Journal of Agricultural Big Data >
Research and Application of Citrus Big Data
Received date: 2021-02-10
Online published: 2021-05-18
Using the citrus industry chain model as the basic framework, this paper proposes an explicit definition of Citrus Big Data. For each of the four main parts of the citrus industry chain, i.e., production resources, planting operations, processing, storage and transportation, and marketing, the composition of data, acquisition methods and challenges in applications of its core data resources are analyzed, respectively. Applications of typical data technologies such as product standardization, image recognition, meteorology forecasting, data visualization, and digital traceability in the citrus industry are systematically reviewed. Cases studies on Coca Cola orange juice, Chongqing citrus and Gannan umbilical orange are presented, demonstrating that big data technology is playing a more and more important role in the citrus industry, aiding green farming, disease and insect pest control, production increase, and improvement of fruit commodity rates. In China, research on citrus has entered a stage of rapid development since 2007, and has made great progresses in talent training, fundamental research, and transformation and application of scientific research results, but research on the application of the new generation information technologies such as big data and artificial intelligence, is still limited. Major citrus production regions in southern China are actively exploring the feasibility route of digital transformation for citrus industry. As a core resource for industrial upgrading and digital transformation, Citrus Big Data has a wide range of applications and great potential for growth. Application of Citrus Big Data in China is still in its infancy, facing many challenges such as equality in science and technology, data sharing, development of key models and algorithms, etc.
Qiuzi Wen‑Han, Yongqiang Zheng, Yang Liu . Research and Application of Citrus Big Data[J]. Journal of Agricultural Big Data, 2021 , 3(1) : 33 -44 . DOI: 10.19788/j.issn.2096-6369.210104
| 1 | 毛胜勇,叶植材.中国统计年鉴2018[M].北京: 中国统计出版社, 2018. |
| 1 | Mao S Y, Ye Z C.China Statistical Yearbook 2018[M].Beijing: China Statistics Press, 2018. |
| 2 | 中华人民共和国国家统计局.中国统计年鉴2020[M].北京: 中国统计出版社, 2020. |
| 2 | National Bureau of statistics of the people's Republic of China.China Statistical Yearbook2020[M].Beijing:China Statistics Press, 2020. |
| 3 | 新华社.中共中央国务院关于构建更加完善的要素市场化配置体制机制的意见[E]. http://www.gov.cn/zhengce/2020-04/09/content_5500622.htm, 2020-04-09. |
| 3 | Xinhua News Agency.pinions of the CPC Central Committee and the State Council on building a more perfect market-oriented allocation system and mechanism of factors[E].http://www.gov.cn/zhengce/2020-04/09/content_5500622.htm, 2020-04-09. |
| 4 | 姜侯, 杨雅萍, 孙九林. 农业大数据研究与应用[J]. 农业大数据学报, 2019, 1(01): 5-10. |
| 4 | Jiang H, Yang Y P, Sun J L. Research and application of agricultural big data[J]. Journal of agricultural big data, 2019, 1(01): 5-10. |
| 5 | Smith Adam. An Inquiry into the Nature and Causes of the Wealth of Nations[M]. County Fife: OUP Oxford, 2008 |
| 6 | 郁义鸿, 管锡展. 产业链纵向控制与经济规制[M]. 上海: 复旦大学出版社, 2006. |
| 6 | Yu Y H, Guan X Z. Vertical control of industrial chain and economic regulation[M].Shanghai: Fudan University Press, 2006. |
| 7 | Manyika, Chui M, Brown B, et al. Big data: The next frontier for innovation, competition, and productivity,[R/OL]McKinsey Global Institute, 2011. . |
| 8 | Lu H S,Ying Y B,Zhu D R, et al. Temperature influence for Fourier transform near-infrared transmittance measurement of citrus fruit soluble solids contents[C]. Boston, Massachusetts, United States, 2006. |
| 9 | Rashidi M, Keshavarzpour F. Classification of Tangerine Size and Shape Based on Mass and Outer Dimensions[J].Agricultural Engineering Research Journal, 2011, 1 (3): 51–54. |
| 10 | Annamalai P. Citrus Yield Mapping System Using Machine Vision[D]. A Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science (2004) |
| 11 | Patel H.N., Jain R.K., Joshi M.V.. Automatic Segmentation and Yield Measurement of Fruit using Shape Analysis[J]. International Journal of Computer Applications, 2012,45(7):19-24. |
| 12 | U.-O. Dorj, M. Lee, D.-U. Imaan. A new method for tangerine tree flower recognition[J].Computer Applications for Bio-technology, Multimedia, and Ubiquitous City, 2012,CCIS353:49-56. |
| 13 | Dorj Ulzii-Orshikh, Lee Malrey, Sang-seok Yun. An yield estimation in citrus orchards via fruit detection and counting using image processing[J]. Computers and Electronics in Agriculture,2017,140： 103-112 |
| 14 | Csillik O, Cherbini J, Johnson R, et al. Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks[J]. Drones, 2018, 2(4): 39. |
| 15 | Ok A O, Ozdarici-Ok A. DETECTION OF CITRUS TREES FROM UAV DSMS[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, 4： 27-34. |
| 16 | Osco L P, Arruda M, Junior J M, et al. A Convolutional Neural Network Approach for Counting and Geolocating Citrus-Trees in UAV Multispectral Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 160:97-106. |
| 17 | Redmon J., Farhadi A., 2018. Yolov3: An incremental improvement. arXiv preprint, arXiv:1804.02767. |
| 18 | Ren S., He K., Girshick R., Sun J. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. In Advances in neural information processing systems (NIPS2015), 91-99. |
| 19 | Ampatzidis Y, Partel V. UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence[J]. Remote Sensing, 2019, 11(4). |
| 20 | Skaria, Mani: People, Arthropods. Weather and Citrus Diseases[J]. Diseases of Fruits and Vegetables Volume I: Diagnosis and Management, Springer Netherlands, 2004, 10.1007/1-4020-2606-4(Chapter 7):307-337. |
| 21 | Abdulridha J, Batuman O, Ampatzidis Y. UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning[J]. Remote Sensing, 2019, 11(11):1373. |
| 22 | Ampatzidis Y, Partel V, Bo M, et al. Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence[J]. Computers and Electronics in Agriculture, 164. |
| 23 | Sharif, Muhammad, Khan, et al. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection, Computers and Electronics in Agriculture, Volume 150, 2018, Pages220-234, |
| 24 | Garcia-Ruiz, Sankaran, Maja, et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-in-fected citrus trees[J]. Comput. Electron. Agric. 91, 106-115. |
| 25 | Partel V, Nunes L, Stansly P, et al. Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence[J]. Computers and Electronics in Agriculture, 2019, 162:328-336. |
| 26 | Li H C,Yu C,Xia J J,et al. A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area[J]. Advances in Atmospheric Sciences,2019,36(10). |
| 27 | Burke Amanda,Snook Nathan,David John Gagne II,et al. Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting[J]. Weather and Forecasting,2020,35(1). |
| 28 | Mrunalini R. Badnakhe,Surya S. Durbha,Adinarayana Jagarlapudi,et al. Evaluation of Citrus Gummosis disease dynamics and predictions with weather and inversion based leaf optical model[J]. Computers and Electronics in Agriculture,2018,155: 130-141 |
| 29 | 金国花,杨军,李翔翔,李迎春：江西省柑橘生产现状及气象服务需求分析[J].气象与减灾研究, 2019, 42(4):301-305 |
| 29 | Jin G H,Yang J, Li X X, et al.Analysis of citrus production status and meteorological service demand in Jiangxi Province[J]. Meteorology and disaster reduction, 2019, 42(4): 301-305. |
| 30 | Lou W P, Qiu X F, Wu L H, et al.Scheme of Weather-Based Indemnity Indices for Insuring Against Freeze Damage to Citrus Orchards in Zhejiang,China[J].Agricultural Sciences in China,2009,8(11):1321-1331. |
| 31 | Rosenzweig Cynthia,Phillips Jennifer,Goldberg Richard,et al. Potential impacts of climate change on citrus and potato production in the US[J]. Agricultural Systems,1996,52(4):455-479. |
| 32 | Rijmenam[EB/OL]. [2013-7-18]. |
| 33 | 郑永强, 王娅, 杨琼, 等. 重庆三峡库区鲍威尔脐橙花期叶片矿质营养诊断[J]. 中国农业科学, 2018, 51(12). |
| 33 | Zheng Y Q, Wang Y, Yang Q, et al. Leaf Nutritional Diagnosis of Powell Navel Orange at Flowering Stage in Chongqing Three Gorges Reservoir Area[J].Scientia Agricultura Sinica, 2018, 51(12). |
| 34 | 王娅. 重庆三峡库区柑橘叶片营养综合诊断技术研究[D].重庆: 西南大学, 2019. |
| 34 | Wang Y. Study on Leaf Nutritional Diagnosis of Citrus in Chongqing Three Gorges Reservoir Area[D].Chongqing: Southwest University, 2019. |
| 35 | 赵丹, 杨肖华, 胡晶晶, 等. 大数据看我国柑橘市场[J]，营销界，2019, 31：30-33. |
| 35 | Zhao D, Yang X H, Hu J J, et al. Big data insights on China’s citrus market [J], Marketing, 2019, 31: 30-33. |
| 36 | 郭文武, 叶俊丽, 邓秀新. 新中国果树科学研究70 年—柑橘[J]，果树学报, 2019, 36(10）: 1264-1272. |
| 36 | Guo W W, Ye J L, Deng X X. Fruit scientific research in New China in the past 70 years:Citrus[J], Journal of Fruit Science,2019, 36(10）: 1264-1272. |
| 37 | 罗春霞, 甘国平, 黄强,等. 荆门市柑橘产业转型发展的对策研究[J]. 中国农业文摘:农业工程, 2019, 031(002):39-40. |
| 37 | Luo C X, Gan G P, Huang Q, et al. Research on methods of transformation and development for the citrus Industry in Jingmen city [J], Agricultural Science and Engineering in China, 2019, 031(002): 39-40. |
| 38 | 周洁珺, 曾兰. 广东云浮市柑橘产业转型升级研究[J]. 统计与管理, 2017, 000(012):66-67. |
| 38 | Zhou J J and Zeng L. Research on the transformation and upgrading of citrus industry in Yunfu city of Guangdong [J], Statistics and Management, 2017, 000(012):66-67. |
/
| 〈 |
|
〉 |
