Smart Agriculture ›› 2019, Vol. 1 ›› Issue (4): 12-30.doi: 10.12133/j.smartag.2019.1.4.201909-SA003
Previous Articles Next Articles
Huang Yanbo
Received:
2019-09-24
Revised:
2019-11-22
Online:
2019-10-30
Published:
2019-12-24
CLC Number:
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.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.2019.1.4.201909-SA003
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 , Peña-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 , Peña 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. |
[1] | WANG Lin, LIANG Jian, MENG Fanyu, MENG Yang, ZHANG Yongtao, LI Zhenhai. Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data [J]. Smart Agriculture, 2021, 3(2): 15-22. |
[2] | HAN Dong, WANG Pengxin, ZHANG Yue, TIAN Huiren, ZHOU Xijia. Progress of Agricultural Drought Monitoring and Forecasting Using Satellite Remote Sensing [J]. Smart Agriculture, 2021, 3(2): 1-14. |
[3] | YANG Feifei, LIU Shengping, ZHU Yeping, LI Shijuan. Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing [J]. Smart Agriculture, 2021, 3(2): 35-44. |
[4] | DAI Shengpei, LUO Hongxia, ZHENG Qian, HU Yingying, LI Hailiang, LI Maofen, YU Xuan, CHEN Bangqian. Comparison of Remote Sensing Estimation Models for Leaf Area Index of Rubber Plantation in Hainan Island [J]. Smart Agriculture, 2021, 3(2): 45-54. |
[5] | SHU Meiyan, CHEN Xiangyang, WANG Xiqing, MA Yuntao. Estimation of Maize Leaf Area Index and Aboveground Biomass Based on Hyperspectral Data [J]. Smart Agriculture, 2021, 3(1): 29-39. |
[6] | ZHANG Jian, XIE Tianjin, YANG Wanneng, ZHOU Guangsheng. Research Status and Prospect on Height Estimation of Field Crop Using Near-Field Remote Sensing Technology [J]. Smart Agriculture, 2021, 3(1): 1-15. |
[7] | CHEN Ailian, LI Jiayu, ZHANG Shengjun, ZHU Yuxia, ZHAO Sijian, SUN Wei, ZHANG Qiao. Application of Satellite Remote Sensing Yield Estimation Technology in Regional Revenue Protection Crop Insurance: A Case of Soybean [J]. Smart Agriculture, 2020, 2(3): 139-152. |
[8] | HONG Wei, XU Baohua, LIU Shengping. Design and Experimental Research of Long-Term Monitoring System for Bee Colony Multiple Features [J]. Smart Agriculture, 2020, 2(2): 105-114. |
[9] | WANG Peilong , TANG Zhiyong. Application Analysis and Prospect of Nanosensor in the Quality and Safety of Agricultural Products [J]. Smart Agriculture, 2020, 2(2): 1-10. |
[10] | Yang Chenghai. Airborne remote sensing systems for precision agriculture applications [J]. Smart Agriculture, 2020, 2(1): 1-22. |
[11] | Cao Hongxin, Ge Daokuo, Zhang Wenyu, Zhang Weixin, Cao Jing, Liang Wanjie, Xuan Shouli, Liu Yan, Wu Qian, Sun Chuanliang, Zhang Lingling, Xia Ji‘an, Liu Yongxia, Chen Yuli, Yue Yanbin, Zhang Zhiyou, Wan Qian, Pan Yue, Han Xujie, Wu Fei. Developmental analysis and application examples for agricultural models [J]. Smart Agriculture, 2020, 2(1): 147-162. |
[12] | Liu Yuan, Zhou Qingbo, Yu Qiangyi, Wu Wenbin. Analysis of spatial pattern and ecological service value changes of large-scale regional paddy fields based on remote sensing data [J]. Smart Agriculture, 2020, 2(1): 43-57. |
[13] | Yu Fenghua, Xu Tongyu, Guo Zhonghui, Du Wen, Wang Dingkang, Cao Yingli. Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI) [J]. Smart Agriculture, 2020, 2(1): 77-86. |
[14] | Huang Wenjiang, Shi Yue, Dong Yingying, Ye Huichun, Wu Mingquan, Cui Bei, Liu Linyi. Progress and prospects of crop diseases and pests monitoring by remote sensing [J]. Smart Agriculture, 2019, 1(4): 1-11. |
[15] | Lan Yubin, Deng Xiaoling, Zeng Guoliang. Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing [J]. Smart Agriculture, 2019, 1(2): 1-19. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||