Аннотації

Автор(и):
Mingxin Huang
Дата публікації:

11.03.2019

Анотація (укр):

Створено теоретичну основу дослідження для вирішення задачі створення інформаційної технології моніторингу врожайності сільськогосподарських культур на основі аналізу мультиспектральних зображень, отриманих шляхом дистанційного зондування. Створена на основі цієї технології геоінформаційна система повинна моніторити та прогнозувати врожайність аналізуючи супутникові часові ряди зображень для виявлення кількісних та якісних показників врожайності, можливих захворювань рослин тощо. Описана в роботі задача є актуальною в умовах екологічної невизначеності. Виявлено, що через продовольчу кризу, наслідком якої є постійне зростання вартості продовольчих товарів, сільське господарство все більше функціонує в умовах невизначеності та ризику, що потребує застосування спеціальних методів дослідження.

Анотація (рус):

Анотація (англ):

A theoretical basis for research was created to solve the problem of creating an information technology for monitoring the yield of agricultural crops based on the analysis of multispectral images obtained by remote sensing. The geoinformation system created on the basis of this technology should monitor and yield the analysis of satellite time series of images to identify quantitative and qualitative indicators of yield, possible diseases of plants, and the like. The problem described in the work is relevant in conditions of environmental uncertainty. It is revealed that as a result of the food crisis, the consequence of which is a constant increase in the cost of food products, agriculture is increasingly functioning in conditions of uncertainty and risk, which requires the use of special research methods.

Література:

References:

  1. Turn the tide on plastic. Our daily choices matter: 5 ways to reduce our reliance on plastic. Retrieved from: http://www.fao.org/home/en/ [in English]
  2. Mingxin, Huang, Vatskel, Vladimir. (2019) Digital image analysis technologies for decision support systems in agricultural. Management of development of complex systems, 37, 164 167.
  3. Kuchansky, A., Biloshchytskyi, A. (2015). Prediction of time series by selective comparison with the sample. Eastern-European Journal of Enterprise Technologies, 6/4 (78), 13 18.
  4. Kuchansky, A., Nikolenko, V., Rachenko, A. (2015). A method for identifying trends in financial time series based on trend models of forecasting. Management of Development of Complex Systems, 24, 84 89.
  5. Kuchansky, A., Nikolenko, V. (2015). Pattern matching method for time-series forecasting. Management of Development of Complex Systems, 22 (1), 101 106.
  6. Kuchansky, A., Biloshchytskyi, A., Andrashko, Yu., Biloshchytska, S., Shabala, Ye., Myronov, O. (2018). Development of adaptive combined models for predicting time series based on similarity identification. Eastern-European Journal of Enterprise Technologies, 1/4 (91), 32 42. DOI: 10.15587/1729-4061.2018.121620.
  7. Biloshchytskyi, A.A. (2012). Vector method of goal-setting projects in design-vector space. Management of development of complex systems, 11, 110 114.
  8. Berzlev, A. (2013). Methods of pre-forecasting fractal time series analysis. Management of development of complex systems, 16, 76 81.
  9. Berzlev, A. (2013). The current state of information systems of time series forecasting. Management of development of complex systems, 13, 78 82.
  10. Petitjean, F., Inglada, J., Gançarski, P. (2012). Satellite image time series analysis under time warping. IEEE Trans. Geosci. Remote Sens, 50 (8), 3081 3095.
  11. Eerens, H., Haesen, D., Rembold, F., Urbano, F., Tote, C., Bydekerke, L. (2014). Image time series processing for agriculture monitoring. Environmental Modelling and Software, 53, 154 162.
  12. Drone data collection and analytics for agriculture. Quantify plant and soil health, improve productivity and maximize field output. Retrieved from: https://www.precisionhawk.com/agriculture [in English]
  13. Kolesnikova, E.V. (2013). Modeling poorly structured project management systems. Bul. Odes. Polytechnic. University, 3(42), 127 131.
  14. Oganov, A.V. & Gogunsky, V.D. (2013). Use the Theory of Constrains in PMO implementation at the organization.GESJ: Computer Science and Telecommunications, 4(40), 59 65.
  15. Rach, V., Rossoshanskaya, O., Medvedeva, O. (2010). Status and Trends in the Development of the Trend Project Management Methodology. Management of the development of complex systems, 3, 118 122.
  16. Morozov, V., Kalnichenko, O. & Liubyma, I. (2017). Managing projects configuration in development distributed information systems. 2nd IEEE International Conference on Advances Information and Communication, P.154–157. doi: 10.1109/aiact.2017.8020088
  17. Kuchansky, A., Biloshchytskyi, A., Andrashko, Yu., Vatskel, V., Biloshchytska, S., Danchenko, O., Vatskel, I. (2018). Combined models for forecasting the air pollution level in infocommunication systems for the environment state monitoring. 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS). Lviv, 2018. P. 125–130. DOI: 10.1109/IDAACS-SWS.2018.8525608
  18. Babu, A.J., Thirumalaivasan, D., Venugopal, K. (2006). STAO: a component architecture for raster and time series modeling. Environ. Model. Softw., 21 (5), 653 664.
  19. Rasinmäki, J. (2003). Modelling spatio-temporal environmental data. Environ. Model. Softw., 18 (10), 877 886.
  20. MATLAB per l’intelligenza artificiale. Progettare modelli e sistemi guidati dall'AI. Retrieved from: http://www.mathworks.it/ [in English].
  21. The R Project for Statistical Computing. Retrieved from: http: //www.r-project [in English].
  22. GRASS GIS Bringing advanced geospatial technologies to the world. Retrieved from: http://grass.osgeo.org/ [in English].
  23. Clark Labs. TerrSet Software Features. Retrieved from: http://clarklabs.org/ [in English].
  24. Harris Geospatial Solutions. Retrieved from: http://www.exelisvis.com/ [in English].
  25. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Harlan, J.C. (1974). Monitoring the Vernal Advancements and Retro Gradation of Natural Vegetation. Greenbelt, MD, USA, 371.
  26. MARS-OP predicts worldwide crop yields. Retrieved from: http://www.marsop.info/marsop3/ [in English]
  27. USGS FEWS NET Data Portal. Retrieved from: http://earlywarning.usgs.gov/fews/ [in English]
  28. Becker-Reshef, I., Justice, C., Sullivan, M., Vermote, E., Tucker, C., Anyamba, A., Small, J., Pak, E., Masuoka, E., Schmaltz, J., Hansen, M., Pittman, K., Birkett, C., Williams, D., Reynolds, C., Doorn, B. (2010). Monitoring global croplands with coarse resolution earth observations: the Global Agriculture Monitoring (GLAM) Project. Remote Sens., 2 (6), 1589-1609.
  29. United States Department of Agriculture Foreign Agricultural Service. Retrieved from: http://www.pecad.fas.usda.gov/cropexplorer/ [in English]
  30. Díaz, L., Bröring, A., McInerney, D., Libertá, G., Foerster, T. (2013). Publishing sensor observations into Geospatial Information Infrastructures: a use case in fire danger assessment. Environ. Model. Softw., 48 (10), 65-80.
  31. Dubois, G., Schulz, M., Skøien, J., Bastin, L., Peedell, S. (2013). eHabitat, a multipurpose Web Processing Service for ecological modeling. Environ. Model. Softw., 41 (3), 123 133.
  32. Blower, J.D., Gemmell, A.L., Griffiths, G.H., Haines, K., Santokhee, A., Yang, X. (2013). A Web Map Service implementation for the visualization of multidimensional gridded environmental data. Environ. Model. Softw., 47 (9), 218-224.
  33. Jönsson, P., Eklundh, L. (2004). TIMESAT e a program for analysing time-series of satellite sensor data. Comput. Geosci, 30, 833 845.
  34. Climate Hazards Group. Retrieved from: http://chg.geog.ucsb.edu/ [in English]
  35. AgrometShell (AMS) Software for crop yield forecasting initiated by the Food and Agriculture Organization of the United Nations. Retrieved from: http: // www.hoefsloot.com/agrometshell.htm [in English]
  36. Posudin, Yu.І. (2003). Methods for measuring environmental parameters: Textbook. World, 288.
  37. Reed, B.C., White, M., Brown, J.F. (2003). Remote sensing phenology. Phenology: an Integrative Environmental Science, 39, 365 381.
  38. White, M.A., Nemani, R.R. (2006). Real-time monitoring and short-term forecasting of land surface phenology. Remote Sensing of Environment, 104(1), 43 49.
  39. Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1), 106 115.
  40. Verbesselt, J., Hyndman, R., Zeileis, A., Culvenor, D. (2010). Phenological Change Detection while Accounting for Abrupt and Gradual Trends in Satellite Image Time Series. Remote Sensing of Environment, 114(12), 29702980.
  41. Loeffler, C., Ligtenberg, A. and Moschytz, G. (1989). Practical Fast 1-D DCT Algorithms with 11 Multiplications. Proc. Int’l. Conf. on Acoustics, Speech, and Signal Processing (ICASSP '89), 988991.
  42. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195 213.
  43. Fulton, J.P., Sobolik, C.J., Shearer, S.A., Higgins, S.F., Burks, T.F. (2009). GrainYield Monitor Flow Sensor For Accuracy For Simulated Varying Field Slopes, Applied in Engineering in Agriculture, 25 (1), 15 21.
  44. Atherton, B.C., Morgan, M.T., Shearer, S.A., Stombaugh, T.S., Ward, A.D. (1999). Site-Specific Farming: A Perspective on Information Needs, Benefits and Limitations. Soil and Water Conservation Society, 54 (Second Quarter), 455 461.