Low-cost Technological Strategies for Smallholders Sustainability: A Review

Eduardo Cornejo-Velázquez; Otilio Arturo Acevedo-Sandoval; Hugo Romero-Trejo; Alfredo Toriz-Palacios

doi:10.4067/S0718-27242020000100105

Authors

Eduardo Cornejo-Velázquez Department of Strategic Planning and Technology Direction, Autonomous Popular University of the State of Puebla, 17 sur 711, Barrio de Santiago, 72410, Puebla, Mexico https://orcid.org/0000-0002-0653-9459
Otilio Arturo Acevedo-Sandoval Institute of Basic Sciences and Engineering, Autonomous University of the State of Hidalgo, Pachuca – Tulancingo Km 4.5, Mineral de la Reforma, Hidalgo, Mexico https://orcid.org/0000-0003-0475-7003
Hugo Romero-Trejo Institute of Basic Sciences and Engineering, Autonomous University of the State of Hidalgo, Pachuca – Tulancingo Km 4.5, Mineral de la Reforma, Hidalgo, Mexico https://orcid.org/0000-0002-3660-758X
Alfredo Toriz-Palacios Department of Strategic Planning and Technology Direction, Autonomous Popular University of the State of Puebla, 17 sur 711, Barrio de Santiago, 72410, Puebla, Mexico

DOI:

https://doi.org/10.4067/S0718-27242020000100105%20

Keywords:

technological strategic, low-cost technologies, smallholders, remote sensing

Abstract

Facing the challenges of the 21st century, into the agricultural sector have been designing strategies focused on the management of ecosystem resources, risk management associated with crops and the promotion of sustainable growth of agricultural communities. These strategies have been configured considering functional and competitive levels for open agricultural production systems, and usually based on low-cost technologies such that Wireless Sensor Networks (WSN), Internet of Things (IoT), Unmanned Aerial Vehicles (UAV), Cloud Computing, and Computational Algorithms. This approach allows the configuration, planning, and implementation of technological strategies for the agricultural sector, impacting in a positive way, generating higher production levels and intensive production cycles to strengthen the smallholder farmers.

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Author Biography

Eduardo Cornejo-Velázquez, Department of Strategic Planning and Technology Direction, Autonomous Popular University of the State of Puebla, 17 sur 711, Barrio de Santiago, 72410, Puebla, Mexico

Estudiantes del programa de Doctorado en Planeación Estratégica y Dirección de Tecnologías en la Universidad Autónoma del Estado de Puebla y Profesor de Tiempo Completo en la Universidad Autónoma del Estado de Hidalgo

References

Alahi, M. E. E., Pereira-Ishak, N., & Burkitt, L. (2018). An Internet-of-Things enabled Smart Sensing System for Nitrate Monitoring. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2018.2809669

Antle, J. M., Jones, J. W., & Rosenzweig, C. E. (2017). Next generation agricultural system data, models and knowledge products: Introduction. Agricultural Systems, 155, 186–190. https://doi.org/10.1016/J.AGSY.2016.09.003

Balafoutis, A. T., Beck, B., Fountas, S., Tsiropoulos, Z., Vangeyte, J., van der Wal, T., … Pedersen, S. M. (2017). Smart Farming Technologies – Description, Taxonomy and Economic Impact. In S. M. Pedersen & K. M. Lind (Eds.), Precision Agriculture: Technology and Economic Perspectives (pp. 21–77). Springer, Cham. https://doi.org/10.1007/978-3-319-68715-5_2

Barrero, O., & Perdomo, S. A. (2018). RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Precision Agriculture, 1–14. https://doi.org/10.1007/s11119-017-9558-x

Bayala, J., Zougmoré, R., Dayamba, S. D., & Olivier, A. (2017). Editorial for the Thematic Series in Agriculture &Food Security: Climate-Smart Agriculture Technologies in West Africa: learning from the ground AR4D experiences. Agriculture & Food Security, 6(1), 40. https://doi.org/10.1186/s40066-017-0117-5

Burkart, A., Hecht, V. L., Kraska, T., & Rascher, U. (2018). Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution. Precision Agriculture, 19(1), 134–146. https://doi.org/10.1007/s11119-017-9504-y

Castaldi, F., Pelosi, F., Pascucci, S., & Casa, R. (2017). Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agriculture, 18(1), 76–94. https://doi.org/10.1007/s11119-016-9468-3

Chand, K. G., Chand, K. G., Sidhendra, M., & Hussain, D. M. A. (2018). Soil nutrient measurement in paddy farming using IoT. International Journal of Engineering & Technology, 7(2.7), 356–358. https://doi.org/10.14419/ijet.v7i2.7.10718

Chiriboga, M. (2015). Pequeñas economías: reflexiones sobre la agricultura familiar campesina. (FAO, Ed.). Quito, Ecuador: FAO.

Domingues Franceschini, M., Bartholomeus, H., van Apeldoorn, D., Suomalainen, J., & Kooistra, L. (2017). Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production. Sensors, 17(6), 1428. https://doi.org/10.3390/s17061428

Dorigo, W. A., Zurita-Milla, R., de Wit, A. J. W., Brazile, J., Singh, R., & Schaepman, M. E. (2007). A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9(2), 165–193. https://doi.org/10.1016/J.JAG.2006.05.003

EU. (2016). A strategic approach to EU agricultural. Brussels, Belgium. Retrieved from https://ec.europa.eu/programmes/horizon2020/sites/horizon2020/files/agri_strategypaper_web_1.pdf

FAOb. (2015). Climate-Smart Agriculture: A call for action. (FAO, Ed.), Synthesis of the Asia-Pacific Regional Workshop Bangkok, Thailand, 18 to 20 June 2015. Retrieved from http://www.fao.org/3/a-i4904e.pdf

FAOd. (2017). El estado de la seguridad alimentaria y la nutrición en el mundo.

Ferrández-Pastor, F., García-Chamizo, J., Nieto-Hidalgo, M., & Mora-Martínez, J. (2018). Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context. Sensors, 18(6), 1731. https://doi.org/10.3390/s18061731

Føre, M., Frank, K., Norton, T., Svendsen, E., Alfredsen, J. A., Dempster, T., … Berckmans, D. (2017). Precision fish farming: A new framework to improve production in aquaculture. Biosystems Engineering. https://doi.org/10.1016/J.BIOSYSTEMSENG.2017.10.014

Gongal, A., Silwal, A., Amatya, S., Karkee, M., Zhang, Q., & Lewis, K. (2016). Apple crop-load estimation with over-the-row machine vision system. Computers and Electronics in Agriculture, 120, 26–35. https://doi.org/10.1016/J.COMPAG.2015.10.022

Hamouda, Y. E. M., & Msallam, M. M. (2018). Smart heterogeneous precision agriculture using wireless sensor network based on extended Kalman filter. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3386-4(

Handique, B. K., Khan, A. Q., Goswami, C., Prashnani, M., Gupta, C., & Raju, P. L. N. (2017). Crop Discrimination Using Multispectral Sensor Onboard Unmanned Aerial Vehicle. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 87(4), 713–719. https://doi.org/10.1007/s40010-017-0443-9

Hunt, E. R., Horneck, D. A., Spinelli, C. B., Turner, R. W., Bruce, A. E., Gadler, D. J., … Hamm, P. B. (2018). Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precision Agriculture, 19(2), 314–333. https://doi.org/10.1007/s11119-017-9518-5

Hunt, L. A., & Boote, K. J. (1998). Data for model operation, calibration, and evaluation (pp. 9–39). Springer, Dordrecht. https://doi.org/10.1007/978-94-017-3624-4_2

Janssen, S. J. C., Porter, C. H., Moore, A. D., Athanasiadis, I. N., Foster, I., Jones, J. W., & Antle, J. M. (2017). Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. Agricultural Systems, 155, 200–212. https://doi.org/10.1016/J.AGSY.2016.09.017

Jayaraman, P., Yavari, A., Georgakopoulos, D., Morshed, A., & Zaslavsky, A. (2016). Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt. Sensors, 16(11), 1884. https://doi.org/10.3390/s16111884

Jiménez-Brenes, F. M., López-Granados, F., de Castro, A. I., Torres-Sánchez, J., Serrano, N., & Peña, J. M. (2017). Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling. Plant Methods, 13(1), 55. https://doi.org/10.1186/s13007-017-0205-3

Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., & Wang, J. (2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141–152. https://doi.org/10.1016/J.EJA.2017.11.002

Jones, J., Antle, J., Basso, B., Boote, K., Conant, R., Foster, I., … Wheeler, T. (2017). Brief history of agricultural systems modeling. Agricultural Systems, 155, 240–254. https://doi.org/10.1016/J.AGSY.2016.05.014

Karim, F., Karim, F., & Frihida, A. (2017). Monitoring system using web of things in precision agriculture. Procedia Computer Science, 110, 402–409. https://doi.org/10.1016/J.PROCS.2017.06.083

Khan, Z., Rahimi-Eichi, V., Haefele, S., Garnett, T., & Miklavcic, S. J. (2018). Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging. Plant Methods, 14(1), 20. https://doi.org/10.1186/s13007-018-0287-6

Kim, H. G., Park, J.-S., & Lee, D.-H. (2018). Potential of Unmanned Aerial Sampling for Monitoring Insect Populations in Rice Fields. Florida Entomologist —, 101(2), 330–334. https://doi.org/10.1653/024.101.0229

Krienke, B., Ferguson, R. B., Schlemmer, M., Holland, K., Marx, D., & Eskridge, K. (2017). Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor. Precision Agriculture, 18(6), 900–915. https://doi.org/10.1007/s11119-017-9534-5

Kruize, J. W., Wolfert, J., Scholten, H., Verdouw, C. N., Kassahun, A., & Beulens, A. J. M. (2016). A reference architecture for Farm Software Ecosystems. Computers and Electronics in Agriculture, 125, 12–28. https://doi.org/10.1016/j.compag.2016.04.011

Martínez, J., Egea, G., Agüera, J., & Pérez-Ruiz, M. (2017). A cost-effective canopy temperature measurement system for precision agriculture: a case study on sugar beet. Precision Agriculture, 18(1), 95–110. https://doi.org/10.1007/s11119-016-9470-9

McCarthy, N., Lipper, L., & Zilberman, D. (2018). Economics of Climate Smart Agriculture: An Overview. In Climate Smart Agriculture Building Resilience to Climate Change (pp. 31–47).

Mekala, M. S., & Viswanathan, P. (2018). CLAY-MIST: IoT-Cloud Enabled CMM index for Smart Agriculture Monitoring System. Measurement. https://doi.org/10.1016/J.MEASUREMENT.2018.10.072

Montoya, E. A. Q., Colorado, S. F. J., Muñoz, W. Y. C., & Golondrino, G. E. C. (2017). Propuesta de una Arquitectura para Agricultura de Precisión Soportada en IoT. RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação, (24), 39–56. https://doi.org/10.17013/risti.24.39–56

Musat, G.-A., Colezea, M., Pop, F., Negru, C., Mocanu, M., Esposito, C., & Castiglione, A. (2018). Advanced services for efficient management of smart farms. Journal of Parallel and Distributed Computing, 116, 3–17. https://doi.org/10.1016/J.JPDC.2017.10.017

Oldeland, J., Große-Stoltenberg, A., Naftal, L., & Strohbach, B. J. (2017). The Potential of UAV Derived Image Features for Discriminating Savannah Tree Species. In The Roles of Remote Sensing in Nature Conservation (pp. 183–201). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-64332-8_10

Pallottino, F., Biocca, M., Nardi, P., Figorilli, S., Menesatti, P., & Costa, C. (2018). Science mapping approach to analyze the research evolution on precision agriculture: world, EU and Italian situation. Precision Agriculture, 1–16. https://doi.org/10.1007/s11119-018-9569-2

Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Dalla Corte, V. F., & de Vargas Mores, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2017.12.002

Popović, T., Latinović, N., Pešić, A., Zečević, Ž., Krstajić, B., & Djukanović, S. (2017). Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study. Computers and Electronics in Agriculture, 140, 255–265. https://doi.org/10.1016/J.COMPAG.2017.06.008

Raut, R., Varma, H., Mulla, C., & Pawar, V. R. (2018). Soil Monitoring, Fertigation, and Irrigation System Using IoT for Agricultural Application. In Intelligent Communication and Computacional Technologies (pp. 67–73). Springer, Singapore. https://doi.org/10.1007/978-981-10-5523-2_7

Rodríguez, S., Gualotuña, T., & Grilo, C. (2017). A System for the Monitoring and Predicting of Data in Precision Agriculture in a Rose Greenhouse Based on Wireless Sensor Networks. Procedia Computer Science, 121, 306–313. https://doi.org/10.1016/j.procs.2017.11.042

Romero, M., Luo, Y., Su, B., & Fuentes, S. (2018). Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Computers and Electronics in Agriculture, 147, 109–117. https://doi.org/10.1016/J.COMPAG.2018.02.013

Schut, A. G. T., Traore, P. C. S., Blaes, X., & de By, R. A. (2018). Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites. Field Crops Research, 221, 98–107. https://doi.org/10.1016/J.FCR.2018.02.018

Segun, A., Sunday, O. I., Ogunti, E. O., & Akingbade, F. K. (2018). Solution to Bird Pest on Cultivated Grain Farm: A Vision Controlled Quadcopter System Approach. International Journal of Engineering & Technology, 7(10). Retrieved from www.ijert.org

Singh, R., & Singh, · G S. (2017). Traditional agriculture: a climate-smart approach for sustainable food production. Energy, Ecology and Environment, 2(5), 296–316. https://doi.org/10.1007/s40974-017-0074-7

Valentini, R. (2017). the global agricultural System and climate change: challenges and opportunities for the russian federation * актуальнаЯ тЕМа. FINANCE: THEORY AND PRACTICE, 21(6), 5671–2017. https://doi.org/10.26794/2587

West, J. S., Canning, G. G. M., Perryman, S. A., & King, K. (2017). Novel Technologies for the detection of Fusarium head blight disease and airborne inoculum. Tropical Plant Pathology, 42(3), 203–209. https://doi.org/10.1007/s40858-017-0138-4

Yang, S., Yang, X., & Mo, J. (2018). The application of unmanned aircraft systems to plant protection in China. Precision Agriculture, 19(2), 278–292. https://doi.org/10.1007/s11119-017-9516-7

Zhang, Y., Wang, L., & Duan, Y. (2016). Agricultural information dissemination using ICTs: A review and analysis of information dissemination models in China. Information Processing in Agriculture, 3(1), 17–29. https://doi.org/10.1016/J.INPA.2015.11.002

Zilberman, D., Goetz, R., & Garrido, A. (2018). Climate Smart Agriculture Building Resilience to Climate Change (Springer). Roma, Italia. https://doi.org/10.1007/978-3-319-61194-5