217 research outputs found

    A plant-like battery : a biodegradable power source ecodesigned for precision agriculture

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    The natural environment has always been a source of inspiration for the research community. Nature has evolved over thousands of years to create the most complex living systems, with the ability to leverage inner and outside energetic interactions in the most efficient way. This work presents a flow battery profoundly inspired by nature, which mimics the fluid transport in plants to generate electric power. The battery was ecodesigned to meet a life cycle for precision agriculture (PA) applications; from raw material selection to disposability considerations, the battery is conceived to minimize its environmental impact while meeting PA power requirements. The paper-based fluidic system relies on evaporation as the main pumping force to pull the reactants through a pair of porous carbon electrodes where the electrochemical reaction takes place. This naturally occurring transpiration effect enables to significantly expand the operational lifespan of the battery, overcoming the time-limitation of current capillary-based power sources. Most relevant parameters affecting the battery performance, such as evaporation flow and redox species degradation, are thoroughly studied to carry out device optimization. Flow rates and power outputs comparable to those of capillary-based power sources are achieved. The prototype practicality has been demonstrated by powering a wireless plant-caring device. Standardized biodegradability and phytotoxicity assessments show that the battery is harmless to the environment at the end of its operational lifetime. Placing sustainability as the main driver leads to the generation of a disruptive battery concept that aims to address societal needs within the planetary environmental boundaries. A biodegradable battery inspired by the transpiration pull of liquids in plants has been ecodesigned to power wireless sensors and then be safely biodegraded or composted, resembling the way a plant comes back to nature at the end of its lifecycle

    Simultaneous fruit detection and size estimation using multitask deep neural networks

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    The measurement of fruit size is of great interest to estimate the yield and predict the harvest resources in advance. This work proposes a novel technique for in-field apple detection and measurement based on Deep Neural Networks. The proposed framework was trained with RGB-D data and consists of an end-to-end multitask Deep Neural Network architecture specifically designed to perform the following tasks: 1) detection and segmentation of each fruit from its surroundings; 2) estimation of the diameter of each detected fruit. The methodology was tested with a total of 15,335 annotated apples at different growth stages, with diameters varying from 27 mm to 95 mm. Fruit detection results reported an F1-score for apple detection of 0.88 and a mean absolute error of diameter estimation of 5.64 mm. These are state-of-the-art results with the additional advantages of: a) using an end-to-end multitask trainable network; b) an efficient and fast inference speed; and c) being based on RGB-D data which can be acquired with affordable depth cameras. On the contrary, the main disadvantage is the need of annotating a large amount of data with fruit masks and diameter ground truth to train the model. Finally, a fruit visibility analysis showed an improvement in the prediction when limiting the measurement to apples above 65% of visibility (mean absolute error of 5.09 mm). This suggests that future works should develop a method for automatically identifying the most visible apples and discard the prediction of highly occluded fruits.This work was partly funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-094222-B-I00[PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). The work of Jordi Gene Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union-NextGenerationEU.Peer ReviewedPostprint (published version

    Simultaneous fruit detection and size estimation using multitask deep neural networks

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    The measurement of fruit size is of great interest to estimate the yield and predict the harvest resources in advance. This work proposes a novel technique for in-field apple detection and measurement based on Deep Neural Networks. The proposed framework was trained with RGB-D data and consists of an end-to-end multitask Deep Neural Network architecture specifically designed to perform the following tasks: 1) detection and segmentation of each fruit from its surroundings; 2) estimation of the diameter of each detected fruit. The methodology was tested with a total of 15,335 annotated apples at different growth stages, with diameters varying from 27 mm to 95 mm. Fruit detection results reported an F1-score for apple detection of 0.88 and a mean absolute error of diameter estimation of 5.64 mm. These are state-of-the-art results with the additional advantages of: a) using an end-to-end multitask trainable network; b) an efficient and fast inference speed; and c) being based on RGB-D data which can be acquired with affordable depth cameras. On the contrary, the main disadvantage is the need of annotating a large amount of data with fruit masks and diameter ground truth to train the model. Finally, a fruit visibility analysis showed an improvement in the prediction when limiting the measurement to apples above 65% of visibility (mean absolute error of 5.09 mm). This suggests that future works should develop a method for automatically identifying the most visible apples and discard the prediction of highly occluded fruits.This work was partly funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-094222-B-I00[PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142 GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). The work of Jordi Gené Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU.info:eu-repo/semantics/publishedVersio

    An overview of IoT architectures, technologies, and existing open-source projects

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    Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: Today’s needs for monitoring and control of different devices in organizations require an Internet of Things (IoT) platform that can integrate heterogeneous elements provided by multiple vendors and using different protocols, data formats and communication technologies. This article provides a comprehensive review of all the architectures, technologies, protocols and data formats most commonly used by existing IoT platforms. On this basis, a comparative analysis of the most widely used open source IoT platforms is presented. This exhaustive comparison is based on multiple characteristics that will be essential to select the platform that best suits the needs of each organization.This research/work has been supported by GAIN (Galician Innovation Agency) and the Regional Ministry of Economy, Employment and Industry, Xunta de Galicia under grant COV20/00604 through the ERDF Galicia 2014-2020; and by grant PID2019-104958RB-C42 (ADELE) funded by MCIN/AEI/10.13039/501100011033 . Funding for open access charge: Universidade da Coruña/CISUG.Xunta de Galicia; COV20/0060

    Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation

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    The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5 mm and R2 = 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE = 2.93 mm and R2 = 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_SizingThis work was partly funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). The work of Jordi Gené Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU. We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisition, and Pieter van Dalfsen and Dirk de Hoog from Wageningen University & Research for additional data collection used in the case study.info:eu-repo/semantics/publishedVersio

    Assessment of greenhouse emissions of the green bean through the static enclosure technique

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    : Urban green installations are extensively promoted to increase sustainable and accessible food production and simultaneously improve the environmental performance and liveability of city buildings. In addition to the multiple benefits of plant retrofitting, these installations may lead to a consistent increase in biogenic volatile organic compounds (BVOCs) in the urban environment, especially indoors. Accordingly, health concerns could limit the implementation of building-integrated agriculture. In a building-integrated rooftop greenhouse (i-RTG), throughout the whole hydroponic cycle, green bean emissions were dynamically collected in a static enclosure. Four representative BVOCs, α-pinene (monoterpene), β-caryophyllene (sesquiterpene), linalool (oxygenated monoterpene) and cis-3-hexenol (LOX derivate), were investigated in the samples collected from two equivalent sections of a static enclosure, one empty and one occupied by the i-RTG plants, to estimate the volatile emission factor (EF). Throughout the season, extremely variable BVOC levels between 0.04 and 5.36 ppb were found with occasional but not significant (P > 0.05) variations between the two sections. The highest emission rates were observed during plant vegetative development, with EFs equivalent to 78.97, 75.85 and 51.34 ng g-1 h-1 for cis-3-hexenol, α-pinene, and linalool, respectively; at plant maturity, all volatiles were either close to the LLOQ (lowest limit of quantitation) or not detected. Consistent with previous studies significant relationships (r ≥ 0.92; P < 0.05) were individuated within volatiles and temperature and relative humidity of the sections. However, correlations were all negative and were mainly attributed to the relevant effect of the enclosure on the final sampling conditions. Overall, levels found were at least 15 folds lower than the given Risk and LCI values of the EU-LCI protocol for indoor environments, suggesting low BVOC exposure in the i-RTG. Statistical outcomes demonstrated the applicability of the static enclosure technique for fast BVOC emissions survey inside green retrofitted spaces. However, providing high sampling performance over entire BVOCs collection is recommended to reduce sampling error and incorrect estimation of the emissions

    Analytical design methodology for wind power permanent magnet synchronous generators

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    In this paper a novel analytical design methodology for wind power permanent magnet synchronous generators is presented. This kind of electric generator plays a major role in small-scale wind energy conversion systems up to 10 kW. The proposed diameter-cubed sizing equation is based both on the generator requirements, imposed by the application, and the design parameters that rely on the designer criteria. The magnetic field waveforms of both the permanent magnets field and the armature field are considered from the first moment through the winding factors, as well as the slots effects given by the Carter factor. The analytical model of the permanent magnet synchronous generator is validated with the finite element method, showing good agreement, both with no load and under load. As the generator is unsaturated, the main source of divergence between the analytical and the finite element model are the iron losses, due to the nonuniform magnetic field distribution

    Lohkoketjuteknologian hyödyntäminen ruoan tuotantoketjuissa : kirjallisuustutkimus

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    Tässä maisteritutkielmassa kartoitetaan lohkoketjuteknologian hyödyntämisen mahdollisuutta Suomen ruoantuotantoketjuissa. Lohkoketjuteknologia on vertaisverkkoperiaatteella (Peer to Peer) toimiva tietokanta, joka hajauttaa verkkotoimintaa tehokkaaksi toimintaympäristöksi ilman keskitettyä instituutiota. Lohkoketjuteknologian innovaatio kätkeytyy lohkoketjun muuttumattomuuteen, jonka kryptografinen suojausjärjestelmä takaa, että kerran laitettua tietoa ei voida muuttaa. Kiinnostus teknologiaan on kasvanut maailmalla finanssilaitosten otettua sen käyttöön. Tämän seurauksena maataloudessa ja ruoan tuotannossa siihen liittyvät tutkimukset ovat lisääntyneet. Lohkoketjuteknologian avulla ruoan seurantatiedot kirjoitetaan lohkoketjuun, jolloin tietojen luotettavuus on taattu ja ruoan turvallisuus varmistettu. Samalla lohkoketjuteknologian hajautettu järjestelmä yhdistää reaaliaikaisesti ruoan toimitusketjun kaikki osapuolet luotettavasti, turvallisesti, avoimesti ja läpinäkyvästi tuottamaan arvoa koko tuotantoketjun tasolla. Tutkielman tavoitteena on antaa ilmiöstä kokonaiskuva, kuten mitä lohkoketjuteknologia on, miten sitä voidaan soveltaa ruoantuotantoketjussa ja kenelle se voisi tuottaa arvoa. Ilmiö on uusi suomalaisessa ruontuotantoketjussa ja tämän takia tutkielma toteutetaan poikkitieteellisesti kirjallisuuskatsauksena ja sinisen meren strategian avulla. Yhdistämällä eri näkökulmia on mahdollista kartoittaa laaja-alaisesti ja kokonaisvaltaisesti ilmiötä sekä teorian toimivuutta. Tutkielman aineisto on kerätty kartoittavan kirjallisuuskatsauksen avulla. Kerätty tutkimusaineisto on koottu Helsingin yliopiston Helka-tietokannasta ja osittain Google Scholarin avulla. Tutkielman aineisto koostu 20 tieteellisestä artikkelista, jotka linkittyvät lohkoketjuteknologian hyödyntämiseen maataloudessa tai ruon tuotantoketjussa Tutkimustulosten perusteella lohkoketjuteknologian käyttöönotto on alkuvaiheessa teknologisten esteiden takia. Vaikka Suomessa on kehitetty maailman parhaita seurantajärjestelmiä varmistamaan ruoan turvallisuutta, toimitusketju ei ole avoin, läpinäkyvä ja tasapuolinen kaikkien osapuolten kesken. Lohkoketjuteknologialla on suuri potentiaali muuttaa nykyistä tuotantoketjua, jos Suomessa hyödynnetään suotuisia strategioita ja politiikkaa sen käyttöönotossa. Teknologia hyödyntäminen tarvitsee strategioita, jotka ohjaavat sen kehitystä maantaloudessa. Lisäksi sen hyödyntämisessä tarvitaan suotuisaa poliittista ilmapiiriä. Tutkielman aikana nousi esiin useita jatkotutkimusideoita lohkoketjuteknologian hyödyntämisen mahdollisuuksista. Voitaisiin esimerkiksi tutkia, miten lohkoketjuteknologiaa voisi hyödyntää rehukaupan hintasopimuksissa.This master's thesis explores the possibility of utilizing blockhain technology in Finnish food production chains. The blockchain technology is a distributed database that decentralizes network operations into an efficient operating environment, without a centralized institution. The innovation in blockchain technology lies in the concensus protocols of the blockchain, whose cryptographic security system ensures that once placed, information cannot be forged from there. In that reason interest in blockchain technology has grown in the world since the introduction of financial institutions and as a result, research in agriculture and food production has grown around the phenomenon. With blockchain technology, the food trace tracking data is written to the blockchain, whereby the security of the data is guaranteed and the safety of the food is ensured. At the same time, the blockchain technology decentralized system connects real-time food supply chain to all parties in real time, reliably, securely, openly, transparently to create value at the entire supply chain level. The aim of this study is to give an overall picture of the phenomenon, such as what blockchain technology is, how it can be applied in the food production chain and to whom it could provide value. The phenomenon is new in the Finnish food supply chains and because of this the study is carried out interdisciplinary in the treatise as a literature review and with the help of the strategy of the Blue Sea strategy. It is possible to survey the phenomenon and the functionality of the theory widely and comprehensively by connecting different research methods. By combining different research methods, it is possible to map the phenomenon and the functionality of the theory extensively and comprehensively. In the scoping mapping, the research materials have been collected with the help of a mapping literature review. The collected research material has been compiled from the Helka database of the University of Helsinki and partly with the help of Google choolar. The material of the dissertation consists of 20 scientific articles related to the utilization of blockchain technology in agriculture or in the food production chain. Based on the research results, blockchain technology is in its infancy due to technological barriers. Even though in Finland has developed the world's best monitoring systems to ensure food safety, at the same time, however, the supply chain is not open, transparent and fair between the parties. For this reason, blockchain technology has a huge potential to revolutionize the current production chain if exploited by favorable strategies and policies in its deployment. Technology needs strategies to guide development in agriculture and its exploitation requires favorable political climate to support it. Further research would be needed for example on how the use of blockchain technology in feed futures trading, such as how a transparent, real-time and open ecosystem of blockchain technology affects feed price fixing

    Machine learning models for traffic classification in electromagnetic nano-networks

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    The number of nano-sensors connected to wireless electromagnetic nano-network generates different traffic volumes that have increased dramatically, enabling various applications of the Internet of nano-things. Nano-network traffic classification is more challenging nowadays to analyze different types of flows and study the overall performance of a nano-network that connects to the Internet through micro/nanogateways. There are traditional techniques to classify traffic, such as port-based technique and load-based technique, however the most promising technique used recently is machine learning. As machine learning models have a great impact on traffic classification and network performance evaluation in general, it is difficult to declare which is the best or the most suitable model to address the analysis of large volumes of traffic collected in operational nano-networks. In this paper, we study the classification problem of nano-network traffic captured by micro/nano-gateway, and then five supervised machine learning algorithms are used to analyze and classify the nano-network traffic from traditional traffic. Experimental analysis of the proposed models is evaluated and compared to show the most adequate classifier for nano-network traffic that gives very good accuracy and performance score to other classifiers.This work was supported in part by the ‘‘Agencia Estatal de Investigación’’ of ‘‘Ministerio de Ciencia e Innovación’’ of Spain under Project PID2019-108713RB-C51/MCIN/AEI/10.13039/501100011033, and in part by the ‘‘Agència de Gestió d’Ajuts Universitaris i de Recerca’’ (AGAUR) of the ‘‘Generalitat de Catalunya’’ under Grant 2021FI_B2 00091.Postprint (published version

    Paper-based ZnO self-powered sensors and nanogenerators by plasma technology

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    Nanogenerators and self-powered nanosensors have shown the potential to power low-consumption electronics and human-machine interfaces, but their practical implementation requires reliable, environmentally friendly and scalable, processes for manufacturing and processing. This article presents a plasma synthesis approach for the fabrication of piezoelectric nanogenerators (PENGs) and self-powered sensors on paper substrates. Polycrystalline ZnO nanocolumnar thin films are deposited by plasma-enhanced chemical vapour deposition on common paper supports using a microwave electron cyclotron resonance reactor working at room temperature yielding high growth rates and low structural and interfacial stresses. Applying Kinetic Monte Carlo simulation, we elucidate the basic shadowing mechanism behind the characteristic microstructure and porosity of the ZnO thin films, relating them to an enhanced piezoelectric response to periodic and random inputs. The piezoelectric devices are assembled by embedding the ZnO films in PMMA and using Au electrodes in two different configurations: laterally and vertically contacted devices. We present the response of the laterally connected devices as a force sensor for low-frequency events with different answers to the applied force depending on the impedance circuit, i.e. load values range, a behaviour that is theoretically analyzed. The vertical devices reach power densities as high as 80 nW/cm2 with a mean power output of 20 nW/cm2. We analyze their actual-scenario performance by activation with a fan and handwriting. Overall, this work demonstrates the advantages of implementing plasma deposition for piezoelectric films to develop robust, flexible, stretchable, and enhanced-performance nanogenerators and self-powered piezoelectric sensors compatible with inexpensive and recyclable supportsComment: 30 pages, 8 figures in main tex
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