全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

成像技术在农业领域中的应用
The Application of Imaging Techniques in Agricultural Field

DOI: 10.12677/HJAS.2019.911144, PP. 1032-1040

Keywords: 成像技术,热成像,光谱成像,荧光成像,激光成像
Imaging Techniques
, Thermal Imaging, Spectral Imaging, Fluorescence Imaging, Laser Imaging

Full-Text   Cite this paper   Add to My Lib

Abstract:

成像技术已广泛应用到农业生产的诸多领域,成像技术的应用拓宽了人类的认知范围和研究手段、大大提高了生产效率和节约成本、推动了产业升级、加速了农业现代化智能化的发展进程,对未来农业的智能化和可持续发展有重要意义。本文综合国内外优秀研究成果,在简要阐述了成像技术的基础上,总结了国内外成像技术在农业生产中的应用情况,分析了各技术的优势和劣势,并展望了成像技术在农业领域上的可能应用研究领域及方向。
Imaging technology has been widely applied to various fields of agricultural production. The application of imaging technology broadened the scope of human cognition and research means, greatly improved production efficiency and save costs, promoted industrial upgrading and accelerated the development process of agricultural modernization and intelligence, which is of great significance to the future intelligent and sustainable development of agriculture. In this paper, we summarized the outstanding research results at home and abroad and elaborate, analyzed the advantages and disadvantages of each imaging technology, and looked forward to the possible application research fields and directions of imaging technology in agricultural field.

References

[1]  Zou, X.-B., Zhao, J.-W. and Li, Y.-X. (2007) Apple Color Grading Based on Organization Feature Parameters. Pattern Recognition Letters, 28, 2046-2053.
https://doi.org/10.1016/j.patrec.2007.06.001
[2]  Zou, X.-B., Zhao, J.-W., Li, Y.-X., et al. (2010) In-Line Detection of Apple Defects Using Three Color Cameras System. Computers and Electronics in Agriculture, 70, 129-134.
https://doi.org/10.1016/j.compag.2009.09.014
[3]  徐小龙. 基于红外热成像技术的植物病害早期检测的研究[D]: [硕士学位论文]. 杭州: 浙江大学, 2012.
[4]  周建民, 周其显, 刘燕德. 红外热成像技术在农业生产中的应用[J]. 农机化研究, 2010, 32(2): 1-4.
[5]  张若岚, 陈洁. 从单波段到超光谱——面向多维信息感知的红外光谱成像技术[J]. 红外技术, 2014, 36(4): 257-264
[6]  卢劲竹, 蒋焕煜, 崔笛. 荧光成像技术在植物病害检测的应用研究进展[J]. 农业机械学报, 2014, 45(4): 243-252.
[7]  岳娟. 激光三维成像关键技术[D]: [博士学位论文]. 北京: 中国科学院大学, 2017.
[8]  穆金虎, 陈玉泽, 冯慧, 等. 作物育种学领域新的革命: 高通量的表型组学时代[J]. 植物科学学报, 2016, 34(6): 962-971.
[9]  李真, 史智兴, 王成, 等. 红外热成像技术在作物胁迫检测方面的应用[J]. 农机化研究, 2016, 38(1): 232-237.
[10]  Fuller, M.P. and Wisniewski, M. (1998) The Use of Infrared Thermal Imaging in the Study of Ice Nucleation and Freezing of Plants. Journal of Thermal Biology, 23, 81-89.
https://doi.org/10.1016/S0306-4565(98)00013-8
[11]  Wang, M., Ling, N., Zhu, Y.-Y., et al. (2012) Thermographic Visualization of Leaf Response in Cucumber Plants Infected with the Soil-Borne Pathogen Fusarium ox-ysporum f. sp. Cucumerinum. Plant Physiology and Biochemistry, 61, 153-161.
https://doi.org/10.1016/j.plaphy.2012.09.015
[12]  刘亚. 基于远红外热成像的叶温变化与玉米苗期耐旱性的研究[J]. 中国农业科学, 2009, 42(6): 2192-2201.
[13]  王冰, 崔日鲜, 王月福. 基于远红外成像技术的花生苗期抗旱性鉴定[J]. 中国油料作物学报, 2011, 33(6): 632-636.
[14]  王道杰. 油菜抗旱性及鉴定方法与指标[J]. 西北农业学报, 2012, 21(5): 108-113.
[15]  Meron, M., Tsipris, J., Orlov, V., et al. (2010) Crop Water Stress Mapping for Site-Specific Irrigation by Thermal Imagery and Artificial Reference Surfaces. Precision Agriculture, 11, 148-162.
https://doi.org/10.1007/s11119-009-9153-x
[16]  徐惠荣, 应义斌. 红外热成像在树上柑桔识别中的应用研究[J]. 红外与毫米波学报, 2004, 23(5): 353-355.
[17]  李泽东, 李宏宁, 方玉萍, 等. 黄瓜霜霉病害的窄带多光谱图像光谱分类和评估研究[J]. 云南师范大学学报, 2011, 31(6): 63-69.
[18]  王一杰, 杨智慧, 成军虎. 多光谱成像技术在食品营养品质检测方面的应用进展[J]. 食品工业科技, 2019(9): 1-13.
[19]  潘锐, 熊勤学, 张文英. 数字图像技术及其在作物表型研究中的应用研究进展[J]. 长江大学学报(自科版), 2016, 13(21): 38-41, 46.
[20]  许洪. 多光谱、超光谱成像探测关键技术研究[D]: [博士学位论文]. 天津: 天津大学, 2008.
[21]  Pu, H.-B., Kamruz-zaman, M. and Sun, D.-W. (2015) Selection of Feature Wavelengths for Developing Multispectral Imaging Systems for Quality, Safety and Authenticity of Muscle Foods: A Review. Trends in Food Science and Technology, 45, 86-104.
https://doi.org/10.1016/j.tifs.2015.05.006
[22]  Lohumi, S., Lee, S., Lee, H., et al. (2015) A Review of Vibrational Spectroscopic Techniques for the Detection of Food Authenticity and Adulteration. Trends in Food Science & Technolo-gy, 46, 85-98.
https://doi.org/10.1016/j.tifs.2015.08.003
[23]  桂江生, 吴子娴, 顾敏, 等. 高光谱成像技术在农业中的应用概述[J]. 浙江农业科学, 2017, 58(7): 1101-1105.
[24]  张巍. 基于高光谱成像技术的蓝莓内部品质检测方法的研究[D]: [硕士学位论文]. 沈阳: 沈阳农业大学, 2016.
[25]  Gan, F.-P., Wang, R.-S., Ma, A.-N., et al. (2002) Investiga-tion on Physiological Status of Regional Vegetation Using Push Broom Hyperspectral Imager Data. Journal of Integra-tive, 44, 983-989.
[26]  Kuska, M., Wahabzada, M., Leucker, M., et al. (2015) Hyperspectral Phenotyping on the Mi-croscopic Scale: Towards Automated Characterization of Plant-Pathogen Interactions. Plant Methods, 11, 1-15.
https://doi.org/10.1186/s13007-015-0073-7
[27]  Juan, X., Stephen, S., Muhammad, S., et al. (2010) Detection of Sprout Damage in Canada Western Red Spring Wheat with Multiple Wavebands Using Visible/Near-Infrared Hyper-spectral Imaging. Biosystems Engineering, 106, 188-194.
https://doi.org/10.1016/j.biosystemseng.2010.03.010
[28]  Wallays, C., Missotten, B., De Baerdemaeker, J., et al. (2009) Hyperspectral Waveband Selection for On-Line Measurement of Grain Cleanness. Biosystems Engineering, 104, 1-7.
https://doi.org/10.1016/j.biosystemseng.2009.05.011
[29]  刘伟, 刘长虹, 郑磊. 基于支持向量机的多光谱成像稻谷品种鉴定[J]. 农业工程学报, 2014, 30(10): 145-151.
[30]  许学, 马卉, 王钰, 等. 基于多光谱成像技术的小麦品种快速无损鉴定[J]. 中国农学通报, 2019, 35(15): 14-19.
[31]  冯雷, 柴荣耀, 孙光明, 等. 基于多光谱成像技术的水稻叶瘟检测分级方法研究[J]. 光谱学与光谱分析, 2009, 29(10): 2730-2733.
[32]  孙光明, 杨凯盛, 张传清, 等. 基于多光谱成像技术的大麦赤霉病识别[J]. 农业工程学报, 2009, 25(增刊2): 204-207.
[33]  姜伟杰, 孙明. 基于多光谱成像的番茄叶片叶绿素含量预测建模方法研究[J]. 光谱学与光谱分析, 2011, 31(3): 758-761.
[34]  刘奕彤, 宋玉柱, 马昕宇, 等. 基于多光谱成像技术的玉米氮素营养诊断方法研究[J]. 农机化研究, 2018, 40(2): 148-153.
[35]  孙俊, 金夏明, 毛罕平, 等. 基于高光谱图像光谱与纹理信息的生菜氮素含量检测[J]. 农业工程学报, 2014, 30(10): 167-173.
[36]  王丽凤, 张长利, 赵越, 等. 高光谱成像技术的玉米叶片氮含量检测模型[J]. 农机化研究, 2017, 11(11): 140-146, 68.
[37]  冯慧, 熊立仲, 陈国兴, 等. 基于高光谱成像和主成分分析的水稻茎叶分割[J]. 激光生物学报, 2015, 24(1): 31-38.
[38]  李美凌, 邓飞, 刘颖, 等. 基于高光谱图像的水稻种子活力检测技术研究[J]. 浙江农业学报, 2015, 27(1): 1-6.
[39]  Christian, N., Zhao, G.-P., Nicole, D., et al. (2015) Using Hyperspectral Imaging to Determine Germination of Native Australian Plant Seeds. Journal of Photo-chemistry and Photobiology B: Biology, 145, 19-24.
https://doi.org/10.1016/j.jphotobiol.2015.02.015
[40]  杨小玲, 由昭红, 成芳. 高光谱成像技术检测玉米种子成熟度[J]. 光谱学与光谱分析, 2016, 36(12): 4028-4033.
[41]  贾敏, 欧中华. 高光谱成像技术在果蔬品质检测中的应用[J]. 激光生物学报, 2018, 27(2): 119-126.
[42]  张保华, 李江波, 樊书祥, 等. 高光谱成像技术在果蔬品质与安全无损检测中的原理及应用[J]. 光谱学光谱分析, 2014, 34(10): 2743-2751.
[43]  索少增, 刘翠玲, 吴静珠, 等. 高光谱图像技术检测梨表面农药残留试验研究[J]. 食品科学技术学报, 2011, 29(6): 73-77.
[44]  李增芳, 楚秉泉, 章海亮, 等. 高光谱成像技术无损检测赣南脐橙表面农药残留研究[J]. 光谱学与光谱分析, 2016, 36(12): 4034-4038.
[45]  Jiang, J.-B., Qiao, X.-J. and He, R.-Y. (2016) Use of Near-Infrared Hyperspectral Images to Identify Moldy Peanuts. Journal of Food Engineering, 169, 284-290.
https://doi.org/10.1016/j.jfoodeng.2015.09.013
[46]  White Noel, D.G., et al. (2016) Detection of Fungal Infection and Ochratoxin A Contamination in Stored Barley Using Near-Infrared Hyperspectral Imaging. Journal of Stored Prod-ucts Research, 147, 162-173.
https://doi.org/10.1016/j.biosystemseng.2016.03.010
[47]  Rajkumar, P., Wang, N., Elmasry, G., et al. (2012) Studies on Banana Fruit Quality and Maturity Stages Using Hyperspectral Imaging. Journal of Food Engineering, 108, 194-200.
https://doi.org/10.1016/j.jfoodeng.2011.05.002
[48]  薛建新, 张淑娟, 张晶晶. 基于高光谱成像技术的沙金杏成熟度判别[J]. 农业工程学报, 2015, 31(11): 300-307.
[49]  Bock, C.H., Poole, G.H., Parker, P.E., et al. (2010) Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging. Critical Reviews in Plant Sciences, 29, 59-107.
https://doi.org/10.1080/07352681003617285
[50]  梁琨, 杜莹莹, 卢伟, 等. 基于高光谱成像技术的小麦籽粒赤霉病识别[J]. 农业机械学报, 2016, 47(2): 309-315.
[51]  李勋兰, 易时来, 何绍兰, 等. 高光谱成像技术的柚类品种鉴别研究[J]. 光谱学与光谱分析, 2015, 35(9): 2639-2643.
[52]  吴龙国. 基于高光谱成像技术的土壤水盐及番茄植株水分诊断机理与模型研究[D]: [博士学位论文]. 银川: 宁夏大学, 2017.
[53]  王春萍, 雷开荣, 李正国, 等. 低温胁迫对水稻幼苗不同叶龄叶片叶绿素荧光特性的影响[J]. 植物资源与环境学报, 2012, 21(3): 38-43.
[54]  Pereira, F., Milori, D., Pereira, E.R., et al. (2011) Laser-Induced Fluorescence Imaging Method to Monitor Citrus Greening Disease. Computers and Electronics in Agriculture, 79, 90-93.
https://doi.org/10.1016/j.compag.2011.08.002
[55]  Sankaran, S. and Ehsani, R. (2012) Detection of Huanglong-bing Disease in Citrus Using Fluorescence Spectroscopy. Transactions of the ASABE, 55, 313-320.
https://doi.org/10.13031/2013.41241
[56]  杨昊谕, 于海业, 刘煦, 等. 叶绿素荧光PCA-SVM分析的黄瓜病虫害诊断研究[J]. 光谱学与光谱分析, 2010, 30(11): 3018-3021.
[57]  陈兵, 王克如, 李少昆, 等. 病害胁迫对棉叶光谱反射率和叶绿素荧光特性的影响[J]. 农业工程学报, 2011, 27(9): 86-93.
[58]  杨昊谕. 基于叶绿素荧光光谱分析的植物生理信息检测技术研究[D]: [博士学位论文]. 长春: 吉林大学, 2010.
[59]  Su, W.-H., Fennimore, S.A. and Slaughter, D.C. (2019) Fluorescence Imaging for Rapid Monitoring of Translocation Behaviour of Systemic Markers in Snap Beans for Automated Crop/Weed Discrimination. Biosystems Engineering, 186, 156-167.
https://doi.org/10.1016/j.biosystemseng.2019.07.009
[60]  张石锐, 董大明, 郑文刚, 等. 农田土壤水分含量的激光诱导荧光光谱表征[J]. 光谱学与光谱分析, 2012, 32(10): 2623-2627.
[61]  董佳. 荧光光谱成像技术在中药品质检测中的应用研究[D]: [硕士学位论文]. 广州: 暨南大学, 2015.
[62]  季云飞, 耿林, 冯国旭. 激光成像技术的新发展[J]. 激光与红外, 2015, 45(12): 1413-1417.
[63]  张文英, 李承道, 等. 作物表型研究方法[M]. 北京: 科学出版社, 2017.
[64]  王志翀, 何雄奎, 李天, 等. 基于激光成像技术的农药雾滴飘移评价方法研究[J]. 农业工程学报, 2019, 35(9): 73-79.
[65]  方圣辉, 汪琳, 周颖, 等. 基于点云的植株表型构建和反射率方向性分析[J]. 激光与红外, 2018, 48(8): 965-972.
[66]  姜丹, 刘卉, 邱权. 基于Seekur的农田机器人激光避障设计与仿真[J]. 农机化研究, 2015, 37(7): 151-155.
[67]  季宇寒, 徐弘祯, 张漫, 等. 基于激光雷达的农田环境点云采集系统设计[J]. 农业机械学报, 2019(S1): 1-7.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133