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Internet of Food and Farm 2020

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... Crop monitoring takes into consideration one or more of the following points:  Environmental conditions including humidity, temperature, solar radiation, fertilization, and pesticide application, for which data can be collected through WSNs and IoT sensors [80].  Crop diseases, including visual data that can be collected with high-resolution cameras, which may be fixed or mobile via UAVs [81]. ...
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Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers’ profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.
... Crop monitoring takes into consideration one or more of the following points:  Environmental conditions including humidity, temperature, solar radiation, fertilization, and pesticide application, for which data can be collected through WSNs and IoT sensors [80].  Crop diseases, including visual data that can be collected with high-resolution cameras, which may be fixed or mobile via UAVs [81]. ...
Article
Full-text available
Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers’ profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.
... Like the Future Internet programme, it was part of a larger programme with other IoT large-scale pilots in other sectors and domains (EU, 2022a;Guillen et al., 2017). IoF2020 built on the ecosystem that was formed in the previous projects with existing and new partners (Sundmaeker et al., 2016;Verdouw et al., 2017). The heart of the project was formed by 19 use case projects that were organized in five trials, representing several agricultural subsectors such as arable, meat, dairy, fruit, etc. Experienced partners from the previous projects combined all their knowledge and experience in an integrated, multidisciplinary project approach to facilitate large-scale implementation of digital IoT solutions designed to have a real impact. ...
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CONTEXT Digital technologies nowadays play a major role in innovation within the agri-food domain. The evolution of IT systems has currently arrived at a level that involves complex systems integration and business ecosystems in which many stakeholders in different roles are involved. A new paradigm for digital innovation is needed that copes with this increased complexity. OBJECTIVE This paper presents an empirically informed framework for analysing and designing viable, sustainable digital innovation ecosystems in the agri-food domain. METHODS The research is based on a series of European large-scale public-private innovation projects from 2011 to 2021 with a total budget of 73 M€. They involved hundreds of stakeholders that were developing a large number of digital solutions through which a digital innovation ecosystem for agri-food was formed. In a longitudinal study, a conceptual framework was used to analyse these projects and describe how the digital innovation ecosystem has developed. Lessons learnt are translated into a number of design principles and an organizational approach to foster digital innovation ecosystems in agri-food. RESULTS AND CONCLUSIONS The conceptual framework consists of 6 key concepts: (i) innovation strategy, (ii) innovation organization, (iii) innovation network that contains (iv) the innovation process and (v) the innovation object and finally (vi) an innovation infrastructure. Along these 6 concepts, lessons learnt and in total 21 design principles are derived from analysing the projects forming a basis for the organizational framework. At the core of this framework is a lean multi-actor approach to trials and use case development interacting with a set of multidisciplinary activities: (i) developing a common technical collaboration infrastructure, (ii) identifying value streams with user engagement, (iii) engaging the right partners and stakeholders at the right time supported by strategic project planning and dynamic management. The most important conclusion is that effective, successful and quick use of appropriate IT in agri-food requires that actors should not be analysed in isolation from both their technological and business environment. Another consequence is that a ‘minimal viable ecosystem’ only emerges after considerable time, resources and ingenuity is invested and may require outside (government) intervention. SIGNIFICANCE Results from this paper can be used both by public and private stakeholders to diagnose and improve digital innovation projects and develop viable, sustainable digital innovation ecosystems in agri-food.
... Agriculture, on the other hand, has been undergoing the fourth revolution in recent decades as a result of the incorporation of Information and Communications Technologies into conventional agriculture (Sundmaeker et al., 2016). Machine Learning and Big Data Analytics, Remote Sensing, IoT, and UAVs are all promising technologies that might help agricultural systems innovate (Walter et al., 2017;Wolfert et al., 2017). ...
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In order to commercialize in the industry, various sensors and electrical gadgets are used to keep prices low in a few fields. Unmanned aerial vehicles (UAVs) can be utilized for surveillance, pesticide and insecticide application, and bioprocessing mistake detection to save money and improve the abilities of agricultural experts. Both single-mode and multi-mode UAV systems will perform admirably in this application. This chapter examines the constraints of the internet of things and UAV connectivity in remote areas, as well as smart agriculture application scenarios. In addition, the benefits and uses of employing the internet of things (IoT) and UAVs in agriculture were discussed. On the basis of several elements such as geographical, technological, and business, a system model has been presented. For various IoT applications, the architecture includes enabling technologies, scalability, intelligence, and supportability. Finally, interoperability issues are examined in depth in order to uncover the complications that arise during coordination between UAV and IoT components.
... Agriculture has undergone a fourth transformation (Farming 4.0) in recent years as ICT has been integrated into conventional farming practices (Sundmaeker et al., 2016). Technologies like Unmanned Aerial Vehicles (UAVs) popularly known as drones, Remote Sensing, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics (BDA), etc. are especially promising and have the potential to usher a new era in agricultural practices (Walter et al., 2017;Wolfert et al., 2017). ...
Technical Report
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This document status and scope of application of drones in agriculture in India includes about unmanned Aerial Vehicle (drone), applications of drones in agriculture and allied sectors, status of drone uses in Indian agriculture, manufacturing/Import of drones in India, policy framework, training and testing on Drone, Challenges with Use of drones in agriculture, way forward and way forward.
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Sustainable agriculture is currently being challenged under climate change scenarios since extreme environmental processes disrupt and diminish global food production. For example, drought-induced increases in plant diseases and rainfall caused a decrease in food production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow analyzing a large amount of data to understand agricultural production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow the processing and analysis of large amounts of heterogeneous data for which intelligent IT and high-resolution remote sensing techniques are required. However, the selection of ML algorithms depends on the types of data to be used. Therefore, agricultural scientists need to understand the data and the sources from which they are derived. These data can be structured, such as temperature and humidity data, which are usually numerical (e.g., float); semi-structured, such as those from spreadsheets and information repositories, since these data types are not previously defined and are stored in No-SQL databases; and unstructured, such as those from files such as PDF, TIFF, and satellite images, since they have not been processed and therefore are not stored in any database but in repositories (e.g., Hadoop). This study provides insight into the data types used in Agricultural Big Data along with their main challenges and trends. It analyzes 43 papers selected through the protocol proposed by Kitchenham and Charters and validated with the PRISMA criteria. It was found that the primary data sources are Databases, Sensors, Cameras, GPS, and Remote Sensing, which capture data stored in Platforms such as Hadoop, Cloud Computing, and Google Earth Engine. In the future, Data Lakes will allow for data integration across different platforms, as they provide representation models of other data types and the relationships between them, improving the quality of the data to be integrated.
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During the last several decades, smart agriculture approaches have become increasingly popular as a means of increasing agricultural productivity. The main goals of this research were to increase crop yield and allow farmers to monitor and manage the growth of their plants in their fields using a smart platform of IoT to control the flow of water based on the moisture of the soil and provide real-time surveillance to farmers who live far away from their farms. Anyone with a smartphone can use it, and once set up; it does not need to be maintained. In this study, two levels of controller were proposed: publisher and subscriber. The master and controller of the whole sensor is the publisher, who serves as the central coordinator. In technical terms, a subscriber is referred to as a water sprinkler unit. Based on the information given by the publisher, the water sprinkler unit will deliver water to the agricultural area once the publisher advises the subscriber. In the agriculture area, four Raspberry Pi systems, Arduino boards, and soil moisture sensors were used in an experiment that yielded a successful outcome.KeywordsIoTPublisherSubscriberRaspberry Pi
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The environmental assessment of using optical crop sensors for variable rate nitrogen application (VRNA) has been limited by the lack of a robust method to quantify site- and technology-specific impacts. This study aimed to (1) present a comparative life cycle assessment (LCA) of a conventional winter wheat production system with and without using a crop sensor for VRNA applied to an Austrian case study. Special emphasis was placed on simulating site-specific field emissions with the DeNitrification-DeComposition (DNDC) biogeochemical soil model; (2) assess the environmental impacts of only the fertilization process; and (3) compare soil emissions simulated by the DNDC with soil emissions coming from a benchmark ecoinvent wheat production process. Three nitrogen fertilization schemes – one conventional and two VRNA – were modeled. Two functional units were used – 1 ha of cultivated winter wheat and 1 kg of winter wheat produced. The system boundary includes tillage, seeding, plant protection, nitrogen fertilization, and harvesting processes. Information communication technologies (ICT) – manufacturing of the sensor, internet and computer manufacturing and usage – were also included within the boundary. Local and global environmental impacts attributed to nitrogen emissions due to fertilization were evaluated in this LCA, including climate change (CC), fine particulate matter formation (FPMF), freshwater eutrophication (FE), freshwater ecotoxicity (FET), terrestrial acidification (TA), marine eutrophication (ME), and human noncarcinogenic toxicity (HTnc). The CC of the fertilization process was 1,662.8 kg CO2 eq./ha with conventional nitrogen application versus 1,518.8 kg CO2 eq./ha as the lower of the two VRNA results, an 8.6% reduction due to less fertilizer applied. Fertilization was found to be responsible for more than 80% of the total emissions that impact CC, 55% of the FET, 44% of the HTnc, 96% of the FE and 96% of the TA. The largest greenhouse gas (GHG) emitters were soil N emissions as simulated by the DNDC, followed by the fertilizer manufacturing process in all of the impacts, except for FET and HTnc, where fertilizer production was the highest contributor. ICT components contributed less than 1% to all of the impacts assessed. The amount of applied N fertilizer has a greater influence on NH3 and NO3 indirect soil emissions than on direct N2O emissions. This study demonstrates that using optical crop sensors for VRNA could have a limited but positive environmental impact and highlights the importance of applying site-specific soil models to estimate field emissions.
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