021.10.009
QIN C J, LI Y. Research on automatic identification technology of crop diseases and pests: based on video surveillance and UAV platforms. Agricultural Mechanization Research, 2021,43(10):42-45, 50. (in Chinese with English abstract)
doi: 10.13427/j.cnki.njyi.2021.10.009
[19]   刘子毅.基于图谱特征分析的农业虫害检测方法研究.杭州:浙江大学,2017:72-97.
LIU Z Y. Detection of agricultural pest insects based on imaging and spectral feature analysis. Hangzhou: Zhejiang University, 2017:72-97. (in Chinese with English abstract)
[20]   沈娟,杨玫.农产品重金属污染及其检测技术.安徽农学通报,2020,26(17):136-137. DOI:10.16377/j.cnki.issn1007-7731.2020.17.059
SHEN J, YANG M. Heavy metal pollution in agricultural products and its detection technology. Anhui Agricultural Science Bulletin, 2020,26(17):136-137. (in Chinese with English abstract)
doi: 10.16377/j.cnki.issn1007-7731.2020.17.059
[21]   周利航,薛科宇,赵哲,等.原子荧光技术在农产品重金属检测中的应用.江西农业,2019(4):125. DOI:10.19394/j.cnki.issn1674-4179.2019.04.104
ZHOU L H, XUE K Y, ZHAO Z, et al. Application of atomic fluorescence technology in the detection of heavy metals in agricultural products. Jiangxi Agriculture, 2019(4):125. (in Chinese with English abstract)
doi: 10.19394/j.cnki.issn1674-4179.2019.04.104
[22]   SHEN T T, KONG W W, LIU F, et al. Rapid determination of cadmium contamination in lettuce using laser-induced breakdown spectroscopy. Molecules, 2018,23(11):1-15. DOI:10.3390/molecules23112930
doi: 10.3390/molecules23112930
[23]   代学城.微探原子吸收光谱法在农产品重金属检测中的应用.农业与技术,2020,40(21):40-41. DOI:10.19754/j.nyyjs.20201115013
DAI X C. The application of micro probe atomic absorption spectrometry in the detection of heavy metals in agricultural products. Agriculture and Technology, 2020,40(21):40-41. (in Chinese with English abstract)
doi: 10.19754/j.nyyjs.20201115013
[24]   盖梦.原子荧光技术在农产品重金属检测中的运用.粮食科技与经济,2020,45(7):122-123. DOI:10.16465/j.gste.cn431252ts.20200733
GAI M. Application of atomic fluorescence technology in the detection of heavy metals in agricultural products. Food Science and Technology and Economy, 2020,45(7):122-123. (in Chinese with English abstract)
doi: 10.16465/j.gste.cn431252ts.20200733
[25]   MASSAOUTI M, DASKALAKI C, GORODETSKY A, et al. Detection of harmful residues in honey using terahertz time-domain spectroscopy. Applied Spectroscopy, 2013,67(11):1264-1269. DOI:10.1366/13-07111
doi: 10.1366/13-07111
[26]   BAEK S H, KANG J H, HWANG Y H, et al. Detection of methomyl, a carbamate insecticide, in food matrices using terahertz time-domain spectroscopy. Journal of Infrared Millimeter and Terahertz Waves, 2016,37(5):486-497. DOI:10.1007/s10762-015-0234-9
doi: 10.1007/s10762-015-0234-9
[27]   MAENG I, BAEK S H, KIM H Y, et al. Feasibility of using terahertz spectroscopy to detect seven different pesticides in wheat flour. Journal of Food Protection, 2014,77(12):2081-2087. DOI:10.4315/0362-028X.JFP-14-138
doi: 10.4315/0362-028X.JFP-14-138
[28]   蔺磊,吴瑞梅,刘木华,等.噻菌灵农药的表面增强拉曼光谱分析.光谱学与光谱分析,2015,35(2):404-408. DOI:10.3964/j.issn.1000-0593(2015)02-0404-05
LIN L, WU R M, LIU M H, et al. Surface-enhanced Raman spectroscopy analysis of thiabendazole pesticide. Spectroscopy and Spectral Analysis, 2015,35(2):404-408. (in Chinese with English abstract)
doi: 10.3964/j.issn.1000-0593(2015)02-0404-05
[29]   DONG T, LIN L, HE L, et al. Density functional theory analysis of deltamethrin and its determination in strawberry by surface enhanced Raman spectroscopy. Molecules, 2018,23(6):1458. DOI:10.3390/molecules23061458
doi: 10.3390/molecules23061458
[30]   漆海霞.畜禽舍关键环境因子检测系统的研究.广州:华南农业大学,2016.
QI H X. Study on detection system of key environmental factors in livestock and poultry house. Guangzhou: South China Agricultural University, 2016. (in Chinese with English abstract)
[31]   李颖,付金宇,侯永超.有害气体检测的电化学技术的应用发展.科学技术与工程,2018,18(3):132-141. DOI:10.3969/j.issn.1671-1815.2018.03.021
LI Y, FU J Y, HOU Y C. Application development of electrochemical sensors technology for harmful gases detecting. Science Technology and Engineering, 2018,18(3):132-141. (in Chinese with English abstract)
doi: 10.3969/j.issn.1671-1815.2018.03.021
[32]   王志琛.光导型及光伏型红外探测器多种技术参数检测系统.长春:长春理工大学,2015.
WANG Z C. The detection system for various technical parameters of photoconductive and photovoltaic infrared detector. Changchun: Changchun University of Science and Technology, 2015. (in Chinese with English abstract)
[33]   郭东东.基于三轴加速度传感器的山羊行为特征识别研究.太原:太原理工大学,2015.
GUO D D. Research on 3D acceleration sensor recognition for goats behavior recognition. Taiyuan: Taiyuan University of Science and Technology, 2015. (in Chinese with English abstract)
[34]   王俊,谭骥,张海洋,等.基于无线传感器网络的奶牛运动行为实时监测系统.家畜生态学报,2018,39(10):45-52. DOI:10.3969/j.issn.1673-1182.2018.10.009
WANG J, TAN J, ZHANG H Y, et al. A real-time monitoring system for cow behavior based on wireless sensor network. Acta Ecologae Animalis Domastici, 2018,39(10):45-52. (in Chinese with English abstract)
doi: 10.3969/j.issn.1673-1182.2018.10.009
[35]   黄孟选,李丽华,许利军,等.RFID技术在动物个体行为识别中的应用进展.中国家禽,2018,40(22):39-44. DOI:10.16372/j.issn.1004-6364.2018.22.009
HUANG M X, LI L H, XU L J, et al. Application progress of RFID technology in individual animal behavior recognition. China Poultry, 2018,40(22):39-44. (in Chinese with English abstract)
doi: 10.16372/j.issn.1004-6364.2018.22.009
[36]   李卓,杜晓冬,毛涛涛,等.基于深度图像的猪体尺检测系统.农业机械学报,2016,47(3):311-318. DOI:10.6041/j.issn.1000-1298.2016.03.044
LI Z, DU X D, MAO T T, et al. Pig dimension detection system based on depth image. Transactions of the Chinese Society of Agricultural Machinery, 2016,47(3):311-318. (in Chinese with English abstract)
doi: 10.6041/j.issn.1000-1298.2016.03.044
[37]   刘龙申,沈明霞,柏广宇,等.基于机器视觉的母猪分娩检测方法研究.农业机械学报,2014,45(3):237-242. DOI:10.6041/j.issn.1000-1298.2014.03.039
LIU L S, SHEN M X, BO G Y, et al. Sows parturition detection method based on machine vision. Transactions of the Chinese Society of Agricultural Machinery, 2014,45(3):237-242. (in Chinese with English abstract)
doi: 10.6041/j.issn.1000-1298.2014.03.039
[38]   赵凯旋,何东健,王恩泽.基于视频分析的奶牛呼吸频率与异常检测.农业机械学报,2014,45(10):258-263. DOI:10.6041/j.issn.1000-1298.2014.10.040
ZHAO K X, HE D J, WANG E Z. Detection of breathing rate and abnormity of dairy cattle based on video analysis. Transactions of the Chinese Society of Agricultural Machinery, 2014,45(10):258-263. (in Chinese with English abstract)
doi: 10.6041/j.issn.1000-1298.2014.10.040
[39]   赵晓洋.基于动物发声分析的畜禽舍环境评估.杭州:浙江大学,2019.
ZHAO X Y. Environmental assessment of livestock houses based on vocal analysis of animals. Hangzhou: Zhejiang University, 2019. (in Chinese with English abstract)
[40]   黎煊,赵建,高云,等.基于深度信念网络的猪咳嗽声识别.农业机械学报,2018,49(3):179-186. DOI:10.6041/j.issn.1000-1298.2018.03.022
LI X, ZHAO J, GAO Y, et al. Recognition of pig cough sound based on deep belief nets. Transactions of the Chinese Society of Agricultural Machinery, 2018,49(3):179-186. (in Chinese with English abstract)
doi: 10.6041/j.issn.1000-1298.2018.03.022
[41]   黄福任,贾博,徐洪东,等.母羊发情声音数字化识别模型的建立.中国畜牧杂志,2019,55(12):8-12. DOI:10.19556/j.0258-7033.20190527-06
HUANG F R, JIA B, XU H D, et al. Establishment of digital recognition model for ewe estrus sound. Chinese Journal of Animal Science, 2019,55(12):8-12. (in Chinese with English abstract)
doi: 10.19556/j.0258-7033.20190527-06
[42]   曾宝国,刘美岑.基于物联网的水产养殖水质实时监测系统.计算机系统应用,2013,22(6):53-56. DOI:10.3969/j.issn.1003-3254.2013.06.011
ZENG B G, LIU M C. Real-time water quality of aquaculture monitoring system based on the Internet of Things. Computer Systems and Applications, 2013,22(6):53-56. (in Chinese with English abstract)
doi: 10.3969/j.issn.1003-3254.2013.06.011
[43]   杨平平.一种基于二维码的农产品追溯方法.电子技术与软件工程,2020(17):203-204.
YANG P P. A method for traceability of agricultural products based on QR codes. Electronic Technology and Software Engineering, 2020(17):203-204. (in Chinese with English abstract)
[44]   侯敬熙.基于二维码的猪肉溯源系统开发研究.广州:华南农业大学,2018.
HOU J X. Research on the development of pork traceability system based on QR code. Guangzhou: South China Agricultural University, 2018. (in Chinese with English abstract)
[45]   焦玉聪,张立新,黄庆林,等.基于RFID及二维码的肉制品质量安全溯源系统.江苏农业科学,2017,45(5):215-218.DOI:10.15889/j.issn.1002-1302.2017.05.060
JIAO Y C, ZHANG L X, HUANG Q L, et al. Meat product quality and safety traceability system based on RFID and QR code. Jiangsu Agricultural Sciences, 2017,45(5):215-218. (in Chinese with English abstract)
doi: 10.15889/j.issn.1002-1302.2017.05.060
[46]   夏俊,凌培亮,虞丽娟,等.水产品全产业链物联网追溯体系研究与实践.上海海洋大学学报,2015,24(2):303-313.
XIA J, LING P L, YU L J, et al. Research and practice of the Internet of Things traceability system for the entire aquatic product industry chain. Journal of Shanghai Ocean University, 2015,24(2):303-313. (in Chinese with English abstract)
[47]   刘世明,陈建宏,张宗平,等.基于RFID的供港蔬菜安全监管溯源系统.计算机系统应用,2014,23(2):42-47.DOI:10.3969/j.issn.1003-3254.2014.02.007
LIU S M, CHEN J H, ZHANG Z P, et al. Vegetables supplied to Hong Kong safety supervision traceability system based on RFID. Computer Systems and Applications, 2014,23(2):42-47. (in Chinese with English abstract)
doi: 10.3969/j.issn.1003-3254.2014.02.007
[48]   胡梦杰,孟凡婷.基于物联网技术的冷链智能化应用研究.物联网技术,2020,10(3):64-66, 69. DOI:10.16667/j.issn.2095-1302.2020.03.018
HU M J, MENG F T. Research on the application of cold chain intelligence based on Internet of Things technology. Internet of Things Technology, 2020,10(3):64-66, 69. (in Chinese with English abstract)
doi: 10.16667/j.issn.2095-1302.2020.03.018
[49]   霍文慧,李敬兆.基于物联网的智慧物流监测系统.物联网技术,2020,10(9):19-23. DOI:10.16667/j.issn.2095-1302.2020.09.005
HUO W H, LI J Z. A smart logistics monitoring system based on the Internet of Things. Internet of Things Technology, 2020,10(9):19-23. (in Chinese with English abstract)
doi: 10.16667/j.issn.2095-1302.2020.09.005
[50]   周一青,潘振岗,翟国伟,等.第五代移动通信系统5G标准化展望与关键技术研究.数据采集与处理,2015,30(4):714-724. DOI:10.16337/j.1004-9037.2015.04.002
ZHOU Y Q, PAN Z G, ZHAI G W, et al. Standardization and key technologies for future fifth generation of mobile communication systems. Journal of Data Acquisition and Processing, 2015,30(4):714-724. (in Chinese with English abstract)
doi: 10.16337/j.1004-9037.2015.04.002
[51]   董慧,黄世震.基于LoRa技术的智慧农业系统设计与实现.微型机与应用,2017,36(22):106-108. DOI:10.19358/j.issn.1674-7720.2017.22.028
DONG H, HUANG S Z. Design and implementation of intelligent agriculture system based on LoRa technology. Microcomputer and its Applications, 2017,36(22):106-108. (in Chinese with English abstract)
doi: 10.19358/j.issn.1674-7720.2017.22.028
[52]   刘玮,董江波,刘娜,等.NB-IoT关键技术与规划仿真方法.电信科学,2016():144-148. DOI:10.11959/j.issn.1000-0801.2016326
LIU W, DONG J B, LIU N, et al. NB-IoT key technology and design simulation method. Telecommunications Science, 2016():144-148. (in Chinese with English abstract)
doi: 10.11959/j.issn.1000-0801.2016326
[53]   李道亮.农业物联网导论.北京:科学出版社,2012.
LI D L. Introduction to Agricultural Internet of Things. Beijing: Science Press, 2012. (in Chinese)
[54]   李道亮.敢问水产养殖路在何方?智慧渔场是发展方向.中国农村科技,2018(1):43-46. DOI:10.3969/j.issn.1005-9768.2018.01.018
LI D L. Where is the way to aquaculture?The smart fishery is the direction of development. China Rural Science and Technology, 2018(1):43-46. (in Chinese)
doi: 10.3969/j.issn.1005-9768.2018.01.018
[55]   陈英义,程倩倩,方晓敏,等.主成分分析和长短时记忆神经网络预测水产养殖水体溶解氧.农业工程学报,2018,34(17):183-191. DOI:10.11975/j.issn.1002-6819.2018.17.024
CHEN Y Y, CHENG Q Q, FANG X M, et al. Principal component analysis and long short-term memory neural network for predicting dissolved oxygen in water for aquaculture. Transactions of the CSAE, 2018,34(17):183-191. (in Chinese with English abstract)
doi: 10.11975/j.issn.1002-6819.2018.17.024
[56]   陈英义,成艳君,杨玲,等.基于改进深度信念网络的池塘养殖水体氨氮预测模型研究.农业工程学报,2019,35(7):195-202. DOI:10.11975/j.issn.1002-6819.2019.07.024
CHEN Y Y, CHENG Y J, YANG L, et al. Prediction model of ammonia-nitrogen in pond aquaculture water based on improved multi-variable deep belief network. Transactions of the CSAE, 2019,35(7):195-202. (in Chinese with English abstract)
doi: 10.11975/j.issn.1002-6819.2019.07.024
[57]   孟祥宝,黄家怿,谢秋波,等.基于自动巡航无人驾驶船的水产养殖在线监控技术.农业机械学报,2015,46(3):276-281. DOI:10.6041/j.issn.1000-1298.2015.03.040
MENG X B, HUANG J Y, XIE Q B, et al. Online monitoring equipment for aquaculture based on unmanned automatic cruise boat. Transactions of the Chinese Society for Agricultural Machinery, 2015,46(3):276-281. (in Chinese with English abstract)
doi: 10.6041/j.issn.1000-1298.2015.03.040
[58]   黄鸿兵,吴光红.浅述江苏省水产品质量安全及监管体系.食品安全质量检测学报,2014,5(1):94-98.
HUANG H B, WU G H. Brief introduction of aquatic product quality safety and supervision system of Jiangsu Province. Journal of Food Safety and Quality, 2014,5(1):94-98. (in Chinese with English abstract)
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