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Assessment of benefits resulting from the use of computer control systems in precision agriculture

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Języki publikacji
EN
Abstrakty
EN
Rising food prices mean that solutions are sought that will reduce the costs of agricultural production. One area of research is precision agriculture. This solution requires a computerized plant growth monitoring system. The paper presents a computer system for monitoring plant growth, developed for the needs of precision agriculture for small agricultural areas. The work contains a description of the monitoring system divided into the key elements of the process. An exemplary method of preparing orthophotomaps of the area was presented. The method of making maps that can be implemented on a personal computer has been described. The paper describes the most frequently used Geologic Indicators and Vegetation Index. A test of determining the coefficients was carried out on exemplary acreage with an area of 5.28 ha. Typical positioning systems for agricultural machines are discussed. The DGPS (Differential Global Positioning System) navigation method was used in the tests, which confirmed that it can be used in precision agriculture for small agricultural areas while maintaining sufficient positioning accuracy. The presented system was tested during one cycle of vegetation of winter barley sown with the no-plowing method. On this basis, an analysis of savings resulting from the reduction of the amount of fertilizers and plant care products used was carried out. The proposed system does not require complicated computer systems. It was designed in such a way that it can be implemented on standard PC equipment cooperating with a short-range drone equipped with a standard RGB (Red Green Blue) camera.
Rocznik
Strony
art. no. e144582
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
  • Opole University of Technology, Faculty of Electrical Engineering Automatic Control and Informatics, ul. Prószkowska 76, 45-758 Opole, Poland
Bibliografia
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  • [3] P. Sharifi, “Sustainable Agriculture: And Introduction to Extensive and Intensive Agriculture,” J. Eng. Appl. Sci., vol. 12, no. 10, pp. 2747–2751, 2017, doi: 10.36478/jeasci.2017.2747.2751 .
  • [4] D. Anders and M.R. Rząsa, “The possibility of composting animal waste products,” Environ. Prot. Eng., vol. 33. no. 2, pp. 7–15, 2007.
  • [5] H.Ü. Evcim, A. Dĕgirmencioğlu, G. Özgünaltay Ertuğrul, and ̇I. Aygün, “Advancements and transitions in technologies for sustainable agricultural production,” Econ. Environ. Stud., vol. 12, no. 4, pp. 459–466, 2012.
  • [6] J. Barwicki, K. Mazur, W.J. Wardal, M. Majchrzak, and K. Borek, “Monitoring of typical field work in different soil conditions using remote sensing – a literature review and some concepts for the future,” Agricult. Eng., vol. 19, no. 3, pp. 5–13, 2015.
  • [7] V. Mosorov, D. Sankowski, Ł. Mazurkiewicz, and T. Dyakowski, “The ‘best-correlated pixels’ method for solid mass flow measurement using electrical capacitance tomography,” Meas. Sci. Technol., vol. 13, no. 12, pp. 1810–1814, 2002.
  • [8] I. Tahmasbian, N.K. Morgan, S.H. Bai, M.W. Dunlop, and A.F. Moss, “Comparison of hyperspectral imaging and near-infrared spectroscopy to determine nitrogen and carbon concentrations in wheat,” Remote Sens., vol. 13, p. 1128, 2021, doi: 10.3390/rs13061128.
  • [9] J. Zhu, Q. Xu, J. Yao, X. Zhang, and Ch. Xu, “The changes of leaf reflectance spectrum and leaf functional traits of osmanthus fragrans are related to the parasitism of cuscuta japonica,” Appl. Sci., vol. 11, p. 1937, 2021, doi: 10.3390/app11041937.
  • [10] “Uncovering spectral signatures in a pecan orchard,” MicaSense. [Online]. Available: https://micasense.com/uncovering-spectral-signatures-in-a-pecan-orchard/ [Accessed: 14. July 2022].
  • [11] “dji-p4-multispectral-agriculture-drone,” Ghostysky. [Online]. Available: https://www.ghostysky.com/product/dji-p4-multispectral-agriculture-drone/ [Accessed: 14. July 2022].
  • [12] E. Wiśnieska, “Teledetekcja i fotogrametria obszarów leśnych” [Online]. Available: https://www.geomatyka.lasy.gov.pl/aktualnosci/-/asset/_publisher/PD11goFHmwok/content/teledetekcja-i-fotogrametria-obszarow-lesnych [Accessed: 13 Dec. 2020].
  • [13] R.M. Chauhan, “Advantages and challeging in E Agriculture,” Orient. J. Comput. Sci. Technol., vol. 8, no. 3, pp. 228–233, 2015.
  • [14] M.A. Mahboob, B. Genc, T. Celik, S. Ali, and I. Atif, “Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data from Landsat,” J. S. Afr. Inst. Min. Metall., vol. 19, pp. 279–289, 2019, doi: 10.17159/2411-9717/2019/v119n3a7.
  • [15] L. Cabrera-Bosquet, G. Molero, A.M. Stellacci, J. Bort, S. Nogués, and J.L. Araus, “NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions,” Cereal Res. Commun., vol. 39, no. 1, pp.147–159, 2011, doi: 10.1556/CRC.39.2011.1.15.
  • [16] G. Melillos and D.G. Hadjimitsis, “Using simple ratio (SR) vegetation index to detect deep manmade infrastructures in Cyprus,” in Proc. SPIE 11418, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV, 2020, p. 114180E, doi: 10.1117/12.2557893.
  • [17] J. Qi, A. Chehbouni, A.R. Huete, Y.H. Kerr, and S. Sorooshian, “Modified soil adjusted vegetation index,” Remote Sens. Environ., vol. 48, no. 2, pp. 119–126, 1994, doi: 10.1016/0034-4257(94)90134-1.
  • [18] L.S. Eng, R. Ismail, W. Hashim, and A. Baharum, “The use of VARI, GIL, and vigreen formulas in detecting vegetation in aerial images,” Int. J. Technol., vol. 10, no. 7, 2019, pp. 1385–1394, doi: 10.14716/ijtech.v10i7.3275.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bd710477-1e1a-4c5d-ad21-c3c02cf78814
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