Big Data for weed control and crop protection

Weed Research

Volumn 57, Issue 4, 2017, Pages 218-233

  van Evert, F K ^a   Fountas, S ^b   Jakovetic, D ^c,d   Crnojevic, V ^c,d   Travlos, I ^b   Kempenaar, C ^a  

^a WAGENINGEN UNIVERSITY   (Netherlands)

^b AGRICULTURAL UNIVERSITY OF ATHENS   (Greece)

^c UNIVERSITY OF NOVI SAD   (Serbia)

^d UNIVERSITY OF NOVI SAD   (Serbia)

Author keywords

data ownership; data sharing; graphical model; multivariate regression; neural network; semantics; support vector machine

Indexed keywords

AGRICULTURAL MANAGEMENT; AGROINDUSTRY; ALGORITHM; ARTIFICIAL NEURAL NETWORK; GRAPHICAL METHOD; HERBICIDE; MULTIVARIATE ANALYSIS; SUPPORT VECTOR MACHINE; WEED CONTROL;

NETHERLANDS;

EID: 85019946776     PISSN: 00431737     EISSN: 13653180     Source Type: Journal    
DOI: 10.1111/wre.12255     Document Type: Review

References (107)

1. Agrimetrics (2016) Agrimetrics. Available at: http://www.agrimetrics.co.uk/ (last accessed 18 April 2017).
2. Allemang D & Hendler J (2011) Semantic Web for the working ontologist: effective modeling in RDFS and OWL. Elsevier, Amsterdam, The Netherlands.
3. Apache (2016) Apache Hadoop. Available at: https://hadoop.apache.org/ (last accessed 18 April 2017).
4. Bakker T, Van Asselt CJ, Bontsema J, Müller J & Van Straten G (2006) An autonomous weeding robot for organic farming. In: 5th International Conference on Field and Service Robotics, Vol. 25. Springer, Port Douglas, Australia.
5. Bakker T, van Asselt K, Bontsema J, Muller J & van Straten G (2010) Systematic design of an autonomous platform for robotic weeding. Journal of Terramechanics 47, 63–73.
6. Balcan M-F, Kanchanapally V, Liang Y & Woodruff D (2014) Improved Distributed Principal Component Analysis. Available at: arxiv.org/abs/1408.5823. (last accessed 18 April 2017).
7. Baral S, Kumar Tripathy A & Bijayasingh P (2011) Yield prediction using artificial neural networks. In: Computer Networks and Information Technologies: Second International Conference on Advances in Communication, Network, and Computing, CNC 2011, Bangalore, India, March 10–11, 2011. Proceedings. (eds VV Das, J Stephen & Y Chaba), 315–317. Springer, Berlin, Heidelberg.
8. Beauchamp TL & Childress JF (2001) Principles of biomedical ethics. Oxford University Press, USA.
9. Been TH, Schomaker CH & Molendijk LPG (2004) NemaDecide: a decision support system for the management of potato cyst nematodes. In: Decision support systems in potato production. Symposium proceedings Potato modelling conference, Edinburgh, March 2003. Wageningen (eds DKL Mackerron & AJ Haverkort). Academic Press, Wageningen.
10. Been TH, Schomaker CH & Molendijk LPG (2007) NemaDecide, a decision support system for the management of potato cyst nematodes. Phytopathology 97, S152.
11. Berners-Lee T, Hendler J & Lassila O (2001) The semantic web. Scientific American 248, 28–37.
12. Berti A, Bravin F & Zanin G (2003) Application of decision-support software for postemergence weed control. Weed Science 51, 618–627.
13. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York.
14. Blackmore S, Godwin RJ & Fountas S (2003) The analysis of spatial and temporal trends in yield map data over six years. Biosystems Engineering 84, 455–466.
15. Brdar S, Ćulibrk D, Marinković B, Crnobarac J & Crnojević V (2011) Support vector machines with features contribution analysis for agricultural yield prediction. In: Proceedings of the Second International Workshop on Sensing Technologies in Agriculture, Forestry and Environment (EcoSense 2011), 43–47. Belgrade.
16. Chang F, Dean J, Ghemawat S et al. (2008) Bigtable: a distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS) 26, 4.
17. Christensen S, Sogaard HT, Kudsk P et al. (2009) Site-specific weed control technologies. Weed Research 49, 233–241.
18. Codd EF (1970) A relational model of data for large shared data banks. Communications of the ACM 13, 377–387.
19. Davies A & Frigola R (2014) Probabilistic Models for Big Data. Available at: http://www.rogerfrigola.com/doc/bigdata.pdf. University of Cambridge. (last accessed 18 April 2017).
20. De Almeida J & Freitas H (2012) Exotic flora of continental Portugal–a new assessment. Bocconea 24, 231–237.
21. De Mauro A, Greco M & Grimaldi M (2016) A formal definition of Big Data based on its essential features. Library Review 65, 122–135.
22. Donoho DL, Maleki A & Montanari A (2009) Message-passing algorithms for compressed sensing. Proceedings of the National Academy of Sciences 106, 18914–18919.
23. Dreier J & Kerschbaum F (2011) Practical privacy-preserving multiparty linear programming based on problem transformation. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom), 916–924. IEEE.
24. Duchi J, Hazan E & Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12, 2121–2159.
25. Duchi JC, Jordan MI & Wainwright MJ (2014) Privacy aware learning. Journal of the Association for Computing Machinery 61, 38.
26. Duke CS & Porter JH (2013) The ethics of data sharing and reuse in biology. BioScience 63, 483–489.
27. Early R, Bradley BA, Dukes JS et al. (2016) Global threats from invasive alien species in the twenty-first century and national response capacities. Nature Communications 7, 12485.
28. Eddy PR, Smith AM, Hill BD, Peddle DR, Coburn CA & Blackshaw RE (2008) Hybrid segmentation - Artificial Neural Network classification of high resolution hyperspectral imagery for Site-Specific Herbicide Management in agriculture. Photogrammetric Engineering and Remote Sensing 74, 1249–1257.
29. Facchinei F, Scutari G & Sagratella S (2015) Parallel selective algorithms for big data optimization. IEEE Transactions on Signal Processing 63, 1874–1889.
30. Foley JA (2011) Can we feed the world and sustain the planet? Scientific American 305, 60–65.
31. Foley JA, Ramankutty N, Brauman KA et al. (2011) Solutions for a cultivated planet. Nature 478, 337–342.
32. Fountas S, Wulfsohn D, Blackmore BS, Jacobsen HL & Pedersen SM (2006) A model of decision-making and information flows for information-intensive agriculture. Agricultural Systems 87, 192–210.
33. Fountas S, Carli G, Sorensen CG et al. (2015) Farm management information systems: current situation and future perspectives. Computers and Electronics in Agriculture 115, 40–50.
34. Fumanal B, Chauvel B & Bretagnolle F (2007) Estimation of pollen and seed production of common ragweed in France. Annals of Agricultural and Environmental Medicine 14, 233–236.
35. Fung GM & Mangasarian OL (2013) Privacy-preserving linear and nonlinear approximation via linear programming. Optimization Methods and Software 28, 207–216.
36. Gerber E, Schaffner U, Gassmann A, Hinz H, Seier M & MÜLLER-SCHÄRER H (2011) Prospects for biological control of Ambrosia artemisiifolia in Europe: learning from the past. Weed Research 51, 559–573.
37. Gerhards R, Sokefeld M, Knuf D & Kuhbauch W (1996) Mapping and geostatistical analysis of weed distribution in sugarbeet fields for site-specific weed management. Journal of Agronomy and Crop Science-Zeitschrift Fur Acker Und Pflanzenbau 176, 259–266.
38. Guisan A & Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecological Modelling 135, 147–186.
39. Haverkort AJ & Kempenaar C (2016) Recent advances in biotechnology and information technology in the potato industry. In: Proceedings Crop Protection in Northern Britain, 183–190.
40. Hinton GE, Osindero S & Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural computation 18, 1527–1554.
41. Hong M, Razaviyayn M, Luo Z-Q & Pang J-S (2015) A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data.” Available at: http://arxiv.org/abs/1511.02746 (last accessed 18 April 2017).
42. Hsieh C-J, Si S & Dhillon IS (2014) A divide-and-conquer solver for kernel support vector machines. Proceedings of the 31st International Conference on Machine Learning, Proceedings of Machine Learning Research (PMLR) 32(1), 566–574, 2014.
43. Hunt LA, Jones JW, Hoogenboom G et al. (1994) Input and output file structures for crop simulation models. In: Crop Modeling and Related Environmental Data: A Focus on Applications for Arid and Semiarid Regions in Developing Countries. (eds PF Uhlir & GC Carter), 35–72. CODATA Commission on Global Change.
44. Jaiswal P, Avraham S, Ilic K et al. (2005) Plant Ontology (PO): a Controlled Vocabulary of Plant Structures and Growth Stages. Comparative and Functional Genomics 6(7–8). doi: 10.1002/cfg.496.
45. Jakovetić D, Xavier J & Moura JM (2014) Fast distributed gradient methods. IEEE Transactions on Automatic Control 59, 1131–1146.
46. Jordan MI (2004) Graphical models. Statistical Science 19, 140–155.
47. Kashyap H, Ahmed HA, Hoque N, Roy S & Bhattacharyya DK (2015) Big data Analytics in Bioinformatics: A Machine Learning Perspective. Available at: https://arxiv.org/abs/1506.05101 (last accessed 18 April 2017).
48. Kastens TL & Featherstone AM (1996) Feedforward backpropagation neural networks in prediction of farmer risk preferences. American Journal of Agricultural Economics 78, 400–415.
49. Kempenaar C, Hejting S & Michielsen JM (2014a) Perspectives for site specific application of soil herbicides in arable farming. In: 12th International Conference on Precision Agriculture (ICPA), Sacramento, CA, USA.
50. Kempenaar C, Van Evert FK & Been T (2014b) Use of vegetation indices in variable rate application of potato haulm killing herbicides. In: 12th International Conference on Precision Agriculture (ICPA), Sacramento, CA, USA.
51. Kempenaar C, Van Evert FK, Been T, Kocks CG & Westerdijk CE (2016) Towards data-intensive, more sustainable farming: advances in predicting crop growth and use of variable rate technology in arable crops in the Netherlands. In: 13th International Conference on Precision Agriculture (ICPA), St. Louis, MO, USA.
52. King G (2007) An introduction to the Dataverse network as an infrastructure for data sharing.
53. Klein LJ, Marianno FJA, Albrecht CM, Freitag M & Hamann HF (2015) PAIRS: A scalable geo-spatial data analytics platform. In: 2015 IEEE Conference on Big Data 1290–1298.
54. Kueffer C, Pyšek P & Richardson DM (2013) Integrative invasion science: model systems, multi-site studies, focused meta-analysis and invasion syndromes. New Phytologist 200, 615–633.
55. Kutter T, Tiemann S, Siebert R & Fountas S (2011) The role of communication and co-operation in the adoption of precision farming. Precision Agriculture 12, 2–17.
56. Lafferty J, McCallum A & Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning (ICML). Vol. 1, 282–289.
57. Lawson LG, Pedersen SM, Sorensen CG et al. (2011) A four nation survey of farm information management and advanced farming systems: a descriptive analysis of survey responses. Computers and Electronics in Agriculture 77, 7–20.
58. Low Y, Gonzalez J, Kyrola A, Bickson D, Guestrin C & Hellerstein JM (2010) GraphLab: A New Parallel Framework for Machine Learning. In: Conference on Uncertainty in Artificial Intelligence (UAI). Available at https://arxiv.org/abs/1408.2041 (last accessed 18 April 2017).
59. Luo J & Cardina J (2012) Germination patterns and implications for invasiveness in three Taraxacum (Asteraceae) species. Weed Research 52, 112–121.
60. Marko O, Brdar S, Panic M, Lugonja P & Crnojevic V (2016) Soybean varieties portfolio optimisation based on yield prediction. Computers and Electronics in Agriculture 127, 467–474.
61. Marz N & Warren J (2015) Big Data: principles and best practices of scalable realtime data systems. Manning Publications, Greenwich, CT, USA.
62. Mepham B (2005) Bioethics: an introduction for the biosciences. Oxford University Press, Oxford, UK.
63. Merfield CN (2016) Robotic weeding's false dawn? Ten requirements for fully autonomous mechanical weed management Weed Research 56, 340–344.
64. Murali NS, Secher BJ, Rydahl P & Andreasen FM (1999) Application of information technology in plant protection in Denmark: from vision to reality. Computers and Electronics in Agriculture 22, 109–115.
65. Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R & Muharemagic E (2015) Deep learning applications and challenges in big data analytics. Journal of Big Data 2, 1.
66. Nozari E, Tallapragada P & Cortés J (2016) Differentially private distributed convex optimization via objective perturbation. In: American Control Conference (ACC), Boston, MA, USA.
67. Oerke E-C & Dehne H-W (2004) Safeguarding production—losses in major crops and the role of crop protection. Crop Protection 23, 275–285.
68. Ostrouchov G, Chen W-C, Schmidt D & Patel P (2012) pbdR –Programming with Big Data in R. Available at: http://r-pbd.org/ (last accessed 18 April 2017).
69. Parsons DJ, Benjamin L, Clarke J et al. (2009) Weed Manager—a model-based decision support system for weed management in arable crops. Computers and Electronics in Agriculture 65, 155–167.
70. Pedersen SM, Fountas S, Blackmore BS, Gylling M & Pedersen JL (2004) Adoption and perspectives of precision farming in Denmark. Acta Agriculturae Scandinavica Section B-Soil and Plant Science 54, 2–8.
71. Pěknicová J & Berchová-Bímová K (2016) Application of species distribution models for protected areas threatened by invasive plants. Journal for Nature Conservation 34, 1–7.
72. Perez-Ortiz M, Pena JM, Gutierrez PA, Torres-Sanchez J, Hervas-Martinez C & Lopez-Granados F (2016) Selecting patterns and features for between- and within-crop-row weed mapping using UAV-imagery. Expert Systems with Applications 47, 85–94.
73. Prider J, Correll R & Warren P (2012) A model for risk-based assessment of Phelipanche mutelii (branched broomrape) eradication in fields. Weed Research 52, 526–534.
74. Pyšek P, Chytrý M, Pergl J, Sadlo J & Wild J (2012) Plant invasions in the Czech Republic: current state, introduction dynamics, invasive species and invaded habitats. Preslia 84, 575–629.
75. Rahaman MM, Chen D, Gillani Z, Klukas C & Chen M (2015) Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Frontiers in Plant Science 6, 619.
76. Ramírez-Albores JE, Bustamante RO & Badano EI (2016) Improved predictions of the geographic distribution of invasive plants using climatic niche models. PLoS ONE 11, e0156029.
77. Rogers EM (1995) Diffusion of innovations, 4th edn. The Free Press, New York, NY, USA.
78. Rosenzweig C, Jones JW, Hatfield JL et al. (2013) The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. Agricultural and Forest Meteorology 170, 166–182.
79. Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW & Pluemer L (2010) Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 74, 91–99.
80. Rydahl P (2004) A Danish decision support system for integrated management of weeds. Aspects of Applied Biology. Advances in Applied Biology: Providing New Opportunities for Consumers and Producers in the 21st Century 72, 4, 3–53.
81. Sarwate AD & Chaudhuri K (2013) Signal processing and machine learning with differential privacy: algorithms and challenges for continuous data. IEEE signal processing magazine 30, 86–94.
82. Sawyer JE (2010) Experiences Developing and Using a Regional Corn Nitrogen Response Trial Database. In: ASA, CSSA, SSSA International Meetings, Long Beach CA. Available at: https://scisoc.confex.com/scisoc/2010am/webprogram/Paper59153.html (last accessed 18 April 2017).
83. Shrestha R, Arnaud E, Mauleon R et al. (2010) Multifunctional crop trait ontology for breeders' data: field book, annotation, data discovery and semantic enrichment of the literature. AoB Plants 2010, plq008. doi: 10.1093/aobpla/plq008
84. Šikoparija B, Smith M, Skjøth CA et al. (2009) The Pannonian plain as a source of Ambrosia pollen in the Balkans. International Journal of Biometeorology 53, 263–272.
85. Slavakis K, Giannakis GB & Mateos G (2014) Modeling and optimization for big data analytics:(statistical) learning tools for our era of data deluge. IEEE signal processing magazine 31, 18–31.
86. Smola AJ & Schölkopf B (2004) A tutorial on support vector regression. Statistics and Computing 14, 199–222.
87. Song W, Zhou W, Jin Z et al. (2005) Germination response of Orobanche seeds subjected to conditioning temperature, water potential and growth regulator treatments. Weed Research 45, 467–476.
88. Sørensen CG, Fountas S, Nash E et al. (2010) Conceptual model of a future farm management information system. Computers and Electronics in Agriculture 72, 37–47.
89. Steiner JL, Sadler EJ, Wilson G et al. (2009) Stewards watershed data system: system design and implementation. Transactions of the ASABE 52, 1523–1533.
90. Straub ET (2009) Understanding technology adoption: theory and future directions for informal learning. Review of Educational Research 79, 625–649.
91. Thibaudon M, Hamberger C, Guilloux L & Massot R (2010) Ragweed pollen in France: origin, diffusion, exposure. European Annals of Allergy and Clinical Immunology 42, 209.
92. Thuiller W, Lafourcade B, Engler R & Araújo MB (2009) BIOMOD–a platform for ensemble forecasting of species distributions. Ecography 32, 369–373.
93. Travlos IS (2013a) Competition between ACCase-inhibitor resistant and susceptible sterile wild oat (Avena sterilis) biotypes. Weed Science 61, 26–31.
94. Travlos IS (2013b) Responses of invasive silverleaf nightshade (Solanum elaeagnifolium) populations to varying soil water availability. Phytoparasitica 41, 41–48.
95. Travlos I, Paspatis E & Psomadeli E (2008) Allelopathic potential of Oxalis pes-caprae tissues and root exudates as a tool for integrated weed management. Journal of Agronomy 7, 202–205.
96. Van Evert FK, Spaans EJA, Krieger SD, Carlis JV & Baker JM (1999a) A database for agroecological research data: I. Data model. Agronomy Journal 91, 54–62.
97. Van Evert FK, Spaans EJA, Krieger SD, Carlis JV & Baker JM (1999b) A database for agroecological research data: II. A relational implementation. Agronomy Journal 91, 62–71.
98. Van Evert FK, Samsom J, Polder G et al. (2011) A robot to detect and control broad-leaved dock (Rumex obtusifolius L.) in grassland. Journal of Field Robotics 28, 264–277.
99. Vatsavai RR, Ganguly A, Chandola V, Stefanidis A, Klasky S & Shekhar S (2012) Spatiotemporal data mining in the era of big spatial data: algorithms and applications. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on analytics for big geospatial data, 1–10. ACM.
100. Wainwright MJ & Jordan MI (2008) Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning 1, 1–305.
101. Weeraddana PC, Athanasiou G, Jakobsson M, Fischione C & Baras JS (2014) On the Privacy of Optimization Approaches. Available at: https://arxiv.org/abs/1210.3283 (last accessed 18 April 2017).
102. White JW & Van Evert FK (2008) Publishing agronomic data. Agronomy Journal 100, 1396–1400.
103. White JW, Hunt LA, Boote KJ et al. (2013) Integrated description of agricultural field experiments and production: a The ICASA Version 2.0 data standards. Computers and Electronics in Agriculture 96, 1–12.
104. Wytock M & Kolter JZ (2013) Sparse gaussian conditional random fields: algorithms, theory, and application to energy forecasting. In: ICML (3), 1265–1273.
105. Xie P, Bilenko M, Finley T, Gilad-Bachrach R, Lauter K & Naehrig M (2014) Crypto-Nets: Neural Networks Over Encrypted Data. Available at: https://arxiv.org/abs/1412.6181. (last accessed 18 April 2017).
106. Yan F, Sundaram S, Vishwanathan S & Qi Y (2013) Distributed autonomous online learning: regrets and intrinsic privacy-preserving properties. IEEE Transactions on Knowledge and Data Engineering 25, 2483–2493.
107. Zhang Q & Pierce FJ (2013) Agricultural Automation: fundamentals and Practices. CRC Press, Boca Raton, FL, USA.