Abstract
Brown Planthopper (BPH) is one of the most dangerous insects that cause damage to rice. Aphids infected rice fields with low productivity can be lost even. Dealing with this situation, the Plant Protection industry has invented the light trap - a device based on the specific activity of insects phototaxis. These measures are considered effective and less costly today. However, the current light traps are usually installed next to the home of the staff assigned to manage light traps for easy tracking without attention to the impact of environmental factors around. Currently, the plant protection industry wants more scientific basis in light traps arranged so they want to review and make the factors of climate and geography in the light traps installed but not yet performed. In this paper, we propose an approach to find appropriate positions to replace light traps based on a combination between weather factors and geographical factors with data on infected areas by BPH with various infection levels exhibited on the maps based on Cellular Automata method. We present the simulation results with 8 considered cases to determine positions for light traps in an area of more than 1400 square kilometres including 84 communes in Can Tho city, one of the largest rice granaries in Vietnam.
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References
Ba BB, Nguyen HV, Nguyen HH, Ho CV (2006) A manual for preventing brown backed rice plant hoppers and rice stunts, National Agricultural Extension Center, Ministry of Agriculture and Rural Development 1–10
Vergara BS (1992) A farmer’s primer on growing rice, Int. Rice Res Inst
Catindig JLA, Arida GS, Baehaki SE, Bentur JS, Cuong LQ, Norowi M, Rattanakarn W, Sriratanasak W, Xia J, Lu Z (2009) Situation of s in Asia, s: new threats to the sustainability of intensive rice production systems in Asia, Los Bañ,os (Philippines): International Rice Research Institute (IRRI). 191–220
Phan CH, Huynh HX, Drogoul A (2010) An agent-based aproach to the simulation of Brown Plant Hopper (BPH) invasion in the Mekong Delta. In: International conference on computing and communication technologies, IEEE, pp 227–232
Vo QC, Vo MQ, Nguyen CHT, Ho CV (2013) Management of BPH data on light traps for rice farming in the Mekong Delta. In: Proceedings of GIS application national conference, pp 282–290
Bao HL, Tai TP, Long HV, Hiep XH, Bernard P (2013) Designing a brown s surveillance network based on wireless sensor network approach. arXiv:1312.3692, 1–6
Luong HH, Huynh HX (2014) Graph-based model for geographic coordinate search based on balltree structure. In: Proceeding of the 17th national Conference in Vietnam, pp 116–123
Quang TC, Vo M, Nguyen C, Ho C (2013) Managing The Brown Plant Hopper Caught By Light-Traps For Supporting Of Rice Cultivation In The Mekong Delta. National Scientific Conference Of Gis Applications 2013 In Vietnam. pp 282–290
Bao HL, Tai TP, Long HV, Hiep XH, Bernard P (2013) Designing a brown surveillance network based on wireless sensor network approach, arXiv:1312.3692, 1–6
Lam BH, Phan TT, Huynh HX, Pottier B (2016) A synchronous network for Brownplant hoppers surveillance based on hexagonal cellular automata. In: International conference on nature of computation and communication, Springer International Publishing, Springer International Publishing, pp 97–112
Truong XV, Huynh XH, Le NM, Drogoul A (2012) Modeling a surveillance network based on unit disk graph technique - application for monitoring the invasion of insects in mekong delta region. In: The 15th international conference on principles and practice of multi-agent systems (PRIMA 2012), pp 228–242
Kim BTN (2008) Curriculum Theoretical Optimal Methods and Algorithms. Bach Khoa - Hanoi Publishing House, pp 135–178
Nguyen VGN, Huynh HX, Drogoul A (2012) Toward an agent-based multi-scale recommendation system for brown plant hopper control. In: 2012 Sixth UKSim/AMSS European symposium on, IEEE, pp 9–14
Nguyen VGN, Huynh HX, Vo TT, Drogoul A (2011) On weather affecting to brown plant hopper invasion using an agent-based model. In: Proceedings of the international conference on management of emergent digital EcoSystems, ACM Press, pp 150–157
Pottier B, YvesLucas P (2015) Dynamic networks “Netgen: objectives, Asynchronous network for BPH surveillance based on hexagonal CA 19 installation, use, and programming” Technical report
Maeda K, Sakama C (2007) Identifying cellular automata rules. J Cell Autom 2:1–20
Otuka A, Watanabe T, Suzuki Y, Matsumura M, Furuno A, Chino M (2005) A migration analysis of the rice Nilaparvata lugens from the Philippines to East Asia with three-dimensional computer simulations. Popul Ecol 47:143–150. https://doi.org/10.1007/s10144-005-0216-1
Otuka A (2009) Migration of rice s and simulation techniques, s: new threats to the sustainability of intensive rice production systems in Asia, Los Bañ,os (Philippines): International Rice Research Institute (IRRI), 343–356
Checker VG, Sharma M (2021) Signalling during insect plant interaction. In: Singh IK, Singh A (eds) Plant-pest interactions: from molecular mechanisms to chemical ecology. https://doi.org/10.1007/978-981-15-2467-7∖_9. Springer, Singapore
Karegowda AG et al Deep learning solutions for agricultural and farming activities. Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan
Senthilnathan K, Shanmugam B, Goyal D, Annapoorani I, Samikannu R (2021) Deep learning applications and intelligent decision making in engineering. IGI Global. https://doi.org/10.4018/978-1-7998-2108-3
Huynh VK, Nguyen VGN, Huynh HX (2014) Simulation of BPH density in Dong Thap province based on interpolation techniques and multi agent system. In: Scientific research conference, Dong Thap University, pp 1–11
Jarkko K (2005) Theory of cellular automata: A survey. Theor Comput Sci 334:3–33
Wolfram S (1982) Cellular Automata as Simple Self-Organizing Systems, Caltech preprint CALT-68-938, 1–12
Guan Q, Shi X, Huang M, Lai C (2015) A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures, International Journal of Geographical Information Science
Nagare RM, Bhattacharya P, Khanna J, Schincariol RA (2015) Coupled cellular automata of frozen soil processes, SOIL 130–116
Shiffman D, Fry S, Marsh Z (2012) Chapter 7: cellular automata, The Nature of Code: Simulating Natural Systems with Processing, The Nature of Code, 323–354
Tran HV, Pottier B (2015) Cyber-physical systems and mixed simulations. Master thesis, 3–4
Itami RM (1994) Simulating spatial dynamics: cellular automata theory. Landsc Urban Plan 30 (12):27–47. https://doi.org/10.1016/0169-2046(94)90065-5. ISSN 0169-2046
Lam VP (2007) Things to know about s and control measures. Lao Dong - Ha Noi publishing house. pp 7
Pottier B, Lucas P (2015) Dynamic networks “Netgen: objectives, A synchronous network for BPH surveillance based on hexagonal CA 19 installation, use, and programming” Technical report
Watanabe T, Furuno A, Chino M, Otuka A, Matsumura M, Suzuki Y (2005) Development of a numerical simulation model for long-range migration of rices. Agr Forest Meteorol 197–209
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Huynh, H.X., Phan, N.M.L., Luong, H.H. et al. Brown Planthopper Sensor Network Optimization Based on Climate and Geographical Factors using Cellular Automata Technique. Mobile Netw Appl 26, 1311–1328 (2021). https://doi.org/10.1007/s11036-021-01763-z
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DOI: https://doi.org/10.1007/s11036-021-01763-z