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Application of Precision Agriculture Technology in Podlaskie Voivodeship

). Postęp i nowe koncepcje w rolnictwie precyzyjnym. Inżynieria Rolnicza, 9 (107), 19-21. Gaultney, L. (2008). Economic and environmental benefits of precision agriculture in fruit and vegetable production . Book of Abstracts: International Symposium – Precision Agriculture for Fruits and Vegetables. Orlando. 6-9.01.2008. Gozdowski, D., Samborski, S., Sioma, S. (2007). Rolnictwo precyzyjne . Wydawnictwo SGGW. Warszawa. ISBN 978-83-7244-858-3. Jadczyszyn, T. (1998). System Rolnictwa Precyzyjnego. III. Nawożenie w rolnictwie precyzyjnym. Frag. Agron

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Automated evaluation of agricultural damage using UAV survey

References [1] Molnár, A., Stojcsics, D., Lovas, I., “Precision agricultural and game damage analysis application for unmanned aerial vehicles”, INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND INFORMATICS 1:(10) pp. 38-43. (2016) [2] Molnár, A., Stojcsics, D., “GPGPU accelerated large scale 3D object reconstruction application for fixed and rotary wing unmanned aerial vehicles”, INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS - ENGINEERING & DEVELOPMENT 1:(10) pp. 189-194. (2016) [3] Harwin, S., Lucieer, A., “Assessing the Accuracy of

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Study On Correlation Of Data From Yield Monitoring System And Hand Samples

References ADAMCHUK, V. et al. 2004. EC04-704 Precision agriculture: Listening to the story told by yield maps. Historical Materials from University of Nebraska - Lincoln Extension.s Paper 710. [online]. [Retrieved 2012-06-19]. Retrieved from: http://digitalcommons.unl. edu/ ARSLAN, S. - COLVIN, T.S. 2002a. An evaluation of the response of yield monitors and combines to varying yields. In Precision Agriculture, vol. 3, 2002, pp. 107-122. AUERNHAMMER, H. - DEMMEL, M. - MUHR, K. - ROTTMEIER, J. - WILD, K. 1993. Yield measurements

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Solutions for the automation of operational monitoring activities for agricultural and forestry tasks

systems able to operate following Precision Agriculture and Precision Forestry approaches for agroforestry companies. Indeed, each of those applications should be supported by a technological platform that has the ability to perform the collection, the processing, the analysis, and the evaluation of the raw data, as well as the use of the obtained information, in a sequential and integrated way. To ensure the management and the processing of all these functions, the platform must be supported by adequate information system with the capability to satisfy the specific

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Development of a WSN for Greenhouse Microclimate Distribution Monitoring

.d.). [11] J.R. Mahan, K.M. Yeater, Agricultural applications of a low-cost infrared thermometer, Comput. Electron. Agric. 64 (2008) 262-267. doi: 10.1016/j.compag.2008.05.017. [12] S. Advanticsys, Advanticsys web site., (n.d.). [13] Raspberry, Raspberry Pi2 B official web site., (n.d.). [14] N. Katsoulas, K.P. Ferentinos, A. Tzounis, T. Bartzanas, C. Kittas, Operation reliability of wireless sensor networks in greenhouse conditions, (2011). [15] Kipp-Zonen, SP Lite2 Pyranometer official web site., (n.d.).

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A Review on Current and Emerging Application Possibilities for Unmanned Aerial Vehicles

Abstract

This paper presents a review on current and emerging application possibilities for unmanned aerial vehicles (UAVs). The introduction section of the paper briefly describes some of the application areas in which drones are currently being used. The next chapters of the paper describe more detailly the use of UAVs for aerial photography, filming, security and logistics, GIS, land and water surveys. The main focus of the last chapters is on the advantages and the disadvantages of the drones usage in precision agriculture, wildlife and nature observations and archaeology. The last chapters also provide information on how the advanced information technology solutions can be implemented in order to provide means for fighting invasive species, to increase the yield of certain crops, to monitor and predict flooding, wildfires and other disasters, etc. This paper provides only overview of the most interesting and widely available applications of the UAVs, but there are also many other more specific and dedicated solutions for implementation of the drones for different purposes.

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Modelling groundwater flow and nitrate transport: a case study of an area used for precision agriculture in the middle part of the Vistula River valley, Poland

., 1998. Aquifer Hydraulics: A Comprehensive Guide to Hydrogeologic Data Analysis . John Wiley & Sons, New York, 58 pp. Baum, R., Wojszczuk, K. & Wawrzynowicz, J., 2012. Miejsce i rola rolnictwa precyzyjnego w koncepcji zrównoważonego rozwoju gospodarstw rolnych [Place and role of precision agriculture at concept of sustainable development of farms]. Ekonomia i Środowisko 1, 71–83. Bieciński, P.A., 1960. Nowyj metod opredelenija koeffi-cienta wodootdaczi wodonosnych płastow [A new method for determining the storage coefficient of aquifers]. Gidrotehnika

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Effectiveness of Chlorophyll Meter Measurement in Winter Wheat at Field Scale Level

estimating nitrogen concentration of winter wheat at different growth stages. In Precision Agriculture , vol. 11 , no. 4, pp. 335‒357. DOI: 10.1007/s11119-010-9165-6. LIANG, H. ‒ HUANG, W. ‒ LIU, L. ‒ WANG, J. ‒ ZHAO, C. ‒ MA, Y. 2004. Variable rate nitrogen application algorithm based on SPAD measurements and its influence on winter wheat. In Geoscience and Remote Sensing Symposium , 2004. IGARSS ‘04: Proceedings. 2004 IEEE International, vol. 6 , pp. 4059‒4062. DOI:10.1109/IGARSS.2004.1370022 LICHTFOUSE, E. ‒HAMELIN, M. ‒NAVARRETE, M. ‒DEBAEKE, P. 2011

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Continuous field soil moisture content mapping by means of apparent electrical conductivity (ECa) measurement

-13. Cook, P.G., Walker, G.R., Buselli, G., Potts, I., Dodds, A.R., 1992. The application of electromagnetic techniques to groundwater recharge investigations. Journal of Hydrology, 130, 201-229. Corwin, D.L., Lesch, S.M., 2003. Application of soil electrical conductivity to precision agriculture. Theory, principles and guidelines. Agron. J., 95, 455-471. Corwin, D.L., Lesch, S.M., 2005. Apparent electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, 46, 11-43. Corwin, D.L., Rhoades, J

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Automated evaluation of agricultural damage using UAV survey

-grain insects using deep learning. Computers and Electronics in Agriculture 145, 319–325. [4] Ha, J. G., Moon, H., Kwak, J. T., Hassan, S. I., Dang, M., Lee, O. N., Park, H. Y. (2017), Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. Journal of Applied Remote Sensing 11(4), art. no 042621. [5] Uzal, L. C., Grinblat, G. L., Namías, R., Larese, M. G., Bianchi, J. S., Morandi, E. N., Granitto, P. M. (2018), Seed-per-pod estimation for plant breeding using deep learning. Computers and Electronics in

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