Relevant bibliographies by topics / Smart agriculture

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Smart agriculture'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "Smart agriculture"

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Dankan Gowda, V., M. Sandeep Prabhu, M. Ramesha, Jayashree M. Kudari, and Ansuman Samal. "Smart Agriculture and Smart Farming using IoT Technology." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012038. http://dx.doi.org/10.1088/1742-6596/2089/1/012038.

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Abstract It has become easier to access agriculture data in recent years as a result of a decline in digital breaches between agricultural producers and IoT technologies. These future technologies can be used to boost productivity by cultivating food more sustainably while also preserving the environment, thanks to improved water use and input and treatment optimization. The Internet of Things (IoT) enables the production of agricultural process-supporting systems. Referred to as remote monitoring systems, decision support tools, automated irrigation systems, frost protection systems, and fertilisation systems, respectively. Farmers and researchers must be provided with a detailed understanding of IoT applications in agriculture as a result of the knowledge described above. This study is about using Internet of Things (IoT) technologies and techniques to enhance agriculture. This article is meant to serve as an introduction to IoT-based applications in agriculture by identifying need for such tools and explaining how they support agriculture.
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Beyerer, Jürgen, Georg Bretthauer, and Thomas Längle. "Smart agriculture." at - Automatisierungstechnik 69, no. 4 (April 1, 2021): 275–77. http://dx.doi.org/10.1515/auto-2021-2049.

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鲜, 雨航. "Big Data, Smart Agriculture, Agricultural Information." Advances in Social Sciences 11, no. 08 (2022): 3121–26. http://dx.doi.org/10.12677/ass.2022.118428.

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Azadi, Hossein, Saghi Movahhed Moghaddam, Stefan Burkart, Hossein Mahmoudi, Steven Van Passel, Alishir Kurban, and David Lopez-Carr. "Rethinking resilient agriculture: From Climate-Smart Agriculture to Vulnerable-Smart Agriculture." Journal of Cleaner Production 319 (October 2021): 128602. http://dx.doi.org/10.1016/j.jclepro.2021.128602.

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John, Lizy Kurian. "Smart Agriculture and Smart Memories." IEEE Micro 42, no. 1 (January 1, 2022): 4–6. http://dx.doi.org/10.1109/mm.2021.3138625.

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Singh, Gurjeet, Naresh Kalra, Neetu Yadav, Ashwani Sharma, and Manoj Saini. "SMART AGRICULTURE: A REVIEW." Siberian Journal of Life Sciences and Agriculture 14, no. 6 (December 25, 2022): 423–54. http://dx.doi.org/10.12731/2658-6649-2022-14-6-423-454.

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Agriculture is regarded as one of the most crucial sectors in guaranteeing food security. However, as the world’s population grows, so do agri-food demands, necessitating a shift from traditional agricultural practices to smart agriculture practices, often known as agriculture 4.0. It is critical to recognize and handle the problems and challenges related with agriculture 4.0 in order to fully profit from its promise. As a result, the goal of this research is to contribute to the development of agriculture 4.0 by looking into the growing trends of digital technologies in the field of agriculture. A literature review is done to examine the scientific literature pertaining to crop farming published in the previous decade for this goal. This thorough examination yielded significant information on the existing state of digital technology in agriculture, as well as potential future opportunities.
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Adamides, George. "A Review of Climate-Smart Agriculture Applications in Cyprus." Atmosphere 11, no. 9 (August 25, 2020): 898. http://dx.doi.org/10.3390/atmos11090898.

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Climate-smart agriculture is an approach for developing agricultural strategies to modernize agricultural systems using digital techniques, aiming for sustainable agriculture and ensuring food security under climate change. This article provides a systematic literature review of smart agriculture technologies towards climate-smart agriculture in Cyprus, including robotics, Internet of Things, and remote sensing. The paper starts with a review of climate-smart agriculture, presenting its importance in terms of optimizing agricultural production processes in order to address the interlinked challenges of food security and climate change. An extensive literature review of works published in the areas of robotics, Internet of Things, and remote sensing is undertaken, with particular attention paid to works carried out in relation to agriculture in Cyprus. The paper analyzes aspects of the climate-smart agriculture research situation in Cypriot agriculture, identifies gaps, and concludes with new directions.
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Sadiku, Matthew N. O., Mahamadou Tembely, and Sarhan M. Musa. "Climate-Smart Agriculture." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 2 (February 28, 2017): 148–49. http://dx.doi.org/10.23956/ijarcsse/v7i2/01202.

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Rakhmatullaevna, Makhmudova Nargiza. "“smart” agriculture tools." ACADEMICIA: AN INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL 11, no. 2 (2021): 430–34. http://dx.doi.org/10.5958/2249-7137.2021.00372.4.

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Hmielowski, Tracy. "Climate-Smart Agriculture." CSA News 62, no. 2 (February 2017): 4–7. http://dx.doi.org/10.2134/csa2017.62.0201.

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Dissertations / Theses on the topic "Smart agriculture"

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Grossi, Francesca <1983&gt. "Climate smart agriculture : beyond the theoretical definition." Doctoral thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/11973.

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It has been long recognized that effective climate policies need to account for economic, environmental and social impacts, exemplifying the notion of “triple-wins”. In the context, of rapid population growth and urbanization rate, this rationale has acquired even more impetus through the notion of climate smart agriculture applied to urban and peri-urban agriculture (UPA), i.e. farming practices that take place within or on the fringe of a city. Notwithstanding, this increasing popularity of UPA as climate-smart strategy, substantial challenges still remain, especially in terms of impacts’ assessment. This research aims to contribute to fill existing knowledge gaps, through the development of triple-wins comparative analysis, enabling the practical investigation of UPA´s impacts, and thus a better understanding of whether and to which extent these farming practices can support the building of more resilient and sustainable cities in low and middle-income countries. Accordingly, it builds upon a multi-methods research design comprising of: qualitative content analysis, case study research, and a multiple criteria decision analysis method, the PROMETHEE II.
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Mattos, Adriane Wassmassdorf. "Smart farming." Master's thesis, Universidade de Lisboa, Faculdade de Arquitetura, 2016. http://hdl.handle.net/10400.5/14196.

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Giua, Carlo <1994&gt. "Smart Farming in Italian agriculture: essays on adoption and diffusion dynamics shaping the agricultural digital transition." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10379/1/C.%20Giua%2C%202022.%20Smart%20Farming%20in%20Italian%20agriculture%20-%20essays%20on%20adoption%20and%20diffusion%20dynamics%20shaping%20the%20agricultural%20digital%20transition.pdf.

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Smart Farming Technologies (SFT) is a term used to define the set of digital technologies able not only to control and manage the farm system, but also to connect it to the many disruptive digital applications posed at multiple links along the value chain. The adoption of SFT has been so far limited, with significant differences at country-levels and among different types of farms and farmers. The objective of this thesis is to analyze what factors contributes to shape the agricultural digital transition and to assess its potential impacts in the Italian agri-food system. Specifically, this overall research objective is approached under three different perspectives. Firstly, we carry out a review of the literature that focuses on the determinants of adoption of farm-level Management Information Systems (MIS), namely the most adopted smart farming solutions in Italy. Secondly, we run an empirical analysis on what factors are currently shaping the adoption of SFT in Italy. In doing so, we focus on the multi-process and multi-faceted aspects of the adoption, by overcoming the one-off binary approach often used to study adoption decisions. Finally, we adopt a forward-looking perspective to investigate what the socio-ethical implications of a diffused use of SFT might be. On the one hand, our results indicate that bigger, more structured farms with higher levels of commercial integration along the agri-food supply chain are those more likely to be early adopters. On the other hand, they highlight the need for the institutional and organizational environment around farms to more effectively support farmers in the digital transition. Moreover, the role of several other actors and actions are discussed and analyzed, by highlighting the key role of specific agri-food stakeholders and ad-hoc policies, with the aim to propose a clearer path towards an efficient, fair and inclusive digitalization of the agrifood sector.
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Pivoto, Dieisson. "Smart farming : concepts, applications, adoption and diffusion in southern Brazil." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/178439.

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O Smart Farming (SF) é um novo conjunto de tecnologias que podem ser usadas para melhorar a tomada de decisões e a automação em atividades agrícolas. Para isso, alguns agricultores começaram a utilizar a Internet das Coisas (IoT), que é uma tecnologia que permite que os objetos sejam detectados ou controlados remotamente em infraestruturas de rede existentes. Esse processo tende a criar oportunidades para uma integração mais direta do mundo físico com sistemas baseados em computador, gerando maior eficiência, precisão e benefícios econômicos para os usuários de SF. Além das novas áreas como IoT, Computação em Nuvem, Cognitive Computing e Big Data, dois campos contribuíram para o desenvolvimento de SF: Agricultura de Precisão (AP) e Tecnologia da Informação (TI).A presente tese analisou o processo de inovação no contexto da SF, desde a produção de conhecimento científico até a fase de difusão dessas tecnologias na agricultura, sendo que, o objeto de estudo contemplou as propriedades rurais de grãos. A discussão e análise realizadas no trabalho têm como base teórica o aporte da economia evolucionária e o paradigma tecnoeconômico usado para analisar revoluções tecnológicas. O trabalho consistiu de três etapas metodológicas distintas A primeira, de caráter exploratório, foi realizada por meio de entrevistas com especialistas de diferentes áreas, visando melhor compreender o tema estudado. Na segunda etapa, realizou-se um levantamento na literatura científica acerca do tema. De posse dessas informações, operacionalizou-se uma pesquisa empírica para analisar a adoção dessas tecnologias no ambiente real. Para isso, foram aplicados 119 questionários com produtores de grãos da região Sul do Brasil (Paraná, Santa Catarina e Rio Grande do Sul), sendo adotada amostragem estratificada, pois foram considerados produtores cujas propriedades produzissem 50% ou mais da receita bruta em grãos.Com base nos resultados, foi possível inferir que as tecnologias de SF encontram-se no processo de gestação e emergência. Observou-se um intenso desenvolvimento científico em tecnologias como IoT e ambientes inteligentes, bem como um forte efeito de "spillover" de outras indústrias para a agricultura. Entretanto, espera-se que nos próximos anos, o número de inovações disponíveis ao mercado na área de SF cresça. Os principais fatores de adoção de SF observados no trabalho foram: a) aumento de produtividade, b) melhor qualidade de processo, c) redução de custos, e d) maior conhecimento de áreas cultivadas. Da mesma forma, alguns fatores aumentaram a adoção de tecnologias em diferentes intensidades e maneiras. A educação teve o efeito significativo e positivo na adoção de tecnologias georeferenciadas de amostragem de solo A adoção do piloto de pulverização do piloto automático e softwares de gerenciamento teve influência positiva do tamanho da área. Os resultados da tese sinalizaram que um maior grau de escolaridade, tende a aumentar probabilidade de adoção dessas tecnologias. As principais barreiras que atrasam a entrada dos produtores de grãos na SF foram: a) o preço dos equipamentos, b) baixa qualificação do trabalho rural c) a precariedade do acesso à Internet nas regiões rurais brasileiras, e d) necessidade de inserir muitos dados e informações em software. Verificou-se assim que as máquinas empregadas nos sistemas produtivos de grãos estão passando pelo processo de digitalização, especialmente pelo aumento da disponibilidade de equipamentos com sensores e processos automatizados. No entanto, na percepção do produtor rural, grande número de técnicos e consultores ainda não está adaptado ao novo contexto da agricultura. Com isso, permanece o questionamento acerca da capacidade do produtor e dos consultores técnicos de acompanhar e aproveitar o potencial das tecnologias de SF na tomada de decisão na propriedade rural. Os resultados desse trabalho, inéditos no contexto brasileiro, avançam no sentido de compreender a difusão da SF no contexto brasileiro.
Smart Farming (SF) is a modern set technologies that can be used to improve decision making and automation throughout agricultural activities. To accomplish this, some farmers are using the Internet of Things (IoT), which is new technology that allows objects to be sensed or controlled remotely across existing network infrastructures. Further, it can create opportunities for more direct integration of the physical world into computer-based systems, which can result in improved efficiency, accuracy, and economic benefits for SF users. Besides the new areas such as IoT, Cloud Computing, Cognitive Computing and Big Data, two fields have contributed to the development of SF: Precision Agriculture (PA) and Information Technology (IT). The present study analyzed SF’s innovative processes, beginning with the production of scientific knowledge through to SF’s final diffusion of these technologies into agriculture. The discussion and analysis are based on the theoretical contributions of the evolutionary economy and the techno-economic paradigms and were used to analyze technological revolutions. The work consisted of three distinct methodological steps First, to better understand the subject being studied, interviews were conducted with researchers and market professionals, from different areas, such as agriculture, electronics engineering and mechanization. During the second stage, text mining was used to analyze scientific literature on SF. In the third step an empirical research was carried out to analyze the adoption of SF technologies in real environment. To operationalize this step, a questionnaire was sent to grain farmers from the southern region of Brazil, which included Paraná, Santa Catarina, and Rio Grande do Sul. Since these grain' farmers produced 50% or more of the gross revenue in grains were included in the database. After the surveys were completed, the empirical data was used to analyze the adoption of these technologies. Based on the results, it was possible to infer that SF technologies are in the process of gestation and emergence. There has been intense scientific development in technologies, such as IoT and smart environments. Additionally, there has been a strong spillover effect from industries to agriculture. Because of this, it is expected that the number of SF innovations available to the market will grow over the next several years The study indicated main factors that a farmer chose to adopt SF were: potential increase in productivity, better process quality, cost reduction, and a greater knowledge of cultivated areas. Additionally, adding in these factors, education had the positive effect on the adoption of georeferenced soil sampling. The adoption of an autopilot spray pilot and management software was positively influenced by the size of the area. The results of the study have indicated that a higher level of schooling tends to increase the probability of adopting these technologies. It was also found that high equipment costs, the low qualification of rural workers, the precariousness of Internet access in Brazilian rural regions, and the need to insert a lot of data and information in specific programs available to take advantage of SF technologies are the main barriers faced by grain producers, which contribute to their delay in implementing SF technologies. Additionally, it has been verified that the machines used in the grain production systems are becoming digitalized—the availability of equipment with sensors and automated processes are rapidly increasing. However, from the famers’ perception, many technicians and consultants, such as agronomists and agricultural engineers, have not yet adapted to the new context of agriculture, with growing implementation of SF technologies amongst farmers. Thus, the question remains whether farmers and technical consultants can take advantage of available SF technologies and, if so, whether they can use these technologies to help them make decisions and monitor their farming practices. The results of this research can be used to further understand how SF technologies are being used among Brazilian grain producers.
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ALIEV, KHURSHID. "Internet of Things Applications and Artificial Neural Networks in Smart Agriculture." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2697287.

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Internet of Things (IoT) is receiving a great attention due to its potential strength and ability to be integrated into any complex systems and it is becoming a great tool to acquire data from particular environment to the cloud. Data that are acquired from Wireless Sensor Nodes(WSN) could be predicted using Artificial Neural Network(ANN) models. One of the use case fields of IoT is smart agriculture and there are still issues on developing low cost and power efficient WSN using advanced radio technologies for short and long-range applications and implementation of prediction tools. This is the reason why the target of this thesis is to develop a low cost and power efficient WSN and IoT based control system and analyze the best predictive model for such systems. With this purpose, we developed BLESensor node for short-range IoT applications and Internet of Plant(IoP) for long distance smart agriculture applications. A non-linear prediction model is developed in order to forecast acquired data from sensor nodes. BLESensor node Experimental test results reveal that newly developed BLESensor node has a good impact on the improved lifetime and applications could possibly make this emerging technological area more useful. The Android software has been tested on Samsung Galaxy SM-T311, running Android 4.4.2 and it works without any issues and it is supposed to work on all other Android devices equipped with BLE. The working temperature range of the BLESensor node is supposed to work goes from -20 °C to 70 °C due to battery temperature limits. The system has been tested in the climatic chamber (Challenge 250 from Angelantoni) present at the Neuronica Lab, which allowed the sensor to be software calibrated. Several measurements have been proven that each node offers an uncertainty of 1.2 °C for temperature. These values are acceptable for the type of application for which they are intended. The power consumption has been measured directly from scope analysis and simulating the code step by step and calculations resulted that the lifetime of the node lasts for a month. Considering a normal use of these sensors with a reasonable sampling time the lifetime could be increased. IoP node IoP node is a prototype device that works with WiFi protocol and collects temperature, humidity and soil moisture data of plants to the cloud. For IoP node, we have implemented a firmware, tested a prototype device and designed the PCB in OrCAD software and generated a Gerber file and developed an android application. Prediction model Comparisonofthreenon-linearmodelswithOakdatasetresultedinbetterperformance of NNARX model and we used NNARX model to predict 10 days step ahead maximum and minimum temperature and described the results of performances. The performance given by trained models in terms of Mean Square Error (MSE) for maximum temperature prediction provided an error of 0.8826 on unseen data for the month of September. Similarly, the performance of model predicting minimum temperature was tested and it resulted in an error value of 0.944. In conclusion, this work must be intended only as a proof-of-concept, although, the developed BLESensor system, IoP prototype device and predictive models showed expected optimum results, both in terms of functionalities and usability.
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Casten, Carlberg Carl Johan, and Elsa Jerhamre. "Artificial Intelligence in Agriculture : Opportunities and Challenges." Thesis, Uppsala universitet, Avdelningen för datorteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-443576.

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Artificial Intelligence (AI) is increasingly used in different parts of society for providing decision support in various activities. The agricultural sector is anticipated to benefit from an increased usage of AI and smart devices, a concept called smart farming technologies. Since the agricultural sector faces several simultaneous challenges, such as shrinking marginals, complicated pan-European regulations, and demands to mitigate the environmental footprint, there are great expectations that smart farming will benefit both individual farmers and industry stakeholders. However, most previous research focuses only on a small set of characteristics for implementing and optimising specific smart farming technologies, without considering all possible aspects and effects. This thesis investigates both technical and non-technical opportunities and hurdles when implementing AI in Swedish agricultural businesses. Three sectors in agriculture are scrutinized: arable farming, milk production and beef production. As a foundation for the thesis, a literature review revises former research on smart farming. Thereafter, an interview study with 27 respondents both explores the susceptibility and maturity of smart farming technologies and provides examples of technical requirements of three chosen applications of AI in agriculture. Findings of the study include a diverse set of aspects that both enable and obstruct the transition. Main identified opportunities are the importance smart farming has on the strategic agendas of several industry stakeholders, the general trend towards software technology as a service through shared machinery, the vast amount of existing data, and the large interest from farmers towards new technology. Contrasting, the thesis identifies main hurdles as technical and legislative challenges to data ownership, potential cybersecurity threats, the need for a well-articulated business case, and the sometimes lacking technical knowledge within the sector. The thesis concludes that the macro trend points towards a smart farming transition but that the speed of the transformation will depend on the resolutions for the identified obstacles.
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Arakelyan, Irina. "Climate-smart agriculture and rural livelihoods : the case of the dairy sector in Malawi." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28796.

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Over the last decade climate-smart agriculture (CSA) has been promoted as a new approach to deal with the impacts of climate change on agriculture while simultaneously trying to mitigate emissions and improve food security. This approach suggests that these multiple goals – adaptation, mitigation and food security - could be achieved simultaneously by adopting specific technologies. At its core, CSA describes agricultural interventions that can 1) sustainably increase agricultural productivity, and hence food security and farm incomes; 2) help adapt and build resilience of agricultural systems to climate change; and 3) reduce greenhouse gas emissions from agriculture (including crops, livestock and fisheries). The main focus of CSA is on smallholder producers, many of whom are already marginalized by existing food production systems, their livelihoods increasingly affected by changes in climate. Unsustainable agricultural practices are common amongst these groups. However, there is an increasing awareness of the need to sustain the natural resource base in order to maintain or increase productivity. Malawi is one of the poorest and least developed countries in the world, with chronic food insecurity affecting large parts of the population, and climate variability increasingly noticeable across the country. Agriculture is practiced predominantly on small holdings, with more than 80% of the population depending on land-based income. In this context, the introduction of climate-smart projects and technologies with the potential to deliver triple wins could improve farmers’ incomes and food security, increase their resilience to climate change impacts, as well as deliver global benefits via climate change mitigation. This dissertation looks at the adoption levels of various, potentially climate-smart agricultural practices by smallholder dairy farmers in Malawi, with the view of establishing the current level of engagement in these practices, and identifying the factors that influence adoption. Results show the importance of the socio-economic and institutional factors in explaining the probability of adopting different agricultural practices. In particular, the findings indicate the importance of well-informed and targeted extension support as one of the major enabling factors for the adoption of improved practices. The findings further show that farmers’ climate change perceptions play a key role in the adoption of climate-smart practices. Overall, the thesis concludes that a number of currently unsustainable dairy farm management practices could be improved upon to achieve double or triple-win benefits within a reasonably short timescale, many of them at low cost. In addition, limited adoption rates of several sustainable practices that are already in place could be improved with the provision of more training, knowledge sharing and extension advice and support on the benefits of these practices. However, the thesis argues that before implementing projects and policies that promise triple wins, a careful evaluation of benefits, including mitigation, adaptation, and food security, and risks must be carried out, as triple wins will not be achievable in many cases due to the local and external constraints including lack of skills and knowledge, and lack of funding. In this respect, whether climate-smart agriculture could become a globally sustainable approach to the climate change problem in agriculture, remains to be seen.
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BOTTA, ANDREA. "Agri.Q - Sustainable Rover for Precision Agriculture." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2963950.

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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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Books on the topic "Smart agriculture"

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Ouda, Samiha, and Abd El-Hafeez Zohry. Climate-Smart Agriculture. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93111-7.

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Bhattacharyya, Pratap, Himanshu Pathak, and Sharmistha Pal. Climate Smart Agriculture. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9132-7.

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Lipper, Leslie, Nancy McCarthy, David Zilberman, Solomon Asfaw, and Giacomo Branca, eds. Climate Smart Agriculture. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61194-5.

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Mkomwa, Saidi, and Amir Kassam, eds. Conservation agriculture in Africa: climate smart agricultural development. Wallingford: CABI, 2022. http://dx.doi.org/10.1079/9781789245745.0000.

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Abstract This book is about Conservation Agriculture (the use of no tillage systems) to preserve soil structure and integrity. It has become an increasingly important step towards sustainable farming. This publication brings together conservation agriculture and climate smart decision making processes for the first time, focusing on Africa. This book brings to the fore scientific and empirical evidence about Conservation Agriculture in Africa, articulated by the Second Africa Congress on Conservation Agriculture (2ACCA) held in Johannesburg in 2018. It describes how farmers in Africa are successfully adopting Conservation Agriculture as an alternative to the unsustainable conventional farming practices and as a solution to loss of agricultural productivity, soil erosion and land degradation, climate change challenges and ever-increasing food insecurity. This work discusses how Conservation Agriculture can support the implementation of the African Union's Malabo Declaration and Agenda 2063 which calls for climate smart agricultural development. It provides development-oriented case studies and scientific evidence relevant to all stakeholders in the public, private and civil sectors who are engaged in building policy, institutional and human capacity to accelerate the mainstreaming of Conservation Agriculture across Africa.
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Boumerdassi, Selma, Mounir Ghogho, and Éric Renault, eds. Smart and Sustainable Agriculture. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88259-4.

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Popkova, Elena G., and Bruno S. Sergi, eds. Smart Innovation in Agriculture. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7633-8.

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Frühauf, Manfred, Georg Guggenberger, Tobias Meinel, Insa Theesfeld, and Sebastian Lentz, eds. KULUNDA: Climate Smart Agriculture. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-15927-6.

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Zhang, Qin, ed. Encyclopedia of Smart Agriculture Technologies. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89123-7.

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Rosenstock, Todd S., Andreea Nowak, and Evan Girvetz, eds. The Climate-Smart Agriculture Papers. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92798-5.

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Pakeerathan, Kandiah, ed. Smart Agriculture for Developing Nations. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8738-0.

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Book chapters on the topic "Smart agriculture"

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Zinke-Wehlmann, Christian, and Karel Charvát. "Introduction of Smart Agriculture." In Big Data in Bioeconomy, 187–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_14.

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AbstractSmart agriculture is a rising area bringing the benefits of digitalization through big data, artificial intelligence and linked data into the agricultural domain. This chapter motivates the use and describes the rise of smart agriculture.
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Zohry, Abd El-Hafeez, and Samiha Ouda. "Integration Between Crop-Smart, Water-Smart and Soil-Smart Practices." In Climate-Smart Agriculture, 67–89. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93111-7_4.

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Liu, Bing, and Yan Zhu. "Climate-Smart Agriculture." In Encyclopedia of Smart Agriculture Technologies, 1–8. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_251-1.

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Doloi, Hemanta, Ray Green, and Sally Donovan. "Agriculture." In Planning, Housing and Infrastructure for Smart Villages, 49–70. Abingdon, Oxon ; New York, NY : Routledge is an imprint of the Taylor & Francis Group, an Informa Business, 2019.: Routledge, 2018. http://dx.doi.org/10.1201/9781351261081-4.

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González-Sánchez, Emilio J., Manuel Moreno-Garcia, Amir Kassam, Saidi Mkomwa, Julio Roman-Vazquez, Oscar Veroz-Gonzalez, Rafaela Ordoñez-Fernandez, et al. "Climate smart agriculture for Africa: the potential role of conservation agriculture in climate smart agriculture." In Conservation agriculture in Africa: climate smart agricultural development, 66–84. Wallingford: CABI, 2022. http://dx.doi.org/10.1079/9781789245745.0003.

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Abstract To achieve the challenges raised in Agenda 2063 and the Malabo Declaration, new agricultural techniques need to be promoted. Practical approaches to implement climate smart agriculture and sustainable agriculture, able to deliver at field level, are required. These include sustainable soil and land management that allows different user groups to manage their resources, including water, crops, livestock and associated biodiversity, in ways that are best suited to the prevailing biophysical, socio-economic and climatic conditions. The adoption of locally adapted sustainable soil management practices is needed to support climate change mitigation and adaptation from the agricultural perspective. In this sense, Conservation Agriculture (CA) can be adapted to local conditions, and help achieve the key objectives. The application of CA principles brings multiple benefits, especially in terms of soil conservation, but also for mitigating climate change. In fact, CA has the ability to transform agricultural soils from being carbon emitters into carbon sinks, because of no-tillage (NT) techniques and the return to the soil of diverse crop biomass from above-ground parts of plants and from diverse roots systems and root exudates. Similarly, fossil energy use decreases due to the reduction in agricultural operations, and so less CO2 is emitted to the atmosphere. Lower greenhouse gas (GHG) emissions in CA also result, because of reduced and more efficient use of inputs. Scientific studies confirm the sequestration potential of increased soil organic carbon (SOC) stocks on croplands in Africa on each of the continent's major bioclimatic areas. Coefficients of SOC sequestration for Africa are presented in this chapter.
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Ohletz, Janel Louise. "Regenerative Agriculture." In Encyclopedia of Smart Agriculture Technologies, 1–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89123-7_252-1.

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da Silveira, Franco, and Fernando Gonçalves Amaral. "Agriculture 4.0." In Encyclopedia of Smart Agriculture Technologies, 1–5. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89123-7_207-2.

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da Silveira, Franco, and Fernando Gonçalves Amaral. "Agriculture 4.0." In Encyclopedia of Smart Agriculture Technologies, 1–5. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89123-7_207-1.

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da Silveira, Franco, and Fernando Gonçalves Amaral. "Agriculture 4.0." In Encyclopedia of Smart Agriculture Technologies, 1–5. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_207-3.

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Saiz-Rubio, Verónica. "Digital Agriculture." In Encyclopedia of Smart Agriculture Technologies, 1–8. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_13-1.

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Conference papers on the topic "Smart agriculture"

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Aqeel-ur-Rehman. "Smart Agriculture." In 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT). IEEE, 2017. http://dx.doi.org/10.1109/icieect.2017.7916576.

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Rakhymberdina, Marzhan Yessenbekovna, Marzhan Anuarbekovna Sadenova, Natalya Anatolyevna Kulenova, Utegenova Meruyert Erkinovna, and Jiri Jaromir Klemes. "Smart Green Agriculture on Industrially Polluted Agricultural Landscapes." In 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE, 2021. http://dx.doi.org/10.23919/splitech52315.2021.9566460.

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Vargas, Ruben, Bennett Miller, Gabriel Anhalzer, Mohammed Al Hasani, Heinz Boehmer Fiehn, Jiashu Yang, Soham Tamhane, and Alan Mickelson. "Smart Agriculture in Uganda." In 2019 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2019. http://dx.doi.org/10.1109/ghtc46095.2019.9033021.

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Jeyaselvi, M., M. Sathya, and BVP Prasad. "IoT Based Smart Agriculture." In 2022 8th International Conference on Applied System Innovation (ICASI). IEEE, 2022. http://dx.doi.org/10.1109/icasi55125.2022.9774472.

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Mahajan, Palvi. "Internet of things revolutionizing Agriculture to Smart Agriculture." In 2021 2nd Global Conference for Advancement in Technology (GCAT). IEEE, 2021. http://dx.doi.org/10.1109/gcat52182.2021.9587896.

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Mishra, Devesh, Tanuja Pande, Krishna Kant Agrawal, Ali Abbas, Akhilesh Kumar Pandey, and Ram Suchit Yadav. "Smart agriculture system using IoT." In the Third International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3339311.3339350.

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BĂLĂCEANU, Cristina, George SUCIU, Romulus CHEVEREȘAN, Marius DOBREA, and Andreea IOSIF. "Monitoring Solutions For Smart Agriculture." In Air and Water Components of the Environment 2019 Conference. Casa Cărţii de Ştiinţă, 2019. http://dx.doi.org/10.24193/awc2019_17.

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Chandavale, Anjali, Abhijeet Dixit, Aditya Khedkar, and Rucha B. Kolekar. "Automated Systems for Smart Agriculture." In 2019 IEEE Pune Section International Conference (PuneCon). IEEE, 2019. http://dx.doi.org/10.1109/punecon46936.2019.9105686.

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Barrettino, Diego. "Sensor Systems for Smart Agriculture." In 2022 IEEE 13th Latin America Symposium on Circuits and System (LASCAS). IEEE, 2022. http://dx.doi.org/10.1109/lascas53948.2022.9789078.

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Sokullu, Radosveta. "LoRa Based Smart Agriculture Network." In 2022 8th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE). IEEE, 2022. http://dx.doi.org/10.1109/eeae53789.2022.9831210.

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Reports on the topic "Smart agriculture"

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Parikh, Sanjai J., and Emilie Winfield. Climate-Smart Agriculture: Biochar Amendments. U.S. Department of Agriculture, California Climate Hub, January 2020. http://dx.doi.org/10.32747/2020.7303346.ch.

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This fact sheet is the final installment of a four-part climate-smart agriculture series exploring the relationship between carbon farming, soil health, and soil amendments on CA croplands and rangelands. This fact sheet focuses on biochar amendments and previous fact sheets address the benefits of compost and pulverized rock. The series is intended for members of the technical assistance community who advise CA growers on climate-smart agriculture.
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Winfield, Emilie. Climate-Smart Agriculture: Rock Amendments. U.S. Department of Agriculture, California Climate Hub, January 2020. http://dx.doi.org/10.32747/2020.7304495.ch.

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This fact sheet is the third installment of a four-part climate-smart agriculture series exploring the relationship between carbon farming, soil health, and soil amendments on CA croplands and rangelands. This fact sheet focuses on pulverized rock amendments and other fact sheets address the benefits of compost and biochar. The series is intended for members of the technical assistance community who advise CA growers on climate-smart agriculture.
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Winfield, Emilie. Climate-Smart Agriculture: Compost Amendments. U.S. Department of Agriculture, California Climate Hub, January 2020. http://dx.doi.org/10.32747/2020.7304494.ch.

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This fact sheet is the second installment of a four-part climate-smart agriculture series exploring the relationship between carbon farming, soil health, and soil amendments on CA croplands and rangelands. This fact sheet focuses on compost and subsequent fact sheets will address the benefits of biochar and pulverized rock. The series is intended for members of the technical assistance community who advise CA growers on climate-smart agriculture.
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Barooah, Prapti, Muzna Fatima Alvi, Claudia Ringler, and Vishal Pathak. Gender, agriculture policies and climate smart agriculture in India. Washington, DC: International Food Policy Research Institute, 2023. http://dx.doi.org/10.2499/p15738coll2.136537.

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Research Institute (IFPRI), International Food Policy. Trade, climate change, and climate-smart agriculture. Washington, DC: International Food Policy Research Institute, 2017. http://dx.doi.org/10.2499/9780896292949_05.

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Blom-Zandstra, Greet, and Anne Elings. Workshops on Climate Smart Agriculture in Algeria. Lelystad: Wageningen Research Foundation (WR) business units Agrosystems Research and Greenhouse Horticulture, 2017. http://dx.doi.org/10.18174/444735.

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Harmayani, Eni. Indonesia tempe production supported by smart agriculture. Edited by Ria Ernunsari and Sara Phillips. Monash University, September 2022. http://dx.doi.org/10.54377/c6a8-a95c.

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Winfield, Emilie. Climate-Smart Agriculture: Soil Health & Carbon Farming. U.S. Department of Agriculture, California Climate Hub, January 2020. http://dx.doi.org/10.32747/2020.7303347.ch.

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This fact sheet is the first installment of a four-part climate smart agriculture series exploring the relationship between carbon farming, soil health, and soil amendments on CA croplands and rangelands. Subsequent fact sheets will address the benefits of compost, pulverized rock, and biochar as amendments. The series is intended for members of the technical assistance community who advice CA growers on climate smart agriculture.
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Downing, Thomas. Climate smart agriculture: Mapping guidance on climate change. Evidence on Demand, October 2013. http://dx.doi.org/10.12774/eod_hdoct2013.downing.

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Research Institute (IFPRI), International Food Policy. Climate-smart agriculture practices based on precision agriculture: the case of maize in western Congo. Washington, DC: International Food Policy Research Institute, 2017. http://dx.doi.org/10.2499/9780896292949_07.

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