Research progress of intelligent perception and analytics of agricultural information

YUE Xuejun; CAI Yulin; WANG Linhui; LIU Yongxin; WANG Jian; HONG Jinbao

doi:10.7671/j.issn.1001-411X.202008044

Journal of South China Agricultural University > 2020 > 41(6): 14-28. > DOI: 10.7671/j.issn.1001-411X.202008044

YUE Xuejun, CAI Yulin, WANG Linhui, et al. Research progress of intelligent perception and analytics of agricultural information[J]. Journal of South China Agricultural University, 2020, 41(6): 14-28. DOI: 10.7671/j.issn.1001-411X.202008044

Citation:

PDF (3223 KB)

Research progress of intelligent perception and analytics of agricultural information

1.
College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
2.
School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China

More Information

Received Date: August 30, 2020
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

In modern agriculture, agricultural producers need to know the farmland environment and the growth state of crop in a real-time, accurate and comprehensive manner, and make corresponding analysis, induction and decision of obtained information. Intelligent sensing and analysis technology of agricultural information plays an indispensable role in modern agriculture. In this review, we discussed two aspects of agricultural intelligent sensing and information analysis technology, focused on the research progress of agricultural information intelligent perception technology and agricultural information analysis method based on agricultural internet of things and big data at home and abroad, introduced the application of intelligent decision-making technology based on agricultural information in agricultural machinery and equipment intellectualization. The problems existing in application of agricultural sensors were summarized. Some suggestions were put forward for the development of agricultural information perception, information analysis technology, agricultural database technology and intelligent decision-making technology to provide a reference for the development of intelligent agriculture in future.
- agricultural information,
- intellisense,
- agricultural sensor,
- information analysis,
- internet of things,
- agricultural big data

FullText(HTML)

References (102)

References

[1]	马明, 赵新子, 蔺相志, 等. 电子农情监测物联网: 农业的重要增产措施[J]. 农业开发与装备, 2017(6): 30-31.
[2]	刘迁迁, 宫哲. 加强农情监测技术应用提升三农金融服务质量[J]. 农业经济, 2016(4): 17-18.
[3]	孟祥宝, 谢秋波, 彭宾, 等. 农情综合信息智能监控体系的开发及应用[J]. 机电工程技术, 2014(6): 144-148.
[4]	王大正, 任博, 刘珠明. 不同类型农情监测系统间数据共享方案研究[J]. 中国农机化学报, 2019, 50(12): 154-159.
[5]	DUAN Q, YANG R, CHEN Y. Automatic Identifying query interfaces of deep web based on preclassification-SVM[J]. Sensor Lett, 2013, 11(6): 1389-1395. doi: 10.1166/sl.2013.2910
[6]	岳学军, 王叶夫, 洪添胜, 等. 基于信道测试的橘园WSN网络部署试验[J]. 农业机械学报, 2013, 44(5): 213-218.
[7]	曹惠茹, 李业谦, 岳学军, 等. 无线多媒体传感网络不同部署环境下信号传播特性试验[J]. 测试技术学报, 2016, 30(5): 389-393.
[8]	CAO H, LIU Y, YUE X, et al. Cloud-assisted UAV data collection for multiple emerging events in distributed WSNs[J]. Sensors, 2017, 17(8): 1818. doi: 10.3390/s17081818
[9]	CAO H R, YANG Z, YUE X J, et al. An optimization method to improve the performance of unmanned aerial vehicle wireless sensor networks [J]. Int J Distrib Sens N, 2017, 13(4): 1-10. doi: 10.1177/1550147717705614.
[10]	葛文杰, 赵春江. 农业物联网研究与应用现状及发展对策研究[J]. 农业机械学报, 2014, 45(7): 222-230.
[11]	TERENCE S, PURUSHOTHAMAN G. Systematic review of internet of things in smart farming[J]. Trans Emerging Tel Tech, 2020, 31: e3958.
[12]	胡亮, 曹艳, 唐江云, 等. 基于物联网的玉米病害环境监测系统研究与实现[J]. 中国农学通报, 2020, 36(22): 154-164.
[13]	刘子成. 土壤环境监测技术的现状及发展趋势[J]. 江西农业, 2020(14): 16.
[14]	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.
[15]	ANTONUCCI F, PALLOTTINO F, COSTA C, et al. Development of a rapid soil water content detection technique using active infrared thermal methods for in-field applications [J]. Sensors, 2011, 11: 10114-10128.
[16]	蔡坤, 徐兴, 俞龙, 等. 基于LVDS传输线延时检测技术的土壤含水率传感器[J]. 农业机械学报, 2016, 47(12): 315-322.
[17]	蔡坤, 岳学军, 洪添胜, 等. 基于RC网络相频特性的土壤含水率传感器设计[J]. 农业工程学报, 2013, 29(7): 36-43.
[18]	GENOT V, COLINET G, BOCK L, et al. Near infrared reflectance spectroscopy for estimating soil characteristics valuable in the diagnosis of soil fertility[J]. J Near Infrared Spec, 2011, 19(2): 117-138. doi: 10.1255/jnirs.923
[19]	HONG M K, MYUNG-CHUL K, SMITH A E. Simultaneous determination of 2, 4-D, dicamba, and mecoprop in soil leachates by gas chromatography with electron capture detection[J]. J Aoac Int, 1996, 79(4): 998-1004.
[20]	邓小蕾, 李民赞, 武佳, 等. 集成GPRS、GPS、ZigBee的土壤水分移动监测系统[J]. 农业工程学报, 2012, 28(9): 130-135.
[21]	李民赞, 姚向前, 杨玮, 等. 基于卤钨灯光源和多路光纤的土壤全氮含量检测仪研究[J]. 农业机械学报, 2019, 50(11): 169-174.
[22]	秦琳, 黄世群, 仲伶俐, 等. 杜马斯燃烧法和凯氏定氮法在土壤全氮检测中的比较研究[J]. 中国土壤与肥料, 2020(4): 258-265.
[23]	王儒敬, 陈天娇, 汪玉冰, 等. 基于深度稀疏学习的土壤近红外光谱分析预测模型[J]. 发光学报, 2017, 38(1): 109-116.
[24]	张俊卿, 高钧, 陈翔宇, 等. 土壤钾离子非接触电导检测装置设计与试验[J]. 农业机械学报, 2018, 49(S1): 367-371.
[25]	李颉, 张小超, 苑严伟, 等. 北京典型耕作土壤养分的近红外光谱分析[J]. 农业工程学报, 2012, 28(2): 183-186.
[26]	张俊宁, 方宪法, 张小超, 等. 基于激光诱导击穿光谱的土壤钾素检测[J]. 农业机械学报, 2014, 45(10): 294-299.
[27]	代艳娜, 刘青海, 蒲继锋, 等. 超高效液相色谱-串联质谱法检测芹菜和土壤中灭蝇胺及其代谢物三聚氰胺残留[J]. 食品安全质量检测学报, 2020, 11(15): 5020-5026.
[28]	杨学灵, 魏嘉良, 蔡子洋, 等. 超声波萃取−气相色谱法测定土壤和沉积物中异丙胺[J]. 广州化工, 2020, 48(15): 140-141.
[29]	陈二阳, 袁姜红, 黎忠文, et al. 基于JSON的土壤环境监测物联网感知源信任评价模型研究 [J/OL]. 西安理工大学学报, 2020. https://kns.cnki.net/kcms/detail/61.1294.N.20200708.1442.002.html.
[30]	岳学军, 刘永鑫, 洪添胜, 等. 基于土壤墒情的自动灌溉控制系统设计与试验[J]. 农业机械学报, 2013, 44(S2): 241-246.
[31]	彭炜峰, 刘芳, 李光林, 等. 丘陵地区农田土壤信息监测系统的研究[J]. 农机化研究, 2021, 43(4): 65-69.
[32]	孙通. 农业气象物联网在蔬菜大棚中的应用[J]. 现代农业科技, 2020(16): 164.
[33]	蔡坤, 洪添胜, 岳学军, 等. 基于误码检测机制的滴灌系统红外光雨水传感器的设计[J]. 农业工程学报, 2012, 28(24): 70-77.
[34]	王斌, 刘雪梅, 张国强, 等. 猪舍生态环境监测和清洁控制系统的设计[J]. 农业工程学报, 2020, 36(3): 55-62.
[35]	YUE X J, HONG T S, XU X, et al. High-performance humidity sensors based on double-layer ZnO-TiO₂ nanofibers via electrospinning[J]. Chin Phys Lett, 2011, 28(9): 090701. doi: 10.1088/0256-307X/28/9/090701
[36]	马瑞丽. 基于物联网的农业气象监测的实现研究[J]. 农业技术与装备, 2020(7): 124-125.
[37]	刘恺, 谭泗桥, 刘志杰, 等. 稻田农情监测技术研究进展及发展趋势分析[J]. 企业技术开发, 2019, 38(4): 38-41.
[38]	黄华宁. 我国首个“作物病虫草害监测预警研究中心”成立 [J]. 科学种养, 2020(3): 63.
[39]	WANG J, NAKANO K, OHASHI S, et al. Detection of external insect infestations in jujube fruit using hyperspectral reflectance imaging[J]. Biosyst Eng, 2011, 108(4): 345-351. doi: 10.1016/j.biosystemseng.2011.01.006
[40]	李震, 洪添胜, 王建, 等. 柑橘全爪螨虫害快速检测仪的研制与试验[J]. 农业工程学报, 2014, 30(14): 49-56.
[41]	田有文, 程怡, 王小奇, 等. 基于高光谱成像的苹果虫害检测特征向量的选取[J]. 农业工程学报, 2014, 30(12): 132-139.
[42]	GRIFFEL L M, DELPARTE D, EDWARDS J. Using support vector machines classification to differentiate spectral signatures of potato plants infected with potato virus Y[J]. Comput Electron Agr, 2018, 153: 318-324.
[43]	ROMER C, BÜRLING K, HUNSCHE M, et al. Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with support vector machines[J]. Comput Electron Agr, 2011, 79: 180-188.
[44]	KAUR R, KANG S S. An enhancement in classifier support vector machine to improve plant disease detection[C]∥ IEEE International Conference on Moocs. IEEE, 2016.
[45]	许良凤, 徐小兵, 胡敏, 等. 基于多分类器融合的玉米叶部病害识别[J]. 农业工程学报, 2015, 31(14): 194-201.
[46]	赵小娟, 叶云, 冉耀虎. 基于物联网的茶树病虫害监测预警系统设计与实现[J]. 中国农业信息, 2019, 31(06): 107-115.
[47]	朱静波, 李闰枚, 董伟, 等. 基于数据预处理的病虫草害农田小气候监测系统设计[J]. 现代农业科技, 2017(15): 277-279.
[48]	WATCHAREERUETAI U, OHNISHI N. A New Color-based Lawn Weed Detection Method and Its Integration with Texture-based Methods: A Hybrid Approach [J]. 電気学会論文誌, c 電子·情報·システム部門誌, 2011, 131(2): 355-366 WATCHAREERUETAI U, OHNISHI N. A new color-based lawn weed detection method and its integration with texture-based methods: A hybrid approach[J]. IEEJ T Electr Electr, 2011, 131(2): 355-366.
[49]	WAJAHAT K, FRANCISCD G, JON N, et al. Exploiting affine invariant regions and leaf edge shapes for weed detection[J]. Comput Electron Agr, 2015, 118: 290-299.
[50]	BAKHSHIPOUR A, JAFARI A. Evaluation of support vector machine and artificial neural networks in weed detection using shape features[J]. Comput Electron Agr, 2018, 145: 153-160.
[51]	LOUARGANT M, JONES G, FAROUX R, et al. Unsupervised classification algorithm for early weed detection in row-crops by combining spatial and spectral information[J]. Remote Sen, 2018, 10(5): 761. doi: 10.3390/rs10050761
[52]	DAVID H, FERAS D, TRISTAN P, et al. A rapidly deployable classification system using visual data for the application of precision weed management[J]. Comput Electron Agr, 2018, 148: 107-120.
[53]	牟海维, 朱春辉. 植物生理传感器的研究现状与应用展望[J]. 农业与技术, 2020, 40(13): 31-32.
[54]	JONES C L, MANESS N O, STONE M L, et al. Chlorophyll estimation using multispectral reflectance and height sensing[J]. T Asabe, 2007, 50(5): 1867-1872. doi: 10.13031/2013.23938
[55]	BARESEL J R P, RISCHBECK P, HU Y, et al. Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat[J]. Comput Electron Agr, 2017, 140: 25-33.
[56]	孙红, 邢子正, 张智勇, 等. 基于RED-NIR的主动光源叶绿素含量检测装置设计与试验[J]. 农业机械学报, 2019, 50(S1): 175-181.
[57]	岳学军, 全东平, 洪添胜, 等. 柑橘叶片叶绿素含量高光谱无损检测模型[J]. 农业工程学报, 2015, 31(1): 294-302.
[58]	冯伟, 朱艳, 姚霞, 等. 小麦氮素积累动态的高光谱监测[J]. 中国农业科学, 2008, 41(7): 1937-1946.
[59]	黄双萍, 洪添胜, 岳学军, 等. 基于高光谱的柑橘叶片磷含量估算模型实验[J]. 农业机械学报, 2013, 44(4): 202-207.
[60]	黄双萍, 洪添胜, 岳学军, 等. 基于高光谱的柑橘叶片氮素含量多元回归分析[J]. 农业工程学报, 2013, 29(5): 132-138.
[61]	黄双萍, 岳学军, 洪添胜, 等. 不同物候期柑橘叶片钾素水平预测建模[J]. 江苏大学学报(自然科学版), 2013, 34(5): 529-535.
[62]	岳学军, 全东平, 洪添胜, 等. 不同生长期柑橘叶片磷含量的高光谱预测模型[J]. 农业工程学报, 2015, 31(8): 207-213.
[63]	岳学军, 全东平, 洪添胜, 等. 基于流形学习算法的柑橘叶片氮含量光谱估测模型"[J]. 农业机械学报, 2015, 46(6): 244-250.
[64]	陈学深, 黄柱健, 马旭, 等. 基于触觉感知的水稻行弯度测量装置设计与试验[J]. 农业机械学报, 2020, 51(2): 45-53.
[65]	邹金秋. 农情监测数据获取及管理技术研究[D]. 北京: 中国农业科学院, 2012.
[66]	曾小红, 王强. 国内外农业信息技术与网络发展概况[J]. 中国农学通报, 2011, 27(8): 468-473.
[67]	卢丽娜. 世界农业信息化进程及发展趋势[J]. 中国信息界, 2007(1): 85-91.
[68]	王强, 曾小红. 国内外农业数据资源和网络发展概况[J]. 世界农业, 2008(11): 61-64.
[69]	方利, 姚敏, 岳建伟, 等. 多源海量统计遥感数据集成管理技术研究[J]. 地理信息世界, 2010, 8(1): 56-60.
[70]	方利, 易文斌, 岳建伟, 等. 统计遥感基础地理数据库标准研究与编制[J]. 测绘与空间地理信息, 2010, 33(1): 14-17.
[71]	徐世卫. 农业大数据与农产品监测预警[J]. 中国农业科技导报, 2014, 16(5): 14-20.
[72]	ZHAO H, WANG F, YANG Q. Origin traceability of peanut kernels based on multi-element fingerprinting combined with multivariate data analysis[J]. J Food Agr, 2020, 100(10): 4040-4048.
[73]	刘彪. “云计算”和大数据在“互联网+”时代的应用[J]. 电子技术与软件工程, 2020(12): 201-213.
[74]	崔晓军, 高子航. 基于GIS与云计算的温州市农业大数据可视化平台研究[J]. 电脑编程技巧与维护, 2020(4): 113-115.
[75]	顾静秋. 农业数据智能感知与分析关键技术研究 [D]; 北京: 北京交通大学, 2018.
[76]	赵献立, 王志明. 机器学习算法在农业机器视觉系统中的应用[J]. 江苏农业科学, 2020, 48(12): 226-231.
[77]	LI H, LEE W S, WANG K. Identifying blueberry fruit of different growth stages using natural outdoor color images[J]. Comput Electron Agr, 2014, 106: 91-101.
[78]	王永波邝炳洽. 基于机器学习的花卉分类算法研究[J]. 现代计算机, 2013(9): 21-24.
[79]	周军, 蔡建, 郭俊先, 等. 基于机器视觉的核桃仁特征提取与分级方法研究[J]. 江苏农业科学, 2018, 46(11): 175-179.
[80]	KURTULMUS F, ÜNAL H. Discriminating rapeseed varieties using computer vision and machine learning[J]. Expert Syst App, 2015, 42(4): 1880-1891. doi: 10.1016/j.eswa.2014.10.003
[81]	罗匡男, 彭琳, 齐伟恒. 基于机器视觉的三七叶片病斑识别[J]. 江苏农业科学, 2017, 45(24): 209-212.
[82]	刘永娟. 基于计算机视觉技术的玉米发育期识别研究 [D]. 无锡: 江南大学, 2017.
[83]	ZHANG S W, WU X W, YOU Z H, et al. Leaf image based cucumber disease recognition using sparse representation classification[J]. Comput Electron Agr, 2017, 134: 135-141. doi: 10.1016/j.compag.2017.01.014
[84]	徐邮邮, 李西灿, 尚璇, 等. 基于半监督模糊识别的土壤含水量高光谱估测模型研究[J]. 地理与地理信息科学, 2019, 35(2): 33-37.
[85]	吴四茜. 一种相似青梅品级半监督智能认知方法研究 [D]; 合肥: 合肥工业大学, 2019.
[86]	周俊, 陈钦, 梁泉. 基于强化学习的农业移动机器人视觉导航[J]. 农业机械学报, 2014, 45(2): 53-58. ZHOU J, CHEN Q, LIANG Q. Vision navigation of agricultural mobile robot based on reinforcement learning [J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(2): 53-8.
[87]	SUN L, YANG Y, HU J, et al. Reinforcement learning control for water-efficient agricultural irrigation[C]//IEEE International Conference on Ubiquitous Computing and Communications. IEEE, 2017.
[88]	CHEN Y, ZHU H, OZKAN H E. Development of a variable-rate sprayer with laser scanning sensor to synchronize spray outputs to tree structures[J]. T Asabe, 2012, 55(3): 773-781. doi: 10.13031/2013.41509
[89]	GIL E, LLORENS J, LLOP J, et al. Variable rate sprayer: Part 2: Vineyard prototype: Design, implementation, and validation[J]. Comput Electron Agr, 2013, 95: 136-150.
[90]	曾立. 作物间变量喷施作业机器人及控制设计[D]. 哈尔滨: 哈尔滨理工大学, 2018.
[91]	苑严伟, 李树君, 方宪法, 等. 氮磷钾配比施肥决策支持系统[J]. 农业机械学报, 2013, 44(8): 240-244.
[92]	郝云鹏. 基于GIS的玉米变量施肥作业自动决策系统研究 [D]. 长春; 吉林农业大学, 2017.
[93]	KAREN L, MICHAEL P, AMANDA A, et al. A decision-support system for analyzing tractor guidance technology[J]. Comput Electron Agr, 2018, 153: 115-25. doi: 10.1016/j.compag.2018.08.014
[94]	HAMEED I A. Intelligent coverage path planning for agricultural robots and autonomous machines on three-dimensional terrain[J]. J Intell Robot Syst, 2014, 74(3/4): 965-983.
[95]	刘刚, 康熙, 夏友祥, 等. 基于GNSS农田平整全局路径规划方法与试验[J]. 农业机械学报, 2018, 49(5): 27-33.
[96]	孟志军, 刘卉, 王华, 等. 农田作业机械路径优化方法[J]. 农业机械学报, 2012, 43(6): 147-152.
[97]	张亚娇. 农机自动驾驶监控终端关键技术研究与系统开发 [D]. 广州: 华南农业大学, 2016.
[98]	CHI Z, NOBORU N. Development of a multi-robot tractor system for agriculture field work[J]. Comput Electron Agr, 2017, 142: 79-90.
[99]	曹如月, 李世超, 季宇寒, 等. 基于蚁群算法的多机协同作业任务规划[J]. 农业机械学报, 2019, 50(S1): 34-39.
[100]	郭娜, 胡静涛. 基于Smith-模糊PID控制的变量喷药系统设计及试验[J]. 农业工程学报, 2014, 30(8): 56-64.
[101]	曹如月, 李世超, 魏爽, 等. 基于Web-GIS的多机协同作业远程监控平台设计[J]. 农业机械学报, 2017, 48(S1): 52-57.
[102]	向抚, 宋玉娥, 叶紫文. 石门县农情信息工作发展现状及对策[J]. 作物研究, 2018, 32(S1): 85-86.

[1]	PENG Xu, RAO Yuan, QIAO Yan. Detection method of abnormal agricultural data based on broad convolution neural network[J]. Journal of South China Agricultural University, 2022, 43(2): 113-121. DOI: 10.7671/j.issn.1001-411X.202103050
[2]	LI Cheng’en, ZOU Xiangjun, LIAN Guoping, LIN Junqiang, ZHANG Po, CHEN Mingyou, YE Lei. Data set construction method of virtual assembly classification detection network for agricultural machinery[J]. Journal of South China Agricultural University, 2021, 42(6): 117-125. DOI: 10.7671/j.issn.1001-411X.202104005
[3]	ZHAO Chunjiang. Current situations and prospects of smart agriculture[J]. Journal of South China Agricultural University, 2021, 42(6): 1-7. DOI: 10.7671/j.issn.1001-411X.202108039
[4]	ZENG Jingyuan, HONG Tiansheng, YANG Zhou, LI Fu. Design and experiment of real-time monitoring system for orchard irrigation based on internet of things[J]. Journal of South China Agricultural University, 2020, 41(6): 145-153. DOI: 10.7671/j.issn.1001-411X.202005009
[5]	GAO Peng, XIE Jiaxing, SUN Daozong, CHEN Wenbin, YANG Mingxin, ZHOU Ping, WANG Weixing. Prediction models of soil moisture content and electrical conductivity in citrus orchard based on internet of things and LSTM[J]. Journal of South China Agricultural University, 2020, 41(6): 134-144. DOI: 10.7671/j.issn.1001-411X.202007024
[6]	ZHANG Bo, HUANG Zhihong, LAN Yubin, WU Lili, HE Binbin, ZENG Ming. "Internet+" agricultural aviation services platform architecture design and practice[J]. Journal of South China Agricultural University, 2016, 37(6): 38-45. DOI: 10.7671/j.issn.1001-411X.2016.06.006
[7]	WANG Danni, BAO Shitai, WANG Chunlin, TANG Lisheng. Agricultural high temperature disaster monitoring based on meteorological data mining in Guangdong Province[J]. Journal of South China Agricultural University, 2015, 36(2): 106-112. DOI: 10.7671/j.issn.1001-411X.2015.02.019
[8]	Journal of South China Agricultural University[J]. Journal of South China Agricultural University, 2008, 29(4).
[9]	Journal of South China Agricultural University[J]. Journal of South China Agricultural University, 2006, 27(4): F0003-F0003.

Cited By

Cited by

Periodical cited type(8)

1.	刘丽新，姚天旭，张文韬，周一名，刘庆天，王鑫，翁文安，张博文，金峰，邵玺文. 基于群体生长及微气象因子对苏打盐碱稻区不同水稻产量层次差异解析. 东北农业科学. 2023(02): 6-12 .
2.	谢慧敏，吴可，刘文奇，韦国良，陆献，李壮林，韦善清，梁和，江立庚. 海藻肥与微生物菌剂部分替代化肥对水稻产量及其构成因素的影响. 作物杂志. 2022(01): 161-166 .
3.	刘淼，梁正伟. 低氮高密增微肥对苏打盐碱地水稻产量和氮肥利用率的影响. 土壤与作物. 2021(03): 245-255 .
4.	陈丽鹏. 探讨不同栽培模式下水稻的产量差异. 种子科技. 2020(02): 112+115 .
5.	赵玉兰，丁秀文. 优良食味粳稻南粳3908高产栽培技术研究. 现代农业科技. 2020(09): 18-19 .
6.	李小波，黄光福，施继芳，王春荣，张玉娇，程卯，胡建，张石来，胡凤益. 多年生稻云大107产量潜力分析. 中国稻米. 2020(04): 35-39 .
7.	刘赵月，李蕊彤，李晶，顾万荣，左师宇，任晓松，左月桃，魏湜. 盐碱胁迫下京尼平苷对玉米种子萌发及根系AsA-GSH循环的影响. 江苏农业学报. 2020(04): 842-850 .
8.	罗玉琼，严博，吴可，谢慧敏，梁和，江立庚. 免耕和稻草还田对稻田土壤肥力和水稻产量的影响. 作物杂志. 2020(05): 133-139 .

Other cited types(8)

Get Citation

PDF

XML

Article views (1507) PDF downloads (1781) Cited by(16)

Turn off MathJax

Article Contents

Abstract

References

Research progress of intelligent perception and analytics of agricultural information