Global-referenced navigation grids for off-road vehicles and environments

[EN] The presence of automation and information technology in agricultural environments seems no longer questionable; smart spraying, variable rate fertilizing, or automatic guidance are becoming usual management tools in modern farms. Yet, such techniques are still in their nascence and offer a lively hotbed for innovation. In particular, significant research efforts are being directed toward vehicle navigation and awareness in off-road environments. However, the majority of solutions being developed are based on occupancy grids referenced with odometry and dead-reckoning, or alternatively based on GPS waypoint following, but never based on both. Yet, navigation in off-road environments highly benefits from both approaches: perception data effectively condensed in regular grids, and global references for every cell of the grid. This research proposes a framework to build globally referenced navigation grids by combining three-dimensional stereo vision with satellite-based global positioning. The construction process entails the in-field recording of perceptual information plus the geodetic coordinates of the vehicle at every image acquisition position, in addition to other basic data as velocity, heading, or GPS quality indices. The creation of local grids occurs in real time right after the stereo images have been captured by the vehicle in the field, but the final assembly of universal grids takes place after finishing the acquisition phase. Vehicle-fixed individual grids are then superposed onto the global grid, transferring original perception data to universal cells expressed in Local Tangent Plane coordinates. Global referencing allows the discontinuous appendage of data to succeed in the completion and updating of navigation grids along the time over multiple mapping sessions. This methodology was validated in a commercial vineyard, where several universal grids of the crops were generated. Vine rows were correctly reconstructed, although some difficulties appeared around the headland turns as a consequence of unreliable heading estimations. Navigation information conveyed through globally referenced regular grids turned out to be a powerful tool for upcoming practical implementations within agricultural robotics. (C) 2011 Elsevier B.V. All rights reserved.The author would like to thank Juan Jose Pena Suarez and Montano Perez Teruel for their assistance in the preparation of the prototype vehicle, Veronica Saiz Rubio for her help during most of the field experiments, Ratul Banerjee for his contribution in the development of software, and Luis Gil-Orozco Esteve for granting permission to perform multiple tests in the vineyards of his winery Finca Ardal. Gratitude is also extended to the Spanish Ministry of Science and Innovation for funding this research through project AGL2009-11731.Rovira Más, F. (2011). Global-referenced navigation grids for off-road vehicles and environments. Robotics and Autonomous Systems. 60(2):278-287. https://doi.org/10.1016/j.robot.2011.11.007S27828760

Application of a Mamdani-type fuzzy rule-based system to segment periventricular cerebral veins in susceptibility-weighted images

This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients. © Springer International Publishing Switzerland 2016.Postprint (author's final draft

GPS data conditioning for enhancing reliability of automated off-road vehicles

[EN] The practical implementation of precision agriculture at a large scale has not occurred yet for several reasons. Among them, the lack of uniformity and reliability in global positioning has discouraged many producers from adopting advanced solutions which, while considered to add a significant value to their production systems, cannot be incorporated without guarantees of minimum levels of long-term consistency. Although substantial improvements are constantly being introduced by receiver manufacturers, positioning errors can appear at the final stages of the localization process, resulting in inaccuracies and anomalies normally undetected by embedded quality filters. This article proposes an actuation protocol to enhance the robustness of GPS information for practical agricultural applications. The algorithm embodying this strategy merges partially-acquired raw strings into complete US National Marine Electronics Association messages whose information fields are checked for consistency. Once data qualifies as stable, other logic filters are applied to reinforce the likelihood of obtaining proper locations. Extensive field tests demonstrated that the algorithm was able to discard most erroneous positions due to typical GPS errors and poor signal reception in complex agricultural environments. However, the phenomena of coordinate quantization and random outliers were still present, which indicates that further redundancy is necessary to avoid unreliable outcomes. In this regard, positive results for supplementary consistency from GPS-based vehicle heading and speed are anticipated.Our gratitude is extended to the Spanish Ministry of Science and Innovation for funding this research through project AGL2009-11731.Rovira 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. https://doi.org/10.1177/0954407012454976521535227

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

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

Bifocal Stereoscopic Vision for Intelligent Vehicles

The numerous benefits of real-time 3D awareness for autonomous vehicles have motivated the incorporation of stereo cameras to the perception units of intelligent vehicles. The availability of the distance between camera and objects is essential for such applications as automatic guidance and safeguarding; however, a poor estimation of the position of the objects in front of the vehicle can result in dangerous actions. There is an emphasis, therefore, in the design of perception engines that can make available a rich and reliable interval of ranges in front of the camera. The objective of this research is to develop a stereo head that is capable of capturing 3D information from two cameras simultaneously, sensing different, but complementary, fields of view. In order to do so, the concept of bifocal perception was defined and physically materialized in an experimental bifocal stereo camera. The assembled system was validated through field tests, and results showed that each stereo pair of the head excelled at a singular range interval. The fusion of both intervals led to a more faithful representation of reality

Bed load transport and incipient motion below a large gravel bed river bend

A new data set of bed load measurements in a cross-section at the exit of a river bend is presented. Data are analyzed to identify processes that contribute to the morphodynamic stability of gravel bed meanders. It is shown that boundary shear stress and bed material texture are strongly coupled, resulting in an almost equal mobility at incipient motion over the bend point bar in relation to channel flow stage. Conversely, for conditions above bankfull an excess of fine sediment towards the inner-bank, likely related to more intense crosswise flux and grain size sorting, results in size selective transport in relation to the local bed material. We suggest that bed armoring and structuring, as well as crosswise sediment flux, add stability to the outer-bank pool, while the point bar is eroded by large floods and restored by moderate flows. Results reveal the strong feedback of processes at different scales promoting stability at bends of gravel bed rivers.info:eu-repo/semantics/acceptedVersio

Melanoma expression analysis with Big Data technologies

Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work was funded in part by Grants TIN2014-58304-R (Ministerio de Ciencia e Innovación) and P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz de Investigación, Desarrollo e Innovación). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Measuring iron deposits within focal lesions in patients presenting clinically isolated syndrome

Postprint (author's final draft