Advances and Progress of Agricultural Machinery and Sensing Technology Fusion

doi:10.12133/j.smartag.2020.2.4.202002-SA003

Abstract

Abstract:

Agricultural machinery and equipment are important foundations for transforming agricultural development methods and promoting sustainable agricultural development, as well as are the key areas and core supports for promoting agricultural modernization. In order to clarify the development ideas of agricultural machinery informatization and find the key development directions, and vigorously promote the development of agricultural machinery intelligentization, the development status of foreign agricultural machinery and sensing technology fusion were analyzed in this article, and five major development characteristics: 1) development towarding digitalization, automation and informationization, 2) applying sensing technology to the design and manufacturing of agricultural machinery equipment, 3) rapidly developing of animal husbandry machinery sensing technology, 4) focusing on resource conservation and environmental protection, and sensing technology promoting sustainable agricultural development, and 5) towarding intelligent control, automatic operation and driving comfort development were summarized. Among them, some advanced intelligent agricultural machinery were introduced, including the German Krone BiGX700 self-propelled silage harvester, an automatic weeding and fertilization robot developed by the Queensland University of Technology in Australia—Agbot II, and John Deere CP690 self-propelled baler Cotton machine, etc. After that, the new characteristics of the development of agricultural mechanization in China were summarize, and the viewpoint was pointed out that although the current development of agricultural mechanization in China had achieved remarkable results, there were still problems such as low intelligence and informatization of agricultural machinery, and insufficient fusion of agricultural machinery and informatization. Then the prospects for the development of China's agricultural machinery and sensing technology fusion were put forward, including 1) promoting the development of intelligent perception technology and navigation technology research, 2) promoting the intelligentization of agricultural machinery and equipment, and building an agricultural intelligent operation system, 3) promoting the research of agricultural machinery autonomous operation technology and the construction of unmanned farms, and 4) strengthening the technical standard formulation of agricultural machinery informatization and the training of compound talents. The fusion of agricultural machinery and sensing technology can realize the effective and diversified fusion of agricultural mechanization and sensing technology, maximize the guiding effect of informatization, improve the efficiency of agricultural production in China, and promote the development of digital agriculture and modern agriculture.

Key words: agricultural machinery, sensing technology, fusion, smart operating system, unmanned farm, intelligent perception, navigation

CLC Number:

CHEN Xuegeng, WEN Haojun, ZHANG Weirong, PAN Fochu, ZHAO Yan. Advances and Progress of Agricultural Machinery and Sensing Technology Fusion[J]. Smart Agriculture, 2020, 2(4): 1-16.

References

1	刘明霄. 习近平: 给农业插上科技的翅膀[EB/OL].(2013-11-28)[2020-01-11]. .
2	中国政府网. 国务院关于加快推进农业机械化和农机装备产业转型升级的指导意见[EB/OL]. (2018-12-29) [2020-01-11]..
3	新华网. 中共中央国务院印发《乡村振兴战略规划(2018－2022年)》[EB/OL]. (2018-09-26) [2020-01-12]..
4	张桃林. 在2019年全国农业机械化工作会议上的讲话（摘要）[J]. 中国农机化学报, 2019, 40(3): 1-6.
4	ZHANG T. Speech at the national conference on agricultural mechanization in 2019(abstract)[J]. Journal of Chinese Agricultural Mechanization, 2019, 40(3): 1-6.
5	肖俊华, 王瑾, 王锋德. 我国农机企业研发投入现状及对策[J]. 农业工程, 2019, 9(8): 1-5.
5	XIAO J, WANG J, WANG F. Situation and countermeasures of agricultural machinery enterprises R&D investment in China[J]. Agricultural Engineering, 2019, 9(8): 1-5.
6	本刊评论员. 把握趋势科学施策培育经济发展新动能[J]. 中国经贸导刊, 2016(29): 1.
6	Commentator of this journal. Grasp the trend and implement scientific strategies to cultivate new momentum for economic development[J]. China Economic & Trade Herald, 2016(29): 1.
7	孔繁涛. 发展中的农业信息学[J]. 广东农业科学, 2012, 39(15): 219-223.
7	KONG F. Development of agricultural informatics[J]. Guangdong Agricultural Sciences, 2012, 39(15): 219-223.
8	陶卫民. 国外农机技术发展趋势[J]. 湖南农机, 2001(4): 21-22.
8	TAO W. Development trend of foreign agricultural machinery technology[J]. Agricultural Machinery of Hunan, 2001(4): 21-22.
9	中国社会科学网. 数字农业关键技术发展的国际经验[EB/OL]. (2020-08-05) [2020-01-12].
9	.
10	慕农. 德国的农业机械[J]. 当代农机, 2019(6): 50-51.
10	MU N. German agricultural machinery[J]. Contemporary Farm Machinery, 2019(6): 50-51.
11	李瑾, 冯献, 郭美荣, 等. “互联网+”现代农业发展模式的国际比较与借鉴[J]. 农业现代化研究, 2018, 39(2): 194-202.
11	LI J, FENG X, GUO M. The international comparison, tendency and reference of the development mode of "Internet+" modern agriculture[J]. Research of Agricultural Modernization, 2018, 39(2): 194-202.
12	孙绍华. 数字化设计技术及其在农业机械设计中的应用研究[J]. 农机使用与维修, 2018(10): 17-18.
12	SUN S. Digital design technology and its application in agricultural machinery design[J]. Agricultural Mechanization Using & Maintenance, 2018(10): 17-18.
13	杜岳峰, 傅生辉, 毛恩荣, 等. 农业机械智能化设计技术发展现状与展望[J]. 农业机械学报, 2019, 50(9): 1-17.
13	DU Y, FU S, MAO E, et al. Development situation and prospects of intelligent design for agricultural machinery[J]. Transactions of the CSAM, 2019, 50(9): 1-17.
14	陈学庚. 科技创新开拓农业机械化新篇章[J]. 中国农村科技, 2019(5): 30-35.
14	CHEN X. Scientific and technological innovation opens up a new chapter in agricultural mechanization[J]. China Rural Science and Technology, 2019(5): 30-35.
15	豆贺, 高艳芳, 胡逢时, 等. 国内外牧草收获机械发展现状及趋势[J]. 现代化农业, 2017, 450(1): 61-64.
15	DOU H, GAO Y, HU F, et al. Current situation and trend of pasture harvesting machinery in China and abroad[J]. Modern Agriculture, 2017, 450(1): 61-64.
16	吴利华, 贾玉斌, 王志强, 等. 对草原畜牧业机械化的思考[J]. 农村牧区机械化, 2014(4): 15-17.
16	WU L, JIA Y, WANG Z, et al. Thinking on mechanization of grassland animal husbandry[J]. Mechanization of Rural Pastoral Area, 2014(4): 15-17.
17	杨莉. 牧草割草机研究现状与发展趋势[J]. 中国农机化学报, 2019, 40(11): 35-40, 72.
17	YANG L. Research situation and development tendency of herbage mower[J]. Journal of Chinese Agricultural Mechanization, 2019, 40(11): 35-40, 72.
18	JR L K, SOUGH E C, MCDONELL E E, et al. The effect of wide swathing on wilting times and nutritive value of alfalfa haylage[J]. Journal of Dairy Science, 2010, 93(4): 1770-1773.
19	JOE S. Meet the world's most powerful forage harvester[J]. Farmer's Weekly, 2018(18040): 50.
20	王姣. 巨龙传奇——KRONE(科罗尼)BIG X系列自走式青贮收获机[J]. 农业机械, 2013(2): 18-26.
20	WANG J. The legend of the dragon—KRONE BIG X series self-propelled silage harvester[J]. Agricultural Machinery, 2013(2): 18-26.
21	农机通. 科罗尼BiGX1180自走式青贮饲料收获机[EB/OL]. [2020-01-15]. .
22	农机通. 科罗尼BiGX700自走式青贮饲料收获机[EB/OL]. [2020-01-15]. .
23	方宪法. 我国农业机械化技术自主创新能力研究[D]. 北京: 中国农业大学, 2007.
23	FANG X. Research on independent innovation ability of agricultural mechanization technology in China[D]. Beijing: China Agricultural University, 2007.
24	易牧网. U40犊牛自动喂奶机[EB/OL]. [2020-01-14]. .
25	ORTIZ B V, BALKCOM K B, DUZY L, et al. Evaluation of agronomic and economic benefits of using RTK-GPS-based auto-steer guidance systems for peanut digging operations[J]. Precision Agriculture, 2013, 14(4): 357-375.
26	BODDIE W S, KUN L. Global food safety product management: A holistic, integrated, strategic approach (proposed)[J]. Health and Technology, 2014(2): 135-143.
27	倪瑜.中美农业发展概况对比分析[J].新产经, 2018(12): 88-95.
27	NI Y. Comparative analysis of the general situation of agricultural development in China and the United States[J]. New Industrial Economy, 2018(12): 88-95.
28	唐汉, 王金武, 徐常塑, 等. 化肥减施增效关键技术研究进展分析[J]. 农业机械学报, 2019, 50(4): 1-19.
28	TANG H, WANG J, XU C, et al. Research progress analysis on key technology of chemical fertilizer reduction and efficiency increase[J]. Transactions of the CSAM, 2019, 50(4): 1-19.
29	TechnologiesVeris. Planter and tillage based platform for mapping and managing soil variability[EB/OL]. [2019-12-29]. .
30	沈成杰. 变量喷雾系统设计及喷雾流量控制特性试验研究[D]. 镇江: 江苏大学, 2009.
30	CHEN C. Variable-rate spraying system design and experimental study on spray flow-rate control characteristic[D]. Zhenjiang: Jiangsu University, 2009.
31	罗锡文, 廖娟, 邹湘军, 等. 信息技术提升农业机械化水平[J]. 农业工程学报, 2016, 32(20): 1-14.
31	LUO X, LIAO J, ZOU X, et al. Enhancing agricultural mechanization level through information technology[J]. Transactions of the CSAE, 2016, 32(20): 1-14.
32	The Queensland University of Technology. Case study: AgBot II. [EB/OL]. [2020-01-14]. .
33	FarmingQUT Future. AgBot II robotic site-specific crop and weed management tool. [EB/OL]. [2020-01-14]. .
34	刘成良, 林洪振, 李彦明, 等. 农业装备智能控制技术研究现状与发展趋势分析[J]. 农业机械学报, 2020, 51(1): 1-18.
34	LIU C, LIN H, LI Y, et al. Analysis on status and development trend of intelligent control technology for agricultural machinery[J]. Transactions of the CSAM, 2020, 51(1): 1-18.
35	NORAC. Tailored control for any operation[EB/OL]. [2020-01-15]. .
36	DeerJohn. CP690 Cotton Picker[EB/OL]. [2020-01-03]. .
37	王玉狮, 张文博. 国外农机品牌销售店的经验值得借鉴[J]. 农机科技推广, 2013(10): 49, 51.
37	WANG Y, ZHANG W. The experience of foreign agricultural machinery brand sales shops is worth learning from[J]. Agriculture Machinery Technology Extension, 2013(10): 49, 51.
38	宁学贵. 德国农机制造企业给我们的启示[J]. 现代农业装备, 2015(5): 64-66.
38	NING X. Enlightenment from German agricultural machinery manufacturing enterprises[J]. Modern Agricultural Equipment, 2015(5): 64-66.
39	国家统计局[EB/OL]. [2019-12-16]. .
40	中国政府网. 国务院关于加快推进农业机械化和农机装备产业转型升级的指导意见[EB/OL]. (2020-01-08) [2020-01-20]. .
41	中国政府网. 国务院政策例行吹风会[EB/OL]. (2018-12-19) [2020-01-21]..
42	施威, 孙梦迪, 曹成铭. 农机社会化服务组织创新研究——以山东省为例[J]. 农机化研究, 2016(11): 1-6.
42	SHI W, SUN M, CAO C. Innovative study on social service organization of agricultural machinery in Shandong province[J]. Journal of Agricultural Mechanization Research, 2016(11): 1-6.
43	中华人民共和国农业农村部. 张桃林副部长在2019年全国农业机械化工作会议上的讲话[EB/OL]. (2019-03-22) [2019-12-23]. .
44	齐飞, 朱明, 周新群, 等. 农业工程与中国农业现代化相互关系分析[J]. 农业工程学报, 2015, 31(1): 1-10.
44	QI F, ZHU M, ZHOU X, et al. Discussion on classification method of protected agricultural engineering technology[J]. Transactions of the CSAE, 2015, 31(1): 1-10.
45	中国政府网. 主要农作物全程机械化取得明显进展[EB/OL]. (2020-01-08) [2020-01-20]. .
46	杨宏伟. 主要作物生产全程机械化研讨会召开[J]. 农机科技推广, 2018(7): 21.
46	YANG H. The main crop production mechanization seminar was held[J]. Agricultural Machinery Technology Promotion, 2018(7): 21.
47	徐跃进. 浙江省丘陵山区农业机械化发展研究[D]. 杭州: 浙江大学, 2017.
47	XU Y. Study on agricultural mechanization development in hilly and mountain areas of Zhejiang province [D]. Hangzhou: Zhejiang University, 2017.
48	姚春生, 何丽虹, 陈谦, 等. 农业机械化信息化融合研究[J]. 中国农机化学报, 2017, 38(8): 1-8, 54.
48	YAO C, HE L, CHEN Q, et al. Research on fusion of agricultural mechanization and informatization[J]. Journal of Chinese Agricultural Mechanization, 2017, 38(8): 1-8, 54.
49	吴海华, 胡小鹿, 方宪法, 等. 智能农机装备技术创新进展及发展重点研究[J]. 现代农业装备, 2020, 41(3): 2-10.
49	WU H, HU X, FANG X, et al. Technology innovation progress and development key research of intelligent agricultural machinery equipment[J]. Modern Agricultural Equipment, 2020, 41(3): 2-10.
50	武建设, 陈学庚. 新疆兵团棉花生产机械化发展现状问题及对策[J]. 农业工程学报, 2015, 31(18): 5-10.
50	WU J, CHEN X. Present situation, problems and countermeasures of cotton production mechanization development in Xinjiang Production and Construction Corps[J]. Transactions of the CSAE, 2015, 31(18): 5-10.
51	中国政府网. 国务院关于加快推进农业机械化和农机装备产业转型升级的指导意见[EB/OL]. (2018-12-29) [2019-12-26]..
52	罗锡文. 对我国农业机械化科技创新的思考[J]. 农机科技推广, 2019(2): 4-7.
52	LUO X. Thoughts on the technological innovation of agricultural mechanization in China[J]. Agriculture Machinery Technology Extension, 2019(2): 4-7.
53	胡小鹿, 梁学修, 张俊宁, 等. 中国智能农机装备标准体系框架构建与研制建议[J/OL]. 智慧农业(中英文):1-8 [2020-10-27]..
53	HU X, LIANG X, ZHANG J, et al. Construction of standard system framework for intelligent agricultural machinery in China[J/OL]. Smart Agriculture: 1-8 [2020-10-27]..
54	郑文钟. 国外智能化农机装备简介[J]. 新农村, 2016(2): 34-35.
54	ZHANG W. Brief introduction of foreign intelligent agricultural machinery equipment[J]. New Countryside, 2016(2): 34-35.
55	中华人民共和国农业农村部. 农业部关于印发《全国农业机械化发展第十三个五年规划》的通知[EB/OL]. (2017-01-05) [2019-12-28]. .

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