From Parallel Plants to Smart Plants: Intelligent Control and Management for Plant Growth

Mengzhen Kang; Fei-Yue Wang

doi:10.1109/JAS.2017.7510487

Volume 4 Issue 2

Apr. 2017

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(2): 161-166

Mengzhen Kang and Fei-Yue Wang, "From Parallel Plants to Smart Plants: Intelligent Control and Management for Plant Growth," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 161-166, Apr. 2017. doi: 10.1109/JAS.2017.7510487

Citation:

Mengzhen Kang and Fei-Yue Wang, "From Parallel Plants to Smart Plants: Intelligent Control and Management for Plant Growth," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 161-166, Apr. 2017. doi: 10.1109/JAS.2017.7510487

Citation:

PDF( 5442 KB)

From Parallel Plants to Smart Plants: Intelligent Control and Management for Plant Growth

doi: 10.1109/JAS.2017.7510487

Mengzhen Kang^{1, 2, 3
,},
Fei-Yue Wang^{4, 5, 6
,
,}

1.
Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences (SKL-MCCS, CASIA), Beijing 100190, China
2.
Qingdao Academy of Intelligent Industries, Qingdao 266000, China
3.
Beijing Engineering Research Center of Intelligent Systems and Technology, Beijing 100190, China
4.
Key Laboratory of Management and Control for Complex Systems (SKL-MCCS), Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing 100190, China
5.
School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
6.
Research Center for Military Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073, China

Funds:

This work was supported by the National High Technology Research and Development Program (863 program) of China 2012AA101906-2

the National Natural Science Foundation of China 3140030594

More Information

Author Bio:
Mengzhen Kang (M'16) is an associate professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. Her current research interests include parallel agriculture and computational plant sciences. She has published over 50 scientific papers in her research field. She received her Ph.D. degree in pattern recognition and intelligent systems from Institution of Automation, Chinese Academy of Sciences in 2003. From 2005-2006, she was a postdoctoral fellow with DigiPlante, INRIA in France and Plant Sciences, Wageningen University in Netherland. She organized the international conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Application (PMA'12 and FSPMA2016) as the general co-chair from China (e-mail: mengzhen.kang@ia.ac.cn)
Corresponding author: Fei-Yue Wang (S'87-M'89-SM'94-F'03) received his Ph. D. in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a professor and director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Overseas Chinese Talents Program from the State Planning Council and "100 Talent Program" from CAS, and in 2002, was appointed as the director of the Key Lab of Complex Systems and Intelligence Science, CAS. In 2011, he became the State Specially Appointed Expert and the Director of The State Key Laboratory of Management and Control for Complex Systems. Dr. Wang's current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editor in-Chief of the International Journal of Intelligent Control and Systems (1995–2000), the Founding EiC of the IEEE ITS Magazine (2006–2007), the EiC of the IEEE Intelligent Systems (2009–2012), and the EiC of the IEEE Transactions on ITS (2009–2016). Currently he is the EiC of the China's Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, ASME conferences. He was the President of IEEE ITS Society (2005–2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007–2008), and the Vice President of the ACM China Council (2010–2011). Since 2008, he is the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang is elected Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the 2nd Class National Prize in the Natural Sciences of China and awarded the Outstanding Scientist by ACM for his work in intelligent control and social computing. He received the IEEE ITS Outstanding Application and Research Awards in 2009 and 2011, and the IEEE SMC Norbert Wiener Award in 2014. Corresponding author of this paper. (email:feiyue.wang@ia.ac.cn)
Received Date: 2017-01-15
Accepted Date: 2017-03-06

Abstract

Abstract

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description (the crop model), prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed.
- Artificial intelligence,
- cropping plan,
- management system,
- precision agriculture,
- plant model

FullText(HTML)

References(37)

References

[1]	P. C. Robert, "Precision agriculture: a challenge for crop nutrition management, " Plant Soil, vol. 247, no. 1, pp. 143-149, Nov. 2002.
[2]	R. Gebbers and V. I. Adamchuk, "Precision agriculture and food security, " Science, vol. 327, no. 5967, pp. 828-831, Feb. 2010.
[3]	G. van Straten, H. Challa, and F. Buwalda, "Towards user accepted optimal control of greenhouse climate, " Comput. Electron. Agric., vol.26, no.3, pp.221-238, May2000. doi: 10.1016/S0168-1699(00)00077-6
[4]	C. T. de Wit, "Photosynthesis of leaf canopies, " Agricultural Research Reports, no.663, 1965. https://www.researchgate.net/publication/241870363_Photosynthesis_of_Leaf_Canopies
[5]	L. F. M. Marcelis, E. Heuvelink, and J. Goudriaan, "Modelling biomass production and yield of horticultural crops: a review, " Sci. Hort. , vol. 74, no. 1-2, pp. 83-111, Apr. 1998.
[6]	C. A. Medina-Ruíz, I. A. Mercado-Luna, G. M. Soto-Zarazúa, I. Torres-Pacheco, and E. Rico-García, "Mathematical modeling on tomato plants: A review, " Afr. J. Agr. Res. , vol. 6, no. 33, pp. 6745-6749, Dec. 2011.
[7]	B. H. E. Vanthoor, C. Stanghellini, E. J. van Henten, and P. H. B. de Visser, "A methodology for model-based greenhouse design: Part 1, a greenhouse climate model for a broad range of designs and climates, " Biosyst. Eng. , vol. 110, no. 4, pp. 363-377, Dec. 2011.
[8]	B. H. E. Vanthoor, P. H. B. de Visser, C. Stanghellini, and E. J. van Henten, "A methodology for model-based greenhouse design: Part 2, description and validation of a tomato yield model, " Biosyst. Eng. , vol. 110, no. 4, pp. 378-395, Dec. 2011.
[9]	F. Rodríguez, L. J. Yebra, M. Berenguel, and S. Dormido, "Modelling and simulation of greenhouse climate using dymola, " in Proc. 15th Triennial World Congress, Barcelona, Spain, 2002. https://www.researchgate.net/publication/242737053_MODELLING_AND_SIMULATION_OF_GREENHOUSE_CLIMATE_USING_DYMOLA
[10]	I. Impron, S. Hemming, and G. P. A. Bot, "Simple greenhouse climate model as a design tool for greenhouses in tropical lowland, " Biosyst. Eng. , vol. 98, no. 1, pp. 79-89, Sep. 2007.
[11]	S. L. Speetjens, J. D. Stigter, and G. Van Straten, "Towards an adaptive model for greenhouse control, " Comput. Electron. Agric. , vol. 67, no. 1-2, pp. 1-8, Jun. 2009.
[12]	X. J. Yan, W. R. Wang, and J. P. Liang, "Application mode construction of internet of things (IOT) for facility agriculture in Beijing, " Trans. CSAE, vol. 28, no. 4, pp. 149-154, Feb. 2012.
[13]	J. C. Aker, "Dial "A" for agriculture: a review of information and communication technologies for agricultural extension in developing countries, " Agric. Econom. , vol. 42, no. 6, pp. 631-647, Nov. 2011.
[14]	D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, "Mastering the game of Go with deep neural networks and tree search, " Nature, vol. 529, no. 7587, pp. 484-489, Jan. 2016.
[15]	I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques (Fourth Edition). Morgan Kaufmann, 2016. http://www.doc88.com/p-3127644122935.html
[16]	F. Y. Wang, "Parallel control: A method for data-driven and computational control, " Acta Automat. Sinica, vol. 39, no. 4, pp. 293-302, Apr. 2013.
[17]	J. T. Ritchie, U. Singh, D. C. Godwin, and W. T. Bowen, "Cereal growth, development and yield, " in Understanding Options for Agricultural Production, G. Y. Tsuji, G. Hoogenboom, and P. K. Thornton, Eds. Netherlands: Springer, 1998, pp.79-98.
[18]	H. Gijzen, E. Heuvelink, H. Challa, L. F. M. Marcelis, E. Dayan, S. Cohen, and M. Fuchs, "Hortisim: a model for greenhouse crops and greenhouse climate, " Acta Hort. , vol. 456, pp. 441-450, Jan. 1998.
[19]	J. B. Evers, J. Vos, X. Yin, P. Romero, P. E. L. van der Putten, and P. C. Struik, "Simulation of wheat growth and development based on organ-level photosynthesis and assimilate allocation, " J. Exp. Bot. , vol. 61, no. 8, pp. 2203-2216, Mar. 2010.
[20]	H. P. Yan, M. Z. Kang, P. De Reffye, and M. Dingkuhn, "A dynamic, architectural plant model simulating resource-dependent growth, " Ann. Bot., vol.93, no.5, pp.591-602, May2004. doi: 10.1093/aob/mch078
[21]	P. -H. Cournéde, V. Letort, A. Mathieu, M. -Z. Kang, S. Lemaire, S. Trevezas, F. Houllier, and P. de Reffye, "Some parameter estimation issues in functional-structural plant modelling, " Math. Model. Nat. Pheno. , vol. 6, no. 2, pp. 133-159, Mar. 2011.
[22]	M. -Z. Kang, L. -L. Yang, B. -G. Zhang, and P. de Reffye, "Correlation between dynamic tomato fruit-set and source-sink ratio: a common relationship for different plant densities and seasons?, " Ann. Bot. , vol. 107, no. 5, pp. 805-815, Apr. 2011.
[23]	M. Z. Kang, E. Heuvelink, S. M. P. Carvalho, and P. de Reffye, "A virtual plant that responds to the environment like a real one: the case for chrysanthemum, " New Phytol. , vol. 195, no. 2, pp. 384-395, Jul. 2012.
[24]	M.-Z. Kang, J.-B. Evers, J. Vos, and P. de Reffye, "The derivation of sink functions of wheat organs using the GREENLAB model, " Ann. Bot., vol.101, no.8, pp.1099-1108, May2008. https://www.researchgate.net/publication/40095621_The_derivation_of_sink_functions_of_wheat_organs_using_the_GREENLAB_model
[25]	Y. Ma, M. Wen, Y. Guo, B. Li, P.-H. Cournede, and P. de Reffye, "Parameter optimization and field validation of the functional-structural model GREENLAB for maize at different population densities, " Ann. Bot., vol.101, no.8, pp.1185-1194, May2008. http://publications.cirad.fr/une_notice.php?dk=546303
[26]	F. -Y. Wang, "From parallel plants to parallel agriculture: a data driven and computational approach for next generation smart agricultural systems, " Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China, Tech. Rep. Jul. 2006.
[27]	X. -R. Fan, M. -Z. Kang, E. Heuvelink, P. de Reffye, and B. -G. Hu, "A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth, " Ecolog. Model. , vol. 312, pp. 363-373, Sep. 2015.
[28]	F. -Y. Wang, J. Zhang, Q. L. Wei, X. H. Zheng, and L. Li, "PDP: Parallel dynamic programming, " IEEE/CAA J. Automat. Sinica, vol. 4, no. 1, pp. 1-5, Jan. 2017.
[29]	A. A. Feng and N. R. Gohel, "Real-time user profile platform for targeted online advertisement and personalization, " U. S. Patent 20080015878, Jan. 17, 2008.
[30]	F. -Y. Wang, "Artificial plant systems: from computer simulations to computational experiments, " Center for Intelligent Control and Systems Engineering, Chinese Academy of Sciences (CASIA), Beijing, China, Tech. Rep. Sep. 2001.
[31]	J. Dury, N. Schaller, F. Garcia, A. Reynaud, and J. E. Bergez, "Models to support cropping plan and crop rotation decisions: A review, " Agron. Sustain. Dev. , vol. 32, no. 2, pp. 567-580, Apr. 2012.
[32]	L. M. R. dos Santos, P. Michelon, M. N. Arenales, and R. H. S. Santos, "Crop rotation scheduling with adjacency constraints, " Ann. Oper. Res. , vol. 190, no. 1, pp. 165-180, Oct. 2011.
[33]	L. Wu, F. -X. Le Dimet, P. de Reffye, B. -G. Hu, P. -H. Cournéde, and M. -Z. Kang, "An optimal control methodology for plant growth-case study of a water supply problem of sunflower, " Math. Comput. Simulat. , vol. 82, no. 5, pp. 909-923, Jan. 2012.
[34]	A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.
[35]	M. Z. Kang, T. Corpetti, J. Hua, and P. de Reffye, "Reconstructing LAI series by filtering technique and a dynamic plant model, " in Remote Sensing of Biomass-Principles and Applications, T. Fatoyinbo, Ed. InTech Press, 2012, pp. 217-228.
[36]	F. Y. Wang, "The emergence of intelligent enterprises: From CPS to CPSS, " IEEE Intell. Syst. , vol. 25, no. 4, pp. 85-88, Jul. --Aug. 2010.
[37]	V. Letort, P. Mahe, P.-H. Cournéde, P. de Reffye, and B. Courtois, "Quantitative genetics and functional-structural plant growth models: Simulation of quantitative trait loci detection for model parameters and application to potential yield optimization, " Ann. Bot., vol.101, no.8, pp.1243-1254, May2008. http://www.oalib.com/paper/4098707

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(8)

Get Citation

PDF

XML

Article Metrics

Article views (1764) PDF downloads(422)

From Parallel Plants to Smart Plants: Intelligent Control and Management for Plant Growth

doi: 10.1109/JAS.2017.7510487

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Export File

Citation

Format

Content