research-article

A review on the practice of big data analysis in agriculture

Authors:

Andreas Kamilaris,

Andreas Kartakoullis,

Francesc X. Prenafeta-BoldAuthors Info & Claims

Computers and Electronics in Agriculture, Volume 143, Issue C

Pages 23 - 37

https://doi.org/10.1016/j.compag.2017.09.037

Published: 01 December 2017 Publication History

Abstract

Survey on the practice of big data analysis in agriculture.Detailed review of 34 high-impact relevant research studies.Discussion on the status and potential of big data analysis in agriculture.Open problems and challenges, barriers for wider adoption and use.Ways to overcome barriers and potential future applications in agriculture. To tackle the increasing challenges of agricultural production, the complex agricultural ecosystems need to be better understood. This can happen by means of modern digital technologies that monitor continuously the physical environment, producing large quantities of data in an unprecedented pace. The analysis of this (big) data would enable farmers and companies to extract value from it, improving their productivity. Although big data analysis is leading to advances in various industries, it has not yet been widely applied in agriculture. The objective of this paper is to perform a review on current studies and research works in agriculture which employ the recent practice of big data analysis, in order to solve various relevant problems. Thirty-four different studies are presented, examining the problem they address, the proposed solution, tools, algorithms and data used, nature and dimensions of big data employed, scale of use as well as overall impact. Concluding, our review highlights the large opportunities of big data analysis in agriculture towards smarter farming, showing that the availability of hardware and software, techniques and methods for big data analysis, as well as the increasing openness of big data sources, shall encourage more academic research, public sector initiatives and business ventures in the agricultural sector. This practice is still at an early development stage and many barriers need to be overcome.

References

[1]

AgGateway, 2005. {Online} Available at: <http://www.aggateway.org/Home.aspx> (accessed 2017).

[2]

Akinboro, B., 2016. Bringing Mobile Wallets to Nigerian Farmers. {Online} Available at: <http://www.cgap.org/blog/bringing-mobile-wallets-nigerian-farmers> (accessed 2017).

[3]

Anon., 2016. AeroFarms. {Online} Available at: <http://aerofarms.com/> (accessed 2017).

[4]

R. Aqeel ur, A. Abbasi, N. Islam, Z. Shaikh, A review of wireless sensors and networks' applications in agriculture, Comput. Stand. Interfaces, 36 (2014) 263-270.

Digital Library

[5]

Armbruster, W.J., MacDonell, M.M., 2014. Informatics to Support International Food Safety. s.l., s.n., pp. 127134.

[6]

Armstrong, L., Diepeveen, D., Maddern, R., 2007. The application of data mining techniques to characterize agricultural soil profiles. s.l., s.n.

[7]

C. Atzberger, Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs, Remote Sens., 5 (2013) 949-981.

[8]

aWhere Inc., 2015. {Online} Available at: <http://www.awhere.com/> (accessed 2017).

[9]

Babinet, Gilles et al., 2015. The New World economy, s.l.: Report addressed to Ms Segolene Royal, Minister of Environment, Sustainable Development and Energy, working group led by Corinne Lepage.

[10]

B. Barrett, I. Nitze, S. Green, F. Cawkwell, Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches, Remote Sens. Environ., 152 (2014) 109-124.

[11]

B. Basso, Spatial validation of crop models for precision agriculture, Agric. Syst., 68 (2001) 97-112.

[12]

W. Bastiaanssen, D. Molden, I. Makin, Remote sensing for irrigated agriculture: examples from research and possible applications, Agric. Water Manage., 46 (2000) 137-155.

[13]

I. Becker-Reshef, Monitoring global croplands with coarse resolution earth observations: the Global Agriculture Monitoring (GLAM) project, Remote Sens., 2 (2010) 1589-1609.

[14]

Bell, J., Butler, C., Thompson, J., 1995. Soil-terrain modeling for site-specific agricultural management. Site-Specific Management for Agricultural Systems, American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, pp. 209227.

[15]

Blue River Technology, 2011. {Online} Available at: <http://www.bluerivert.com/> (accessed 2017).

[16]

R. Bongiovanni, J. Lowenberg-DeBoer, Precision agriculture and sustainability, Precision Agric., 5 (2004) 359-387.

[17]

Bunge, J., 2014. Big data comes to the farm, sowing mistrust: seed makers barrel into technology business, s.l.: Wall Street Journal (Online).

[18]

S.M. Capalbo, J.M. Antle, C. Seavert, Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making, Agric. Syst. (2016).

[19]

I. Carbonell, The ethics of big data in big agriculture, Internet Policy Rev., 5 (2016) 1-13.

[20]

M. Carolan, Publicising food: big data, precision agriculture, and co-experimental techniques of addition, Soc. Ruralis (2016).

[21]

A. Chedad, AP animal production technology: recognition system for pig cough based on probabilistic neural networks, J. Agric. Eng. Res., 79 (2001) 449-457.

[22]

M. Chi, Big data for remote sensing: challenges and opportunities, Proc. IEEE, 104 (2016) 2207-2219.

[23]

J. Cooper, Big data in life cycle assessment, J. Ind. Ecol., 17 (2013) 796-799.

[24]

Cropster, 2007. {Online} Available at: <https://www.cropster.com/> (accessed 2017).

[25]

J. de Leeuw, The potential and uptake of remote sensing in insurance: a review, Remote Sens., 6 (2014) 10888-10912.

[26]

S. Erenturk, K. Erenturk, Comparison of genetic algorithm and neural network approaches for the drying process of carrot, J. Food Eng., 78 (2007) 905-912.

[27]

Farm Hack, 2010. Farm Hack. {Online} Available at: <http://farmhack.org> (accessed January 2017).

[28]

Field to Market, 2015. Fieldprint Calculator. {Online} Available at: <https://www.fieldtomarket.org/fieldprint-calculator/> (accessed 2017).

[29]

Food and Agriculture Organization of the United Nations, 2009. How to Feed the World in 2050., Rome: s.n.

[30]

R. Frelat, Drivers of household food availability in sub-Saharan Africa based on big data from small farms, Proc. Natl. Acad. Sci., 113 (2016) 458-463.

[31]

Fuchs, A., Wolff, H., 2011. Drought and retribution: evidence from a large scale rainfall index insurance in Mexico. s.l., s.n., pp. 1314.

[32]

R.T. Furbank, M. Teste, Phenomics technologies to relieve the phenotyping bottleneck, Trends Plant Sci., 16 (2011) 635-644.

[33]

G.L. Galford, Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil, Remote Sens. Environ., 112 (2008) 576-587.

[34]

R. Gebbers, V. Adamchuk, Precision agriculture and food security, Science, 327 (2010) 828-831.

[35]

K. Giller, Communicating complexity: integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development, Agric. Syst., 104 (2011) 191-203.

[36]

Global Envision, 2006. Unleashing Ugandan farmers potential through mobile phones. {Online} Available at: <https://www.mercycorps.org/research-resources/unleashing-ugandan-farmers-potential-through-mobile-phones> (accessed 2017).

[37]

GODAN, 2015. Global Open Data for Agriculture and Nutrition (GODAN) initiative. {Online} Available at: <http://www.godan.info/> (accessed 2017).

[38]

O. Gonzlez-Recio, M. Toro, A. Bach, Past, present and future of epigenetics applied to livestock breeding, Front. Genet., 6 (2015) 305.

[39]

Grace, D., McDermot, J., 2015. Reducing and Managing Food Scares, Washington, DC: International Food Policy Research, 20142015 Global Food Policy Report.

[40]

GSMA, 2014. mAgri Programme. {Online} Available at: <http://www.gsma.com/mobilefordevelopment/programmes/magri/programme-overview> (accessed 2017).

[41]

P. Gutirrez, Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data, Comput. Electron. Agric., 64 (2008) 293-306.

Digital Library

[42]

F. Hartung, J. Schiemann, Precise plant breeding using new genome editing techniques: opportunities, safety and regulation in the EU, Plant J., 78 (2014) 742-752.

[43]

I. Hashem, The rise of big data on cloud computing: review and open research issues, Inform. Syst., 47 (2015) 98-115.

Digital Library

[44]

. Jwiaka, M. Milkovics, Z. Lakne, A network-science support system for food chain safety: a case from hungarian cattle production, Int. Food Agribusiness Manage. Rev. Special Issue, 19(A) (2016).

[45]

A. Kamilaris, F. Gao, F.X. Prenafeta-Bold, M.I. Ali, Agri-IoT: A Semantic Framework for Internet of Things-Enabled Smart Farming Applications, Reston, VA, USA, s.n., 2016.

[46]

Karmas, A., Karantzalos, K., Athanasiou, S., 2014. Online analysis of remote sensing data for agricultural applications. s.l., OSGeos European conference on free and open source software for geospatial.

[47]

Karmas, A., Tzotsos, A. & Karantzalos, K., 2016. Geospatial Big Data for Environmental and Agricultural Applications. In: s.l.: Springer International Publishing, pp. 353390.

[48]

C. Kempenaar, Wageningen University & Research, s.l., 2016.

[49]

G.-H. Kim, S. Trimi, J.-H. Chung, Big-data applications in the government sector, Commun. ACM, 57 (2014) 78-85.

Digital Library

[50]

J. Kitzes, Shrink and share: humanity's present and future ecological footprint, Philos. Trans. Royal Soc. B: Biol. Sci., 363 (2008) 467-475.

[51]

N. Kshetri, The emerging role of Big Data in key development issues: opportunities, challenges, and concerns, Big Data Soc., 1 (2014).

[52]

S. Liaghat, S.K. Balasundram, A review: the role of remote sensing in precision agriculture, Am. J. Agric. Biol. Sci., 5 (2010) 50-55.

[53]

R. Lokers, Analysis of Big Data technologies for use in agro-environmental science, Environ. Model. Software, 84 (2016) 494-504.

Digital Library

[54]

D.C. Love, An international survey of aquaponics practitioners, PLoS ONE, 9 (2014).

[55]

M.T. Lucas, D. Chhajed, Applications of location analysis in agriculture: a survey, J. Operational Res. Soc., 55 (2004) 561-578.

[56]

C. Ma, H.H. Zhang, X. Wang, Machine learning for Big Data analytics in plants, Trends Plant Sci., 19 (2014) 798-808.

[57]

A. Manickavasagan, D.S. Jayas, N. White, J. Paliwal, Applications of thermal imaging in agriculturea review, Can. Soc. Eng. Agric. Food Biol. Syst., 5 (2005).

[58]

B.G. Marcot, Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement, For. Ecol. Manage., 153 (2001) 29-42.

[59]

McQueen, R., Garner, S., C.G., N.-M., Witten, I.H., 1995. Applying machine learning to agricultural data. Compt. Electron. Agric., 12(1).

[60]

G. Meyer, J.C. Neto, D.D. Jones, T.W. Hindman, Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images, Comput. Electron. Agric., 42 (2004) 161-180.

[61]

A. Mucherino, P. Papajorgji, P. Pardalos, Data Mining in Agriculture, vol. 34, Springer Science & Business Media, 2009.

Digital Library

[62]

C.S. Nandyala, H.-K. Kim, Big and meta data management for U-agriculture mobile services, Int. J. Software Eng. Appl. (IJSEIA), 10 (2016) 257-270.

[63]

S. Nativi, Big data challenges in building the global earth observation system of systems, Environ. Model. Software, 68 (2015) 1-26.

Digital Library

[64]

OADA, 2014. Open Agriculture Data Alliance. {Online} Available at: <http://openag.io/> (accessed 2017).

[65]

W. Oluoch-Kosura, Institutional innovations for small-holder farmers competitiveness in Africa, African J. Agric. Resource Econ., 5 (2010) 227-242.

[66]

M. Ozdogan, Y. Yang, G. Allez, C. Cervantes, Remote sensing of irrigated agriculture: opportunities and challenges, Remote Sens., 2 (2010) 2274-2304.

[67]

PEAT UG, 2016. Plantix. {Online} Available at: <http://plantix.net/> (accessed 2017).

[68]

Pierce, F.J., & N., P., 1999. Aspects of precision agriculture. Advances in agronomy, vol. 67, pp. 185.

[69]

J.A. Pierna, Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds, J. Chemom., 18 (2004) 341-349.

[70]

J. Pretty, Agricultural sustainability: concepts, principles and evidence, Philos. Trans. Royal Soc. London B: Biol. Sci., 363 (2008) 447-465.

[71]

H. Rahman, D. Sudheer Pamidimarri, R. Valarmathi, M. Raveendran, Omics: Applications in Biomedical, Agriculture and Environmental Sciences, s.l., CRC PressI Llc., 2013.

[72]

RIICE Partnership, 2014. Remote sensing-based Information and Insurance for Crops in Emerging economies. {Online} Available at: <http://www.riice.org/> (accessed 2017).

[73]

D. Rodriguez, To mulch or to munch? Big modelling of big data, Agric. Syst., 153 (2017) 32-42.

[74]

T. Sakamoto, A crop phenology detection method using time-series MODIS data, Remote Sens. Environ., 96 (2005) 366-374.

[75]

M. Sawant, R. Urkude, S. Jawale, Organized data and information for efficacious agriculture using PRIDE model, Int. Food Agribusiness Manage. Rev., 19(A) (2016).

[76]

J. Sayer, K. Cassman, Agricultural innovation to protect the environment, Proc. Natl. Acad. Sci. U.S.A., 110 (2013) 8345-8348.

[77]

J.L. Schnase, MERRA analytic services: meeting the big data challenges of climate science through cloud-enabled climate analytics-as-a-service, Comput. Environ. Urban Syst. (2014).

[78]

Schuster, E.W. et al., 2011. Infrastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques. s.l., IEEE.

[79]

J. Schuster, Big data ethics and the digital age of agriculture, Am. Soc. Agric. Biol. Eng., 24 (2017) 20-21.

[80]

R. Senanayake, Sustainable Agriculture: definitions and parameters for measurement, J. Sustain. Agric., 1 (1991) 7-28.

[81]

SenseFly, 2012. Drone applications in agriculture. {Online} Available at: <https://www.sensefly.com/applications/agriculture.html> (accessed 2017).

[82]

D.-H. Shin, M.J. Choi, Ecological views of big data: perspectives and issues, Telematics Inform., 32 (2015) 311-320.

Digital Library

[83]

P. Slavin, Climate and famines: a historical reassessment, Wiley Interdiscipl. Rev.: Clim. Change, 7 (2016) 433-447.

[84]

M.-L. Song, R. Fisher, J.-L. Wang, L.-B. Cui, Environmental performance evaluation with big data: theories and methods, Ann. Oper. Res. (2016) 1-14.

[85]

S. Sonka, Big data: fueling the next evolution of agricultural innovation, J. Innovation Manage., 4 (2016) 114-136.

[86]

M.E. Sykuta, Big data in agriculture: property rights, privacy and competition in Ag data services, Int. Food Agribusiness Manage. Rev. Special Issue, 19(A) (2016).

[87]

Syngenta Foundation for Sustainable Agriculture, 2016. FarmForce. {Online} Available at: <http://www.farmforce.com/> {accessed 2017}.

[88]

Syngenta, 2010. Syngenta Foundation for Sustainable Agriculture: Kilimo Salama An agricultural insurance initiative. {Online} Available at: <https://kilimosalama.wordpress.com/about/> (accessed 2017).

[89]

M. Teke, IEEE, s.l, 2013.

[90]

K. Tesfaye, Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data, Int. Food Agribusiness Manage. Rev., 19(A) (2016) 1-18.

[91]

P. Thenkabail, Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium, Int. J. Remote Sens., 30 (2009) 3679-3733.

[92]

P. Thenkabail, Spectral matching techniques to determine historical land-use/land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR Pathfinder datasets, Photogram. Eng. Remote Sens., 73 (2007) 1029-1040.

[93]

Thomson Reuters, 2017. Web of Science. {Online} Available at: <http://www.webofknowledge.com/> (accessed 2017).

[94]

S. Tripathi, V.V. Srinivas, R.S. Nanjundiah, Downscaling of precipitation for climate change scenarios: a support vector machine approach, J. Hydrol., 330 (2006) 621-640.

[95]

A.C. Tyagi, Towards a second green revolution, Irrigation Drainage, 65 (2016) 388-389.

[96]

A. Urtubia, J. Perez-Correa, A. Soto, P. Pszczolkowski, Using data mining techniques to predict industrial wine problem fermentations, Food Control, 18 (2007) 1512-1517.

[97]

A. Vibhute, S.K. Bodhe, Applications of image processing in agriculture: a survey, Int. J. Comput. Appl., 52 (2012).

[98]

C. Vitolo, Web technologies for environmental Big Data, Environ. Model. Software, 63 (2015) 185-198.

Digital Library

[99]

D. Waga, K. Rabah, Environmental conditions big data management and cloud computing analytics for sustainable agriculture, World J. Comput. Appl. Technol., 2 (2014) 73-81.

[100]

G. Waldhoff, C. Curdt, D. Hoffmeister, G. Bareth, Analysis of multitemporal and multisensor remote sensing data for crop rotation mapping, Int. Arch. Photogram., Remote Sens. Spatial Inform. Sci., 25 (2012) 177-182.

[101]

B.D. Wardlow, S.L. Egbert, J.H. Kastens, Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains, Remote Sens. Environ., 108 (2007) 290-310.

[102]

E. Wari, W. Zhu, A survey on metaheuristics for optimization in food manufacturing industry, Appl. Soft Comput., 46 (2016) 328-343.

Digital Library

[103]

R.H. Weber, R. Weber, Internet of Things, Springer, New York, NY, 2010.

Digital Library

[104]

S. Wolfert, L. Ge, C. Verdouw, M. Bogaardt, Big data in smart farming a review, Agric. Syst., 153 (2017) 69-80.

[105]

World Economic Forum, Big Data, Big Impact: New Possibilities for International Development, World Economic Forum, Geneva, Switzerland, 2012.

[106]

Wu, J., Guo, S., Li, J., Zeng, D., 2016. Big Data Meet Green Challenges: Big Data Toward Green Applications, s.l.: s.n.

[107]

M. Wulder, Opening the archive: how free data has enabled the science and monitoring promise of Landsat, Remote Sens. Environ., 122 (2012) 2-10.

[108]

C. Zhang, J.M. Kovacs, The application of small unmanned aerial systems for precision agriculture: a review, Precision Agric., 13 (2012) 693-712.

Cited By

Chen TLv LWang DZhang JYang YZhao ZWang CGuo XChen HWang QXu YZhang QDu BZhang LTao D(2024)Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and OpportunitiesACM Computing Surveys10.1145/369858957:2(1-37)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3698589
Panoutsopoulos HEspejo-Garcia BRaaijmakers SWang XFountas SBrewster C(2024)Investigating the effect of different fine-tuning configuration scenarios on agricultural term extraction using BERTComputers and Electronics in Agriculture10.1016/j.compag.2024.109268225:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109268
Turkeltaub TPeltin BDagan APaz-Kagan TRave EBaram S(2024)Accounting for the impact of tree size and soil spatial variability on leaching from orchardsComputers and Electronics in Agriculture10.1016/j.compag.2024.108996221:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.108996
Show More Cited By

A review on the practice of big data analysis in agriculture
1. Applied computing
  1. Physical sciences and engineering

Recommendations

Multimedia Big Data Analytics: A Survey

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the ...
Mobile Agent Based New Framework for Improving Big Data Analysis
CLOUDCOM-ASIA '13: Proceedings of the 2013 International Conference on Cloud Computing and Big Data

The rising number of applications serving millions of users and dealing with terabytes of data need to a faster processing paradigms. Recently, there is growing enthusiasm for the notion of big data analysis. Big data analysis becomes a very important ...
Big Data: A Survey

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the ...

Comments

Information & Contributors

Information

Published In

cover image Computers and Electronics in Agriculture

Computers and Electronics in Agriculture Volume 143, Issue C

December 2017

325 pages

ISSN:0168-1699

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2017

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

81
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen TLv LWang DZhang JYang YZhao ZWang CGuo XChen HWang QXu YZhang QDu BZhang LTao D(2024)Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and OpportunitiesACM Computing Surveys10.1145/369858957:2(1-37)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3698589
Panoutsopoulos HEspejo-Garcia BRaaijmakers SWang XFountas SBrewster C(2024)Investigating the effect of different fine-tuning configuration scenarios on agricultural term extraction using BERTComputers and Electronics in Agriculture10.1016/j.compag.2024.109268225:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109268
Turkeltaub TPeltin BDagan APaz-Kagan TRave EBaram S(2024)Accounting for the impact of tree size and soil spatial variability on leaching from orchardsComputers and Electronics in Agriculture10.1016/j.compag.2024.108996221:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.108996
Zhai WXiong XMo GXiao YWu CXu ZPan J(2024)A Bagging-SVM field-road trajectory classification model based on feature enhancementComputers and Electronics in Agriculture10.1016/j.compag.2024.108635217:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.108635
Alves GCruvinel P(2024)Parallel and distributed processing for high resolution agricultural tomography based on big dataMultimedia Tools and Applications10.1007/s11042-023-15686-283:4(10115-10146)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15686-2
Aune TGu HWoldensenbet DLe TSauer J(2023)Spatial experiment identification (SPEX-ID)Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications10.1145/3615888.3627809(1-7)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615888.3627809
Palei SBehera SSethy P(2023)A Systematic Review of Citrus Disease Perceptions and Fruit Grading Using Machine VisionProcedia Computer Science10.1016/j.procs.2023.01.225218:C(2504-2519)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2023.01.225
Kurtser PLowry S(2023)RGB-D datasets for robotic perception in site-specific agricultural operations—A surveyComputers and Electronics in Agriculture10.1016/j.compag.2023.108035212:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.108035
Dooyum Uyeh DGebremedhin KHiablie S(2023)Perspectives on the strategic importance of digitalization for Modernizing African AgricultureComputers and Electronics in Agriculture10.1016/j.compag.2023.107972211:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.107972
Zhang QZhao XHan YYang FPan SLiu ZWang KZhao C(2023)Maize yield prediction using federated random forestComputers and Electronics in Agriculture10.1016/j.compag.2023.107930210:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.107930
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents