Abstract
The food security of most rural sectors depends on traditional agricultural practices. Geomatics tools contribute to traditional agricultural practices through Unmanned Aerial Vehicles (UAVs) in smart agriculture, agricultural mapping and improving sustainable agricultural production. The study’s objective is to explore the application of UAVs in agricultural mapping through a literature review of the use of this tool in agriculture. The methodology of this review performs a literary search on the application of UAVs to explore the structure, dynamics and domain of this area of knowledge. Subsequently, the analysis includes scientific contributions, the most relevant authors, evolution of themes and trends in sustainable agriculture. The results show that the countries with the greatest scientific contribution in this area of knowledge are the United States (USA) and China. The predominant themes are monitoring plant phenology/crop detection, status/evaluation of agricultural soils, irrigation applications/water resources and agricultural yield. UAV use, and their fusion with other technologies is a technological trend contributing to agricultural mapping through thematic maps and object-based image analysis in digitising sustainable farming activities and practices.
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References
Navia, J., Mondragon, I., Patino, D., Colorado, J.: Multispectral mapping in agriculture: terrain mosaic using an autonomous quadcopter UAV. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1351–1358. IEEE (2016). https://doi.org/10.1109/ICUAS.2016.7502606
Tsouros, D.C., Bibi, S., Sarigiannidis, P.G.: A review on UAV-based applications for precision agriculture. Information 10, 349 (2019). https://doi.org/10.3390/info10110349
Aslan, M.F., Durdu, A., Sabanci, K., Ropelewska, E., Gültekin, S.S.: A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses. Appl. Sci. 12, 1047 (2022). https://doi.org/10.3390/app12031047
Jakob, S., Zimmermann, R., Gloaguen, R.: The need for accurate geometric and radiometric corrections of drone-borne hyperspectral data for mineral exploration: MEPHySTo—A toolbox for pre-processing drone-borne hyperspectral data. Remote Sens. (Basel) 9, 88 (2017). https://doi.org/10.3390/rs9010088
Christiansen, M., Laursen, M., Jørgensen, R., Skovsen, S., Gislum, R.: Designing and testing a UAV mapping system for agricultural field surveying. Sensors 17, 2703 (2017). https://doi.org/10.3390/s17122703
Rokhmana, C.A.: The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia. Procedia Environ. Sci. 24, 245–253 (2015). https://doi.org/10.1016/j.proenv.2015.03.032
Yue, J., et al.: Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens. (Basel) 9, 708 (2017). https://doi.org/10.3390/rs9070708
Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I.: A compilation of UAV applications for precision agriculture. Comput. Netw. 172, 107148 (2020). https://doi.org/10.1016/j.comnet.2020.107148
Gašparović, M., Zrinjski, M., Barković, Đ., Radočaj, D.: An automatic method for weed mapping in oat fields based on UAV imagery. Comput. Electron. Agric. 173, 105385 (2020). https://doi.org/10.1016/j.compag.2020.105385
Allred, B., et al.: Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes. Agric. Water Manag. 232, 106036 (2020). https://doi.org/10.1016/j.agwat.2020.106036
Simic Milas, A., Romanko, M., Reil, P., Abeysinghe, T., Marambe, A.: The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images. Int. J. Remote Sens. 39, 5415–5431 (2018). https://doi.org/10.1080/01431161.2018.1455244
Torres-Sánchez, J., Peña, J.M., de Castro, A.I., López-Granados, F.: Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric. 103, 104–113 (2014). https://doi.org/10.1016/j.compag.2014.02.009
Santesteban, L.G., Di Gennaro, S.F., Herrero-Langreo, A., Miranda, C., Royo, J.B., Matese, A.: High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agric. Water Manag. 183, 49–59 (2017). https://doi.org/10.1016/j.agwat.2016.08.026
Holman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., Hawkesford, M.: High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens. (Basel) 8, 1031 (2016). https://doi.org/10.3390/rs8121031
Herwitz, S.R., et al.: Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Comput. Electron. Agric. 44, 49–61 (2004). https://doi.org/10.1016/j.compag.2004.02.006
Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., Zhang, L.: A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PLoS ONE 13, e0196302 (2018). https://doi.org/10.1371/journal.pone.0196302
Kerkech, M., Hafiane, A., Canals, R.: Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Comput. Electron. Agric. 174, 105446 (2020). https://doi.org/10.1016/j.compag.2020.105446
Niknejad, N., Ismail, W., Bahari, M., Hendradi, R., Salleh, A.Z.: Mapping the research trends on blockchain technology in food and agriculture industry: a bibliometric analysis. Environ. Technol. Innov. 21, 101272 (2021). https://doi.org/10.1016/j.eti.2020.101272
Velasco-Muñoz, J., Aznar-Sánchez, J., Belmonte-Ureña, L., López-Serrano, M.: Advances in water use efficiency in agriculture: a bibliometric analysis. Water (Basel) 10, 377 (2018). https://doi.org/10.3390/w10040377
Luo, J., Han, H., Jia, F., Dong, H.: Agricultural Co-operatives in the western world: a bibliometric analysis. J. Clean. Prod. 273, 122945 (2020). https://doi.org/10.1016/j.jclepro.2020.122945
Ragazou, K., Garefalakis, A., Zafeiriou, E., Passas, I.: Agriculture 5.0: a new strategic management mode for a cut cost and an energy efficient agriculture sector. Energies (Basel) 15, 3113 (2022). https://doi.org/10.3390/en15093113
Bouchenine, A., Abdel-Aal, M.A.M.: Towards supply chain resilience with additive manufacturing: a bibliometric survey. Supply Chain Anal. 2, 100014 (2023). https://doi.org/10.1016/j.sca.2023.100014
Khuram, S., Rehman, C., Nasir, N., Elahi, N.S.: A bibliometric analysis of quality assurance in higher education institutions: implications for assessing university’s societal impact. Eval. Program Plann. 99, 102319 (2023). https://doi.org/10.1016/j.evalprogplan.2023.102319
Abdollahi, A., Ghaderi, Z., Béal, L., Cooper, C.: The intersection between knowledge management and organizational learning in tourism and hospitality: a bibliometric analysis. J. Hosp. Tour. Manag. 55, 11–28 (2023). https://doi.org/10.1016/j.jhtm.2023.02.014
Ma, L., et al.: Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. ISPRS Int. J. Geoinf. 6, 51 (2017). https://doi.org/10.3390/ijgi6020051
Chakraborty, M., Khot, L.R., Sankaran, S., Jacoby, P.W.: Evaluation of mobile 3D light detection and ranging based canopy mapping system for tree fruit crops. Comput. Electron. Agric. 158, 284–293 (2019). https://doi.org/10.1016/j.compag.2019.02.012
Nevavuori, P., Narra, N., Linna, P., Lipping, T.: Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models. Remote Sens. (Basel) 12, 4000 (2020). https://doi.org/10.3390/rs12234000
Chhikara, P., Tekchandani, R., Kumar, N., Chamola, V., Guizani, M.: DCNN-GA: a deep neural net architecture for navigation of UAV in indoor environment. IEEE Internet Things J. 8, 4448–4460 (2021). https://doi.org/10.1109/JIOT.2020.3027095
Bose, S., Mazumdar, A., Basu, S.: Evolution of groundwater quality assessment on urban area- a bibliometric analysis. Groundw. Sustain. Dev. 20, 100894 (2023). https://doi.org/10.1016/j.gsd.2022.100894
Miswan, M.S., Hamdan, R., Roffe, N.I., Wurochekke, A.A.: Land used mapping using unmanned aerial vehicle (UAV) along parit rasipan drainage system. Int. J. Sustain. Constr. Eng. Technol. 13 (2022). https://doi.org/10.30880/ijscet.2022.13.04.025
Park, S., Ryu, D., Fuentes, S., Chung, H., O’Connell, M., Kim, J.: Mapping very-high-resolution evapotranspiration from unmanned aerial vehicle (UAV) imagery. ISPRS Int J Geoinf. 10, 211 (2021). https://doi.org/10.3390/ijgi10040211
Tocci, F., et al.: Advantages in using colour calibration for orthophoto reconstruction. Sensors 22, 6490 (2022). https://doi.org/10.3390/s22176490
El Hoummaidi, L., Larabi, A., Alam, K.: Using unmanned aerial systems and deep learning for agriculture mapping in Dubai. Heliyon. 7, e08154 (2021). https://doi.org/10.1016/j.heliyon.2021.e08154
Tagarakis, A.C., Filippou, E., Kalaitzidis, D., Benos, L., Busato, P., Bochtis, D.: Proposing UGV and UAV systems for 3D mapping of orchard environments. Sensors 22, 1571 (2022). https://doi.org/10.3390/s22041571
Grau, J., et al.: Improved accuracy of riparian zone mapping using near ground unmanned aerial vehicle and photogrammetry method. Remote Sens (Basel). 13, 1997 (2021). https://doi.org/10.3390/rs13101997
de Camargo, T., Schirrmann, M., Landwehr, N., Dammer, K.-H., Pflanz, M.: Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops. Remote Sens. (Basel) 13, 1704 (2021). https://doi.org/10.3390/rs13091704
Deng, J., Zhong, Z., Huang, H., Lan, Y., Han, Y., Zhang, Y.: Lightweight semantic segmentation network for real-time weed mapping using unmanned aerial vehicles. Appl. Sci. 10, 7132 (2020). https://doi.org/10.3390/app10207132
Jiang, R., et al.: UAV-based partially sampling system for rapid NDVI mapping in the evaluation of rice nitrogen use efficiency. J. Clean. Prod. 289, 125705 (2021). https://doi.org/10.1016/j.jclepro.2020.125705
Manish, R., Lin, Y.-C., Ravi, R., Hasheminasab, S.M., Zhou, T., Habib, A.: Development of a miniaturized mobile mapping system for in-row, under-canopy phenotyping. Remote Sens. (Basel) 13, 276 (2021). https://doi.org/10.3390/rs13020276
López-Granados, F., et al.: Monitoring vineyard canopy management operations using UAV-acquired photogrammetric point clouds. Remote Sens. (Basel) 12, 2331 (2020). https://doi.org/10.3390/rs12142331
Feng, Q., et al.: Multi-temporal unmanned aerial vehicle remote sensing for vegetable mapping using an attention-based recurrent convolutional neural network. Remote Sens. (Basel). 12, 1668 (2020). https://doi.org/10.3390/rs12101668
Chew, R., et al.: Deep neural networks and transfer learning for food crop identification in UAV images. Drones. 4, 7 (2020). https://doi.org/10.3390/drones4010007
de Castro, A.I., et al.: Mapping cynodon dactylon infesting cover crops with an automatic decision tree-OBIA procedure and UAV imagery for precision viticulture. Remote Sens. (Basel) 12, 56 (2019). https://doi.org/10.3390/rs12010056
Gabrlik, P., Lazna, T., Jilek, T., Sladek, P., Zalud, L.: An automated heterogeneous robotic system for radiation surveys: design and field testing. J. Field Robot. 38, 657–683 (2021). https://doi.org/10.1002/rob.22010
Rangarajan, A.K., Balu, E.J., Boligala, M.S., Jagannath, A., Ranganathan, B.N.: A low-cost UAV for detection of Cercospora leaf spot in okra using deep convolutional neural network. Multimed Tools Appl. 81, 21565–21589 (2022). https://doi.org/10.1007/s11042-022-12464-4
Tian, Y., et al.: Search and rescue under the forest canopy using multiple UAVs. Int. J. Rob. Res. 39, 1201–1221 (2020). https://doi.org/10.1177/0278364920929398
Wang, T., Chen, B., Zhang, Z., Li, H., Zhang, M.: Applications of machine vision in agricultural robot navigation: a review. Comput. Electron. Agric. 198, 107085 (2022). https://doi.org/10.1016/j.compag.2022.107085
Yang, Z., et al.: UAV remote sensing applications in marine monitoring: knowledge visualization and review. Sci. Total Environ. 838, 155939 (2022). https://doi.org/10.1016/j.scitotenv.2022.155939
Valente, J., Hiremath, S., Ariza-Sentís, M., Doldersum, M., Kooistra, L.: Mapping of Rumex obtusifolius in nature conservation areas using very high resolution UAV imagery and deep learning. International J. Appl. Earth Observ. Geoinform. 112, 102864 (2022). https://doi.org/10.1016/j.jag.2022.102864
Nex, F., et al.: UAV in the advent of the twenties: where we stand and what is next. ISPRS J. Photogramm. Remote. Sens. 184, 215–242 (2022). https://doi.org/10.1016/j.isprsjprs.2021.12.006
Edulakanti, S.R., Ganguly, S.: Review article: the emerging drone technology and the advancement of the Indian drone business industry. J. High Technol. Manag. Res. 34, 100464 (2023). https://doi.org/10.1016/j.hitech.2023.100464
Stöcker, C., Bennett, R., Koeva, M., Nex, F., Zevenbergen, J.: Scaling up UAVs for land administration: towards the plateau of productivity. Land Use Policy 114, 105930 (2022). https://doi.org/10.1016/j.landusepol.2021.105930
Volovelsky, U.: Civilian uses of unmanned aerial vehicles and the threat to the right to privacy – An Israeli case study. Comput. Law Secur. Rev. 30, 306–320 (2014). https://doi.org/10.1016/j.clsr.2014.03.008
Kayad, A., et al.: How many gigabytes per hectare are available in the digital agriculture era? A digitization footprint estimation. Comput. Electron. Agric. 198, 107080 (2022). https://doi.org/10.1016/j.compag.2022.107080
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Escandón-Panchana, P., Herrera-Franco, G., Martínez Cuevas, S., Morante-Carballo, F. (2024). Prospects of UAVs in Agricultural Mapping. In: Florez, H., Leon, M. (eds) Applied Informatics. ICAI 2023. Communications in Computer and Information Science, vol 1874. Springer, Cham. https://doi.org/10.1007/978-3-031-46813-1_21
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