Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Advances in high throughput and affordable phenotyping for adapting maize and wheat to climate change

[eng] Supplying sufficient food to an increasing population is one of the most important challenges over the next century. To meet this demand, crop productivity will need to increase while it is being threatened by climate change effects like the increase of temperatures and the intensity of drought periods. Improving crop performance is key for an efficient adaptation to these challenging growing conditions, with crop breeding being one of the pillars. In that sense selecting more productive varieties for specific environments requires a better understanding of plant acclimation to stress conditions, including efficient phenotyping approaches. Plant phenotyping research pursues the development of new methods with high-throughput capacity and affordable to characterize non-destructively plant traits of interest. The main focus of this thesis was to develop and study versatile and precise methodologies with high-throughput capacity in order to improve crop performance assessments, while saving time and costs in the phenotyping tasksof two of the most important cereal crops: maize and wheat. The use of unmanned aerial vehicles (UAV) equipped with imaging sensors (including RGB, multispectral and thermal) permits covering simultaneously hectares of experimental fields fast, precisely, and in a non-destructive way. However, ground evaluations may still be an alternative in terms of cost and spatial resolution. The performance of these methodologies to assess genotypic differences in grain yield was evaluated in maize and wheat under different agronomical and environmental growing conditions such as nutrient deficiency, conservation agriculture, drought and heat stress. On one side, maize studies were performed in trials in Zimbabwe focused on the evaluation of genotypes under either low and normal phosphorus conditions or the application of conservation agriculture together with different top-dressing nitrogen fertilization regimes, to overcome the nutrient poverty of soils. In these studies, vegetation indices, related to parameters informing on the above-ground biomass and assessed during early stages of development, performed well as grain yield indicators. Moreover, during more advanced phenological stages, indices informing on the leaf and the canopy color were the traits that reported a better association with grain yield and N content in leaves. For the case of wheat, evaluations were performed in different latitudes in Spain covering a range of environments and grown under different management conditions, and sampling was performed during the reproductive stage (heading, anthesis and grain filling). In general terms, biomass indicators, such as canopy green biomass inferred from vegetation indices, together with water status indicators, such as canopy temperature, were the most critical traits predicting GY. The delay of senescence in water-limited environments and the photosynthetic efficiency measured by multispectral indices like the photochemical reflectance index (PRI) during anthesis were also relevant traits for GY under the rainfed and late-planting trials, respectively.[cat] La producció de suficient aliment per a una població cada cop més gran és un dels reptes més importants per al pròxim segle. Per assolir la demanda, la productivitat dels cultius han d’augmentar alhora que fan front als efectes del canvi climàtic com increment de les temperatures i la intensitat dels períodes de sequera. La millora de la capacitat dels cultius és un element clau per a l’adaptació a aquestes condicions més exigents i la selecció de varietats més productives sota ambients específics requereix una millor comprensió de l’aclimatació dels cultius als estressos. La recerca en fenotipatge de cultius té com objectiu el desenvolupament de noves metodologies d’alt rendiment capaces de caracteritzar característiques d’interès de les plantes d’una manera no destructiva. Sota condicions de camp, l’aplicació de metodologies tradicionals en experiments grans laboriós i requereix molt de temps. El principal objectiu d’aquesta tesi ha estat el desenvolupament i estudi diferents metodologies de caràcter versàtil, precises i d’alta capacitat per a millorar les mesures de com es desenvolupen els cultius, alhora de que es redueixen els costos i el temps requerit per a fer els mostrejos. El treball es basa en dos dels principals cereals: el blat i el blat de moro. L’ús de vehicles aeris no tripulats (UAV, del anglès Unmanned Aerial Vehicles) equipats amb càmeres i sensors (RGB, multiespectrals i termals) permet mesurar simultàniament hectàrees de camps experimentals d’una manera ràpida, precisa i sense la destrucció de mostra. Tot i així, les mesures a nivell de terra també són una alternativa prou potent pel que fa el cost i la resolució espacial. La capacitat d’aquestes metodologies per a mesurar diferencies genotípiques en el rendiment del blat de moro i el blat ha estat analitzada sota diferents condicions de creixement com la deficiència de nutrients, pràctiques de agricultura de conservació, sequera i altes temperatures. Per una banda, els estudis de blat de moro es van desenvolupar a Zimbabwe i estaven focalitzats en l’avaluació de genotips sota condicions diferents de fòsfor o en l’aplicació de l’agricultura de conservació per combatre la pobresa mineral dels sòls. En aquests estudis, les mesures relacionades amb paràmetres de biomassa aèria durant estadis primerencs de desenvolupament va funcionar bé com a indicadors de rendiment. A més, durant estadis fenològics més avançats, mesures de color de la capçada del cultiu van estar associats tant amb el rendiment com amb el contingut de nitrogen en les fulles. En el cas del blat, les avaluacions es van dur a terme a diferents latituds d’Espanya, cobrint un ampli rang de condicions climàtiques i agronòmiques. Els mostrejos es van realitzar en diferents estadis fenològics. En termes generals, els indicadors de biomassa i d’estat hídric del cultiu han estat de les mesures més correlacionades amb el rendiment. L’endarreriment de la senescència del cultiu en els ambients on l’aigua era el factor més limitant i el potencial fotosintètic mesurat per index multiespectrals durant la floració del cultiu han estat rellevants sota condicions de sequera i sembra tardana, respectivament

High-throughput field phenotyping in cereals and implications in plant ecophysiology

[eng] Global climate change effects on agroecosystems together with increasing world population is already threatening food security and endangering ecosystem stability. Meet global food demand with crops production under climate change scenario is the core challenge in plant research nowadays. Thus, there is an urgent need to better understand the underpinning mechanisms of plant acclimation to stress conditions contributing to obtain resilient crops. Also, it is essential to develop new methods in plant research that permit to better characterize non-destructively plant traits of interest. In this sense, the advance in plant phenotyping research by high throughput systems is key to overcome these challenges, while its verification in the field may clear doubts on its feasibility. To this aim, this thesis focused on wheat and secondarily on maize as study species as they make up the major staple crops worldwide. A large panoply of phenotyping methods was employed in these works, ranging from RGB and hyperspectral sensing to metabolomic characterization, besides of other more conventional traits. All research was performed with trials grown in the field and diverse stressor conditions representative of major constrains for plant growth and production were studied: water stress, nitrogen deficiency and disease stress. Our results demonstrated the great potential of leave-to-canopy color traits captured by RGB sensors for in-field phenotyping, as they were accurate and robust indicators of grain yield in wheat and maize under disease and nitrogen deficiency conditions and of leaf nitrogen concentration in maize. On the other hand, the characterization of the metabolome of wheat tissues contributed to elucidate the metabolic mechanisms triggered by water stress and their relationship with high yielding performance, providing some potential biomarkers for higher yields and stress adaptation. Spectroscopic studies in wheat highlighted that leaf dorsoventrality may affect more than water stress on the reflected spectrum and consequently the performance of the multispectral/hyperspectral approaches to assess yield or any other relevant phenotypic trait. Anatomy, pigments and water changes were responsible of reflectance differences and the existence of leaf-side-specific responses were discussed. Finally, the use of spectroscopy for the estimation of the metabolite profiles of wheat organs showed promising for many metabolites which could pave the way for a new generation phenotyping. We concluded that future phenotyping may benefit from these findings in both the low-cost and straightforward methods and the more complex and frontier technologies.[cat] Els efectes del canvi climàtic sobre els agro-ecosistemes i l’increment de la població mundial posa en risc la seguretat alimentària i l’estabilitat dels ecosistemes. Actualment, satisfer les demandes de producció d’aliments sota l’escenari del canvi climàtic és el repte central a la Biologia Vegetal. Per això, és indispensable entendre els mecanismes subjacents de l’aclimatació a l’estrès que permeten obtenir cultius resilients. També és precís desenvolupar nou mètodes de recerca que permetin caracteritzar de manera no destructiva els trets d’interès. L’avenç del fenotipat vegetal amb sistemes d’alt rendiment és clau per abordar aquests reptes. La present tesi s’enfoca en el blat i secundàriament en el panís com a espècies d’estudi ja que constitueixen els cultius bàsics arreu del món. Un ampli ventall de mètodes de fenotipat s’han utilitzat, des sensors RGB a híper-espectrals fins a la caracterització metabolòmica. La recerca s’ha dut a terme en assajos de camp i s’han avaluat diversos tipus d’estrès representatius de les majors limitacions pel creixement i producció vegetal: estrès hídric i biòtic i deficiència de nitrogen. Els resultats demostraren el gran potencial dels trets del color RGB (des de la planta a la capçada) pel fenotipat de camp, ja que foren indicadors precisos del rendiment a blat i panís sota condicions de malaltia i deficiència de nitrogen i de la concentració de nitrogen foliar a panís. La caracterització metabolòmica de teixits de blat contribuí a esbrinar els processos metabòlics endegats per l’estrès hídric i la seva relació amb comportament genotípic, proporcionant bio-marcadors potencials per rendiments més alts i l’adaptació a l’estrès. Estudis espectroscòpics en blat van demostrar que la dorsoventralitat pot afectar més que l’estrès hídric sobre l’espectre de reflectància i consegüentment sobre el comportament de les aproximacions multi/híper-espectrals per avaluar el rendiment i d’altres trets fenotípics com anatòmics i contingut de pigments. Finalment, l’ús de l’espectroscòpia per l’estimació del contingut metabòlic als teixits de blat resulta prometedor per molts metabòlits, la qual cosa obre les portes per a un fenotipat de nova generació. El fenotipat pot beneficiar-se d’aquestes troballes, tant en els mètodes de baix cost com de les tecnologies més sofisticades i d’avantguarda

Image processing techniques for plant phenotyping using RGB and thermal imagery = Técnicas de procesamiento de imágenes RGB y térmicas como herramienta para fenotipado de cultivos

[eng] World cereal stocks need to increase in order to meet growing demands. Currently, maize, rice, wheat, are the main crops worldwide, while other cereals such as barley, sorghum, oat or different millets are also well placed in the top list. Crop productivity is affected directly by climate change factors such as heat, drought, floods or storms. Researchers agree that global climate change is having a major impact on crop productivity. In that way, several studies have been focused on climate change scenarios and more specifically abiotic stresses in cereals. For instance, in the case of heat stress, high temperatures between anthesis to grain filling can decrease grain yield. In order to deal with the climate change and future environmental scenarios, plant breeding is one of the main alternatives breeding is even considered to contribute to the larger component of yield growth compared to management. Plant breeding programs are focused on identifying genotypes with high yields and quality to act as a parentals and further the best individuals among the segregating population thus develop new varieties of plants. Breeders use the phenotypic data, plant and crop performance, and genetic information to improve the yield by selection (GxE, with G and E indicating genetic and environmental factors). More factors must be taken into account to increase the yield, such as, for instance, the education of farmers, economic incentives and the use of new technologies (GxExM, with M indicating management). Plant phenotyping is related with the observable (or measurable) characteristics of the plant while the crop growing as well as the association between the plant genetic background and its response to the environment (GxE). In traditional phenotyping the measurements are collated manually, which is tedious, time consuming and prone to subjective errors. Nowadays the technology is involved in many applications. From the point of view of plan phenotyping, technology has been incorporated as a tool. The use of image processing techniques integrating sensors and algorithm processes, is therefore, an alternative to asses automatically (or semi-automatically) these traits. Images have become a useful tool for plant phenotyping because most frequently data from the sensors are processed and analyzed as an image in two (2D) or three (3D) dimensions. An image is the arrangement of pixels in a regular Cartesian coordinates as a matrix, each pixel has a numerical value into the matrix which represents the number of photons captured by the sensor within the exposition time. Therefore, an image is the optical representation of the object illuminated by a radiating source. The main characteristics of images can be defined by the sensor spectral and spatial properties, with the spatial properties of the resulting image also heavily dependent on the sensor platform (which determines the distance from the target object).[spa] Las existencias mundiales de cereales deben aumentar para satisfacer la creciente demanda. Actualmente, el maíz, el arroz y el trigo son los principales cultivos a nivel mundial, otros cereales como la cebada, el sorgo y la avena están también bien ubicados en la lista. La productividad de los cultivos se ve afectada directamente por factores del cambio climático como el calor, la sequía, las inundaciones o las tormentas. Los investigadores coinciden en que el cambio climático global está teniendo un gran impacto en la productividad de los cultivos. Es por esto que muchos estudios se han centrado en escenarios de cambio climático y más específicamente en estrés abiótico. Por ejemplo, en el caso de estrés por calor, las altas temperaturas entre antesis y llenado de grano pueden disminuir el rendimiento del grano. Para hacer frente al cambio climático y escenarios ambientales futuros, el mejoramiento de plantas es una de las principales alternativas; incluso se considera que las técnicas de mejoramiento contribuyen en mayor medida al aumento del rendimiento que el manejo del cultivo. Los programas de mejora se centran en identificar genotipos con altos rendimientos y calidad para actuar como progenitores y promover los mejores individuos para desarrollar nuevas variedades de plantas. Los mejoradores utilizan los datos fenotípicos, el desempeño de las plantas y los cultivos, y la información genética para mejorar el rendimiento mediante selección (GxE, donde G y E indican factores genéticos y ambientales). El fenotipado plantas está relacionado con las características observables (o medibles) de la planta mientras crece el cultivo, así como con la asociación entre el fondo genético de la planta y su respuesta al medio ambiente (GxE). En el fenotipado tradicional, las mediciones se clasifican manualmente, lo cual es tedioso, consume mucho tiempo y es propenso a errores subjetivos. Sin embargo, hoy en día la tecnología está involucrada en muchas aplicaciones. Desde el punto de vista del fenotipado de plantas, la tecnología se ha incorporado como una herramienta. El uso de técnicas de procesamiento de imágenes que integran sensores y algoritmos son por lo tanto una alternativa para evaluar automáticamente (o semiautomáticamente) estas características

Workshop proceedings: Information Systems for Space Astrophysics in the 21st Century, volume 1

The Astrophysical Information Systems Workshop was one of the three Integrated Technology Planning workshops. Its objectives were to develop an understanding of future mission requirements for information systems, the potential role of technology in meeting these requirements, and the areas in which NASA investment might have the greatest impact. Workshop participants were briefed on the astrophysical mission set with an emphasis on those missions that drive information systems technology, the existing NASA space-science operations infrastructure, and the ongoing and planned NASA information systems technology programs. Program plans and recommendations were prepared in five technical areas: Mission Planning and Operations; Space-Borne Data Processing; Space-to-Earth Communications; Science Data Systems; and Data Analysis, Integration, and Visualization

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470

The Future Ocean: Final report = Ozean der Zukunft: Abschlussbericht

2006 - 201

Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe

Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs

Farming and earth observation: sentinel-2 data to estimate within-field wheat grain yield

Wheat grain yield (GY) is a crop feature of central importance affecting agricultural, environmental, and socioeconomic sustainability worldwide. Hence, the estimation of within-field variability of GY is pivotal for the agricultural management, especially in the current global change context. In this sense, Earth Observation Systems (EOS) are key technologies that use satellite data to monitor crop yield, which can guide the application of precision farming. Yet, novel research is required to improve the multiplatform integration of data, including data processing, and the application of this discipline in agricultural management. This article provides a novel methodological analysis and assessment of its applications in precision farming. It presents an integration of wheat GY, Global Positioning Systems (GPS), combine harvester data, and EOS Sentinel-2 multispectral bands. Moreover, it compares several indices and machine learning (ML) approaches to map within-field wheat GY. It also analyses the importance of multi-date remote sensing imagery and explores its potential applications in precision agriculture. The study was conducted in Spain, a major European wheat producer. Within-field GY data was obtained from a GPS combine harvester machine for 8 fields over three seasons (2017-2019) and consecutively processed to match Sentinel-2 10 m pixel size. Seven vegetation indices (NDVI, GNDVI, EVI, RVI, TGI, CVI and NGRDI) as well as the biophysical parameter LAI (leaf area index) retrieved with radiative transfer models (RTM) were calculated from Sentinel-2 bands. Sentinel-2 10 m resolution bands alone were also used as variables. Random forest, support vector machine and boosted regressions were used as modelling approaches, and multilinear regression was calculated as baseline. Different combinations of dates of measurement were tested to find the most suitable model feeding data. LAI retrieved from RTM had a slightly improved performance in estimating within-field GY in comparison with vegetation indices or Sentinel-2 bands alone. At validation, the use of multi-date Sentinel-2 data was found to be the most suitable in comparison with single date images. Thus, the model developed with random forest regression (e.g. R-2 = 0.89, and RSME = 0.74 t/ha when using LAI) outperformed support vector machine (R-2 = 0.84 and RSME = 0.92 t/ha), boosting regression (R-2 = 0.85 and RSME = 0.88 t/ha) and multilinear regression (R-2 = 0.69 and RSME = 1.29 t/ha). However, single date images at specific phenological stages (e.g. R-2 = 0.84, and RSME = 0.88 t/ha using random forest at stem elongation) also posed relatively high R-2 and low RMSE, with potential for precision farming management before harvest.A & nbsp;We acknowledge the support of the project PID2019-106650RB-C21 from the Ministerio de Ciencia e Innovacion, Spain. J.S. is a recipient of a FPI doctoral fellowship from the same institution (grant: PRE2020-091907) . J.L.A. acknowledges support from the Institucio Catalana de Recerca i Estudis Avancats (ICREA) , Generalitat de Catalunya, Spain) . S. C.K. is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovacion, Spain. We acknowledge the support of Cerealto Siro Group, together with Cristina de Diego and Javier Velasco, technical staff from the company, by providing the wheat yield data. This research was also supported by the COST Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu)