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Revista Ciencias Técnicas Agropecuarias

versión On-line ISSN 2071-0054

Rev Cie Téc Agr vol.33 no.1 San José de las Lajas ene.-mar. 2024  Epub 09-Dic-2023

 

REVIEW

Application of drones in international and Cuban agriculture. A review

0000-0001-6056-2601María Elena Ruiz PérezI  *  , 0000-0002-8950-0268Roberto García ReyesII  , 0000-0001-6294-6481Neili Machado GarcíaI 

IUniversidad Agraria de La Habana, San José de las Lajas, Mayabeque, Cuba.

IIMinisterio de la Agricultura, Departamento Provincial de Suelos y Fertilizantes, provincia Holguín, Cuba.

ABSTRACT

The introduction of different technologies such as Geographic Information Systems, images obtained from satellites, airplanes, drones, various types of sensors as well as computer systems and tools have caused a revolution in Agriculture. The use of these technologies has always had the interest of using available resources effectively and efficiently, as well as humanizing agricultural work. This article reviews the benefits obtained with the use of drones internationally and also in Cuba. However, works that propose the use of Precision Agriculture in Cuba have also been cited. It is observed that there are still few published works that describe in detail the results obtained that allow their reproducibility and there are many that describe them qualitatively. It is considered that in the particular case of drones, the extension of their use is still very expensive since in a practical way only the GEOCUBA Enterprise has all the infrastructure and trained staff for their most complete use, so different enterprise, institutions and farmers that wish to use them must make large disbursements.

Key words: UAV; Precision Agriculture

INTRODUCTION

Agriculture is the largest consumer of water globally and it is expected that the demand for food and water will increase dramatically in the near future (Rejeb et al., 2022). Furthermore, the increasing consumption of fertilizers and pesticides, together with the intensification of agricultural activities, could generate future environmental challenges. Similarly, arable land is limited and the number of farmers is declining around the world. These challenges accentuate the need for innovative and sustainable agricultural solutions (Tzounis et al., 2017; Elijah et al., 2018; Inoue, 2020; Friha et al., 2021).

The incorporation of new technologies has been identified as a promising option to address these challenges. The so-called Smart Agriculture Brewster et al. (2017); Tang et al. (2021) and precision agriculture Feng et al. (2019); Khanna & Kaur (2019) have emerged as a result of such issues. The first introduces Information and Communication Technologies (ICT) and other cutting-edge innovations in agricultural activities to increase efficiency and effectiveness (Haque et al., 2021). For its part, precision agriculture focuses on site-specific management by dividing the land into homogeneous parts, and each part receives the exact amount of input it requires to optimize crop performance through novel technologies (Feng et al., 2019; Khanna & Kaur, 2019). Among the technologies that have attracted the attention of scholars in this field are wireless sensor networks (WSNs) Zhou et al. (2016); Zheng & Yang (2018), Internet of Things (IoT) Gill et al. (2017); Liu et al. (2019); 2019; He et al. (2021), artificial intelligence (AI) techniques, including machine learning and deep learning Liakos et al. (2018); Shadrin et al. (2019); Parsaeian et al. (2020), information technologies (Jinbo et al. (2019); Zamora-Izquierdo et al. (2019); Hsu et al. (2020),, Big data Gill et al. (2017); Tantalaki et al. (2019) and blockchains (Khan et al., 2020; Pincheira et al., 2021).

In addition to the aforementioned technologies, remote sensing has been considered a technological tool with high potential to improve smart and precision agriculture. Satellites, human-manned aircraft, and drones are popular remote sensing technologies (Tsouros et al., 2019). Drones, known as unmanned aerial vehicles (UAV), unmanned aircraft systems (UAS) and remotely piloted aircraft are of great importance as they have multiple advantages compared to other remote sensing technologies. For example, drones can deliver high-quality, high-resolution images on cloudy days (Manfreda et al., 2018). Furthermore, its availability and transfer speed constitute other benefits (Radoglou-Grammatikis et al., 2020). Compared to airplanes, drones are very cost-effective and easy to set up and maintain (Tsouros et al., 2019).

In Cuba for several years, drone flights have been carried out with different objectives; however, the information obtained for agriculture has still been disseminated in most cases, more as an advertising impact than through reports or scientific publications that can be replicated by other researchers. The objective of this article is to provide summary information on the different uses that drones have had in agriculture internationally; their deficiencies and what they have been so far, their use in Cuba, as well as the challenges faced the extent of its use.

THE DRONES

A drone is a device that can fly on a preset course with the help of an autopilot and GPS coordinates. The device also has normal radio controls. It can be piloted manually in case of breakdown or dangerous situation. Sometimes the term drone is used to refer to the entire system, including ground stations and video systems, however, the term is more commonly used for fixed- or rotary-wing model airplanes and helicopters (Ahirwar et al., 2019). Different types of sensors such as accelerometers, gyroscopes, GPS and barometers can be installed on drones to carry out georeferenced measurements. It is also very common for them to carry cameras to take aerial photographs and videos. The cameras can be of different types depending on the interest in the flight and can be very expensive. Ahirwar et al. (2019) states that drones are classified according to their weight, autonomy, altitude and the radius in which they operate; for civil use, the types shown in Table 1 can mainly be found.

TABLE 1 Classification of drones for civil use (adapted from Acharya et al. (2021))  

Category Weigh (kg) Altitude (m) (asl1) Radius (km) Autonomy (h)
Micro <2 until 70 <5 <1
Mini 2-20 until 915 <25 1-2
Little 20-150 until 1524 <50 1-5

1 above sea level

Despite the advantages that arise, the use of drones also has deficiencies associated among others, with the following aspects: the preparation of the pilot who flies it, the quality of the images obtained, the costs of implementation, its stability, maneuverability and reliability, the engine power that may be limited for certain tasks, the type of battery and its durability, the limitation in flight time, the limitations for data processing, its load capacity, the lack of regulations and the lack of experience (Laliberte et al., 2007; Nebiker et al., 2008; Hardin & Hardin, 2010; Hardin & Jensen, 2011; Laliberte & Rango, 2011; Zhang & Kovacs, 2012; Puri et al., 2017; Lagkas et al., 2018; Manfreda et al., 2018; Dawaliby et al., 2020; Velusamy et al., 2021; Bacco, et al., (2018)..

USE OF DRONES IN AGRICULTURE

Although they were initially used mainly for military purposes, drones can be used in agriculture, with the Japanese being the first to successfully use them for fumigation in the 1980s (Nonami, 2007). Their use has expanded as they can be linked with novel technologies, computing capabilities and integrated sensors to support crop management (e.g. mapping, monitoring, irrigation, plant diagnosis, disaster reduction, early warning systems, wildlife and forest conservation, to name a few (Negash et al., 2019). Similarly, drones could be leveraged in various agricultural activities, including crop and growth monitoring, yield estimation , evaluation of water stress and weeds, pests and disease detection (Inoue, 2020; Panday et al., 2020). They can not only be used for monitoring, estimation and detection purposes based on their sensory data, but also for precision in irrigation and the management of weeds, pests and diseases. In other words, drones are capable of applying water and pesticides in precise quantities according to environmental conditions.

Hunt Jr & Daughtry (2018) state that agricultural tasks with drones can be grouped into three lines: (1) problem exploration, (2) monitoring to prevent yield losses and (3) planning management operations. Each of these lines has different requirements regarding the type of sensor to be used and its calibration, which defines the operating costs. According to this author, line (3) is the most economical, however, in the United States, the majority of farmers still do not obtain benefits from the use of drones for planning management operations. Table 2 shows the requirements for each of these lines.

TABLE 2 Drone requirements for the three lines of use for agriculture (Hunt Jr & Daughtry, 2018

Characteristic Exploration Monitoring Planning
Sensors Camera (visible, Thermal) Multispectral Multispectral, Hyperspectral
Sensor calibration Not required Sight rigorous
Covered area Specific locations Entire field Entire field
Output Georeferenced photos Geographic information system of the Field Decision-making system
Spatial precision low medium high
Orthomosaic is required no It depends on the product yes
Time immediate 1 or 2 days From 3 days to 3 months
Cost low medium high
Economic benefit Not considerable Depends on the action taken Better economic rates
Environmental benefit Not considerable Depends on the action taken Greater reduction of agrochemicals
Aplicacions Check problem locations in the field Potential yield, occurrences of pests, diseases and weeds Variable rate applications

According to Rejeb et al. (2022) Table 3 summarizes some of the benefits of drones in agriculture.

TABLE 3 Some of the benefits of using drones in Agriculture Rejeb et al. (2022)  

Benefit References
Improve spatial and temporal resolution (Gago et al., 2015; Niu et al., 2020; Srivastava et al., 2020)
Facilitate Precision Agriculture (Maimaitijiang et al., 2017; Deng et al., 2018; Kalischuk et al., 2019)
Crop classification and exploration (López-Granados et al., 2016; Moharana y Dutta, 2016; Maimaitijiang et al., 2017; Kalischuk et al., 2019; Melville et al., 2019; Inoue, 2020).
Fertilizer use (Deng et al., 2018; Guan et al., 2019)
Drought monitoring (Su et al., 2018a; Fawcett et al., 2020; Panday et al., 2020)
Biomass estimation (Bendig et al., 2014)
Yield estimation (Inoue, 2020; Panday et al., 2020; Tao et al., 2020)
Disaster reduction (Negash et al., 2019)
Wildlife and forest conservation (Negash et al., 2019; Panday et al., 2020)
Water stress assessment (Su et al., 2018a; Zhang et al., 2019; Inoue, 2020)
Detection of pests, diseases and weeds (Su et al., 2018b; Zhang et al., 2019; Gašparović et al., 2020; Inoue, 2020)

USE OF DRONES IN CUBA

Although at an international level, there is already a distinction between Smart Agriculture and Precision Agriculture, the terminology most used in Cuba has been Precision Agriculture and different papers use tools that are part of it, such as Geographic Information Systems (GIS), Global Positioning Systems (GPS), tractors with computer and sensory equipment for their management, satellite images of different types and drones. However, there are not many scientific articles that have been published, as well as participation in scientific conferences. In the particular case of the use of drones, the articles have been mainly informative with a view to promoting the advantages of using them. The press releases promoting precision agriculture tools are numerous, but logically no technical details of these applications are given. However, their growing popularity, both internationally and nationally, requires frequent reviews of their applications. Although the main objective of this work is a review of the use of drones, publications that use the term Precision Agriculture have also been incorporated with a view to giving a more complete idea of the gradual incorporation of new technologies in Cuban Agriculture.

Hernández et al. (2006) has been the first publication found in which elements of precision agriculture are applied. The investigation was carried out on farm No 101; belonging to the Basic Cooperative Production Unit (UBPC) "La Julia" of the Various Crops Company (ECV), "Batabanó", in the area of an electric central pivot irrigation machine planted with potatoes. The general objective of the research was to propose recommendations for the differentiated application of fertilizers by quadrants for potato cultivation. In the area under study, it was necessary to carry out a study of the fertility and chemical environment of the soil. The applied methodology allowed us to determine the main chemical characteristics of the soil, demonstrating the differences that exist from one quadrant to another. The differentiated fertilization doses for potato cultivation on the aforementioned farm were also calculated, which if implemented, would guarantee a saving of 16.21 t of NPK (9-13-17) and 1.16 t of UREA (46 -0-0), meaning a decrease in the production cost for the ECV by $4,988.53 (MN) and a saving for the Cuban Ministry of Agriculture (MINAG) of $3,278.51 (USD). It is not known if the results were applied.

Lago-González et al. (2011) give an explanation of what precision agriculture is and what its main components are. The authors developed a System for the Generation of Performance Maps that, according to them, was one of the first at an international level. In addition, they explain how the proposed application can be used. Curiously, the system test was carried out in 2007 in Australia due to the existence of machines there where the System for generating performance maps created could be tested. The obtained maps are shown.

Lora (2015) applies GPS and GIS to evaluate the energy consumption of agricultural machinery in the “Niña Bonita” Livestock Company, obtaining that total energy expenses decrease by 16%

Almeida-Maldonado et al. (2017) developed a website with a view to efficiently managing irrigation. Python language was used for its implementation, mainly because it is very flexible; Its code is readable and well organized, which makes maintenance work and further development easier; In addition, it allows the use of libraries in C and C++, which can be used to offer complex functionalities for which creating a library from scratch could be very expensive. Web2Py was used as the development framework, among other reasons, because it offers a very organized structure and syntax.

Sosa-Escalona et al. (2017) present AgroAlert, a tool for predicting the effects of climate change on agriculture. Which provides early warnings of drought in specific crop fields three to six months in advance. AgroAlert is responsible for the organization, storage, handling, analysis and modeling of agroclimatic conditions. Describes the most vulnerable crop areas in terms of soil water conditions and level of salinization. Likewise, it offers the possibility of varying the criteria under which these areas are identified and carrying out the analysis and prediction of the risks.

Guillén et al. (2020) provides a comprehensive review of the origin of drones in the North American military industry. Subsequently, it defines Precision Agriculture and how drones are a tool that allows many of its applications to be carried out. Provides information on types of drones and cameras, as well as the type of images obtained, citing examples of applications abroad. It also establishes the advantages and disadvantages of its use. Among the latter, the cost, interference in airspace, climate and the need for specialized personnel to analyze the images obtained. Finally, the role of the GARP (Automation, Robotics and Perception Group of the Faculty of Electrical Engineering) at the UCLV (Central University “Marta Abreu” of Las Villas) who have carried out the construction of drones as well as the development of the necessary software.

Ríos-Hernández (2021) makes a review of some of the technologies used in Precision Agriculture such as satellite images, autonomous driving machinery, drones, the location of sensors in plots, soil maps, Geographic Information Systems and offers qualitative examples of the results obtained. It states that drones and other tools have been used to identify pests in Cuban sugarcane fields since 2009 in the Jesús Rabí base business unit (UEB) in Matanzas. It is reported that GEOCUBA (Business group formed by the integration of the Cuban Institute of Hydrography and the Cuban Institute of Geodesy and Cartography), INICA (Sugar Cane Research Institute) and CENPALAB (National Center for Production of Laboratory Animals) have participated in these works). However, no bibliographic references are offered where the quantitative results have been published.

Matamoros et al. (2022) present perhaps the most complete work where the use of different types of drones and also other types of images has been decisive for the development of a computer platform for image processing, specialized cartography and Artificial Intelligence algorithms that are applied to satellite and drone images. The main investigations were carried out in the Sur del Jíbaro rice complex located in the La Sierpe municipality of the Province of Sancti Spíritus. Based on prior knowledge of the crop sowing plans, these were incorporated into the cartography to spatially know the entire distribution of fields, their sowing dates and planned tasks with a view to planning drone flights. Three types of drones were used (Phantom 4 Advanced, Delta Sky Walker X8 and Agras MG-1P Drone) with flight ranges of 30 minutes, 1h 30 minutes and 15 minutes respectively. To process the data, the Agisoft Metashape, Pix4dMapper and IA Tierra software were used, developed by specialists from the GEOCUBA Scientific and Technical Research and Consulting Unit. The authors of the research suggest that systematic monitoring of large areas of rice-planted area through the use of drones is complex due to the flight capacity and processing required. This led to the proposal of combined use of 10 meter resolution Sentinel images and greater exploitation of vegetation indices. The images from this satellite allow the evaluation of the state of the crop every 5 days and the detection of anomalies due to the state of humidity or vegetation. In this way, drone surveys are carried out at certain times of the phenological state of the crops and for the detailed study of areas of anomalies. A service that was widely accepted by producers was fumigation using drones. It is noteworthy that Geo Cuba has already created a drone infrastructure, access to satellite images and computer specialists that make the application of all these technologies possible. However, its use by other companies and private producers would imply prohibitive costs.

Finally, Sosa-Franco et al. (2023) discusses the necessary tools of what could be a “smart farm” in the future, creating a computer system that allows automated management through a GIS, images, data of different formats, the information produced by an agricultural farm, as well as such as the possibility of making inquiries about it by non-specialized personnel.

CONCLUSIONS

There is a dizzying growth at the international level in the use of technologies such as Geographic Information Systems, images from satellites, airplanes and lately, very numerously, drones, as well as the link to computer tools and sensors of different types. The above has led to benefits such as improving the spatial and temporal resolutions of agricultural studies, facilitating Precision Agriculture, classifying and exploring crops, applying fertilizers and water efficiently, monitoring drought, estimating biomass and yields, detection of pests, diseases and weeds, disaster reduction and conservation of wildlife and forests. In Cuba, it is observed that there are still few published works that describe in detail the results obtained that allow their reproducibility and there are many that describe them qualitatively. It is considered that, in the particular case of drones, the extension of their use is still very expensive since in a practical way only the GEOCUBA Company has all the infrastructure and trained personnel for their most complete use, so different companies and institutions those who wish to employ them must make large outlays.

REFERENCES

ACHARYA, B.S.; BHANDARI, M.; BANDINI, F.; PIZARRO, A.C.E.; PERKS, M.; JOSHI, D.R.; WANG, S.; DOGWILER, T.; RAY, R.L.; KHAREL, G.: “Unmanned aerial vehicles in hydrology and water management: Applications, challenges, and perspectives”, Water Resources Research, 57(11), 2021, ISSN: 0043-1397. [ Links ]

AHIRWAR, S.; SWARNKAR, R.; BHUKYA, S.; NAMWADE, G.: “Application of drone in agriculture”, International Journal of Current Microbiology and Applied Sciences, 8(01): 2500-2505, 2019, DOI: https://doi.org/10.20546/ijcmas.2019.801.264. [ Links ]

ALMEIDA-MALDONADO, E.; CAMEJO-BARREIRO, L.E.; SANTIESTEBAN-TOCA, C.E.: “La fertirrigación inteligente, pilar de una agricultura sostenible”, Revista Cubana de Ciencias Informáticas, 11(3): 36-49, 2017, ISSN: 2227-1899. [ Links ]

BACCO, M.; BERTON, A.; FERRO, E.; GENNARO, C.; GOTTA, A.; MATTEOLI, S.; PAONESSA, F.; RUGGER, M.; VIRONE, G.; ZANELLA, A.: “vSmart farming: Opportunities, challenges and technology enablers. 2018 IoT Vertical and Topical Summit on Agriculture -Tuscany”, IOT Tuscany, : 1-6, 2018, DOI: https://doi.org/10.1109/IOTTUSCANY.2018.8373043. [ Links ]

BENDIG, J.; BOLTEN, A.; BENNERTZ, S.; BROSCHEIT, J.; EICHFUSS, S.; BARETH, G.: “Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging”, Remote sensing, 6(11): 10395-10412, 2014, ISSN: 2072-4292. [ Links ]

BREWSTER, C.; ROUSSAKI, I.; KALATZIS, N.; FUKAMI, K.; ELLIS, K.: “IoT in agriculture: Designing a Europe-wide large-scale pilot”, IEEE communications magazine, 55(9): 26-33, 2017, ISSN: 0163-6804. [ Links ]

DAWALIBY, S.; ABERKANE, A.; BRADAI, A.: “Blockchain-based IoT platform for autonomous drone operations management”, En: Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and beyond, pp. 31-36, 2020, DOI: . https://doi.org/10.1145/3414045.3415939. [ Links ]

DENG, L.; MAO, Z.; LI, X.; HU, Z.; DUAN, F.; YAN, Y.: “UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras”, ISPRS journal of photogrammetry and remote sensing, 146: 124-136, 2018, ISSN: 0924-2716. [ Links ]

ELIJAH, O.; RAHMAN, T.A.; ORIKUMHI, I.; LEOW, C.Y.; HINDIA, M.N.: “An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges”, IEEE Internet of things Journal, 5(5): 3758-3773, 2018, ISSN: 2327-4662. [ Links ]

FAWCETT, D.; PANIGADA, C.; TAGLIABUE, G.; BOSCHETTI, M.; CELESTI, M.; EVDOKIMOV, A.; BIRIUKOVA, K.; COLOMBO, R.; MIGLIETTA, F.; RASCHER, U.: “Multi-scale evaluation of drone-based multispectral surface reflectance and vegetation indices in operational conditions.”, Rem. Sens., 12(3): 514, 2020. [ Links ]

FENG, X.; YAN, F.; LIU, X.: “Study of wireless communication technologies on Internet of Things for precision agriculture”, Wireless Personal Communications, 108(3): 1785-1802, 2019, ISSN: 0929-6212. [ Links ]

FRIHA, O.; FERRAG, M.A.; SHU, L.; MAGLARAS, L.; WANG, X.: “Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies”, IEEE/CAA Journal of Automatica Sinica, 8(4): 718-752, 2021, ISSN: 2329-9266. [ Links ]

GAGO, J.; DOUTHE, C.; COOPMAN, R.E.; GALLEGO, P.P.; RIBAS-CARBO, M.; FLEXAS, J.; ESCALONA, J.; MEDRANO, H.: “UAVs challenge to assess water stress for sustainable agriculture”, Agricultural water management, 153: 9-19, 2015, ISSN: 0378-3774. [ Links ]

GAŠPAROVIĆ, M.; ZRINJSKI, M.; BARKOVIĆ, D.; RADOČAJ, D.: “An automatic method for weed mapping in oat fields based on UAV imagery”, Computers and Electronics in Agriculture, 173: 105-385, 2020, ISSN: 0168-1699. [ Links ]

GILL, S.S.; CHANA, I.; BUYYA, R.: “IoT based agriculture as a cloud and big data service: the beginning of digital India”, Journal of Organizational and End User Computing (JOEUC), 29(4): 1-23, 2017. [ Links ]

GUAN, S.; FUKAMI, K.; MATSUNAKA, H.; AL-ZAHRANI, M.; TANAKA, R.; NAKANO, H.; SAKAI, T.; NAKANO, K.; CHOI, H.-L.; TAKAHASHI, K.: “Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs”, Remote Sensing, 11(2): 112, 2019, ISSN: 2072-4292. [ Links ]

GUILLÉN, L.; YASELIS, P.P.; MOLINA, O.: “Drones, aplicaciones en la Agricultura de Precisión: una revisión”, Rev. Agricultura Tropical, 6(2): 1-11, 2020, ISSN: 2517-9292. [ Links ]

HAQUE, A.; ISLAM, N.; SAMRAT, N.H.; DEY, S.; RAY, B.: “Smart farming through responsible leadership in bangladesh: possibilities, opportunities, and beyond”, Sustainability, 13(8): 4511, 2021. [ Links ]

HARDIN, P.J.; HARDIN, T.J.: “Small‐scale remotely piloted vehicles in environmental research”, Geography Compass, 4(9): 1297-1311, 2010, ISSN: 1749-8198, DOI: https://doi.org/10.1111/j.1749-8198.2010.00381.x. [ Links ]

HARDIN, P.J.; JENSEN, R.R.: “Small-scale unmanned aerial vehicles in environmental remote sensing: Challenges and opportunities”, GIScience & Remote Sensing, 48(1): 99-111, 2011, ISSN: 1548-1603, DOI: . https://doi.org/10.2747/1548-1603.48.1.99. [ Links ]

HE, Y.; NIE, P.; ZHANG, Q.; LIU, F.: Agricultural Internet of Things: technologies and applications, Ed. Springer, (1st ed. 2021 edition). ed., 2021. [ Links ]

HERNÁNDEZ, P.P.; HERNÁNDEZ, A.P.; VARGAS, R.H.; ZAMORA, H.Y.; DOPICO, V.Y.: “Determinación de normas de fertilización diferenciada para el cultivo de la papa empleando técnicas de agricultura de precisión.”, Revista Ciencias Técnicas Agropecuarias, 15(1), 2006, ISSN: 2071-0054. [ Links ]

HSU, sT-C.; YANG, H.; CHUNG, Y.; HSU, C.: “A creative iot agriculture platform for cloud fog computing, Sustain”, Comput. Inf. Syst, 28: 100-285, 2020. [ Links ]

HUNT JR, R.; DAUGHTRY, C.S.: “What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?”, International journal of remote sensing, 39(15-16): 5345-5376, 2018, ISSN: 0143-1161, 5345-5376, DOI: 10.1080/01431161.2017.1410300. [ Links ]

INOUE, Y.: “Satellite-and drone-based remote sensing of crops and soils for smart farming-a review”, Soil Science and Plant Nutrition, 66(6): 798-810, 2020, ISSN: 0038-0768, DOI: https://doi.org/10.1080/00380768.2020.1738899. [ Links ]

JINBO, C.; XIANGLIANG, C.; BANDINI, F.; LAM, A.: “Agricultural product monitoring system supported by cloud computing”, Cluster Computing, 22(4): 8929-8938, 2019, ISSN: 1386-7857. [ Links ]

KALISCHUK, M.; PARET, M.L.; FREEMAN, J.H.; RAJ, D.; DA SILVA, S.; EUBANKS, S.; WIGGINS, D.; LOLLAR, M.; MAROIS, J.J.; MELLINGER, C.H.: “An improved crop scouting technique incorporating unmanned aerial vehicle-assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon”, Plant disease, 103(7): 1642-1650, 2019, ISSN: 0191-2917, 1642-1650. [ Links ]

KHAN, P.W.; BYUN, Y.-C.; NAMJE P N: “IoT-blockchain enabled optimized provenance system for food industry 4.0 using advanced deep learning”, Sensors, 20(10): 2990, 2020, ISSN: 1424-8220. [ Links ]

KHANNA, A.F.; KAUR, S.: “Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture”, Computers and electronics in agriculture, 157: 218-231, 2019, ISSN: 0168-1699. [ Links ]

LAGKAS, T.; ARGYRIOU, V.; BIBI, S.; SARIGIANNIDIS, P.: “UAV IoT framework views and challenges: Towards protecting drones as “Things””, Sensors, 18(11): 4015, 2018, ISSN: 1424-8220, DOI: https://doi.org/10.3390/s18114015. [ Links ]

LAGO-GONZÁLEZ, C.; SEPÚLVEDA-PEÑA, J.C.; BARROSO-ABREU, R.; FERNÁNDEZ-PEÑA, F.O.; MACIÁ-PÉREZ, F.; LORENZO, J.: “Sistema para la generación automática de mapas de rendimiento. Aplicación en la agricultura de precisión”, Idesia (Arica), 29(1): 59-69, 2011, ISSN: 0718-3429. [ Links ]

LALIBERTE, A.S.; RANGO, A.: “Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands”, GIScience & Remote Sensing, 48(1): 4-23, 2011, ISSN: 1548-1603, DOI: https://doi.org/10.2747/1548-1603.48.1.4. [ Links ]

LALIBERTE, A.S.; RANGO, A.; HERRICK, J.: “Unmanned aerial vehicles for rangeland mapping and monitoring: A comparison of two systems”, En: ASPRS Annual Conference Proceedings, 2007. [ Links ]

LIAKOS, K.G.; BUSATO, P.; MOSHOU, D.; PEARSON, S.; BOCHTIS, D.: “Machine learning in agriculture: A review”, Sensors, 18(8): 2674, 2018, ISSN: 1424-8220. [ Links ]

LIU, S.; GUO, L.; WEBB, H.; YA, X.; CHANG, X.: “Internet of Things monitoring system of modern eco-agriculture based on cloud computing”, Ieee Access, 7: 37050-37058, 2019, ISSN: 2169-3536. [ Links ]

LÓPEZ-GRANADOS, F.; TORRES-SÁNCHEZ, J.; SERRANO-PÉREZ, A.; DE CASTRO, A.I.; MESAS-CARRASCOSA, F.J.; PEÑA, J.M.: “Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds”, Precision agriculture, 17: 183-199, 2016, ISSN: 1385-2256. [ Links ]

LORA, C.D.: “Consumo energético de la maquinaria agrícola con el empleo de técnicas de agricultura de precisión”, Revista Ingeniería Agrícola, 5(2), 2015. [ Links ]

MAIMAITIJIANG, M.; GHULAM, A.; SIDIKE, P.; HARTLING, S.; MAIMAITIYIMING, M.; PETERSON, K.; SHAVERS, E.; ARCIA, J.; PETERSON, J.; KADAM, S.: “Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine”, ISPRS Journal of Photogrammetry and Remote Sensing, 134: 43-58, 2017, ISSN: 0924-2716, DOI: https://doi.org/10.1016/j.isprsjprs.2017.10.011. [ Links ]

MANFREDA, S.; MCCABE, M.F.; MILLER, P.E.; LUCAS, R.; PAJUELO-MADRIGAL, V.; MALLINIS, G.; BEN-DOR, E.; HELMAN, D.; ESTES, L.; CIRAOLO, G.: “On the use of unmanned aerial systems for environmental monitoring”, Remote sensing, 10(4): 641, 2018, ISSN: 2072-4292. [ Links ]

MATAMOROS, C.P.; GARCÍA, R.E.; SOTO, M.F.; MENÉNDEZ, H.P.; MARTÍNEZ, S.F.; CRUZ, I.R.; CAPOTE, F.J.L.; OJEDA, M.D.; MENESES, D.P.; RODRIGUEZ, Q.B.; VALDIVIA, P.O.: “Agricultura de Precisión aplicada a la producción de arroz en Cuba”, En: Informática 2022. XVIII Convención y Feria Internacional. XII Congreso Internacional Geomática, La Habana 21-25 de marzo 2022, La Habana, Cuba, 2022. [ Links ]

MELVILLE, B.; LUCIEER, A.; ARYAL, J.: “Classification of lowland native grassland communities using hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian midlands”, Drones, 3(1): 5, 2019, ISSN: 2504-446X. [ Links ]

MOHARANA, S.; DUTTA, S.: “Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery”, ISPRS journal of photogrammetry and remote sensing, 122: 17-29, 2016, ISSN: 0924-2716. [ Links ]

NEBIKER, S.; ANNEN, A.; SCHERRER, M.; OESCH, D.: “A light-weight multispectral sensor for micro UAV-Opportunities for very high resolution airborne remote sensing”, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 37(B1): 1193-1200, 2008. [ Links ]

NEGASH, L.; KIM, H.-Y.; CHOI, H.-L.: “Emerging UAV applications in agriculture”, En: 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA), Ed. IEEE, pp. 254-257, 2019, DOI: https://doi.org/10.1109/RITAPP.2019, ISBN: 1-72813-118-9. [ Links ]

NIU, H.; HOLLENBECK, D.; ZHAO, T.; WANG, D.; CHEN, Y.Q.: “Evapotranspiration estimation with small UAVs in precision agriculture”, Sensors, 20(22): 6427, 2020, ISSN: 1424-8220, DOI: https://doi.org/10.3390/s20226427. [ Links ]

NONAMI, K.: “Prospect and recent research & development for civil use autonomous unmanned aircraft as UAV and MAV”, Journal of system Design and Dynamics, 1(2): 120-128, 2007, ISSN: 1881-3046. [ Links ]

PANDAY, U.; PRATIHAST, A.; ARYAL, J.: “A review on drone-based data solutions for cereal crops.”, Drones, 4(3): 1-29, 2020, DOI: https://doi.org/10.3390/ drones 403004. [ Links ]

PARSAEIAN, M.; SHAHABI, M.; HASSANPOUR, H.: “Estimating oil and protein content of sesame seeds using image processing and artificial neural network”, Journal of the American Oil Chemists’ Society, 97(7): 691-702, 2020, ISSN: 0003-021X. [ Links ]

PINCHEIRA, M.; VECCHIO, M.; GIAFFREDA, R.; KANHERE, S.S.: “Cost-effective IoT devices as trustworthy data sources for a blockchain-based water management system in precision agriculture”, Computers and Electronics in Agriculture, 180: 105889, 2021, ISSN: 0168-1699. [ Links ]

PURI, V.; NAYYAR, A.; RAJA, L.: “Agriculture drones: A modern breakthrough in precision agriculture”, Journal of Statistics and Management Systems, 20(4): 507-518, 2017, ISSN: 0972-0510. [ Links ]

RADOGLOU-GRAMMATIKIS, P.; SARIGIANNIDIS, P.; LAGKAS, T.; BOSCH, I.: “A compilation of UAV applications for precision agriculture”, Computer Networks, 172: 107148, 2020, ISSN: 1389-1286, DOI: https://doi.org/10.1016/j.comnet.2020.107148. [ Links ]

REJEB, A.; ABDOLLAHI, A.; REJEB, K.; TREIBLMAIER, H.: “Drones in agriculture: A review and bibliometric analysis”, Computers and electronics in agriculture, 198: 107017, 2022, ISSN: 0168-1699. [ Links ]

RÍOS-HERNÁNDEZ, R.: “La Agricultura de Precisión. Una necesidad actual”, Revista Ingeniería Agrícola, 11(1): 67-74, 2021, ISSN: 2306-1545, e-ISSN-2227-8761. [ Links ]

SHADRIN, D.; MENSHCHIKOV, A.; SOMOV, A.; BORNEMANN, G.; HAUSLAGE, J.; FEDOROV, M.: “Enabling precision agriculture through embedded sensing with artificial intelligence”, IEEE Transactions on Instrumentation and Measurement, 69(7): 4103-4113, 2019, ISSN: 0018-9456. [ Links ]

SOSA-ESCALONA, Y.; PEÑA CASADEVALLS, M.; SANTIESTEBAN-TOCA, C.E.: “Sistema para la alerta temprana de los efectos del cambio climático en la agricultura”, Revista Cubana de Ciencias Informáticas, 11(3): 64-76, 2017, ISSN: 2227-1899. [ Links ]

SOSA-FRANCO, I.; PÉREZ-GUERRA, G.; MACHADO-GARCÍA, N.; PÉREZ-RUIZ, M.E.: “Método para el procesamiento de consultas en un Sistema de Información Geográfica”, Revista Ciencias Técnicas Agropecuarias, 32(2, (April-June)), 2023, ISSN: 2071-0054. [ Links ]

SRIVASTAVA, K.; PANDEY, P.C.; SHARMA, J.K.: “An approach for route optimization in applications of precision agriculture using UAVs”, Drones, 4(3): 58, 2020, ISSN: 2504-446X. [ Links ]

SU, J.; COOMBES, M.; LIU, C.; GUO, L.; CHEN, W.-H.: “Wheat drought assessment by remote sensing imagery using unmanned aerial vehicle”, En: 2018 37th Chinese Control Conference (CCC), Ed. IEEE, pp. 10340-10344, 2018a, ISBN: 988-15639-5-X. [ Links ]

SU, J.; LIU, C.; AMADO, M.E.; HU, X.; WANG, C.; XU, X.; LI, Q.; GUO, L.; CHEN, W.-H.: “Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery”, Computers and electronics in agriculture, 155: 157-166, 2018b, ISSN: 0168-1699, DOI: https://doi.org/10.1016/j.compag.2018.10.017. [ Links ]

TANG, Y.; DANANJAYAN, S.; HOU, C.; GUO, Q.; LUO, S.; HE, Y.: “A survey on the 5G network and its impact on agriculture: Challenges and opportunities”, Computers and Electronics in Agriculture, 180: 105895, 2021, ISSN: 0168-1699. [ Links ]

TANTALAKI, N.; SOURAVLAS, S.; ROUMELIOTIS, M.: “Data-driven decision making in precision agriculture: The rise of big data in agricultural systems”, Journal of agricultural & food information, 20(4): 344-380, 2019, ISSN: 1049-6505. [ Links ]

TAO, H.; CHOI, H.-L.; XU, L.; MIAO, M.; YANG, G.; YANG, X.; FAN, L.: “Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images”, Sensors, 20(4): 1231, 2020, ISSN: 1424-8220. [ Links ]

TSOUROS, D.; BIBI, S.; SARIGIANNIDIS, P.: “A review on UAV-based applications for precision agriculture”, Information, 10(11): 349, 2019, ISSN: 2078-2489, DOI: https://doi.org/10.3390/info10110349. [ Links ]

TZOUNIS, A.F.; KATSOULAS, N.; BARTZANAS, T.; KITTAS, C.: “Internet of Things in agriculture, recent advances and future challenges”, Biosystems engineering, 164: 31-48, 2017, ISSN: 1537-5110, DOI: https://doi.org/10.1016/j.biosystemseng.2017.09.007. [ Links ]

VELUSAMY, P.; BARTH, S.R.; MAHENDRAN, R.; NASEER, S.; AMADO, M.E.; CHOI, J.-G.: “Unmanned Aerial Vehicles (UAV) in precision agriculture: Applications and challenges”, Energies, 15(1): 217, 2021, ISSN: 1996-1073, DOI: https://doi.org/10.3390/en15010217. [ Links ]

ZAMORA-IZQUIERDO, M.A.; SANTA, J.; MARTÍNEZ, J.A.; MARTÍNEZ, V.; SKARMETA, A.F.: “Smart farming IoT platform based on edge and cloud computing”, Biosystems engineering, 177: 4-17, 2019, ISSN: 1537-5110. [ Links ]

ZHANG, C.; KOVACS, J.M.: “The application of small unmanned aerial systems for precision agriculture: a review”, Precision agriculture, 13: 693-712, 2012, ISSN: 1385-2256, DOI: https://doi.org/10.1007/s11119-012-9274-5. [ Links ]

ZHANG, L.; ZHANG, H.; NIU, Y.; HAN, W.: “Mapping maize water stress based on UAV multispectral remote sensing”, Remote Sensing, 11(6): 605, 2019, ISSN: 2072-4292. [ Links ]

ZHENG, J.; YANG, W.: “Design of a Precision Agriculture Leakage Seeding System Based on Wireless Sensors.”, International Journal of Online Engineering, 14(5), 2018, ISSN: 1868-1646. [ Links ]

ZHOU, Y.; XIE, Y.; SHAO, L.: “Simulation of the core technology of a greenhouse monitoring system based on a wireless sensor network”, Int. J. Online Eng, 12(05): 43, 2016. [ Links ]

Received: June 05, 2023; Accepted: December 09, 2023

*Author for correspondence: María Elena Ruiz Pérez, e-mail: mruiz@unah.edu.cu

María Elena Ruiz-Pérez. Dr.C., Profesora Titular, Universidad Agraria de La Habana “Fructuoso Rodríguez Pérez”. Carretera Tapaste y Autopista Nacional km 231/2, San José de Las Lajas, Mayabeque, Cuba. CP 32700, e-mail: mruiz@unah.edu.cu

Roberto Alejandro García-Reyes. Ing., Inv., Ministerio de la Agricultura, Departamento Provincial de Suelos y Fertilizantes, provincia Holguín, Cuba, e-mail: ralejandro9409@gmail.com

Neili Machado-García. Dr.C., Profesora Titular, Universidad Agraria de La Habana “Fructuoso Rodríguez Pérez”. Carretera Tapaste y Autopista Nacional km 23 1/2, San José de Las Lajas, Mayabeque, Cuba. CP 32700, e-mail: neili@unah.edu.cu

The authors of this work declare no conflict of interests.

AUTHOR CONTRIBUTIONS: Conceptualization: María Elena Ruiz. Data curation: María Elena Ruiz, Roberto García, Neili Machado. Formal Analysis: María Elena Ruiz, Roberto García, Neili Machado. Investigation: María Elena Ruiz, Roberto García, Neili Machado. Methodology: María Elena Ruiz. Supervision: María Elena Ruiz, Roberto García, Neili Machado. Validation: María Elena Ruiz, Roberto García, Neili Machado. Visualization: María Elena Ruiz, Roberto García, Neili Machado. Writing - original draft: María Elena Ruiz, Roberto García, Neili Machado. Writing - review & editing: María Elena Ruiz, Roberto García, Neili Machado.

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