美国密西西比三角洲农业航空和精准农业技术研发现状、展望与启示

doi:10.12133/j.smartag.2019.1.4.201909-SA003

摘要/Abstract

摘要：

作物生产管理已经进入智慧农业阶段。智慧农业是由最先进的农业信息技术、智能装备以及大量的数据资源所驱动的先进农业科技理念。智慧农业继承了精准农业概念，把农业生产管理由机械化和信息化提高到高度自动化和智能化的农业生产管理。精准农业从上世纪八十年代的粗略监测发展到本世纪10年代的详细监测和控制。在精准农业的发展过程中，农业航空在作物保护和肥料施用方面起到了关键的作用。而在作物保护和肥料精准施用方面，基于全球导航产生的带有空间信息遥感数据配方图是至关重要的。随着现代化农业的发展，农业航空会因更有效的土壤和植物健康监测和更加快速的机电系统响应，在推进精准农业实际应用上显得越发重要。本文具体从美国最重要的农业地区之一密西西比三角洲出发，总体介绍了农业航空在精准农业向智慧农业迈进过程中的状况。重点介绍了美国农业部在密西西比三角洲地区在航空应用技术和低空遥感方面的研发工作；为发展新一代精准农业和智慧农业，进一步研发农业航空技术的问题、挑战和机会进行了讨论；最后提出了中国发展智慧农业建议。

关键词: 航空施药技术, 农业航空, 大数据, 精准农业, 遥感, 智慧农业

Abstract:

Crop production management has advanced into the stage of smart agriculture, which is driven by state-of-the-art agricultural information technology, intelligent equipment and massive data resources. Smart agriculture inherits ideas from precision agriculture and brings agricultural production and management from mechanization and informalization to intelligentization with automatization. Precision agriculture has been developed from strategic monitoring operations in the 1980s to tactical monitoring and control operations in the 2010s. In its development, agricultural aviation has played a key role in serving systems for spray application of crop protection and production materials for precision agriculture with the guidance of global navigation through geospatial prescription mapping derived from remotely-sensed data. With the development of modernized agriculture, agricultural aviation is even more important for advancing precision agricultural practices with more efficient soil and plant health sensing and more prompt and effective system actuation and action. This paper overviews the status of agricultural aviation for precision agriculture to move toward smart agriculture, especially in the Mississippi Delta region, one of the most important agricultural areas in the U.S. The research and development by scientists associated with the Mississippi Delta region are reported. The issues, challenges and opportunities are identified and discussed for further research and development of agricultural aviation technology for next-generation precision agriculture and smart agriculture.

Key words: aerial application technology, agricultural aviation, big data, precision agriculture, remote sensing, smart agriculture

中图分类号:

黄岩波. 美国密西西比三角洲农业航空和精准农业技术研发现状、展望与启示[J]. 智慧农业(中英文), 2019, 1(4): 12-30.

Huang Yanbo. Perspectives and experiences on the development and innovation of agricultural aviation and precision agriculture from the Mississippi Delta and recommendations for China[J]. Smart Agriculture, 2019, 1(4): 12-30.

参考文献

1	InterviewEditorial: Dr . Yanbo Huang talks about from precision agriculture in the United States to future smart agriculture in China[EB/OL]. (2019-03-25) [2019-08-10]. .
2	Thomson S J , Zimba P V , Bryson C T , et al . Potential for remote sensing from agricultural aircraft using digital video[J]. Applied Engineering in Agriculture, 2005, 21(3): 531-537.
3	Lan Y , Thomson S J , Huang Y B , et al . Current status and future directions of precision aerial application for site-specific crop management in the USA[J]. Computers and Electronics in Agriculture, 2010, 74(1): 34-38.
4	Lan Y , Chen S , Fritz B K . Current status and future trends of precision agricultural aviation technologies[J]. International Journal of Agricultural and Biological Engineering, 2017, 10(3): 1-17.
5	Aerial Application [EB/OL]. [2019-10-05]. .
6	Neblette C B . Aerial photography for the study of plant diseases[J]. Photo-Era Mag, 1927, 58: 346.
7	Taubenhaus J J , Ezekiel W N , Neblette C B . Airplane photography in the study of cotton root rot[J]. Phytopathology, 1929, 19: 1025-1029.
8	Steddom K , Jones D , Rush C . A picture is worth a thousand words. American Phytopathological Society[EB/OL]. [2019-09-11]. .
9	Huang Y , Brown M . Advancing to the next generation precision agriculture. In Agriculture & Food Systems to 2050-Global Trends, Challenges and Opportunities[M]. Singapore: World Scientific Publishing, 2018.
10	USDA-ERS . Latest U.S. atgricultural trade data[EB/OL]. [2019-10-15]. .
11	USDA-ERS . 2019. What is agriculture's share of the overall U .S. economy?[EB/OL]. [2019-10-18]. .
12	Winkler J A , Andresen J A , Hatfield J L , et al . Climate change in the midwest: a synthesis report for the National Climate Assessment[J]. Isaconf, 2014, 67(5520):1261-1261.
13	Snipes C E , Nichols S P , Poston D H , et al . Agricultural Practices of the Mississippi Delta[J]. ACS Symposium Series, 2004: 43-60.
14	EU . Directive 2009/128 /EC of the European Parliament and of the Council of 21 October 2009 establishing a framework for Community action to achieve the sustainable use of pesticides[EB/OL]. [2019-08-20]. .
15	About ag aviation . National Agricultral Aviation Association (NAAA)[EB/OL]. [2019-07-09]
16	FactsIndustry . National agricultral aviation association (NAAA)[EB/OL]. [2019-10-01] .
17	IPCadmin . Aerial applications in the USA[EB/OL]. [2019-05-04]. .
18	Eckelkamp M . Aerial application by the numbers[EB/OL]. [2019-08-06]. .
19	Sato A . The RMAX helicopter UAV[C]// Public Report. Aeronautic Operations. Yamaha Motor Co., Ltd., Shizuoka, Japan, 2003.
20	Zhou Z , Zang Y , Lu X , et al . Technology innovation development strategy on agricultural aviation industry for plant protection in China[J]. Transactions of the CSAE, 2013, 29(24): 1-10.
21	He X , Bonds J , Herbst A , et al . Recent development of unmanned aerial vehicle for plant protection in East Asia[J]. International Journal of Agricultural and Biological Engineering, 2017, 10(3): 18-30.
22	Huang Y , Hoffmann W C , Lan Y , et al . Development of a spray system on an unmanned aerial vehicle platform[J]. Applied Engineering in Agriculture, 2009, 25(6): 803-809.
23	Huang Y , Hoffman W C , Lan Y , et al . Development of a low-volume sprayer for an unmanned helicopter[J]. Journal of Agricultural Science. 2014, 7(1): 148-153.
24	Giles D K , Billings R . Unmanned aerial platforms for spraying: deployment and performance[J]. Aspects of Applied Biology, 2014, 122: 63-69.
25	Kelemen D . The future of unmanned aircraft systems: is there a niche in aerial application?[J]. Ag. Aviation, 2013, 9/10: 15-21.
26	ledgerClarion [EB/OL]. [2019-06-09]
27	Sanders . OptiGro system[EB/OL]. [2019-06-07] .
28	USDA-ARS . Remote sensing technique uses agricultural aircraft[EB/OL]. [2019-07-07]. ScienceDaily, 16 April 2005. .
29	USDA-ARS . Agricultural aircraft offer a different view of remote sensing[J]. Agricultural Research magazine, March 2005, 21. .
30	Huang Y , Thomson S J . Characterization of in-swath spray deposition for CP-11TT flat-fan nozzles used in low volume aerial application of crop production and protection materials[J]. Transactions of the ASAB, 2011, 54(6): 1973-1979.
31	Huang Y , Thomson S J . Characterization of spray deposition and drift from a low drift nozzle for aerial application at different application altitudes[J]. International Journal of Agricultural and Biological Engineering, 2011, 4(4): 1-6.
32	Huang Y , Thomson S J , Ortiz B V , et al . Airborne remote sensing assessment of the damage to cotton caused by spray drift from aerially applied glyphosate through spray deposition measurements[J]. Biosystems Engineering, 2010, 107: 212-220.
33	Reddy K N , Ding W , Zablotowicz R M , et al . Biological responses to glyphosate drift from aerial application in non-glyphosate-resistant corn[J]. Pest Management Science, 2010, 66: 1148-1154.
34	Huang Y , Ding W , Thomson S J , et al . Assessing crop injury caused by aerially applied glyphosate drift using spray sampling[J]. Transactions of the ASABE, 2012, 55(3): 725-731.
35	Huang Y , Thomson S J . Evaluation of spray nozzles for aerial application of biological control agents[C]// NAAA Convention, December 5-8, 2016, Long Beach, California, USA, 2016.
36	Teske M E , Thistle H W , Ice G G . Technical advances in modeling aerially applied sprays[J]. Transactions of the ASABE, 2003, 46(4): 985-996.
37	Taguchi G . System of experimental design[R].Unipub/Kraus/American Supplier Institute, Dearborn, Michigan, USA, 1987.
38	Huang Y , Zhan W , Fritz B K , et al . Analysis of impact of various factors on downwind deposition using a simulation method[J]. Journal of ASTM International, 2010, 7(6): 1-11.
39	Huang Y , Zhan W , Fritz B K , et al . Optimizing selection of controllable variables to minimize downwind drift from aerially applied sprays[J]. Applied Engineering in Agriculture, 2012, 28(3): 307-314.
40	Thomson S J , Huang Y , Fritz B K . Atmospheric stability intervals influencing the potential for off-target movement of spray in aerial application[J]. International Journal of Agricultural Science and Technology, 2017, 5(1): 1-17.
41	Huang Y , Thomson S J . Atmospheric stability determination at different time intervals for determination of aerial application timing[J]. Journal of Biosystems Engineering, 2016, 41(4): 337-341.
42	Huang Y , Fisher D K , Silva M , et al . A real-time web tool for safe aerial application to avoid off-target movement of spray induced by stable atmospheric conditions in the Mississippi Delta[J]. Applied Engineering in Agriculture, 2019, 35(1): 31-38.
43	Huang Y , Fisher D K . A web guide system for aerial application to avoid off-target drift caused by temperature inversion through open-source weather stations[C]// ASABE Paper No. 1900193. St. Joseph, MI.ASABE. 2019.
44	Smith L A . Automatic flow control for aerial applications[C]// Applied Engineering in Agriculture, 2001, 17(4): 449-455.
45	Thomson S J , Smith L A , Hanks J E . Evaluation of application accuracy and performance of a hydraulically operated variable-rate aerial application system[J]. Transactions of the ASABE, 2009, 52(3): 715-722.
46	Thomson S J , Huang Y , Hanks J E , et al . Improving flow response of a variable-rate aerial application system by interactive refinement[J]. Computers and Electronics in Agriculture, 2010, 73: 99-104.
47	de Castro A I , Jurado-Expósito M , Pe?a-Barragán J M , et al . Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops[J]. Precision Agriculture, 2012, 13: 302-321.
48	de Castro A I , Torres-Sánchez J , Pe?a J M , et al . An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery[J]. Remote Sensing, 2018, 10(285): 1-21.
49	Pinter P J , Hatfield Jr J L , Schepers J S , et al . Remote sensing for crop management[J]. Photogrammetric Engineering and Remote Sensing, 2003, 69: 647-664.
50	Shaw D R , Kelley F S . Evaluating remote sensing for determining and classifying soybean anomalies[J]. Precision Agriculture, 2005, 6: 421-429.
51	Torres-Sánchez J , López-Granados F , De Castro A I , et al . configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management[J]. PLoS ONE, 2013, 8(3): e58210.
52	Ortiz B V , Thomson S J , Huang Y , et al . Determination of differences in crop injury from aerial application of glyphosate using vegetation indices[J]. Computers and Electronics in Agriculture, 2011, 77: 204-213.
53	Huang Y , Ouellet-Plamondon C M , Thomson S J , et al . Characterizing downwind deposition of the off-target drift from aerially applied glyphosate using RbCl as tracer[J]. International Journal of Agricultural and Biological Engineering, 2017, 10(3): 31-36.
54	Lan Y , Huang Y , Martin D E , et al . Development of an airborne remote sensing system for crop pest management: System integration and verification[J]. Applied Engineering in Agriculture, 2009, 25(4): 607-615.
55	Huang Y , Thomson S J , Lan Y , et al . Multispectral imaging systems for airborne remote sensing to support agricultural production management[J]. International Journal of Agricultural and Biological Engineering, 2010, 3(1): 50-62.
56	Huang Y , Reddy K N , Fletcher R S , et al . UAV low-altitude remote sensing for precision weed management [J]. Weed Technology, 2018, 32: 2-6.
57	Huang Y , Thomson S J , Brand H J , et al . Development and evaluation of low-altitude remote sensing systems for crop production management[J]. International Journal of Agricultural and Biological Engineering, 2016, 9(4): 1-11.
58	Huang Y , Reddy K N . Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management[J]. Proceedings of the Plenary and Lead Papers of the 25th Asian-Pacific Weed Science Society Conference, 2015, 1: 185-192.
59	Huang Y , Brand H J , Pennington D , et al . Determining soybean injury from Dicamba using RGB and CIR images acquired by UAVs[C]// Proceedings of 13th International Conference on Precision Agriculture, St. Louis, MO., 2016b, July 31-August 3, 2016. 6.
60	Huang Y , Brand H J , Sui R , et al . Cotton yield estimation using very high-resolution digital images acquired with a low-cost small unmanned aerial vehicle[J]. Transactions of the ASABE, 2016, 59(6): 1563-1574.
61	International survey of herbicide resistant weeds [EB/OL]. [2019-06-06]. .
62	Reddy K N , Huang Y , Lee M A , et al . Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S Wats.): Hyperspectral reflectance properties of plants and potential for classification[J]. Pest Management Science, 2014, 70: 1910-1917.
63	Lee M A , Huang Y , Nandula V K , et al . Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery[C]// Proceedings SPIE 9108, Sensing for Agriculture and Food Quality and Safety VI, 91080B, 2014.
64	Huang Y , Lee M A , Nandula V K , et al . Hyperspectal imaging for differentiating glyphosate-resistant and glyphosate-susceptible Italian ryegrass[J]. American Journal of Plant Sciences, 2018, 9: 1467-1477.
65	Singh B D , Singh A K . Marker-assisted plant breeding: principles and practices[J]. Springer India, New Delhi, India, 2015.
66	Kumar J , Pratap A , Kumar S . Phenomics in crop plants: trends, options and limitations[M]. Springer India, New Delhi, India, 2015.
67	Awada L , Phillips P W B , Smyth S J . The adoption of automated phenotyping by plant breeders[J]. Euphytica, 2018, 214(148): 1-15.
68	Ehmke T . Unmanned aerial systems for field scouting and spraying[N]. CSA News, 2013, 58(12): 4-9.
69	Yang C , Odvody G N , Thomasson J A , et al . Site-specific management of cotton root rot using airborne and high-resolution satellite imagery and variable-rate technology[J]. Transactions of the ASABE, 2018, 61(3): 849-858.
70	Cowley D C , Moriarty C , Geddes G , et al . UAVs in context: archaeological airborne recording in a national body of survey and record[J]. Drones, 2018, 2(2): 1-16.
71	Huang Y . Infrastructure development for farm-scale remote sensing big data service[C]// Proceedings of SPIE Asia-Pacific Remote Sensing, Honolulu, HI, 2018, September 24-26, 2018, 10780(17): 1-9.
72	Huang Y , Chen Z , Yu T , et al . Agricultural remote sensing big data: management and applications[J]. Journal of Integrative Agriculture, 2018, 17(9): 1915-1931.
73	Pearce J M . The case for open source appropriate technology[J]. Environment, Development and Sustainability, 2012, 14: 425-431.
74	Fisher D K , Gould P J . Open-source hardware is a low-cost alternative for scientific instrumentation and research[J]. Modern Instrumentation, 2012, 1(2): 8-20.
75	projectsThingspeak For IOT [EB/OL]. [2019-10-04].
76	Brookes G , Barfoot P . The income and production effects of biotech crops globally 1996-2009[J]. International Journal of Biotechnology, 2011, 12: 1-49.

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