3,753 research outputs found

    Innovative solutions for the wine sector: the role of startups

    Get PDF
    The economic globalisation has opened new pathways for commerce and triggered a logistical revolution, which in turn has produced enormous technological innovations. In this context, the role of startups is becoming increasingly crucial since they are positioning themselves as innovation enablers among large and small companies. Between these innovations, IoT, Big Data Analytics and Blockchain can be used in various domains, among which the logistics of the whole wine supply chain. Here we will consider some of the issues and needs that arise in this market sector, showing how Wenda, a startup born in Bologna in February 2015 that works to improve sustainability and traceability in Food & Beverage supply chains, has been able to leverage IoT, Big Data Analytics and Blockchain to empower the wine supply chain with solutions that enable wine traceability throughout the distribution and the after-buying-in preservation and commercialisation phases

    The Digitalisation of African Agriculture Report 2018-2019

    Get PDF
    An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

    Launching the Grand Challenges for Ocean Conservation

    Get PDF
    The ten most pressing Grand Challenges in Oceans Conservation were identified at the Oceans Big Think and described in a detailed working document:A Blue Revolution for Oceans: Reengineering Aquaculture for SustainabilityEnding and Recovering from Marine DebrisTransparency and Traceability from Sea to Shore:  Ending OverfishingProtecting Critical Ocean Habitats: New Tools for Marine ProtectionEngineering Ecological Resilience in Near Shore and Coastal AreasReducing the Ecological Footprint of Fishing through Smarter GearArresting the Alien Invasion: Combating Invasive SpeciesCombatting the Effects of Ocean AcidificationEnding Marine Wildlife TraffickingReviving Dead Zones: Combating Ocean Deoxygenation and Nutrient Runof

    IMPLEMENTATION OF THE EU LEGISLATION CONCERNING GENETICALLY MODIFIED ORGANISMS IN THE GERMAN FOOD AND FEED INDUSTRY

    Get PDF
    Paper prepared for presentation at the 10th ICABR International Conference on Agricultural Biotechnology: Facts, Analysis and Policies Ravello (Italy), June 29th to July 2nd, 2006Traceability, Genetic modified organism, Co-Existence, EU Legislation, Germany, Feed industry, Food industry, Regulations (EC) No 1829/2003 and 1830/2003, Industrial Organization, L51, L66,

    Usability of Real Time Data for Cold Chain Monitoring Systems

    Get PDF
    One in every nine people on earth do not have enough food to lead a healthy life, according to The World Food Programme. That\u27s nearly 800 million people. In addition to that, billions of tons of perishable food products are wasted during transportation and logistics before it reaches the end consumers as thousands of people die every day due to hunger related causes. Perishable foods, medicine and other goods impose severe challenges on inventory management. Businesses debate on whether to keep limited stock just to meet demand and fear losing additional customers or keep excess stock and face the risk of expiry of goods. Unlike the transportation of other goods, perishable food products and medicines undergo tremendous degradation in quality as a function of environmental conditions over time. Perishable food products are usually stored in frozen and refrigerated condition at the distribution centers, supermarkets and during the transit in order to preserve the quality of food and extend the shelf life. Even though, temperature controlled supply chain in the food retail sector has become commonplace, there is one major limitation of the current practice in the chilled food chain management. The printed \u27sell-by-date\u27 is not a true indicator of the quality of the product as it does not reflect the temperature variations during distribution at the different stages of the food supply chain. The food quality is severely compromised when actual environmental conditions deviate from the expected conditions. This research proposes the use of real-time sensor data to support supply chain decisions and describe a model for gauging and improving usability on the real-time sensor data. Data reported through the wireless sensor networks could help in predicting the shelf-life of perishable food products and preventing them from spoilage. Use of sensor data would encourage data driven decision making rather than intuition. The findings would encourage businesses operating in the cold chain environment in exploring value added innovation opportunities through internet of things use cases and improve the usability experience and competitiveness of their supply chains via warehouse workers and truck drivers

    Digitalization in food supply chains: A bibliometric review and key-route main path analysis

    Get PDF
    Technological advances such as blockchain, artificial intelligence, big data, social media, and geographic information systems represent a building block of the digital transformation that supports the resilience of the food supply chain (FSC) and increases its efficiency. This paper reviews the literature surrounding digitalization in FSCs. A bibliometric and key-route main path analysis was carried out to objectively and analytically uncover the knowledge development in digitalization within the context of sustainable FSCs. The research began with the selection of 2140 articles published over nearly five decades. Then, the articles were examined according to several bibliometric metrics such as year of publication, countries, institutions, sources, authors, and keywords frequency. A keyword co-occurrence network was generated to cluster the relevant literature. Findings of the review and bibliometric analysis indicate that research at the intersection of technology and the FSC has gained substantial interest from scholars. On the basis of keyword co-occurrence network, the literature is focused on the role of information communication technology for agriculture and food security, food waste and circular economy, and the merge of the Internet of Things and blockchain in the FSC. The analysis of the key-route main path uncovers three critical periods marking the development of technology-enabled FSCs. The study offers scholars a better understanding of digitalization within the agri-food industry and the current knowledge gaps for future research. Practitioners may find the review useful to remain ahead of the latest discussions of technologyenabled FSCs. To the authors’ best knowledge, the current study is one of the few endeavors to explore technology-enabled FSCs using a comprehensive sample of journal articles published during the past five decades

    Blockchain-based life cycle assessment: An implementation framework and system architecture

    Get PDF
    Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology offers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with internet-of-things (IoT) and big data analytics and visualization can help organizations achieve operational excellence in conducting LCA for improving supply chain sustainability. This research develops a framework to guide the implementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, IoT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications

    Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture

    Full text link
    [EN] The term "Agri-Food 4.0" is an analogy to the term Industry 4.0; coming from the concept "agriculture 4.0". Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri-Food domain.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCARISE-2015.Lezoche, M.; Hernández, JE.; Alemany Díaz, MDM.; Panetto, H.; Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry. 117:1-15. https://doi.org/10.1016/j.compind.2020.103187S115117Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014Ait-Mouheb, N., Bahri, A., Thayer, B. B., Benyahia, B., Bourrié, G., Cherki, B., … Harmand, J. (2018). The reuse of reclaimed water for irrigation around the Mediterranean Rim: a step towards a more virtuous cycle? Regional Environmental Change, 18(3), 693-705. doi:10.1007/s10113-018-1292-zAli, J., & Kumar, S. (2011). Information and communication technologies (ICTs) and farmers’ decision-making across the agricultural supply chain. International Journal of Information Management, 31(2), 149-159. doi:10.1016/j.ijinfomgt.2010.07.008Alzahrani, S. M. (2018). Development of IoT mining machine for Twitter sentiment analysis: Mining in the cloud and results on the mirror. 2018 15th Learning and Technology Conference (L&T). doi:10.1109/lt.2018.8368490Amandeep, Bhattacharjee, A., Das, P., Basu, D., Roy, S., Ghosh, S., … Rana, T. K. (2017). Smart farming using IOT. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). doi:10.1109/iemcon.2017.8117219Annosi, M. C., Brunetta, F., Monti, A., & Nati, F. (2019). Is the trend your friend? An analysis of technology 4.0 investment decisions in agricultural SMEs. Computers in Industry, 109, 59-71. doi:10.1016/j.compind.2019.04.003Baio, F. H. R. (2011). Evaluation of an auto-guidance system operating on a sugar cane harvester. Precision Agriculture, 13(1), 141-147. doi:10.1007/s11119-011-9241-6Belaud, J.-P., Prioux, N., Vialle, C., & Sablayrolles, C. (2019). Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Computers in Industry, 111, 41-50. doi:10.1016/j.compind.2019.06.006Nicolaas Bezuidenhout, C., Bodhanya, S., & Brenchley, L. (2012). An analysis of collaboration in a sugarcane production and processing supply chain. British Food Journal, 114(6), 880-895. doi:10.1108/00070701211234390Bhatt, M. R., & Buch, S. (2015). Prediction of formability for sheet metal component using artificial intelligent technique. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). doi:10.1109/spin.2015.7095356Birkel, H. S., & Hartmann, E. (2019). Impact of IoT challenges and risks for SCM. Supply Chain Management: An International Journal, 24(1), 39-61. doi:10.1108/scm-03-2018-0142Boehlje, M. (1999). Structural Changes in the Agricultural Industries: How Do We Measure, Analyze and Understand Them? American Journal of Agricultural Economics, 81(5), 1028-1041. doi:10.2307/1244080Bonney, L., Clark, R., Collins, R., & Fearne, A. (2007). From serendipity to sustainable competitive advantage: insights from Houston’s Farm and their journey of co‐innovation. Supply Chain Management: An International Journal, 12(6), 395-399. doi:10.1108/13598540710826326Boshkoska, B. M., Liu, S., Zhao, G., Fernandez, A., Gamboa, S., del Pino, M., … Chen, H. (2019). A decision support system for evaluation of the knowledge sharing crossing boundaries in agri-food value chains. Computers in Industry, 110, 64-80. doi:10.1016/j.compind.2019.04.012Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., & Ellis, K. (2017). IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot. IEEE Communications Magazine, 55(9), 26-33. doi:10.1109/mcom.2017.1600528Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 205395171664817. doi:10.1177/2053951716648174Brown, K. (2013). Global environmental change I. Progress in Human Geography, 38(1), 107-117. doi:10.1177/0309132513498837Chilcanan, D., Navas, P., & Escobar, S. M. (2017). Expert system for remote process automation in multiplatform servers, through human machine conversation. 2017 12th Iberian Conference on Information Systems and Technologies (CISTI). doi:10.23919/cisti.2017.7975913Choi, J., In, Y., Park, C., Seok, S., Seo, H., & Kim, H. (2016). Secure IoT framework and 2D architecture for End-To-End security. The Journal of Supercomputing, 74(8), 3521-3535. doi:10.1007/s11227-016-1684-0Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65-80. doi:10.1016/j.biosystemseng.2013.09.006Del Borghi, A., Gallo, M., Strazza, C., & Del Borghi, M. (2014). An evaluation of environmental sustainability in the food industry through Life Cycle Assessment: the case study of tomato products supply chain. Journal of Cleaner Production, 78, 121-130. doi:10.1016/j.jclepro.2014.04.083Devarakonda, R., Shrestha, B., Palanisamy, G., Hook, L., Killeffer, T., Krassovski, M., … Lazer, K. (2014). OME: Tool for generating and managing metadata to handle BigData. 2014 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2014.7004476Nascimento, A. F. do, Mendonça, E. de S., Leite, L. F. C., Scholberg, J., & Neves, J. C. L. (2012). Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics. Scientia Agricola, 69(6), 393-401. doi:10.1590/s0103-90162012000600008Dolan, C., & Humphrey, J. (2000). Governance and Trade in Fresh Vegetables: The Impact of UK Supermarkets on the African Horticulture Industry. Journal of Development Studies, 37(2), 147-176. doi:10.1080/713600072Dragincic, J., Korac, N., & Blagojevic, B. (2015). Group multi-criteria decision making (GMCDM) approach for selecting the most suitable table grape variety intended for organic viticulture. Computers and Electronics in Agriculture, 111, 194-202. doi:10.1016/j.compag.2014.12.023Dworak, V., Selbeck, J., Dammer, K.-H., Hoffmann, M., Zarezadeh, A., & Bobda, C. (2013). Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems. Sensors, 13(2), 1523-1538. doi:10.3390/s130201523Eisele, M., Kiese, R., Krämer, A., & Leibundgut, C. (2001). Application of a catchment water quality model for assessment and prediction of nitrogen budgets. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7-8), 547-551. doi:10.1016/s1464-1909(01)00048-xElsayed, K. M. F., Ismail, T., & S. Ouf, N. (2018). A Review on the Relevant Applications of Machine Learning in Agriculture. IJIREEICE, 6(8), 1-17. doi:10.17148/ijireeice.2018.681Esteso, A., Alemany, M. M. E., & Ortiz, A. (2017). Métodos y Modelos Deterministas e Inciertos para la Gestión de Cadenas de Suministro Agroalimentarias. Dirección y Organización, 41-46. doi:10.37610/dyo.v0i0.509Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706GERHARDS, R., GUTJAHR, C., WEIS, M., KELLER, M., SÖKEFELD, M., MÖHRING, J., & PIEPHO, H. P. (2011). Using precision farming technology to quantify yield effects attributed to weed competition and herbicide application. Weed Research, 52(1), 6-15. doi:10.1111/j.1365-3180.2011.00893.xGovindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. doi:10.1016/j.ijpe.2013.12.028Gumaste, S. S., & Kadam, A. J. (2016). Future weather prediction using genetic algorithm and FFT for smart farming. 2016 International Conference on Computing Communication Control and automation (ICCUBEA). doi:10.1109/iccubea.2016.7860028Hashem, H., & Ranc, D. (2016). A review of modeling toolbox for BigData. 2016 International Conference on Military Communications and Information Systems (ICMCIS). doi:10.1109/icmcis.2016.7496565Hefnawy, A., Elhariri, T., Cherifi, C., Robert, J., Bouras, A., Kubler, S., & Framling, K. (2017). Combined use of lifecycle management and IoT in smart cities. 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). doi:10.1109/skima.2017.8294112Hosseini, S. H., Tang, C. Y., & Jiang, J. N. (2014). Calibration of a Wind Farm Wind Speed Model With Incomplete Wind Data. IEEE Transactions on Sustainable Energy, 5(1), 343-350. doi:10.1109/tste.2013.2284490Hu, Y., Zhang, L., Li, J., & Mehrotra, S. (2016). ICME 2016 Image Recognition Grand Challenge. 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). doi:10.1109/icmew.2016.7574663A. Irmak, J. W. Jones, W. D. Batchelor, S. Irmak, K. J. Boote, & J. O. Paz. (2006). Artificial Neural Network Model as a Data Analysis Tool in Precision Farming. Transactions of the ASABE, 49(6), 2027-2037. doi:10.13031/2013.22264Jeon, S., Kim, B., & Huh, J. (2017). Study on methods to determine rotor equivalent wind speed to increase prediction accuracy of wind turbine performance under wake condition. Energy for Sustainable Development, 40, 41-49. doi:10.1016/j.esd.2017.06.001Joly, P.-B. (2005). Resilient farming systems in a complex world — new issues for the governance of science and innovation. Australian Journal of Experimental Agriculture, 45(6), 617. doi:10.1071/ea03252Joshi, R., Banwet, D. K., & Shankar, R. (2009). Indian cold chain: modeling the inhibitors. British Food Journal, 111(11), 1260-1283. doi:10.1108/00070700911001077Kamata, T., Roshanianfard, A., & Noguchi, N. (2018). Heavy-weight Crop Harvesting Robot - Controlling Algorithm. IFAC-PapersOnLine, 51(17), 244-249. doi:10.1016/j.ifacol.2018.08.165Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194. doi:10.1016/j.ijpe.2019.05.022Kamilaris, 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.2017.09.037Kelepouris, T., Pramatari, K., & Doukidis, G. (2007). RFID‐enabled traceability in the food supply chain. Industrial Management & Data Systems, 107(2), 183-200. doi:10.1108/02635570710723804Khan, S. F., & Ismail, M. Y. (2018). An Investigation into the Challenges and Opportunities Associated with the Application of Internet of Things (IoT) in the Agricultural Sector-A Review. Journal of Computer Science, 14(2), 132-143. doi:10.3844/jcssp.2018.132.143Kladivko, E. J., Helmers, M. J., Abendroth, L. J., Herzmann, D., Lal, R., Castellano, M. J., … Villamil, M. B. (2014). Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems. Journal of Soil and Water Conservation, 69(6), 532-542. doi:10.2489/jswc.69.6.532Ko, T., Lee, J., & Ryu, D. (2018). Blockchain Technology and Manufacturing Industry: Real-Time Transparency and Cost Savings. Sustainability, 10(11), 4274. doi:10.3390/su10114274KÖK, M. S. (2009). Application of Food Safety Management Systems (ISO 22000/HACCP) in the Turkish Poultry Industry: A Comparison Based on Enterprise Size. Journal of Food Protection, 72(10), 2221-2225. doi:10.4315/0362-028x-72.10.2221Kvíz, Z., Kroulik, M., & Chyba, J. (2014). Machinery guidance systems analysis concerning pass-to-pass accuracy as a tool for efficient plant production in fields and for soil damage reduction. Plant, Soil and Environment, 60(No. 1), 36-42. doi:10.17221/622/2012-pseLamsal, K., Jones, P. C., & Thomas, B. W. (2016). Harvest logistics in agricultural systems with multiple, independent producers and no on-farm storage. Computers & Industrial Engineering, 91, 129-138. doi:10.1016/j.cie.2015.10.018Laube, P., Duckham, M., & Palaniswami, M. (2011). Deferred decentralized movement pattern mining for geosensor networks. International Journal of Geographical Information Science, 25(2), 273-292. doi:10.1080/13658810903296630Li, F.-R., Gao, C.-Y., Zhao, H.-L., & Li, X.-Y. (2002). Soil conservation effectiveness and energy efficiency of alternative rotations and continuous wheat cropping in the Loess Plateau of northwest China. Agriculture, Ecosystems & Environment, 91(1-3), 101-111. doi:10.1016/s0167-8809(01)00265-1Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. doi:10.3390/s18082674Meichen, L., Jun, C., Xiang, Z., Lu, W., & Yongpeng, T. (2018). Dynamic obstacle detection based on multi-sensor information fusion. IFAC-PapersOnLine, 51(17), 861-865. doi:10.1016/j.ifacol.2018.08.086Louwagie, G., Northey, G., Finn, J. A., & Purvis, G. (2012). Development of indicators for assessment of the environmental impact of livestock farming in Ireland using the Agri-environmental Footprint Index. Ecological Indicators, 18, 149-162. doi:10.1016/j.ecolind.2011.11.003Luque, A., Peralta, M. E., de las Heras, A., & Córdoba, A. (2017). State of the Industry 4.0 in the Andalusian food sector. Procedia Manufacturing, 13, 1199-1205. doi:10.1016/j.promfg.2017.09.195Malhotra, S., Doja, M. ., Alam, B., & Alam, M. (2017). Bigdata analysis and comparison of bigdata analytic approches. 2017 International Conference on Computing, Communication and Automation (ICCCA). doi:10.1109/ccaa.2017.8229821Mayer, J., Gunst, L., Mäder, P., Samson, M.-F., Carcea, M., Narducci, V., … Dubois, D. (2015). «Productivity, quality and sustainability of winter wheat under long-term conventional and organic management in Switzerland». European Journal of Agronomy, 65, 27-39. doi:10.1016/j.eja.2015.01.002McGuire, S., & Sperling, L. (2013). Making seed systems more resilient to stress. Global Environmental Change, 23(3), 644-653. doi:10.1016/j.gloenvcha.2013.02.001Mekala, M. S., & Viswanathan, P. (2017). A Survey: Smart agriculture IoT with cloud computing. 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS). doi:10.1109/icmdcs.2017.8211551Mishra, S., Mishra, D., & Santra, G. H. (2016). Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper. Indian Journal of Science and Technology, 9(38). doi:10.17485/ijst/2016/v9i38/95032Mocnej, J., Seah, W. K. G., Pekar, A., & Zolotova, I. (2018). Decentralised IoT Architecture for Efficient Resources Utilisation. IFAC-PapersOnLine, 51(6), 168-173. doi:10.1016/j.ifacol.2018.07.148Mohanraj, I., Gokul, V., Ezhilarasie, R., & Umamakeswari, A. (2017). Intelligent drip irrigation and fertigation using wireless sensor networks. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR). doi:10.1109/tiar.2017.8273682Montecinos, J., Ouhimmou, M., Chauhan, S., & Paquet, M. (2018). Forecasting multiple waste collecting sites for the agro-food industry. Journal of Cleaner Production, 187, 932-939. doi:10.1016/j.jclepro.2018.03.127Yandun Narváez, F., Gregorio, E., Escolà, A., Rosell-Polo, J. R., Torres-Torriti, M., & Auat Cheein, F. (2018). Terrain classification using ToF sensors for the enhancement of agricultural machinery traversability. Journal of Terramechanics, 76, 1-13. doi:10.1016/j.jterra.2017.10.005Nguyen, T., ZHOU, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264. doi:10.1016/j.cor.2017.07.004Nilsson, E., Hochrainer-Stigler, S., Mochizuki, J., & Uvo, C. B. (2016). Hydro-climatic variability and agricultural production on the shores of Lake Chad. Environmental Development, 20, 15-30. doi:10.1016/j.envdev.2016.09.001Nolan, P., Paley, D. A., & Kroeger, K. (2017). Multi-UAS path planning for non-uniform data collection in precision agriculture. 2017 IEEE Aerospace Conference. doi:10.1109/aero.2017.7943794Oberholster, C., Adendorff, C., & Jonker, K. (2015). Financing Agricultural Production from a Value Chain Perspective. Outlook on Agriculture, 44(1), 49-60. doi:10.5367/oa.2015.0197Opara, L. U., & Mazaud, F. (2001). Food Traceability from Field to Plate. Outlook on Agriculture, 30(4), 239-247. doi:10.5367/000000001101293724Ott, K.-H., Aranı́bar, N., Singh, B., & Stockton, G. W. (2003). Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts. Phytochemistry, 62(6), 971-985. doi:10.1016/s0031-9422(02)00717-3Panetto, H. (2007). Towards a classification framework for interoperability of enterprise applications. International Journal of Computer Integrated Manufacturing, 20(8), 727-740. doi:10.1080/09511920600996419Paulraj, G. J. L., Francis, S. A. J., Peter, J. D., & Jebadurai, I. J. (2018). Resource-aware virtual machine migration in IoT cloud. Future Generation Computer Systems, 85, 173-183. doi:10.1016/j.future.2018.03.024Pilli, S. K., Nallathambi, B., George, S. J., & Diwanji, V. (2015). eAGROBOT — A robot for early crop disease detection using image processing. 2015 2nd International Conference on Electronics and Communication Systems (ICECS). doi:10.1109/ecs.2015.7124873Pinho, P., Dias, T., Cruz, C., Sim Tang, Y., Sutton, M. A., Martins-Loução, M.-A., … Branquinho, C. (2011). Using lichen functional diversity to assess the effects of atmospheric ammonia in Mediterranean woodlands. Journal of Applied Ecology, 48(5), 1107-1116. doi:10.1111/j.1365-2664.2011.02033.xPrathibha, S. R., Hongal, A., & Jyothi, M. P. (2017). IOT Based Monitoring System in Smart Agriculture. 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). doi:10.1109/icraect.2017.52Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations. Agricultural Systems, 172, 47-59. doi:10.1016/j.agsy.2018.01.022Ribarics, P. (2016). Big Data and its impact on agriculture. Ecocycles, 2(1), 33-34. doi:10.19040/ecocycles.v2i1.54Rosell, J. R., & Sanz, R. (2012). A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81, 124-141. doi:10.1016/j.compag.2011.09.007Roshanianfard, A., Kamata, T., & Noguchi, N. (2018). Performance evaluation of harvesting robot for heavy-weight crops. IFAC-PapersOnLine, 51(17), 332-338. doi:10.1016/j.ifacol.2018.08.200Routroy, S., & Behera, A. (2017). Agriculture supply chain. Journal of Agribusiness in Developing and Emerging Economies, 7(3), 275-302. doi:10.1108/jadee-06-2016-0039Ruiz-Garcia, L., Steinberger, G., & Rothmund, M. (2010). A model and prototype implementation for tracking and tracing agricultural batch products along the food chain. Food Control, 21(2), 112-121. doi:10.1016/j.foodcont.2008.12.003Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big
    corecore