Mapas para mejorar la producción de vino combinando tecnologías de la información y vehículos convencionales

En este trabajo de tesis doctoral se ha desarrollado una nueva metodología para monitorizar un viñedo en diferentes fases de cultivo y mejorar su gestión en campo. El núcleo del método propuesto es la confección de mapas de cultivo con origen local único, resolución variable, y sistema de coordenadas global pero con geometría plana. El sistema desarrollado, además, permite la integración de información proveniente tanto de sistemas de adquisición totalmente automáticos como manuales, así como la comparación y correlación de medidas efectuadas en diferentes etapas de crecimiento e incluso a lo largo de diferentes años. El objetivo último consiste en la proposición de modelos predictivos sobre la producción de uva y potencial enológico del futuro vino. Para ello se aplican nuevas tecnologías en una arquitectura de coste moderado, dotada de la flexibilidad y versatilidad necesaria para que un productor promedio del área mediterránea pueda adaptar el sistema propuesto a sus necesidades particulares, utilizando para ello un vehículo convencional de uso agrícola. La arquitectura propuesta, implementada, y validada en campo consiste en un sistema de percepción basado en visión artificial, un sistema de posicionamiento global con corrección diferencial, y un ordenador de abordo que, mediante la metodología propuesta, combina toda la información adquirida y la transforma en mapas de cultivo compatibles entre sí. El sistema de visión ofrece una técnica simple basada en una cámara monocromática sensible en el rango UV-NIR y acondicionada mediante filtros ópticos que optimizan la ejecución del algoritmo de segmentación dinámica. El programa desarrollado e implementado a bordo de un tractor estándar combina imágenes y posicionamiento del vehículo para generar la información para los mapas en tiempo real de vegetación relativa, que serán posteriormente relacionados con otros mapas de interés, tanto generados de forma automática (desnivel del terreno) como manual (rendimiento, compactación del terreno, acidez, etc.). El control de los sistemas de percepción y posicionamiento también se ha simplificado a través de una única interfaz gráfica, que permite la utilización del sistema por operarios no versados en nuevas tecnologías. Los resultados obtenidos indican que un planteamiento simplificado de la agricultura de precisión es informativo siempre y cuando se cuente con un sistema de gestión de información óptimo. Los mapas de cultivo propuestos sirvieron para establecer correlaciones estadísticamente significativas entre variables clave, cuantificando de manera objetiva la variabilidad espacial en cuanto a cantidad de vegetación, producción de uva, compactación del terreno, o propiedades químicas del mosto. La posibilidad de enriquecer los modelos presentados con información proveniente de campañas sucesivas resulta atractivo para el viticultor, que puede contar con modelos predictivos específicamente adaptados a su explotación y que cada vez serán más precisos. Esta metodología está al alcance de pequeños y medianos productores, ya que prescinde de la compra de imágenes digitales de origen aéreo o remoto, y además no requiere la adquisición de un vehículo específico, lo que facilita la generación de mapas de cultivo mientras se efectúan otras labores agrícolas gracias al uso de redes con referencias globales.Sáiz Rubio, V. (2013). Mapas para mejorar la producción de vino combinando tecnologías de la información y vehículos convencionales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31522TESI

Proximal sensing mapping method to generate field maps in vineyards

[EN] An innovative methodology to generate vegetative vigor maps in vineyards (Vitis vinifera L.) has been developed and pre-validated. The architecture proposed implements a Global Positioning System (GPS) receiver and a computer vision unit comprising a monocular charge-coupled device (CCD) camera equipped with an 8-mm lens and a pass-band near-infrared (NIR) filter. Both sensors are mounted on a medium-size conventional agricultural tractor. The synchronization of perception (camera) and localization (GPS) sensors allowed the creation of globally-referenced regular grids, denominated universal grids, whose cells were filled with the estimated vegetative vigor of the monitored vines. Vine vigor was quantified as the relative percentage of vegetation automatically estimated by the onboard algorithm through the images captured with the camera. Validation tests compared spatial differences in vine vigor with yield differentials along the rows. The positive correlation between vigor and yield variations showed the potential of proximal sensing and the advantages of acquiring top view images from conventional vehicles.Sáiz Rubio, V.; Rovira Más, F. (2013). Proximal sensing mapping method to generate field maps in vineyards. Agricultural Engineering International: CIGR Journal. 15(2):47-59. http://hdl.handle.net/10251/102750S475915

From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management

[EN] The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. This kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth.This research article is part of a project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 737669.Sáiz Rubio, V.; Rovira Más, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy. 10(2):1-21. https://doi.org/10.3390/agronomy10020207S121102Himesh, S. (2018). Digital revolution and Big Data: a new revolution in agriculture. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 13(021). doi:10.1079/pavsnnr201813021Digital Agriculture: Improving Profitabilityhttps://www.accenture.com/_acnmedia/accenture/conversion-assets/dotcom/documents/global/pdf/digital_3/accenture-digital-agriculture-point-of-view.pdfDigital Farming: What Does It Really Mean?http://www.cema-agri.org/publication/digital-farming-what-does-it-really-meanAgriculture Needs to Attract More Young Peoplehttp://www.gainhealth.org/knowledge-centre/worlds-farmers-age-new-blood-neededGenerational Renewalhttps://enrd.ec.europa.eu/enrd-thematic-work/generational-renewal_enWhat is IoT in Agriculture? Farmers Aren’t Quite Sure Despite $4bn US Opportunity—Reporthttps://agfundernews.com/iot-agriculture-farmers-arent-quite-sure-despite-4bn-us-opportunity.htmlPrecision Agriculture Yields Higher Profits, Lower Riskshttps://www.hpe.com/us/en/insights/articles/precision-agriculture-yields-higher-profits-lower-risks-1806.htmlTzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31-48. doi:10.1016/j.biosystemseng.2017.09.007From Dirt to Data: The Second Green Revolution and IoT. Deloitte insightshttps://www2.deloitte.com/insights/us/en/deloitte-review/issue-18/second-green-revolution-and-internet-of-things.html#endnote-sup-9Big Data: The Next Frontier for Innovation, Competition, and Productivity | McKinseyhttps://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovationWolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69-80. doi:10.1016/j.agsy.2017.01.023Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. doi:10.1016/j.compag.2017.09.037How Big Data Will Change Agriculturehttps://proagrica.com/news/how-big-data-will-change-agriculture/Big Data Coordination Platform. Proposal to the CGIAR Fund Councilhttps://cgspace.cgiar.org/handle/10947/4303Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Processes, 7(1), 36. doi:10.3390/pr7010036How AI Is Transforming Agriculturehttps://www.forbes.com/sites/cognitiveworld/2019/07/05/how-ai-is-transforming-agriculture/Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94-111. doi:10.1016/j.biosystemseng.2016.06.014Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Part 2: Operations and systems. Biosystems Engineering, 153, 110-128. doi:10.1016/j.biosystemseng.2016.11.004Ramin Shamshiri, R., Weltzien, C., A. Hameed, I., J. Yule, I., … E. Grift, T. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1-11. doi:10.25165/j.ijabe.20181104.4278Farming 4.0: The Future of Agriculture?https://www.euractiv.com/section/agriculture-food/infographic/farming-4-0-the-future-of-agriculture/Ag Tech Deal Activity More Than Tripleshttps://www.cbinsights.com/research/agriculture-farm-tech-startup-funding-trends/AI, Robotics, And the Future of Precision Agriculturehttps://www.cbinsights.com/research/ai-robotics-agriculture-tech-startups-future/VineScout European Projectwww.vinescout.euPrecision Farming: A New Approach to Crop Managementhttp://agpublications.tamu.edu/pubs/eng/l5177.pdfZhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36(2-3), 113-132. doi:10.1016/s0168-1699(02)00096-0MIAO, Y., MULLA, D. J., & ROBERT, P. C. (2018). An integrated approach to site-specific management zone delineation. Frontiers of Agricultural Science and Engineering, 0(0), 0. doi:10.15302/j-fase-2018230Klassen, S. P., Villa, J., Adamchuk, V., & Serraj, R. (2014). Soil mapping for improved phenotyping of drought resistance in lowland rice fields. Field Crops Research, 167, 112-118. doi:10.1016/j.fcr.2014.07.007Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22-32. doi:10.1016/j.compag.2017.05.001Aravind, K. R., Raja, P., & Pérez-Ruiz, M. (2017). Task-based agricultural mobile robots in arable farming: A review. Spanish Journal of Agricultural Research, 15(1), e02R01. doi:10.5424/sjar/2017151-9573Roldán, J. J., Cerro, J. del, Garzón‐Ramos, D., Garcia‐Aunon, P., Garzón, M., León, J. de, & Barrientos, A. (2018). Robots in Agriculture: State of Art and Practical Experiences. Service Robots. doi:10.5772/intechopen.69874Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Lopez-Granados, F., Brandstoetter, M., Tomic, S., … Debilde, B. (2016). Fleets of robots for environmentally-safe pest control in agriculture. Precision Agriculture, 18(4), 574-614. doi:10.1007/s11119-016-9476-3What’s Slowing the Use of Robots in the Ag Industry?https://www.therobotreport.com/whats-slowing-the-use-of-robots-in-the-ag-industry/Bogue, R. (2016). Robots poised to revolutionise agriculture. Industrial Robot: An International Journal, 43(5), 450-456. doi:10.1108/ir-05-2016-0142Features & Benefits OZ Weeding Robothttps://www.naio-technologies.com/en/agricultural-equipment/weeding-robot-oz/Robotics for Sustainable Broad-Acre Agriculturehttps://www.researchgate.net/publication/283722961_Robotics_for_Sustainable_Broad-Acre_AgricultureThe Ultimate Guide to Agricultural Roboticshttps://www.roboticsbusinessreview.com/agriculture/the_ultimate_guide_to_agricultural_robotics/Kweon, G., Lund, E., & Maxton, C. (2013). Soil organic matter and cation-exchange capacity sensing with on-the-go electrical conductivity and optical sensors. Geoderma, 199, 80-89. doi:10.1016/j.geoderma.2012.11.001Agricultural Robots—Present and Future Applications (Videos Included)https://emerj.com/ai-sector-overviews/agricultural-robots-present-future-applications/Köksal, Ö., & Tekinerdogan, B. (2018). Architecture design approach for IoT-based farm management information systems. Precision Agriculture, 20(5), 926-958. doi:10.1007/s11119-018-09624-8Rovira-Más, F., & Sáiz-Rubio, V. (2013). Crop Biometric Maps: The Key to Prediction. Sensors, 13(9), 12698-12743. doi:10.3390/s130912698Oliver, M. A., & Webster, R. (2014). A tutorial guide to geostatistics: Computing and modelling variograms and kriging. CATENA, 113, 56-69. doi:10.1016/j.catena.2013.09.006Adamchuk, V. ., Hummel, J. ., Morgan, M. ., & Upadhyaya, S. . (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71-91. doi:10.1016/j.compag.2004.03.002Cossell, S., Whitty, M., Liu, S., & Tang, J. (2016). Spatial Map Generation from Low Cost Ground Vehicle Mounted Monocular Camera. IFAC-PapersOnLine, 49(16), 231-236. doi:10.1016/j.ifacol.2016.10.043N. Zhang, & R. K. Taylor. (2001). APPLICATIONS OF A FIELD LEVEL GEOGRAPHIC INFORMATION SYSTEM (FIS) IN PRECISION AGRICULTURE. Applied Engineering in Agriculture, 17(6). doi:10.13031/2013.6829Runquist, S., Zhang, N., & Taylor, R. K. (2001). Development of a field-level geographic information system. Computers and Electronics in Agriculture, 31(2), 201-209. doi:10.1016/s0168-1699(00)00155-1Granular Farm Management Software, Precision Agriculture, Agricultural Softwarehttps://granular.ag/Capterra. Farm Management Softwarewww.capterra.comTop 9 Farm Management Software—Compare Reviews, Features, Pricing in 2019https://www.predictiveanalyticstoday.com/top-farm-management-software/Srivastava, P. K., & Singh, R. M. (2016). GIS based integrated modelling framework for agricultural canal system simulation and management in Indo-Gangetic plains of India. Agricultural Water Management, 163, 37-47. doi:10.1016/j.agwat.2015.08.025Giusti, E., & Marsili-Libelli, S. (2015). A Fuzzy Decision Support System for irrigation and water conservation in agriculture. Environmental Modelling & Software, 63, 73-86. doi:10.1016/j.envsoft.2014.09.020Asfaw, D., Black, E., Brown, M., Nicklin, K. J., Otu-Larbi, F., Pinnington, E., … Quaife, T. (2018). TAMSAT-ALERT v1: a new framework for agricultural decision support. Geoscientific Model Development, 11(6), 2353-2371. doi:10.5194/gmd-11-2353-2018https://dssat.netNavarro-Hellín, H., Martínez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., & Torres-Sánchez, R. (2016). A decision support system for managing irrigation in agriculture. Computers and Electronics in Agriculture, 124, 121-131. doi:10.1016/j.compag.2016.04.003Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609. doi:10.1016/j.rser.2016.11.191Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., & Bosnić, Z. (2019). AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture, 161, 260-271. doi:10.1016/j.compag.2018.04.001Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., … Dicks, L. V. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, 149, 165-174. doi:10.1016/j.agsy.2016.09.009Colaço, A. F., & Molin, J. P. (2016). Variable rate fertilization in citrus: a long term study. Precision Agriculture, 18(2), 169-191. doi:10.1007/s11119-016-9454-9Nawar, S., Corstanje, R., Halcro, G., Mulla, D., & Mouazen, A. M. (2017). Delineation of Soil Management Zones for Variable-Rate Fertilization. Advances in Agronomy, 175-245. doi:10.1016/bs.agron.2017.01.003Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., … Tisserye, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40-50. doi:10.1016/j.compag.2015.05.011Precision Agriculture in Europe: Legal, Social and Ethical Considerations—Think Tankhttp://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_STU(2017)60320

Augmented Perception for Agricultural Robots Navigation

[EN] Producing food in a sustainable way is becoming very challenging today due to the lack of skilled labor, the unaffordable costs of labor when available, and the limited returns for growers as a result of low produce prices demanded by big supermarket chains in contrast to ever-increasing costs of inputs such as fuel, chemicals, seeds, or water. Robotics emerges as a technological advance that can counterweight some of these challenges, mainly in industrialized countries. However, the deployment of autonomous machines in open environments exposed to uncertainty and harsh ambient conditions poses an important defiance to reliability and safety. Consequently, a deep parametrization of the working environment in real time is necessary to achieve autonomous navigation. This article proposes a navigation strategy for guiding a robot along vineyard rows for field monitoring. Given that global positioning cannot be granted permanently in any vineyard, the strategy is based on local perception, and results from fusing three complementary technologies: 3D vision, lidar, and ultrasonics. Several perception-based navigation algorithms were developed between 2015 and 2019. After their comparison in real environments and conditions, results showed that the augmented perception derived from combining these three technologies provides a consistent basis for outlining the intelligent behavior of agricultural robots operating within orchards.This work was supported by the European Union Research and Innovation Programs under Grant N. 737669 and Grant N. 610953. The associate editor coordinating the review of this article and approving it for publication was Dr. Oleg Sergiyenko.Rovira Más, F.; Sáiz Rubio, V.; Cuenca-Cuenca, A. (2021). Augmented Perception for Agricultural Robots Navigation. IEEE Sensors Journal. 21(10):11712-11727. https://doi.org/10.1109/JSEN.2020.3016081S1171211727211

Crop Biometric Maps: The Key to Prediction

[EN] The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular “identity.” This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed.The authors would like to express their gratitude to Edmund Optics, Inc. for supporting the ideas developed in this article with the 2011 Research and Innovation Award, as well as to the Farming by Satellite 2012 Prize sponsored by Claas, Bayer CropScience, and the European GNSS Agency (GSA).Rovira Más, F.; Sáiz Rubio, V. (2013). Crop Biometric Maps: The Key to Prediction. Sensors. 13(9):12698-12743. doi:10.3390/s1309126981269812743139Cox-Foster, D., & vanEngelsdorp, D. (2009). Saving the Honeybee. Scientific American, 300(4), 40-47. doi:10.1038/scientificamerican0409-40Farmers Fuel Growing Market for Imaging Systemshttp://www.photonics.com/Article.aspx?AID=54039Fountas, S., Blackmore, S., Ess, D., Hawkins, S., Blumhoff, G., Lowenberg-Deboer, J., & Sorensen, C. G. (2005). Farmer Experience with Precision Agriculture in Denmark and the US Eastern Corn Belt. Precision Agriculture, 6(2), 121-141. doi:10.1007/s11119-004-1030-zCamera Advances Drive Scientific Researchhttp://www.photonics.com/Article.aspx?AID=51848Bringing Space Science Down to Earthhttp://www.insidegnss.com/node/3185Schueller, J. ., Whitney, J. ., Wheaton, T. ., Miller, W. ., & Turner, A. . (1999). Low-cost automatic yield mapping in hand-harvested citrus. Computers and Electronics in Agriculture, 23(2), 145-153. doi:10.1016/s0168-1699(99)00028-9Sui, R., Thomasson, J. A., Hanks, J., & Wooten, J. (2008). Ground-based sensing system for weed mapping in cotton. Computers and Electronics in Agriculture, 60(1), 31-38. doi:10.1016/j.compag.2007.06.002Jain, A. K., & Pankanti, S. (2008). Beyond Fingerprinting. Scientific American, 299(3), 78-81. doi:10.1038/scientificamerican0908-78Lasers Find Varied Uses in Space Applicationshttp://www.photonics.com/Article.aspx?AID=52252Rovira-Más, F. (2012). Global-referenced navigation grids for off-road vehicles and environments. Robotics and Autonomous Systems, 60(2), 278-287. doi:10.1016/j.robot.2011.11.007Rovira-Más, F., & Banerjee, R. (2012). GPS data conditioning for enhancing reliability of automated off-road vehicles. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 227(4), 521-535. doi:10.1177/0954407012454976Sáiz-Rubio, V., & Rovira-Más, F. (2012). Dynamic segmentation to estimate vine vigor from ground images. Spanish Journal of Agricultural Research, 10(3), 596. doi:10.5424/sjar/2012103-508-11Rovira-Más, F. (2010). Sensor Architecture and Task Classification for Agricultural Vehicles and Environments. Sensors, 10(12), 11226-11247. doi:10.3390/s10121122

Robust estimation of Ackerman angles for front-axle steering vehicles

The multiple benefits of automating steering in agricultural vehicles have resulted in various autoguidance systems commercially available, most of them relying on satellite-based positioning. However, the fact that farm equipment is typically oversized, heavy, and highly powered poses serious challenges to automation in terms of safety and reliability. The objective of this research is to improve the reliability of front-wheel feedback signals as a preliminary stage in the development of stable steering control systems. To do so, the angle turned by each front wheel of a conventional tractor was independently measured by an optical encoder and fused to generate the Ackerman feedback angle. The proposed fusion algorithm analyzes the consistency of each signal with time and checks the coherence between left and right front wheels according to the vehicle steering mechanism. Field experiments demonstrated the benefits of using redundant sensors coupled through logic algorithms for estimating Ackerman angles as the harsh conditions of off-road environments often resulted in the unreliable performance of electronic devices.Sáiz Rubio, V.; Rovira Más, F.; Chatterjee, I.; Molina Hidalgo, JM. (2013). Robust estimation of Ackerman angles for front-axle steering vehicles. Artificial Intelligence Research. 2(2):18-28. doi:10.5430/air.v2n2p18S18282

Design of an Energy-saving Hydrocyclone for Wheat Starch Separation

The nearly unlimited applications and uses of starch for food industry make this natural polymer a unique component; no other constituent can provide consistence and storage stability to such a large variety of foods. Starch can be extracted from agricultural produce through either chemical processes or physical separation. The latter involves the application of centrifugal forces by means of hydrocyclones. A hydrocylcone is a device which separates, through physical methods, two phases of different densities. There are three flows involved: the feed (mixture introduced in the hydrocyclone), the overflow (the least dense part) and the underflow (the densest part). Normally, the underflow part, or commonly known as "heavies", is the desirable part that companies keep, this is, the starch. Despite hydrocyclones are not very expensive devices, current-based hydrocyclones demand high energy rates. This work describes the design and testing of energy-saving hydrocyclones for extracting starch from wheat. Eight prototypes were built and tested at Larsson Mekaniska Verkstad AB (Bromölla, Sweden). This company makes process equipment for the starch industry and was the one with which the author collaborated during the ellaboration of the Degree Project. Six of the eight hydrocyclones were built by Larsson; another was a commercial hydrocyclone and the last one was the one figured out after reading some literature and updates in the hydrocyclones field. The experiments consist of trying the eight hydrocyclones under different conditions, combining concentrations (153 g/L and 237 g/L) and pressures (500 Pa and 700 Pa). The experimental results proved the importance of geometry on hydrocyclone design, and showed the effect of geometrical parameters on the energy-saving properties of cyclones. Four of the eight new models behaved satisfactorily for low energy and high efficiency conditions, obtained with inlet pressures of 500 kPa and starch concentrations of 237 g/L

The role of GNSS in the navigation strategies of cost-effective agricultural robots

[EN] The current state of automation technology has led to a growing interest in the design and development of new use-case agricultural robots that can provide effective solutions to the challenges that agriculture is facing in industrialized countries, especially those derived from labor shortage and ever-increasing production costs. The advent of autonomous moderate-size machines in the field appears as a prospective way of promoting the sustainable production of food in Europe, Japan, and North America. However, there exist important obstacles to the broad expansion of autonomous robots in the field; reliability, safeguarding, system complexity, and cost-efficiency in particular rank high among the impediments to overcome before prototypes move into the production stage. Robot navigation is essential for the successful deployment of autonomous machines in conventional farms, as a minimum level of safety has to be granted at the same time that navigation engines cannot be too sophisticated for solutions to compete with current equipment. In such compromise, global navigation satellite systems play a key role due to its wide range of solutions, and an important number of limitations. In this research, a variety of experiments were conducted to determine the scope of GNSS solutions as a principal component of the navigation system of novel farm robots. Results showed that regardless of the quality of the receiver used, multipath and other uncontrollable errors eventually occur in the field, and therefore signal consistency must be continuously checked by the robot’s navigation engine. Different strategies based on the meticulous analysis of NMEA strings, the optimal combination of GGA and VTG messages, and the trajectory-based redundant estimation of robot planar states are proposed to enhance the integration of GNSS measurements in the navigation engine of agricultural robots. 2014 Elsevier B.V. All rights reserved.Rovira Más, F.; Chatterjee, I.; Sáiz Rubio, V. (2015). The role of GNSS in the navigation strategies of cost-effective agricultural robots. Computers and Electronics in Agriculture. 112:172-183. doi:10.1016/j.compag.2014.12.017S17218311

Mathematical model-based redesign of chickpea harvester reel

Aim of study: This paper presents a mathematical modeling approach to redesign the reels of chickpea harvesters for harvest efficiency.Area of study: A prototype chickpea harvester was designed and evaluated on the Dooshan farm of the University of Kurdistan, Sanandaj, Iran.Material and methods: The strategy used for reducing harvesting losses derived from the dynamic study of the reel applied to the chickpea harvester. The machine was designed such that bats of a power take-off (PTO)-powered reel, in conjunction with passive fingers, harvest pods from anchored plants and throw the pods into a hopper. The trochoid trajectory of the reel bats concerning reel kinematic index, and plant height and spacing was determined for redesigning the reel.Main results: This kinematic design allowed an estimation of the reel orientation at the time of impact. The experimentally validated model offers an accurate and low computational cost method to redesign harvester reels.Research highlights: The new chickpea harvester implemented with a four fixed-bat reel, a height of 40 cm above the ground for the reel axis, and featuring a kinematic index of 2.4 was capable of harvesting pods with harvesting efficiency of over 70%; a significant improvement in harvesting performance