A Smart Decision System for Digital Farming

[EN] New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.This paper has been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR and by the "Ministerio de Ciencia, Innovacion y Universidades" through the "Ayudas para la adquisicion de equipamiento cientifico-tecnico, Subprograma estatal de infraestructuras de investigacion y equipamiento cientifico-tecnico (plan Estatal i+d+i 2017-2020)" (project EQC2018-004988-P).Cambra-Baseca, C.; Sendra, S.; Lloret, J.; Tomás Gironés, J. (2019). A Smart Decision System for Digital Farming. Agronomy. 9(5):1-19. https://doi.org/10.3390/agronomy9050216S11995Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. doi:10.1007/s11036-013-0489-0De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122-135. doi:10.1108/lr-06-2015-0061Haghverdi, A., Leib, B. G., Washington-Allen, R. A., Ayers, P. D., & Buschermohle, M. J. (2015). Perspectives on delineating management zones for variable rate irrigation. Computers and Electronics in Agriculture, 117, 154-167. doi:10.1016/j.compag.2015.06.019Vazquez, J. I., Ruiz-de-Garibay, J., Eguiluz, X., Doamo, I., Renteria, S., & Ayerbe, A. (2010). Communication architectures and experiences for web-connected physical Smart objects. 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). doi:10.1109/percomw.2010.5470521Misra, S., Barthwal, R., & Obaidat, M. S. (2012). Community detection in an integrated Internet of Things and social network architecture. 2012 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/glocom.2012.6503350Atzori, L., Iera, A., & Morabito, G. (2014). From «smart objects» to «social objects»: The next evolutionary step of the internet of things. IEEE Communications Magazine, 52(1), 97-105. doi:10.1109/mcom.2014.6710070Agrivi App http://www.agrivi.com/en/reApollo Project http://apollo-h2020.eu/Cambra, C., Sendra, S., Lloret, J., & Lacuesta, R. (2018). Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming. Sensors, 18(5), 1333. doi:10.3390/s18051333Ortiz, A. M., Hussein, D., Park, S., Han, S. N., & Crespi, N. (2014). The Cluster Between Internet of Things and Social Networks: Review and Research Challenges. IEEE Internet of Things Journal, 1(3), 206-215. doi:10.1109/jiot.2014.2318835Ji, Z., Ganchev, I., O’Droma, M., Zhao, L., & Zhang, X. (2014). A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation. Sensors, 14(12), 22372-22393. doi:10.3390/s141222372Ning, H., & Wang, Z. (2011). Future Internet of Things Architecture: Like Mankind Neural System or Social Organization Framework? IEEE Communications Letters, 15(4), 461-463. doi:10.1109/lcomm.2011.022411.11012

Geosensors to Support Crop Production: Current Applications and User Requirements

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load

Smart agriculture for optimizing photosynthesis using internet of things and fuzzy logic

Photosynthesis is a process that plants need. Plant growth requires sunlight to carry out photosynthesis. At night photosynthesis cannot be carried out by plants. This research proposes an internet of things (IoT) model that can work intelligently to maximize photosynthesis and plant growth using fuzzy logic. The plants used in this research are mustard plants because mustard plants are plants that have broad leaves and require more photosynthesis. The outputs of this proposed model are the activation of light emitting diodes (LED) lights and automatic watering based on input sensors such as soil moisture, temperature, and light intensity which are processed with fuzzy logic. The results show that the use of the IoT model that has been proposed can provide faster and better growth of mustard plants compared with mustard plants without an IoT system and fuzzy logic. This result is also strengthened by comparing the t-test between the two groups, with a significant 95% confidence level. The proposed model in this research is also compared with similar research models carried out previously. This research resulted in a plant height difference of 30.43% higher than the previous research. So, it can conclude that the proposed model can accelerate the growth of mustard plants

Technology and Information Fusion Needs to Address the Food, Energy, Water Systems (FEWS) Nexus Challenges

In response to the Food, Energy, Water Systems (FEWS) Nexus Challenge grant awarded by NSF, the team of investigators led by David Ebert, along with Christian Butzke, Melba Crawford, Phillip Owens, and Dimitrios Peroulis conducted a two-day workshop in Napa, California on November 5th and 6th, 2015. The workshop addressed the emerging issues in the food/energy/water systems throughout the diverse geography of the United States and over various crops and environmental conditions to better understand and model and ultimately devise a solution for the challenges to the FEWS nexus. One of the intended outcomes of the workshop was to generate a report that will chart the research challenges and opportunities for solving these challenges and have an impact on scientific fields including, sensing technology, hydrology, soil science, climate, data fusion, analysis, visualization, and data driven decision 2 making, as well as agricultural production, local and regional economies, sustainability and planning. The information contained in this post-workshop report serves as that foundation.In response to the Food, Energy, Water Systems (FEWS) Nexus Challenge grant awarded by NSF, the team of investigators led by David Ebert, along with Christian Butzke, Melba Crawford, Phillip Owens, and Dimitrios Peroulis conducted a two-day workshop in Napa, California on November 5th and 6th, 2015. The workshop addressed the emerging issues in the food/energy/water systems throughout the diverse geography of the United States and over various crops and environmental conditions to better understand and model and ultimately devise a solution for the challenges to the FEWS nexus. One of the intended outcomes of the workshop was to generate a report that will chart the research challenges and opportunities for solving these challenges and have an impact on scientific fields including, sensing technology, hydrology, soil science, climate, data fusion, analysis, visualization, and data driven decision 2 making, as well as agricultural production, local and regional economies, sustainability and planning. The information contained in this post-workshop report serves as that foundation