References
- ARANGUREN, M. – CASTELLÓN, A. – AIZPURUA, A. 2020. Wheat yield estimation with NDVI values using a proximal sensing tool. In Remote Sensing, vol. 12, no. 17, article no. 2749. DOI: https://doi.org/10.3390/rs12172749
- ASHOURLOO, D. – MOBASHERI, M. R. – HUETE, A. 2014. Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. In Remote Sensing, vol. 6, no. 6, pp. 5107–5123. DOI: https://doi.org/10.3390/rs6065107
- ATANASOV, A. – BANKOVA, A. – ZHECHEVA, G. 2023a. Observation of the vegetation processes of agricultural crops using small unmanned aerial vehicles in Dobrudja region. In Bulgarian Journal of Agricultural Science, vol. 29, no. 1, pp. 176–181. DOI: https://agrojournal.org/29/01-21.html
- ATANASOV, A. – BANKOVA, A. – ZHECHEVA, G. 2023b. Vegetation data processes registered by remote sensing with a small aerial vehicle. In Bulgarian Journal of Agricultural Science, vol. 29 no. 3, pp. 564–569. DOI: https://agrojournal.org/29/03-22.html
- ATANASOV, A. – MIHOVA, G. – MIHAYLOV, R. 2022. Applicability and efficiency of remote sensing of agricultural areas. In Bulgarian Journal of Agricultural Science, vol. 28 no. 5, pp. 933–943. DOI: http://www.agrojournal.org/28/05-21.pdf
- AGROMONITORING. 2024. Available online at: https://home.agromonitoring.com/
- BARKER, J. – ZHANG, N. – SHARON, J. – STEEVES, R. – WANG, X. – WEI, Y. – POLAND, J. 2016. Development of a field-based high-throughput mobile phenotyping platform. In Computers and Electronics in Agriculture, vol. 122, pp. 74–85. DOI: https://doi.org/10.1016/j.compag.2016.01.017
- BELLVERT, J. – MARSAL, J. – GIRONA, J. – GONZALEZ-DUGO, V. – FERERES, E. – USTIN, S. L. – ZARCO-TEJADA, P. J. 2016. Airborne thermal imagery to detect the seasonal evolution of crop water status in peach, nectarine and Saturn peach orchards. In Remote Sensing, vol. 8, no. 1, article no. 39. DOI: https://doi.org/10.3390/rs8010039
- CAA. 2024. Available online at: https://www.caa.bg/bg
- CABRERA-BOSQUET, L. – MOLERO, G. – STELLACCI, A. – BORT, J. – NOGUÉS, S. – ARAUS, J. 2011. NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. In Cereal Research Communications, vol. 39, no. 1, pp. 147–159. DOI: https://doi.org/10.1556/crc.39.2011.1.15
- DEERY, D. M. – REBETZKE, G. J. – JIMENEZ-BERNI, J. A. – JAMES, R. A. – CONDON, A. G. – BOVILL, W. D. – HUTCHINSON, P. – SCARROW, J. – DAVY, R. – FURBANK, R. T. 2016. Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography. In Frontiers in Plant Science, vol. 7, article no. 1808. DOI: https://doi.org/10.3389/fpls.2016.01808
- DEVKOTA, R. – PANT, P. L. – GARTAULA, H. N. – PATEL, K. – GAUCHAN, D. – HAMBLY-ODAME, H. – THAPA, B. – RAIZADA, M. N. 2020. Responsible agricultural mechanization innovation for the sustainable development of Nepal’s hillside farming system. In Sustainability, vol. 12, no. 1, article no. 374. DOI: https://doi.org/10.3390/su12010374
- GOLZARIAN, M. R. – FRICK, R. A. – RAJENDRAN, K. – BERGER, B. – ROY, S. – TESTER, M. – LUN, D. S. 2011. Accurate inference of shoot biomass from high-throughput images of cereal plants. In Plant Methods, vol. 7, article no. 2. DOI: https://doi.org/10.1186/1746-4811-7-2
- GUO, Q. – WU, F. – PANG, S. – ZHAO, X. – CHEN, L. – LIU, J. – XUE, B. – XU, G. – LI, L. – JING, H. – CHU, C. 2018. Crop 3D – a LiDAR based platform for 3D high-throughput crop phenotyping. In Science China Life Sciences, vol. 61, pp. 328–339. DOI: https://doi.org/10.1007/s11427-017-9056-0
- JIANG, L. – SUN, L. – YE, M. – WANG, J. – WANG, Y. – BOGARD, M. – LACAZE, X. – FOURNIER, A. – BEAUCHÊNE, K. – GOUACHE, D. – WU, R. 2019. Functional mapping of N deficiency-induced response in wheat yield-component traits by implementing high-throughput phenotyping. In The Plant Journal, vol. 97, no. 6, pp. 1105–1119. DOI: https://doi.org/10.1111/tpj.14186
- JIN, X. – ZARCO-TEJADA, P. J. – SCHMIDHALTER, U. – REYNOLDS, M. P. – HAWKESFORD, M. J. – VARSHNEY, R. K. – YANG, T. – NIE, C. – LI, Z. – MING, B. – XIAO, Y. – XIE, Y. – LI, S. 2021. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. In IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 1, pp. 200–231. DOI: https://doi.org/10.1109/MGRS.2020.2998816
- MANSOURIALAM, A. – RASEKH, M. – ARDABILI, S. – DADKHAH, M. – MOSAVI, A. 2024. Hyperspectral method integrated with machine learning to predict the acidity and soluble solid content values of kiwi fruit during the storage period. In Acta Technologica Agriculturae, vol. 27, no. 4, pp. 187–193. DOI: https://doi.org/10.2478/ata-2024-0025
- MATESE, A. – DI GENNARO, S. F. 2018. Practical applications of a multisensor UAV platform based on multispectral, thermal and RGB high resolution images in precision viticulture. In Agriculture, vol. 8, no. 7, article no. 116. DOI: https://doi.org/10.3390/agriculture8070116
- MATESE, A. – TOSCANO, P. – DI GENNARO, S. F. – GENESIO, L. – VACCARI, F. P. – PRIMICERIO, J. – BELLI, C. – ZALDEI, A. – BIANCONI, R. – GIOLI, B. 2015. Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. In Remote Sensing, vol. 7, no. 3, pp. 2971–2990. DOI: https://doi.org/10.3390/rs70302971
- MAPIR. 2024. Available online at: https://www.mapir.camera/en-gb/collections/survey3/
- PHANG, S. K. – CHIANG, T. H. A. – HAPPONEN, A. – CHANG, M. M. L. 2023. From satellite to UAV-based remote sensing: A review on precision agriculture. In IEEE Access, vol. 11, pp. 127057–127076. DOI: https://doi.org/10.1109/ACCESS.2023.3330886
- PIX4D. 2024a. Available online at: https://www.pix4d.com/product/pix4dcapture/
- PIX4D. 2024. Available online at: https://www.pix4d.com/product/pix4dmapper-photogrammetry-software/
- POBLETE-ECHEVERRÍA, C. – OLMEDO, G. F. – INGRAM, B. – BARDEEN, M. 2017. Detection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from unmanned aerial vehicle (UAV): A case study in a commercial vineyard. In Remote Sensing, vol. 9, no. 3, article no. 268. DOI: https://doi.org/10.3390/rs9030268
- RAO, U. R. – DEEPAK, B. B. V. L. 2018. Review on application of drone system in precision agriculture. In Procedia Computer Science, vol. 133, pp. 502–509. DOI: https://doi.org/10.1016/j.procs.2018.07.063
- ROMBOLI, Y. – DI GENNARO, S. F. – MANGANI, S. – BUSCIONI, G. – MATESE, A. – GENESIO, L. – VINCENZINI, M. 2017. Vine vigour modulates bunch microclimate and affects the composition of grape and wine flavonoids: An unmanned aerial vehicle approach in a Sangiovese vineyard in Tuscany. In Australian Journal of Grape and Wine Research, vol. 23, no. 3, pp. 368–377. DOI: https://doi.org/10.1111/ajgw.12293
- ROUSE, J. W. – HAAS, R. H. – SCHELL, J. A. – DEERING, D. W. – HARLAN, J. C. 1974. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC Type III Final Report. Greenbelt, MD : NASA/GSFC, 390 pp.
- SINGH, A. – GANAPATHYSUBRAMANIAN, B. – SINGH, A. K. – SARKAR, S. 2016. Machine learning for high-throughput stress phenotyping in plants. In Trends in Plants Science, vol. 21, no. 2, pp. 110–124. DOI: https://doi.org/10.1016/j.tplants.2015.10.015
- SULTANA, S. R. – ALI, A. – AHMAD, A. – MUBEEN, M. – ZIA-UL-HAQ, M. – AHMAD, S. – ERCISLI, S. – JAAFAR, H. Z. E. 2014. Normalized difference vegetation index as a tool for wheat yield estimation: A case study from Faisalabad, Pakistan. In The Scientific World Journal, vol. 2014, no. 1, article no. 725326. DOI: https://doi.org/10.1155/2014/725326
- SUN, W. – DU, Q. 2018. Graph-regularized fast and robust principal component analysis for hyperspectral band selection. In IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 6, pp. 3185–3195. DOI: https://doi.org/10.1109/TGRS.2018.2794443
- ONESOIL. 2024. Available online at: https://onesoil.ai
- VAN DER WAL, T. – ABMA, B. – VIGURIA, A. – PRÉVINAIRE, E. – ZARCOTEJADA, P. J. – SERRUYS, P. – VAN VALKENGOED, E. – VAN DER VOET, P. 2013. Fieldcopter: unmanned aerial systems for crop monitoring services. In: STAFFORD, J. V. (ed.) Precision Agriculture ’13. Wageningen : Wageningen Academic Publishers, 823 pp. ISBN 978-90-8686-778-3. DOI: https://doi.org/10.3920/978-90-8686-778-3
- VANNOPPEN, A. – GOBIN, A. – KOTOVA, L. – TOP, S. – DE CRUZ, L. – VIKSNA, A. – ANISKEVICH, S. – BOBYLEV, L. – BUNTEMEYER, L. – CALUWAERTS, S. – DE TROCH, R. – GNATIUK, N. – HAMDI, R. – REMEDIO, A. R. – SAKALLI, A. – VAN DE VYVER, H. – VAN SCHAEYBROECK, B. – TERMONIA, P. 2020. Wheat yield estimation from NDVI and regional climate models in Latvia. In Remote Sensing, vol. 12, no. 14, article no. 2206. DOI: https://doi.org/10.3390/rs12142206
- XIE, Q. – HUANG, W. – DASH, J. – SONG, X. – HUANG, L. – ZHAO, J. – WANG, R. 2015. Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions. In Advances in Space Research, vol. 56, no. 11, pp. 2365–2373. DOI: https://doi.org/10.1016/j.asr.2015.09.022
- ZHU, M. – ZHANG, J. – ZHU, L. 2021. Variations in growing season NDVI and its sensitivity to climate change responses to green development in mountainous areas. In Frontiers in Environmental Science, vol. 9. DOI: https://doi.org/10.3389/fenvs.2021.678450