Relevant bibliographies by topics / Internet of things, Precision agriculture

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Internet of things, Precision agriculture'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Internet of things, Precision agriculture"

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Alhasnawi, Bilal, Basil Jasim, and Bayadir Issa. "Internet of Things (IoT) for Smart Precision Agriculture." Iraqi Journal for Electrical and Electronic Engineering 16, no. 1 (April 12, 2020): 1–11. http://dx.doi.org/10.37917/ijeee.16.1.4.

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The scarcity of clean water resources around the globe has generated a need for their optimum utilization. Internet of Things (IoT) solutions, based on the application-specific sensors’ data acquisition and intelligent processing, are bridging the gaps between the cyber and physical worlds. IoT based smart irrigation management systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology-based smart system to predict the irrigation requirements of a field using the sensing of ground parameters like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet. The sensing nodes, involved in the ground and environmental sensing, consider soil moisture, air temperature, and relative humidity of the crop field. This mainly focused on wastage of water, which is a major concern of the modern era. It is also time-saving, allows a user to monitor environmental data for agriculture using a web browser and Email, cost-effectiveness, environmental protection, low maintenance and operating cost and efficient irrigation service. The proposed system is made up of two parts: hardware and software. The hardware consists of a Base Station Unit (BSU) and several Terminal Nodes (TNs). The software is made up of the programming of the Wi-Fi network and the system protocol. In this paper, an MQTT (Message Queue Telemetry Transportation) broker was built on the BSU and TU board.
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Wang, Bin Peng. "The Design of Modern Agriculture Control System Based on Internet of Things." Applied Mechanics and Materials 513-517 (February 2014): 1519–22. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1519.

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This paper involves the modern agricultural application control system which is based on internet of things, and this intelligent management system uses intelligent control technology such as S7-300, GSM,WSN and Zigbee to realize the modernization of rural security, agricultural production and residents living fully intelligent managed. This system applies precision agriculture, digital image processing, wireless data transmission and other fields, really combining digital management technology with embedded technology. At the same time, this system which is based on internet of things is the necessary path of modern agriculture informatization strategy. With the mature development of technology of internet of things in modern society, modern agriculture application management system based on internet of things will bring new change to agriculture and high efficiency of agricultural production.
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Gill, Rana. "A Review on Various Techniques to Transform Traditional Farming to Precision Agriculture." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 131–35. http://dx.doi.org/10.17762/turcomat.v12i2.690.

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The agricultural sector is of great importance to fulfill food resources need of the country. Precision Agriculture (PA) with Internet of Things and Wireless Sensor Network is a transformation from traditional farming to smart farming. Wireless sensor networks and Internet of Things are considered as drivers to develop system which can change agriculture sector from manual to automatic. Advancement in the technology have pushed the growth of precision agriculture to very large extent despite of several challenges faced in this area. System for precision agriculture relies on hardware components mainly wireless sensors which act as a source for gathering of real time data. Depending upon the real time date retrieved by sensors automation in agriculture is done by adopting decision-based system. With Precision agriculture productivity is optimized by maintaining sustaniability as crop receives what is acutual requirement on the basis of new techniques and software platforms. This review article includes Inernet of Things (IoT), Wireless Sensors, Wireless communication and challenges faced in this area.
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Evett, Steven R., Susan A. O’Shaughnessy, Manuel A. Andrade, William P. Kustas, M. C. Anderson, H. H. Schomberg, and A. Thompson. "Precision Agriculture and Irrigation: Current U.S. Perspectives." Transactions of the ASABE 63, no. 1 (2020): 57–67. http://dx.doi.org/10.13031/trans.13355.

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Highlights.Precision agriculture (PA) applications in irrigation are stymied by lack of decision support systems.Modern PA relies on sensor systems and near real-time feedback for irrigation decision support and control.Sophisticated understanding of biophysics and biological systems now guides site-specific irrigation.The internet of things (IOT) enables new ways to increase yield per unit of water used and nutrient use efficiency. Keywords: Crop water productivity, Decision support system, Internet of things, Remote sensing, SCADA, Soil water content.
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Solanki, Rutvik. "IoT-based Precision Agriculture Platform: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1419–21. http://dx.doi.org/10.22214/ijraset.2021.38197.

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Abstract: Technological advancements such as the Internet of Things (IoT) and Artificial Intelligence (AI) are helping to boost the global agricultural sector as it is expected to grow by around seventy percent in the next two decades. There are sensor-based systems in place to keep track of the plants and the surrounding environment. This technology allows farmers to watch and control farm operations from afar, but it has a few limitations. For farmers, these technologies are prohibitively expensive and demand a high level of technological competence. Besides, Climate change has a significant impact on crops because increased temperatures and changes in precipitation patterns increase the likelihood of disease outbreaks, resulting in crop losses and potentially irreversible plant destruction. Because of recent advancements in IoT and Cloud Computing, new applications built on highly innovative and scalable service platforms are now being developed. The use of Internet of Things (IoT) solutions has enormous promise for improving the quality and safety of agricultural products. Precision farming's telemonitoring system relies heavily on Internet of Things (IoT) platforms; therefore, this article quickly reviews the most common IoT platforms used in precision agriculture, highlighting both their key benefits and drawbacks
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Ali, Terteil A. A. "Precision Agriculture Monitoring System using Internet of Things (IoT)." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 2961–70. http://dx.doi.org/10.22214/ijraset.2018.4493.

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S., Anulekshmi. "Comprehensive Study and Research on Wireless Sensor Network and Internet of Things for Precision Agriculture." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (March 31, 2020): 150–58. http://dx.doi.org/10.5373/jardcs/v12i4/20201427.

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Spachos, Petros. "Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices." IoT 1, no. 1 (February 21, 2020): 5–20. http://dx.doi.org/10.3390/iot1010002.

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Precision Agriculture (PA) is an ever-expanding field that takes modern technological advancements and applies it to farming practices to reduce waste and increase output. One advancement that can play a significant role in achieving precision agriculture is wireless technology, and specifically the Internet of Things (IoT) devices. Small, inch scale and low-cost devices can be used to monitor great agricultural areas. In this paper, a system for precision viticulture which uses IoT devices for real-time monitoring is proposed. The different components of the system are programmed properly and the interconnection between them is designed to minimize energy consumption. Wireless sensor nodes measure soil moisture and soil temperature in the field and transmit the information to a base station. If the conditions are optimal for a disease or pest to occur, a drone flies towards the area. When the drone is over the node, pictures are captured and then it returns to the base station for further processing. The feasibility of the system is examined through experimentation in a realistic scenario.
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Vuran, Mehmet C., Abdul Salam, Rigoberto Wong, and Suat Irmak. "Internet of underground things in precision agriculture: Architecture and technology aspects." Ad Hoc Networks 81 (December 2018): 160–73. http://dx.doi.org/10.1016/j.adhoc.2018.07.017.

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Lin, Na, Xuping Wang, Yihao Zhang, Xiangpei Hu, and Junhu Ruan. "Fertigation management for sustainable precision agriculture based on Internet of Things." Journal of Cleaner Production 277 (December 2020): 124119. http://dx.doi.org/10.1016/j.jclepro.2020.124119.

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Dissertations / Theses on the topic "Internet of things, Precision agriculture"

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Uludag, Tuba. "LoRaWAN IoT Networks for Precision Agriculture Applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Precision Agriculture (PA) is an emerging technology which enables efficient irrigation by employing the Internet of Things (IoT). We split the thesis in two parts. The first part is estimation of humidity level via experimentation. We focus on measuring Received Signal Strength Indicator (RSSI) to obtain humidity level of the field. Thus, we aim at eliminating the humidity sensors which are very expensive and estimate soil moisture through the variation of RSSI values measured by wireless devices buried underground. In the second part of the thesis, we aim at building an accurate and reliable irrigation system by the help of IoT technology via simulations. The advantage brought by our Wireless Sensor Network (WSN) is twofold: it minimizes the amount of wasted water during irrigation in farming, and it increases the yield with efficient irrigation. For these purposes, we tested the performance of LoRa protocol in different scenarios in both parts of the thesis.
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Chen, Yibo. "Routing algorithm dedicated to environmental data collection : precision agriculture." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22572/document.

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Les Réseaux de Capteurs Sans Fil (RCSF ou Wireless Sensor Network - WSN) sont l'une des technologies les plus importantes du 21ème siècle. La plupart des chercheurs et les analystes estiment que, dans un proche avenir, ces micro-capteurs seront intégrés partout dans l’environnement de notre vie quotidienne. Ces dernières années, l'Internet des Objets (Internet of Things - IoT) est également une des technologies émergentes qui se développe rapidement. Deux nouveaux standards permettent de déployer des réseaux sans fil de faible consommation énergétique connectés à internet : le protocole 6LowPAN (Low power Wireless Personal Area Networks) qui permet notamment d’apporter l’adressage IPv6 aux capteurs grâce à l’encapsulation et la compression des données et le protocole de routage RPL (IPv6 routing protocol for low-power and lossy network) qui permet à l’information de circuler dans les WSN de proche en proche à un faible coût énergétique. Bien que le développement de ces techniques soit extrêmement rapide, plusieurs problèmes causés principalement par le manque de ressources des micro-capteurs (puissance limitée de traitement, problèmes de bande passante et de connexion des liens avec perte de données, problème de ressource énergétique limitée) demeurent et doivent être résolus, notamment pour les applications agro-environnementales
The wireless sensor network (WSN) is one of the most important technologies of the 21st century. Most researchers and technical analysts believe that in the near future, these micro-sensors will be integrated into the environment of our daily lives. In recent years, the IoT (Internet of Things) and WoT (Web of Things) technologies also have great forwarding. Especially, the IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) protocol has allowed the use of IPv6 protocol stack in the field of WSN, thanks to its encapsulation and compression mechanisms in IPv6 packet header. Moreover, the RPL (IPv6 Routing Protocol for Low-power and Lossy Network) provides such a powerful routing function that can be applied for a variety of application scenarios. These two key standards of IoT and WoT technologies for WSN can be used in an IPv6 stack, and they will successfully achieve the connection between Internet and micro-sensors. Thus, due to the availability of IPv6 address (128-bit), all the communicating objects, such as smart device, sensor, and actuator, can be connected to the Internet. That is the greatest advantage brought by the IoT. Although the progress of these techniques is extremely fast, several issues caused by resource constraints of micro-sensor (limited processing power, bandwidth and lossy connection link, and energy), such as QoS, energy efficient, robustness and lifetime of WSN, and the most important, the special requirement of agricultural applications. Notice that Precision Agriculture is are still very challenging and waiting to be solved. Essentially, these open questions would dabble in the aspects like telemedicine, remote home automation, industrial control etc. Thus, the results obtained in this work will have a significant impact on both economic and scientific. Economically, it can offer a solution for WSN to support sustainable development in the field of agriculture automation. While scientifically, we will contribute to the routing protocol standardization of wireless micro-sensors in the domain of environmental monitoring
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Barros, Marcelo Freire de. "Seletor adaptativo de tecnologia de comunicação para nós multitecnológicos em aplicações agrícolas." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-14122016-084744/.

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Os processos de produção e distribuição agrícolas fazem uso de diversas tecnologias usadas para coleta de dados e gerenciamento de recursos e atividades, dentre as quais as Redes de Sensores Sem Fio, Identificação por Radiofrequência e o Wi-Fi. Devido a falhas na infraestrutura e variações ambientais, essas tecnologias possuem diversas limitações, como por exemplo, o atraso com que os dados chegam aos sistemas de gerenciamento, comprometendo a eficiência do processo. Uma solução com nós multitecnológicos, baseada nos princípios da Internet das Coisas, poderia oferecer dados de todo o processo produtivo e de distribuição, em tempo real; mas, para que esta solução seja realmente eficiente, necessita de um seletor adaptativo de tecnologias de comunicação para os \"objetos\" de campo que se adapte ao ambiente em tempo de execução. O objetivo deste trabalho é propor e avaliar a funcionalidade de um seletor adaptativo para esse fim. Para a proposição do seletor, buscou-se embasamento em técnicas adaptativas que oferecessem solução adequada para processos de aprendizagem pelo seletor. Dentre essas técnicas, elegeram-se as Árvores de Decisão Adaptativas para implementar a operação do seletor. A funcionalidade do seletor foi avaliada por simulações, tanto quanto ao aspecto de seleção da opção tecnológica mais adequada para o momento e a situação em questão, quanto à facilidade de se adaptar a mudanças de cenário. Os resultados das avaliações mostram que a simulação inicial, feita a partir de um cenário correspondente à fase inicial de uma cultura de milho não apresenta resultados satisfatórios. Entretanto, após alguns ciclos de aprendizagem do seletor, os resultados evoluem e superaram as exigências de qualidade propostas. Em uma segunda fase, novas simulações foram realizadas, alterando-se o cenário inicial para que, aos poucos, correspondesse à fase de colheita do milho. Nestas simulações, o processo de aprendizagem continuou ocorrendo sempre que as exigências de qualidade deixaram de ser satisfeitas, até que alcançassem a qualidade de comunicação exigida. Estes resultados permitiram concluir pela validade do seletor proposto.
Agricultural production and distribution processes employ different technologies. They are used for data collection and management of resources and activities, among which Wireless Sensor Networks, Radiofrequency Identification and WiFi. These technologies have several limitations due to flaws in infrastructure and environmental variability. For example, data arrive at management systems late, compromising process efficiency. Multi-technology nodes can be a solution to obtain real-time data from the production and distribution processes, particularly if Internet of Things principles are present. Yet, for this solution to be efficient, a communication technology selector for field \"objects\" must be necessary, which must adapt to the environment in run time. Therefore, the goal is to propose and to assess this Selector functionality. This proposition was based on adaptive techniques, which offered an appropriate solution for learning processes by the selector, such as the Adaptive Decision Tree. The selector uses an Adaptive Decision Tree to select the communication technology. The selector functionalities, such as the appropriate technology selection for the moment and how they adapt changes in scenario, were evaluated by the simulation method. Evaluation results show that simulations made from a scenario corresponding to the initial phase of a corn crop did not show satisfactory, but the results evolved and met the quality requirements after some learning cycles. In a second step, new simulations were conducted, changing the scenario slowly to the harvest phase. The learning process continued to occur whenever the quality requirements were no longer met. These results showed the validity of the proposed selector.
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Dube, Elias Edo. "Wireless Farming: a mobile and Wireless Sensor Network based application to create farm field monitoring and plant protection for sustainable crop production and poverty reduction." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20488.

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There is a remarkable growth in the field of Information Communication Technology (ICT) in Developing Countries (DCs). Telecommunication is one of the areas where ICT is recording an ongoing rapid change. Mobile phones are becoming pervasive in daily scenario; and among the beneficiaries of this are farmers. Farmers are using mobile phones in executing their farming business and daily life. At the same time, Wireless Sensor Networks (WSNs) are also showing a result in developed part of our world. WSNs potential in sensing various environmental condition, their affordability and applicability motivated conducting of this master thesis. Therefore, the objective of conducting this master thesis is to investigate and identify how the use of mobile phones in conjunction with WSN enable farmers in Ethiopia monitor and control their farm field. We use firsthand qualitative data we gathered during our field work in Ethiopia to design our proposed prototype. Functional requirements and system design guideless are obtained from observation we make and interviews we carry out on irrigation based farmers around town of Meki in region of Oromia. We use our prototype to demonstrate and evaluate how irrigation based farmers benefit from existence of such system.
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Andrade-Sanchez, Pedro, and John T. Heun. "Things to Know About Applying Precision Agriculture Technologies in Arizona." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2010. http://hdl.handle.net/10150/146428.

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In this publication, we will make the case of what Precision Agriculture (PA) technologies can do to enhance the productivity of farming systems, with particular attention to the case of irrigated agriculture in the semi-arid Arizona. This guide is intended to aid growers to select the right technology when considering the need to acquire new, or upgrade existing equipment.
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Jonarv, Hultgren Susanne, and Philip Tennevall. "Saving resources through smart farming : An IoT experiment study." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17968.

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Context: Smart farming, agritech, is growing in popularity and is starting to develop rapidly with some already existing technology that is implemented in agriculture for both industrial and private use. Objectives: The goal of this thesis is to investigate the benefits and issues with implementing technology in agriculture, agritech. In this thesis the investigation and research is performed by conduction a literature study and an experiment. Realization: A prototype was created to monitor the soil moisture level and calculating the average soil moisture value, then water the plants when needed. This was then compared to a manually watered pot to investigate if agritech could reduce the water usage when maintaining plants. Results: The result of the experiment indicates that it is possible to improve the use of resources such as human labor, time spent on maintaining the plants and water usage. Conclusions: The conclusion of this thesis is with the help of agritech, human workers can spend more time on other tasks and maintain the technology implemented. Instead of observing the plants to see if they need watering and watering them manually. Water usage may also be minimized with the help of sensors that make sure the plants only get watered when needed by constantly checking the soil moisture level.
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Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of the response time. This thesis proposes a detection algorithm that utilizes RNN in predicting sensor drifts from time-series data streams. The detection algorithm was later named; Predictive Sliding Detection Window (PSDW) and consisted of both forecasting and classification models. Three different RNN algorithms, i.e., LSTM, CNN-LSTM, and GRU, were designed to predict sensor drifts using forecasting and classification techniques. The algorithms were compared against each other in terms of relevant accuracy metrics for forecasting and classification. The operability of the solution was investigated by developing a web server that hosted the PSDW algorithm on an AWS computing instance. The resulting forecasting and classification algorithms were able to make reasonably accurate predictions for this particular scenario. More specifically, the forecasting algorithms acquired relatively low RMSE values as ~0.6, while the classification algorithms obtained an average F1-score and accuracy of ~80% but with a high standard deviation. However, the response time was ~5700% slower during the simulation of the HTTP requests. The obtained results suggest the need for future investigations to improve the accuracy of the models and experiment with other computing paradigms for more reliable deployments.
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Torres, Andrei Bosco Bezerra. "Fusão de dados multinível para sistemas de internet das coisas em agricultura inteligente." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/25592.

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TORRES, A. B. B. Fusão de dados multinível para sistemas de internet das coisas em agricultura inteligente. 2017. 71 f. Dissertação (Mestrado em Engenharia de Teleinformática)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2017.
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The usage of Wireless Sensor Networks (WSN) to detect and monitor phenomena isn’t a new concept, with studies dating back to 1980, but it has gained momentum with the expansion of Internet of Things (IoT), which aims to enable day to day objects to sense, identify and analyze our world. For IoT to be viable, it is necessary for the objects/sensors to be low-cost, and that implies a series of limitations: low battery, low processing and storage capabilities, low accuracy, etc. In this context, data fusion techniques can be used to mitigate some of these limitations and make the adoption of low-cost sensors viable. This dissertation proposes a data fusion architecture for IoT, improving sensor accuracy, detecting events/anomalies (such as sensor failure) and enabling automated decision making. As a case study, experimental cultures of precocious dwarf cashew and coconut trees were monitored.
A utilização de Redes de Sensores Sem Fio (RSSF) para detecção de fenômenos e monitoramento de ambientes não é um conceito novo, com estudos iniciados na década de 1980, mas ele tem ganhado força pela expansão da Internet das Coisas (Internet of Things - IoT), que trata de capacitar os objetos ao nosso redor de sensoriar, identificar e analisar o mundo. Para tornar a IoT viável em larga escala, é necessário que os objetos/sensores sejam de baixo custo, e isso implica uma série de limitações: bateria limitada, baixa capacidade processamento e armazenamento, baixa acurácia, dentre outros. Nesse contexto, técnicas de fusão de dados podem ser utilizadas para mitigar algumas das limitações citadas e viabilizar a adoção de sensores de baixo custo. A proposta desta dissertação é uma arquitetura de fusão de dados multinível para IoT para melhorar a acurácia dos sensores, detectar eventos/anomalias (como a falha de sensores) e possibilitar tomadas de decisões automatizadas. Como estudo de caso, foram realizados experimentos em conjunto com a Embrapa em um projeto de pesquisa de Agricultura de Precisão no monitoramento de cultivos experimentais de coco e de caju anão-precoce.
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Nguyen, Quang-Duy. "Interoperability and Upgradability Improvement for Context-Aware Systems in Agriculture 4.0." Thesis, Université Clermont Auvergne‎ (2017-2020), 2020. http://www.theses.fr/2020CLFAC017.

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La prochaine évolution de l’agriculture est l’Agriculture 4.0. Dans ce domaine, les nouvelles technologies de l’Internet des Objets (IdO) et les systèmes contextuels sont utilisés pour améliorer les performances des activités agricoles. Un système contextuel est un système capable de réagir automatiquement et adéquatement en fonction du contexte. Le fait d’utiliser un tel système permet non seulement de réduire la charge de travail des agriculteurs, mais aussi d’améliorer la précision des activités agricoles. Cependant, leur emploi dans le monde rencontre deux obstacles spécifiques. Le premier obstacle est le besoin de mettre régulièrement à jour le système contextuel sans changer sa fonctionnalité. Ce besoin s’appuie sur le fait que l’agriculture est une activité saisonnière, avec un lieu de travail externe, ce qui implique plusieurs facteurs imprévisibles qui influent sur les aspects logiciels et matériels du système. Le deuxième obstacle est l’hétérogénéité de données générées à partir du système contextuel. Dans le domaine agricole, on trouve des capteurs variés observant des phénomènes variés et produisant des données également variées. Représenter ces données est un fait nécessaire pour l’interopérabilité des dispositifs à l’intérieur un système contextuel, ou pour l’interopérabilité de plusieurs systèmes contextuels différents à l’intérieur l’écosystème de l’IdO. Cette thèse propose trois contributions. La première est une architecture s’appuyant sur le principe de microservice. Cette architecture est une pile de services pour les systèmes contextuels, qui permet aux développeurs d’un système de se focaliser sur les objectifs des services plutôt que leurs aspects logiciels et matériels. La deuxième contribution est une ontologie, intitulé CASO, dédiée aux systèmes contextuels. Cette ontologie fournit un vocabulaire pour modéliser les données générées par le système contextuel. De plus, elle inclut un mécanisme pour créer des règles de raisonnement. La troisième contribution est un système d’aide à la décision (SAD) pour l’irrigation automatique, développé à partir d’IRRINOV® , une méthode d’irrigation manuelle. Il fait partie d’un système contextuel dédié à l’irrigation de l’équipe TSCF d’INRAE. Ce SAD est basé sur la pile de services pour les systèmes contextuels,et utilise l’ontologie IRRIG, une spécialisation de CASO dédiée à l’irrigation. Les trois contributions vont être appliquées dans un système contextuel d’irrigation déployé dans l’AgroTechnoPôle, situé à Montoldre, en France
The next evolution of agriculture is Agriculture 4.0. Agriculture 4.0 is about using technologies ofthe Internet of Things (IoT) and Context-Aware Systems (CASs) to increase the performance offarming activities. A CAS can react automatically and adequately to the environment based onits context. Applying CASs in agriculture can reduce farm labor and increase the precision offarming activities. However, it encounters two challenges specific to agriculture. The firstchallenge relies on the need to upgrade a CAS regularly with new computing devices orsoftware programs without changing its functionality. Indeed, natural factors, such as violentweather and wild animals, can damage the computing devices located on farmland. Moreover,after each farming season, farmers may need to upgrade their system with new computingdevices and software programs. The second challenge is the data heterogeneity generated froma CAS. In agriculture, various phenomena involve the need to have different sensor devices thatmake numerous types of measurements and produce heterogeneous data. Representing all ofthese heterogeneous data is necessary for the interoperability of different computing devices ina CAS or the interoperability between different CASs in the IoT ecosystem. This thesis proposesthree contributions. The first contribution addresses the first challenge. It is a new architecturebased on the microservice mindset that allows system developers to focus on the services’goals rather than the computing devices and software programs of a CAS. This newarchitecture is called the stack of services for CASs. The second contribution addresses thesecond challenge. It is a new ontology for CASs named CASO. The ontology provides avocabulary to model heterogeneous data generated from CASs and embodies a mechanism tomake rules for reasoning. The third contribution is to build a decision support system (DSS) forthe irrigation CAS in the research unit TSCF, INRAE. The design of the DSS relies on the stackof services for CASs. Moreover, the DSS uses a new ontology called IRRIG, a specialization ofCASO for irrigation. The DSS is an automation version of the manual irrigation methodIRRINOV®. All the guidelines for farmers in IRRINOV® are transformed into rules for reasoning.The contributions of this thesis are going to be applied to build a smart irrigation CAS deployedin AgroTechnoPôle, located in Montoldre, France
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Zetterman, Björn-Erik Adrian. "Beekeepers usage of IoT : Data collection, sharing and visualization in the domain of beekeeping." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-77814.

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This master thesis is exploring Beekeepers usage of Internet of Things, or “Internet of Bees”. Since most of the prior contributions are focusing on data gathering, the approach to focus on the users needs is central to take next steps in the field of using IoT for Beekeeping. After the introduction a chapter with an overview of current research and commercial solutions are presented. This is followed by a quantitative study with 222 responds, answering what beekeepers like to know about their bees, what platforms used by end users and what the beekeeper as a user expects. An demo of an existing commercial system is set up in real conditions, describing how to mount and configure a demo. Communication, synchronization and presentation is described. A closed user interface and a public user interface are a part of the demonstration. Potential users of this technique are interviewed to gain better understanding of users opinion of the demo. This is followed by another demo using a free of charge app where sound analysis processed with AI is tested. This thesis explains what beekeepers as users of Internet of Things could gain added value to their beekeeping.
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Books on the topic "Internet of things, Precision agriculture"

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Pattnaik, Prasant Kumar, Raghvendra Kumar, and Souvik Pal, eds. Internet of Things and Analytics for Agriculture, Volume 2. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0663-5.

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Buttafuoco, Gabriele, Raj Khosla, Abdul Mouazen, Olivier Naud, and Annamaria Castrignano. Agricultural Internet of Things and Decision Support for Precision Smart Farming. Elsevier Science & Technology Books, 2020.

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Agricultural Internet of Things and Decision Support for Precision Smart Farming. Elsevier, 2020. http://dx.doi.org/10.1016/c2018-0-00051-1.

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Chatterjee, Jyotir Moy, Abhishek Kumar, Pramod Singh Rathore, and Vishal Jain, eds. Internet of Things and Machine Learning in Agriculture. De Gruyter, 2021. http://dx.doi.org/10.1515/9783110691276.

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Jain, Vishal, and Jyotir Moy Chatterjee, eds. Internet of Things and Machine Learning in Agriculture. Nova Science Publishers, 2021. http://dx.doi.org/10.52305/mtxx5116.

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Chou, Timothy, and A. Vincent Vasquez. Precision Construction: Principles, Practices and Solutions for the Internet of Things in Construction. PrecisionStory, 2018.

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Kumar, Raghvendra, Souvik Pal, and Prasant Kumar Pattnaik. Internet of Things and Analytics for Agriculture, Volume 2. Springer, 2019.

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Badnjević, Almir, Mirjana Maksimović, and Enisa Omanović-Mikličanin. Nanofood and Internet of Nano Things: For the Next Generation of Agriculture and Food Sciences. Springer, 2019.

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Book chapters on the topic "Internet of things, Precision agriculture"

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Marcelino, Roderval, Luan C. Casagrande, Renan Cunha, Yuri Crotti, and Vilson Gruber. "Internet of Things Applied to Precision Agriculture." In Online Engineering & Internet of Things, 499–509. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64352-6_46.

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Khelifi, Fekher. "Monitoring System Based in Wireless Sensor Network for Precision Agriculture." In Internet of Things (IoT), 461–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37468-6_24.

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Ponnusamy, Vijayakumar, and Sowmya Natarajan. "Precision Agriculture Using Advanced Technology of IoT, Unmanned Aerial Vehicle, Augmented Reality, and Machine Learning." In Internet of Things, 207–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-52624-5_14.

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Patil, Rahul Keru, and Suhas Shivlal Patil. "Cognitive Intelligence of Internet of Things in Precision Agriculture." In Techno-Societal 2020, 789–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69921-5_79.

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Aishwarya Lakshmi, T., Balaji Hariharan, and P. Rekha. "An Energy Efficient Routing Protocol for Internet of Things Based Precision Agriculture." In Inventive Computation Technologies, 684–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33846-6_74.

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Min, Zhang, Wang Bei, Gao Chunyuan, and Shuai Zhao qian. "Application Study of Precision Agriculture Based on Ontology in the Internet of Things Environment." In Communications in Computer and Information Science, 374–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23226-8_49.

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Marques, Gonçalo, and Rui Pitarma. "An Internet of Things and Wireless Sensor Networks Hybrid Architecture for Precision Agriculture Monitoring." In Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions (2nd Edition), 1863–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51210-1_293.

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Madhumathi, R., T. Arumuganathan, and R. Shruthi. "Internet of Things in Precision Agriculture: A Survey on Sensing Mechanisms, Potential Applications, and Challenges." In Intelligent Sustainable Systems, 539–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2422-3_42.

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Shengduo, Li, and Zou Jian. "Research and Development of Management Platform for Precision Ecological Agriculture Based on the Internet of Things." In Advances in Intelligent and Soft Computing, 387–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29455-6_54.

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Abdul, Ibrahim Muhammad. "Agriculture-Internet of Things (A-IoT)." In Internet of Things, 301–8. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003140443-19.

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Conference papers on the topic "Internet of things, Precision agriculture"

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Singh, Sukhwinder, Parvez Alam, Parteek Kumar, and Sanmeet Kaur. "Internet of Things for Precision Agriculture Applications." In 2019 Fifth International Conference on Image Information Processing (ICIIP). IEEE, 2019. http://dx.doi.org/10.1109/iciip47207.2019.8985688.

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Treboux, Jerome, and Dominique Genoud. "Improved Machine Learning Methodology for High Precision Agriculture." In 2018 Global Internet of Things Summit (GIoTS). IEEE, 2018. http://dx.doi.org/10.1109/giots.2018.8534558.

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Dholu, Manishkumar, and K. A. Ghodinde. "Internet of Things (IoT) for Precision Agriculture Application." In 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2018. http://dx.doi.org/10.1109/icoei.2018.8553720.

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Routray, Sudhir K., Abhishek Javali, Laxmi Sharma, Aritri D. Ghosh, and Anindita Sahoo. "Internet of Things Based Precision Agriculture for Developing Countries." In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019. http://dx.doi.org/10.1109/icssit46314.2019.8987794.

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Stewart, Jacqueline, Robert Stewart, and Sean Kennedy. "Internet of Things — Propagation modelling for precision agriculture applications." In 2017 Wireless Telecommunications Symposium (WTS). IEEE, 2017. http://dx.doi.org/10.1109/wts.2017.7943528.

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Jain, Prachin, Sanat Sarangi, Prakruti Bhatt, and Srinivasu Pappula. "Development of an Energy-efficient Adaptive IoT Gateway Model for Precision Agriculture." In 2018 Global Internet of Things Summit (GIoTS). IEEE, 2018. http://dx.doi.org/10.1109/giots.2018.8534553.

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Kamienski, Carlos, Juha-Pekka Soininen, Markus Taumberger, Stenio Fernandes, Attilio Toscano, Tullio Salmon Cinotti, Rodrigo Filev Maia, and Andre Torre Neto. "SWAMP: an IoT-based Smart Water Management Platform for Precision Irrigation in Agriculture." In 2018 Global Internet of Things Summit (GIoTS). IEEE, 2018. http://dx.doi.org/10.1109/giots.2018.8534541.

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Ali, Terteil A. A., Viraj Choksi, and M. B. Potdar. "Precision Agriculture Monitoring System Using Green Internet of Things (G-IoT)." In 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2018. http://dx.doi.org/10.1109/icoei.2018.8553866.

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El-magrous, Ahmed A., Jason D. Sternhagen, Gary Hatfield, and Qiquan Qiao. "Internet of Things Based Weather-Soil Sensor Station for Precision Agriculture." In 2019 IEEE International Conference on Electro Information Technology (EIT). IEEE, 2019. http://dx.doi.org/10.1109/eit.2019.8833811.

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Salima, Yousfi, Jose Fernando Marin Peira, Gregorio Rincon de la Horra, and Pedro V. Mauri Ablanque. "Remote Sensing Data: Useful Way for the Precision Agriculture." In 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). IEEE, 2019. http://dx.doi.org/10.1109/iotsms48152.2019.8939229.

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Reports on the topic "Internet of things, Precision agriculture"

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Solovyanenko, Nina I. ЮРИДИЧЕСКИЕ СТРАТЕГИИ ЦИФРОВОЙ ТРАНСФОРМАЦИИ АГРАРНОГО БИЗНЕСА. DOI CODE, 2021. http://dx.doi.org/10.18411/0131-5226-2021-70004.

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t.The development of global agricultural production and food trade in recent decades implies a digital transformation and the transition to a new technological order, which is an essential factor for sustainable development. Digitalization of agriculture and the food sector is carried out on the basis of IT 2 platforms, the Internet of Things, cloud computing, big data, artificial intelligence, and blockchain technology. Fragmented and unclear legal mechanisms, slow updating of legal regulation hinder the introduction of digital solutions. A modern regulatory framework based on digital strategies should strengthen the confidence of farmers in "smart agriculture". In Russia, the legal mechanism of strategic planning covers the development of the national platform "Digital Agriculture". Digital strategies also include updating basic legislation.
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Solovyanenko, Nina I. Legal features of innovative (digital) entrepreneurship in the agricultural and food sector. DOI CODE, 2021. http://dx.doi.org/10.18411/0131-5226-2021-70008.

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Modern agricultural production and food trade are involved in the process of digital transformation, which is a cardinal factor of sustainable development and is carried out on the basis of IT platforms, the Internet of Things, cloud computing, big data, artificial intelligence, blockchain technologies. The COVID-19 pandemic has increased the dependence of these sectors of the economy on information and communication technology infrastructure and services. At the same time, the slow updating of legislation, which lags behind the constantly improving digital technologies, not only hinders their implementation, but also is a source of a number of social and legal problems. A modern regulatory framework based on digital strategies should strengthen "smart agriculture". In Russia, the legal mechanism of digital transformation and development of the national platform "Digital Agriculture" should be supported by updated basic legislation.
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