A high-performance IoT solution to reduce frost damages in stone fruits

[EN] Agriculture is one of the key sectors where technology is opening new opportunities to break up the market. The Internet of Things (IoT) could reduce the production costs and increase the product quality by providing intelligence services via IoT analytics. However, the hard weather conditions and the lack of connectivity in this field limit the successful deployment of such services as they require both, ie, fully connected infrastructures and highly computational resources. Edge computing has emerged as a solution to bring computing power in close proximity to the sensors, providing energy savings, highly responsive web services, and the ability to mask transient cloud outages. In this paper, we propose an IoT monitoring system to activate anti-frost techniques to avoid crop loss, by defining two intelligent services to detect outliers caused by the sensor errors. The former is a nearest neighbor technique and the latter is the k-means algorithm, which provides better quality results but it increases the computational cost. Cloud versus edge computing approaches are analyzed by targeting two different low-power GPUs. Our experimental results show that cloud-based approaches provides highest performance in general but edge computing is a compelling alternative to mask transient cloud outages and provide highly responsive data analytic services in technologically hostile environments.This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5. Finally, we thank the farmers for the availability of their resources to be able to asses and improve the IoT monitoring system proposed.Guillén-Navarro, MA.; Martínez-España, R.; López, B.; Cecilia-Canales, JM. (2021). A high-performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience (Online). 33(2):1-14. https://doi.org/10.1002/cpe.529911433

Lucky imaging speckle statistics applied to halo suppression

In ground based astronomy, the Lucky Imaging (LI) technique consists of selecting the best quality pictures among those that have been taken with a short exposure time to freeze the atmosphere distortions. Although it has different advantages, the peak intensity of a star is always surrounded by speckled light which, once averaged, provides the halo. The halo can make it difficult to detect faint companions immersed in it. In this paper, we take advantage of the speckle statistics to remove the halo and so to make more effective current detection techniques. Theoretical predictions are confirmed using experimental LI data. Finally, a photometry algorithm is also proposed.Funding by Ministerio de Economía y Competitividad, project AYA2016-78773-C2-1-P

Caracterización anual del metabolismo de las acículas de Pinus pinaster (Aiton) mediante procedimientos transcriptómicos y metabolómicos

El presente trabajo pretende iniciar una serie de estudios sobre el metabolismo nitrogenado en árboles adultos de la conífera Pinus pinaster (Aiton) en condiciones naturales. Durante un año completo se ha caracterizado el metabolismo de las acículas de árboles de P. pinaster que crecen en el paraje natural de Los Reales de Sierra Bermeja, Estepona (Málaga). Para ello se han seguido dos aproximaciones diferentes. En primer lugar se ha analizado la expresión génica mediante el empleo de un microarray para genes de P. pinaster desarrollado en nuestro laboratorio. Por otra parte se ha observado la acumulación de los distintos metabolitos en las acículas a lo largo del año mediante H1-NMR. Para la determinación de los metabolitos presentes en las muestras se ha generado una base de datos de espectros H1-NMR de 90 metabolitos. Para el análisis de los datos se han empleado herramientas estadísticas de clustering. Una de las principales técnicas que se han empleado para el análisis de las muestras ha sido el análisis de las redes de co-expresión de genes (WGCNA). Los resultados de los análisis de los datos sugieren que las acículas de P. pinaster presentan diferentes niveles de actividad a lo largo del año pudiéndose dividir en dos períodos principales, uno ligado al otoño y al invierno y el otro a la primavera y al verano

Super-Gaussian apodization in ground based telescopes for high contrast coronagraph imaging

We introduce the use of Super-Gaussian apodizing functions in the telescope pupil plane and/or the coronagraph Lyot plane to improve the imaging contrast in ground-based coronagraphs. We describe the properties of the Super-Gaussian function, we estimate its second-order moment in the pupil and Fourier planes and we check it as an apodizing function. We then use Super-Gaussian function to apodize the telescope pupil, the coronagraph Lyot plane or both of them. The result is that a proper apodizing masks combination can reduce the exoplanet detection distance up to a 45% with respect to the classic Lyot coronagraph, for moderately aberrated wavefronts. Compared to the prolate spheroidal function the Super-Gaussian apodizing function allows the planet light up to 3 times brighter. An extra help to increase the extinction rate is to perform a frame selection (Lucky Imaging technique). We show that a selection of the 10% best frames will reduce up to a 20% the detection angular distance when using the classic Lyot coronagraph but that the reduction is only around the 5% when using an apodized coronagraph

A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers

[EN] Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 degrees C in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 degrees C and a coefficient of determination R2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Guillén-Navarro, MA.; Martínez-España, R.; Bueno-Crespo, A.; Morales-García, J.; Ayuso, B.; Cecilia-Canales, JM. (2020). A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers. Sensors. 20(24):1-15. https://doi.org/10.3390/s20247129S1152024Melgarejo-Moreno, J., López-Ortiz, M.-I., & Fernández-Aracil, P. (2019). Water distribution management in South-East Spain: A guaranteed system in a context of scarce resources. Science of The Total Environment, 648, 1384-1393. doi:10.1016/j.scitotenv.2018.08.263Ferrández-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. doi:10.3390/s18061731Liaghat. (2010). A Review: The Role of Remote Sensing in Precision Agriculture. American Journal of Agricultural and Biological Sciences, 5(1), 50-55. doi:10.3844/ajabssp.2010.50.55Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., … Willenbockel, D. (2013). Agriculture and climate change in global scenarios: why don’t the models agree. Agricultural Economics, 45(1), 85-101. doi:10.1111/agec.12091Crookston, R. K. (2006). A Top 10 List of Developments and Issues Impacting Crop Management and Ecology During the Past 50 Years. Crop Science, 46(5), 2253-2262. doi:10.2135/cropsci2005.11.0416gasDutta, R., Morshed, A., Aryal, J., D’Este, C., & Das, A. (2014). Development of an intelligent environmental knowledge system for sustainable agricultural decision support. Environmental Modelling & Software, 52, 264-272. doi:10.1016/j.envsoft.2013.10.004Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918-929. doi:10.1016/j.jhydrol.2018.04.065Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research, 53(5), 3878-3895. doi:10.1002/2016wr019933Coopersmith, E. J., Minsker, B. S., Wenzel, C. E., & Gilmore, B. J. (2014). Machine learning assessments of soil drying for agricultural planning. Computers and Electronics in Agriculture, 104, 93-104. doi:10.1016/j.compag.2014.04.004Mohammadi, K., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., & Gocic, M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture, 117, 214-225. doi:10.1016/j.compag.2015.08.008Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K. (2017). Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 136, 71-78. doi:10.1016/j.compag.2017.01.027Jin, X.-B., Yu, X.-H., Wang, X.-Y., Bai, Y.-T., Su, T.-L., & Kong, J.-L. (2020). Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability, 12(4), 1433. doi:10.3390/su12041433Castañeda-Miranda, A., & Castaño-Meneses, V. M. (2020). Internet of things for smart farming and frost intelligent control in greenhouses. Computers and Electronics in Agriculture, 176, 105614. doi:10.1016/j.compag.2020.105614Tzounis, 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.2017.09.007Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-Art Internet of Things in Protected Agriculture. Sensors, 19(8), 1833. doi:10.3390/s19081833Jawad, H., Nordin, R., Gharghan, S., Jawad, A., & Ismail, M. (2017). Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors, 17(8), 1781. doi:10.3390/s17081781Guillén‐Navarro, M. A., Martínez‐España, R., López, B., & Cecilia, J. M. (2019). A high‐performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience, 33(2). doi:10.1002/cpe.5299Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-

Winter diet of the long-eared owl Asio otus (Strigiformes: Strigidae) in the grasslands of Janos, Chihuahua, Mexico

Abstract Background: The long-eared owl (Asio otus) has a Holarctic distribution, including much of North America. This nocturnal species is considered to be extremely secretive, and poorly known in the Great Plains of the United States and Canada, as well as to México, where no previous studies on its diet have been conducted. Findings: We analyzed 120 pellets collected during January 2007 in roosts in a 2–3 m height mesquite scrub within a grassland area of Reserva Ecológica El Uno, located in the Natural Protected Area Janos. We registered and identified three orders, four families, eight genera and ten species of mammals and two orders and one family of insects. Winter diet is dominated by mammals, especially rodents in both frequency and biomass. Cricetidae and Perognathus flavus were the most frequent family and species, respectively. On the other hand, when analyzing biomass, Sigmodon species were dominant, achieving almost 70% of the consumed biomass. Levin's standardized niche breath based on frequency was calculated as 0.40, while based on biomass was 0.38. Also, two previously unrecorded rodent species were identified as long-eared owl prey. Conclusion: Although 18 different types of items were identified, the long-eared owl tends to be selective, with a single genera (Sigmodon) comprising almost 70% of its consumed biomass during winter. Perognathus flavus was also important in frequency (21%); however, it barely constitutes 2% of the consumed biomass. Keywords: Winter diet, Long-eared owl, Grasslands, Janos, México Resumen El búho orejas largas es una especie Holártica, que se distribuye en gran parte de Norteamérica y que ha sido poco estudiada, especialmente en las Grandes Planicies de Estados Unidos y en México, donde no existe un estudio previo sobre su dieta invernal. Se colectaron y analizaron 120 egagrópilas en la Reserva Ecológica El Uno, dentro del Área Natural Protegida Reserva de la Biósfera Janos. Se identificaron un total de 18 tipos de presa, pero la especie mostró selectividad por dos géneros/especies, ya que cerca del 70% de la biomasa consumida fueron especies del género Sigmodon, mientras que el 21% de las muestras contenían Perognathus flavus. Además, dos especies de roedores identificadas constituyen nuevos registros de presa para la especie

Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

[EN] The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming "Smart" thanks to the analysis of data generated by IoT. This analysis is carried out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI is giving rise to an emerging trend, called AIoT, which is opening up new paths to bring digitization into the new era. However, there is still a big gap between AI and IoT, which is basically in the computational power required by the former and the lack of computational resources offered by the latter. This is particularly true in rural IoT environments where the lack of connectivity (or low-bandwidth connections) and power supply forces the search for "efficient" alternatives to provide computational resources to IoT infrastructures without increasing power consumption. In this paper, we explore edge computing as a solution for bridging the gaps between AI and IoT in rural environment. We evaluate the training and inference stages of a deep-learning-based precision agriculture application for frost prediction in modern Nvidia Jetson AGX Xavier in terms of performance and power consumption. Our experimental results reveal that cloud approaches are still a long way off in terms of performance, but the inclusion of GPUs in edge devices offers new opportunities for those scenarios where connectivity is still a challenge.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 (AEI/FEDER, UE) and RTC-2017-6389-5.Guillén-Navarro, MA.; Llanes, A.; Imbernón, B.; Martínez-España, R.; Bueno-Crespo, A.; Cano, J.; Cecilia-Canales, JM. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing. 77:818-840. https://doi.org/10.1007/s11227-020-03288-w8188407

Aproximaciones metabolómica y transcriptómica a la gestión del metabolismo en las acículas de Pinus pinaster L. aiton

Aproximaciones metabolómica y transcriptómica a la gestión del metabolismo en las acículas de Pinus pinaster L. Aiton Rafael A. Cañas1, Javier Canales1, Carmen Muñoz2, Jose M. Granados1, Ma Belén Pascual1, Concepción Ávila1, María L. García-Martín2, Francisco M. Cánovas1. 1Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Instituto Andaluz de Biotecnología, Universidad de Málaga, Campus Universitario de Teatinos s/n, 29071, MÁLAGA. [email protected] 2Unidad de Nanoimagen, Centro Andaluz de Nanomedicina y Biotecnología (BIONAND), Parque Tecnológico de Andalucía, C/ Severo Ochoa 35, 29590 Campanillas (MÁLAGA) El pino marítimo (Pinus pinaster L. Aiton) es una conífera de hoja perenne con un ciclo de vida largo y cuyas acículas pueden permanecer activas en el árbol varios años. Las coníferas y, en concreto, P. pinaster son especies modelo en el contexto de la producción de madera o de la síntesis de los flavonoides y terpenoides, componentes de la resina. A pesar de ello, el metabolismo y la biología molecular de las hojas (acículas) de las coníferas han sido escasamente estudiados por lo que las relaciones entre los tejidos productores o fuentes y los tejidos consumidores o sumideros no son bien conocidas en este grupo de plantas. En este trabajo nos proponemos el estudio de las acículas desde dos aproximaciones distintas: la metabolómica y la transcriptómica. Para ello se han obtenido muestras de acículas de P. pinaster en condiciones naturales a lo largo de un año completo separando las acículas por su edad. El estudio metabolómico se ha desarrollado mediante H1-NMR para lo que se ha desarrollado una librería de espectros de referencia de 70 metabolitos diferentes. Para el estudio transcriptómico se ha empleado un microarray de cDNA con 8.000 puntos de hibridación (PINARRAY2) y que ha sido desarrollado por nuestro grupo de investigación. Para el estudio de los datos obtenidos se ha empleado un análisis de redes de co-expresión (WGCNA).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Uso eficiente del agua en los frutales del Sureste español

El agua es un patrimonio que hay que proteger, defender y tratar acorde al recurso escaso que es, y así lo indica la DMA (Directiva Marco del Agua, 2000/60/CE). En la Vega Baja del Segura (Alicante, Sureste español) la gestión de los recursos hídricos en las explotaciones agrarias está muy condicionada por su escasez, y el agua es un bien con un aprovechamiento muy intensivo. La agricultura se caracteriza por su larga tradición en la zona, con producciones de excelente calidad y buena competitividad, para lo que es fundamental el buen clima, la calidad de las tierras y la experiencia de los agricultores. Sin embargo el sector agrario está perdiendo importancia, en parte por las limitaciones hidrológicas. La escasez de agua hace que este recurso tenga un alto coste de oportunidad y exige un uso muy eficiente. En la DMA Europea se plantean distintas medidas para promover el uso eficiente y sostenible del agua en los territorios. Precisamente el conocimiento de los niveles de eficiencia que tienen las producciones agrarias en el uso del agua de riego es previo a cualquier posterior iniciativa, ya sea pública o privada. En este trabajo se analiza la eficiencia de los principales frutales de regadío de la zona. Para ello, se han realizado cálculos de distintos indicadores de eficiencia, a partir de datos tomados a agricultores de tres de las principales UDAs (unidades de demanda agraria) que recoge el actual PHDS (Plan Hidrológico de la Demarcación del Segura). Estas UDAs cubren una superficie bruta de 41.700 ha, y tienen sistemas de riegos distintos en función del grado de modernización de las explotaciones. Tras el análisis queda patente que los cultivos con una mayor tradición en la zona (como son limón o granado) son también los que utilizan el agua con más eficiencia

Coronagraphs adapted to atmosphere conditions

In this paper we show new ways to improve the performance of ground-based coronagraphy. We introduce adaptive coronagraphic masks whose profile is a binary version of the instantaneous atmospherically degraded star image. We also propose the hyper-Gaussian profile masks obtained by averaging adaptive masks. In addition, adaptive Lyot stops and hyper-Gaussian Lyot stops are analyzed. Computer simulations show that all these masks outperform the circular hard-edged mask and that a proper mask and stop combination significantly reduces the angular separation at which a faint companion can be detected