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Kumar, Akshatha S., Deepthi S. Kumar, and Manjunath C. R. Soumya K. N. "Impacts of Big Data on Smart Farming." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 577–82. http://dx.doi.org/10.31142/ijtsrd13021.
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Akshatha, S. Kumar, S. Kumar Deepthi, C. R. Manjunath, and K. N. Soumya. "Impacts of Big Data on Smart Farming." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 577–82. https://doi.org/10.31142/ijtsrd13021.
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Farming is the broadest monetary segment and assumes as a critical part in general financial advancement of a country. Technological headway in this field of agribusiness will find a way to build the cultivating exercises. Smart farming has a potential to deliver a productive and sustainable agricultural production, based on a precise and resource efficient approach. Smart farming is an improvement that underscores the utilization of data and correspondence innovation in the digital physical farm administration cycle. New advancements for example the internet of things and cloud computing are relied upon to use this improvement and present more robots and computerized reasoning in cultivation. This is done by the big data gigantic volumes of information with a wide assortment that can be caught, broken down and utilized for basic leadership. This survey expects to pick up understanding into the nest in class of Big data applications in smart farming and distinguish the financial difficulties to be tended to. Akshatha S Kumar | Deepthi S Kumar | Manjunath C R | Soumya K N "Impacts of Big Data on Smart Farming" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd13021.pdf
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Amiri-Zarandi, Mohammad, Rozita A. Dara, Emily Duncan, and Evan D. G. Fraser. "Big Data Privacy in Smart Farming: A Review." Sustainability 14, no. 15 (2022): 9120. http://dx.doi.org/10.3390/su14159120.
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Smart farming aims to improve farming using modern technologies and smart devices. Smart devices help farmers to collect and analyze data regarding different aspects of their business. These data are utilized by various stakeholders, including farmers, technology providers, supply chain investigators, and agricultural service providers. These data sources can be considered big data due to their volume, velocity, and variety. The wide use of data collection and communication technologies has increased concerns about the privacy of farmers and their data. Although some previous studies have reviewed the security aspects of smart farming, the privacy challenges and solutions are not sufficiently explored in the literature. In this paper, we present a holistic review of big data privacy in smart farming. The paper utilizes a data lifecycle schema and describes privacy concerns and requirements in smart farming in each of the phases of this data lifecycle. Moreover, it provides a comprehensive review of the existing solutions and the state-of-the-art technologies that can enhance data privacy in smart farming.
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Roukh, Amine, Fabrice Nolack Fote, Sidi Ahmed Mahmoudi, and Saïd Mahmoudi. "Big Data Processing Architecture for Smart Farming." Procedia Computer Science 177 (2020): 78–85. http://dx.doi.org/10.1016/j.procs.2020.10.014.
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Wolfert, Sjaak, Lan Ge, Cor Verdouw, and Marc-Jeroen Bogaardt. "Big Data in Smart Farming – A review." Agricultural Systems 153 (May 2017): 69–80. http://dx.doi.org/10.1016/j.agsy.2017.01.023.
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Benjelloun, Sarah, Mohamed El Mehdi El Aissi, Younes Lakhrissi, and Safae El Haj Ben Ali. "Data Lake Architecture for Smart Fish Farming Data-Driven Strategy." Applied System Innovation 6, no. 1 (2023): 8. http://dx.doi.org/10.3390/asi6010008.
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Thanks to continuously evolving data management solutions, data-driven strategies are considered the main success factor in many domains. These strategies consider data as the backbone, allowing advanced data analytics. However, in the agricultural field, and especially in fish farming, data-driven strategies have yet to be widely adopted. This research paper aims to demystify the situation of the fish farming domain in general by shedding light on big data generated in fish farms. The purpose is to propose a dedicated data lake functional architecture and extend it to a technical architecture to initiate a fish farming data-driven strategy. The research opted for an exploratory study to explore the existing big data technologies and to propose an architecture applicable to the fish farming data-driven strategy. The paper provides a review of how big data technologies offer multiple advantages for decision making and enabling prediction use cases. It also highlights different big data technologies and their use. Finally, the paper presents the proposed architecture to initiate a data-driven strategy in the fish farming domain.
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Choudhary, Laxmi, and Shashank Swami. "Harnessing the Power of Big Data: Revolutionizing Agriculture." Current Journal of Applied Science and Technology 42, no. 22 (2023): 40–49. http://dx.doi.org/10.9734/cjast/2023/v42i224168.
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Now a day, the latest digital technologies are involved in agriculture field i.e. Big Data. Big Data plays a crucial role in the advancement of smart farming by boosting the productivity of individual farms and removing the risk of a global food crisis by collection and analysis process of Big Data. With the increasing global population and the growing demand for sustainable food production, the agriculture industry leaders and policymakers faces numerous challenges. Fortunately, advancements in technology, particularly in the field of big data analytics, have paved the way for innovative solutions in agriculture, such as smart farming. Smart farming leverages big data to optimize agriculture farming practices i.e. irrigation, fertilization, pest management and crop selection, helps in making real time decisions, improve efficiency, improve operations, boost productivity and increase yields while minimizing resource consumption and environmental impact (such as weather, soil, diseases). Big Data’s help to farmers is by suggesting pesticides the quantity they could use. Hence there arises the need for advanced practical and systematic strategies to correlate the different factors driving the agriculture to derive valuable information out of it. The Big Data has power to develop technologies to achieve the aim of sustainable and smart agriculture with smart farming to enhanced precision farming, predictive analytics, and real time monitoring in agriculture. Smart farming involves the collection and sharing of sensitive information, ranging from crop yields and livestock health to financial data. Safeguarding this data from unauthorized access and maintaining privacy while still allowing for valuable analytics poses a complex ethical and legal dilemma. This digital revolution in agriculture is very promising and will enable the agriculture sector to move to the next level of farm productivity and profitability. This transformation process is not reversible and poised to revolutionize both agriculture and food sector.
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Rao, V. Praveen, and V. Anitha. "Agriculture transformation through Big Data and smart farming." International Journal of Innovative Horticulture 10, no. 2 (2021): 130–37. http://dx.doi.org/10.5958/2582-2527.2021.00012.9.
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Sobue, Shin-ichi. "Space Based Data Usage for Smart Farming." BIO Web of Conferences 80 (2023): 06009. http://dx.doi.org/10.1051/bioconf/20238006009.
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Precision Agriculture and input optimization with knowledge sharing are key for smart farming. The use of new technologies such as satellites, drones, navigation, AI/ML, big data, IoT, cloud-computing makes farming and agriculture smarter and transparent. Use of such advanced technologies, governmental officers and farmers can create evidence-based prescription maps for variable rate application of inputs, such as fertilizers, pesticides, and irrigation. Also, smart farming improve efficiency, reduce costs, simplify forecasting, streamline recording and reporting, and boost the sustainability and environmentally friendly agriculture while addressing todays needs and helping future planning. This paper is a brief overview of space-based tools that are currently available for smart farming and also importance of earth observation for smart farming with some examples on rice crop in Asia.
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Benjelloun, Sarah, Mohamed El Mehdi El Aissi, Younes Lakhrissi, and Safae El Haj Ben Ali. "Big Data Technology Architecture Proposal for Smart Agriculture for Moroccan Fish Farming." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 19 (December 16, 2022): 311–22. http://dx.doi.org/10.37394/23209.2022.19.33.
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As the global population increases rapidly, so does the need for fishing products. Aquaculture is well-developed in Asian countries but is underdeveloped in countries that share Morocco's climate. To meet the rising demands for aquaculture production, it is vital to embrace new digital strategies to manage the massive amount of data generated by the aquaculture environment. By employing Big Data methodologies, aquaculture activity is handled more effectively, resulting in increased production and decreased waste. This phase enables fish farmers and academics to obtain valuable data, increasing their productivity. Although Big Data approaches provide numerous benefits, they have yet to be substantially implemented in agriculture, particularly in fish farming. Numerous research projects investigate the use of Big Data in agriculture, but only some offer light on the applicability of these technologies to fish farming. In addition, no research has yet been undertaken for the Moroccan use case. This study aims to demonstrate the significance of investing in aquaculture powered by Big Data. This study provides data on the situation of aquaculture in Morocco in order to identify areas for improvement. The paper then describes the adoption of Big Data technology to intelligent fish farming and proposes a dedicated architecture to address the feasibility of the solution. In addition, methodologies for data collecting, data processing, and analytics are highlighted. This article illuminates the possibilities of Big Data in the aquaculture business. It demonstrates the technological and functional necessity of incorporating Big Data into traditional fish farming methods. Following this, a concept for an intelligent fish farming system based on Big Data technology is presented.
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Kim, Jun Cheol, Sookhee Kwon, Il Do Ha, and Myung Hwan Na. "Survival analysis for tomato big data in smart farming." Journal of the Korean Data And Information Science Society 32, no. 2 (2021): 361–74. http://dx.doi.org/10.7465/jkdi.2021.32.2.361.
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Iaksch, Jaqueline, Ederson Fernandes, and Milton Borsato. "Digitalization and Big data in smart farming – a review." Journal of Management Analytics 8, no. 2 (2021): 333–49. http://dx.doi.org/10.1080/23270012.2021.1897957.
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Klauser, Francisco. "Surveillance Farm: Towards a Research Agenda on Big Data Agriculture." Surveillance & Society 16, no. 3 (2018): 370–78. http://dx.doi.org/10.24908/ss.v16i3.12594.
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Farming today relies on ever-increasing forms of data gathering, transfer, and analysis. Think of autonomous tractors and weeding robots, chip-implanted animals and underground infrastructures with inbuilt sensors, and drones or satellites offering image analysis from the air. Despite this evolution, however, the social sciences have almost completely overlooked the resulting problematics of power and control. This piece offers an initial review of the main surveillance issues surrounding the problematic of smart farming, with a view to outlining a broader research agenda into the making, functioning, and acting of Big Data in the agricultural sector. For surveillance studies, the objective is also to move beyond the predominant focus on urban space that characterises critical contemporary engagements with Big Data. Smart technologies shape the rural just as much as the urban, and “smart farms” are just as fashionable as “smart cities.”
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Soussi, Abdellatif, Enrico Zero, Roberto Sacile, Daniele Trinchero, and Marco Fossa. "Smart Sensors and Smart Data for Precision Agriculture: A Review." Sensors 24, no. 8 (2024): 2647. http://dx.doi.org/10.3390/s24082647.
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Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.
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Tehreem, Qamar, and Zakaria Bawany Narmeen. "Agri-PAD: a scalable framework for smart agriculture." Agri-PAD: a scalable framework for smart agriculture 29, no. 3 (2023): 1597–605. https://doi.org/10.11591/ijeecs.v29.i3.pp1597-1605.
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More recently, big data tools and technologies have been applied in the agriculture sector leading to major benefits. Many frameworks have been proposed that employ big data technologies in the field of agriculture, however, such existing frameworks are focused on a particular aspect of agriculture and do not consider multiple stakeholders and applications. The objective of this research is to develop a holistic framework named Agri-PAD that encompasses almost all aspects of agriculture including crop selection, crop monitoring, soil monitoring, weather conditions, precision farming, and market demand. The Agri-PAD framework includes three major categories of machine learning based agriculture applications that is precision, recommendation, and enterprise applications. The Agri-PAD framework is capable of providing remote sensing of fields, precision farming, effective supply chain, and support informed decision making leading to enhanced productivity. To validate the efficacy of the proposed framework, the two most prominent agricultural applications, crop production forecasting and crop harvesting recommendation have been investigated and accuracy of 99% has been achieved. We believe that the Agri-PAD framework enables all stakeholders in the agriculture cycle to connect and apply big data analytics at every step leading to a more efficient and smarter agriculture ecosystem.
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Rabhi, Loubna, Noureddine Falih, Lekbir Afraites, and Belaid Bouikhalene. "A functional framework based on big data analytics for smart farming." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1772–79. https://doi.org/10.11591/ijeecs.v24.i3.pp1772-1779.
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Big data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture.
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G. Shaheen Firdous. "Smart Agriculture for Renewable energy Integration Using Cloud and Big Data Analysis." Journal of Information Systems Engineering and Management 10, no. 37s (2025): 123–33. https://doi.org/10.52783/jisem.v10i37s.6384.
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Smart agriculture, often known as precision farming, is a fast-expanding multidisciplinary topic that combines expertise from agriculture, technology, data science, and environmental research. As the population grows and food demand rises, sustainable agriculture requires efficient use of water and energy resources. An integrated and cost-effective Smart Agriculture solution. Small and medium farmers struggle to accept commercial solutions due to their high cost. Renewable Energy Consolidation helps for Advance energy cost-effective agriculture by reducing reliance on fossil fuels for water table pumping. The proposed solution revolves around the cloud as it is crucial in smart farming as it stores key characteristics that are compared to field data. Wireless sensors linked to the cloud collect data from the ground. Machine learning algorithms study the data in real time. This analysis helps farmers understand the status of their crops. Big data provides complete information on rainfall patterns, water cycles, and fertilizer levels. Farmers can use this content to make informed conclusion about crop selection, soil fertility, and harvest regulation. Smart farming uses IoT, cloud computing, and big data analysis to optimize agricultural yields. This helps in enhancing sensors to be more affordable and versatile in data collection, along with advancing computer capabilities for better data analysis and predictions.
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Ouafiq, El Mehdi, Rachid Saadane, and Abdellah Chehri. "Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge." Agriculture 12, no. 3 (2022): 329. http://dx.doi.org/10.3390/agriculture12030329.
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Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights.
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Rabhi, Loubna, Noureddine Falih, Lekbir Afraites, and Belaid Bouikhalene. "A functional framework based on big data analytics for smart farming." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1772. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1772-1779.
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Big <span>data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture</span>.
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Wang, Buyu. "Smart Farming Using the Big Data-Driven Approach for Sustainable Agriculture with IOT." Scalable Computing: Practice and Experience 25, no. 2 (2024): 675–82. http://dx.doi.org/10.12694/scpe.v25i2.2540.
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The study showcases the process of deep learning operated in agriculture, including Deep IoT, which makes the procedure easier using the deep neural system. The use of the IoT in the agrarian sectors makes the evolution of firms more effective. The application of the IoT detector supports the making of grade derivatives in the husbandry department. Marketing of crop finance is two other operations of smart agriculture that help for better harvest farming. Through the IoT technology in the farming industry, agriculturalists can get notifications about the temperature and climate. The method needs professional and qualified employees in the division to properly monitor the system and the methods. The submission of the proper nourishment for the proper crop increases the life duration of the harvest and makes the crop free from menace. The velocity of the manufacture of undeveloped items can also be improved by using the IoT. The function of the BDA and IoT has enlarged for the healthier construction of farming items. The foreword of elegant farming in the rural industry requires more capable and qualified trainers to give the personnel proper teaching. The urbanization of the farming process and the use of elegant and modern technology are well-designed in the time of "Agriculture 3.0".
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Bharat Nagamalla. "Architecting Reliable Data Systems for Smart Agriculture: A Big Data and SRE Perspective." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 556–63. https://doi.org/10.32628/cseit25111253.
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This article explores the transformation of agricultural systems through the integration of Big Data analytics and Site Reliability Engineering (SRE) practices, focusing on the development of robust and reliable data systems for smart agriculture. The article examines the evolution from traditional farming methods to data-driven precision agriculture, highlighting the critical role of IoT sensor networks, real-time analytics, and automated decision support systems. The article investigates the infrastructure requirements, challenges, and solutions in implementing reliable agricultural technology systems, including data collection mechanisms, processing architectures, and rural connectivity solutions. It addresses the importance of SRE practices in maintaining system reliability, incident response, and disaster recovery strategies while examining the implementation of predictive modeling and machine learning applications for crop management. The article also analyzes technical challenges in rural environments, data quality validation, system redundancy, and scalability requirements during peak agricultural seasons. Furthermore, it explores emerging trends and best practices in agricultural technology, emphasizing the importance of sustainable practices and cross-functional team structures in modern farming operations.
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Yin, Baihe. "An Investigation into the Intelligent Ecological Animal Husbandry Based on Embedded Systems." Applied and Computational Engineering 147, no. 1 (2025): 203–12. https://doi.org/10.54254/2755-2721/2025.22729.
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In response to China's imperative for sustainable livestock development and socioeconomic progress, the concurrent advancement of ecological and smart livestock farming is being promoted. The realization of a smart ecological livestock farming model necessitates the intelligent evolution of both equipment and management practices, alongside the application of computer technologies such as big data and cloud computing. However, current systems suffer from poor interoperability and compatibility, which impedes effective data integration and sharing. Furthermore, the existing technological solutions lack adaptability to regional variations, resulting in inadequate technical applicability and suboptimal implementation outcomes. This paper employs literature reviews and technology assessment methodologies to explore the developmental pathways and technological implementations of smart ecological livestock farming. A comprehensive smart ecological livestock farming system based on embedded technology, such as Raspberry Pi and STM32, is designed to facilitate the advancement of smart ecological livestock farming. The conclusion drawn is that smart ecological livestock farming represents an inevitable trajectory for the modernization of the livestock industry.
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Czikkely, Márton, Dorottya Ivanyos, László Ózsvári, and Csaba Fogarassy. "Digitization and big data system of intelligence management in smart dairy farming." Hungarian Agricultural Engineering, no. 38 (2020): 49–55. http://dx.doi.org/10.17676/hae.2020.38.49.
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Suk Oh, Am. "Smart urban farming service model with IoT based open platform." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 320. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp320-328.
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<p>Smart and efficient agricultural production or smart farming using IoT sensors, big-data and cloud service has proven its value for a decade but the effect depends on the agricultural environment of the country or society. In Korea, the population of urban farmers who utilizes small, possibly shared area in farming. Urban farming uses rooftop of the building or even indoor for cropping and many urban farmers may not have sufficient professional farming experiences. However, with information technology like cloud service, many critical farming process can be automated and requires minimal human intervention in monitoring and controlling sensors. In this paper, we propose a smart urban farming model which modifies TTA smart greenhouse standard such that cloud service us integrated with IoT sensors. The hardware design of integrated controller and subsequent software services are specified. This new model can be used to enhance smart urban farming which is one of top 10 agricultural policy of the government.</p>
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Am, suk Oh. "Smart urban farming service model with IoT based open platform." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 320–28. https://doi.org/10.11591/ijeecs.v20.i1.pp320-328.
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Smart and efficient agricultural production or smart farming using IoT sensors, big-data and cloud service has proven its value for a decade but the effect depends on the agricultural environment of the country or society. In Korea, the population of urban farmers who utilizes small, possibly shared area in farming. Urban farming uses rooftop of the building or even indoor for cropping and many urban farmers may not have sufficient professional farming experiences. However, with information technology like cloud service, many critical farming process can be automated and requires minimal human intervention in monitoring and controlling sensors. In this paper, we propose a smart urban farming model which modifies TTA smart greenhouse standard such that cloud service us integrated with IoT sensors. The hardware design of integrated controller and subsequent software services are specified. This new model can be used to enhance smart urban farming which is one of top 10 agricultural policy of the government.
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Mrunal, Mohan Mestry. "Analyzing Smart Agriculture in Terms of IoT." International Journal of Trend in Scientific Research and Development 4, no. 3 (2020): 222–25. https://doi.org/10.5281/zenodo.3892546.
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Agriculture is the major income source in India. Many farmers in India use the traditional way for crop production. Weather conditions in India has changed a lot nowadays. Agriculture in India depends upon weather conditions. There are many traditional ways to increase crop production like Irrigation, greenhouse, fertilizers, etc., but keeping in mind about the current weather conditions we need a smarter way to monitor the weather conditions. Now smart farming is possible because of the Internet of Things IoT . IoT uses different enabling technologies like cloud computing, big data analytics, wireless sensor network, embedded systems. Using IoT, we can track live data like weather conditions, soil moisture, temperature, humidity, soil PH, soil nutrition levels, water level from any place. The motive of this paper is to know about smart farming techniques and technologies in terms of the Internet of Things. Mrunal Mohan Mestry "Analyzing Smart Agriculture in Terms of IoT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30345.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30345/analyzing-smart-agriculture-in-terms-of-iot/mrunal-mohan-mestry
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Cambra Baseca, Carlos, Sandra Sendra, Jaime Lloret, and Jesus Tomas. "A Smart Decision System for Digital Farming." Agronomy 9, no. 5 (2019): 216. http://dx.doi.org/10.3390/agronomy9050216.
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New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management.
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B., M. Sagar, and N. K. Cauvery. "Agriculture Data Analytics in Crop Yield Estimation: A Critical Review." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (2018): 1087–93. https://doi.org/10.11591/ijeecs.v12.i3.pp1087-1093.
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Agriculture is important for human survival because it serves the basic need. A well-known fact that the majority of population (≥55%) in India is into agriculture. Due to variations in climatic conditions, there exist bottlenecks for increasing the crop production in India. It has become challenging task to achieve desired targets in Agri based crop yield. Various factors are to be considered which have direct impact on the production, productivity of the crops. Crop yield prediction is one of the important factors in agriculture practices. Farmers need information regarding crop yield before sowing seeds in their fields to achieve enhanced crop yield. The use of technology in agriculture has increased in recent year and data analytics is one such trend that has penetrated into the agriculture field. The main challenge in using big data in agriculture is identification of effectiveness of big data analytics. Efforts are going on to understand how big data analytics can agriculture productivity. The present study gives insights on various data analytics methods applied to crop yield prediction and also signifies the important lacunae points’ in the proposed area of research.
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Qamar, Tehreem, and Narmeen Zakaria Bawany. "Agri-PAD: a scalable framework for smart agriculture." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (2023): 1597. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1597-1605.
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<span lang="EN-US">More recently, big data tools and technologies have been applied in the agriculture sector leading to major benefits. Many frameworks have been proposed that employ big data technologies in the field of agriculture, however, such existing frameworks are focused on a particular aspect of agriculture and do not consider multiple stakeholders and applications. The objective of this research is to develop a holistic framework named Agri-PAD that encompasses almost all aspects of agriculture including crop selection, crop monitoring, soil monitoring, weather conditions, precision farming, and market demand. The Agri-PAD framework includes three major categories of machine learning based agriculture applications that is precision, recommendation, and enterprise applications. The Agri-PAD framework is capable of providing remote sensing of fields, precision farming, effective supply chain, and support informed decision making leading to enhanced productivity. To validate the efficacy of the proposed framework, the two most prominent agricultural applications, crop production forecasting and crop harvesting recommendation have been investigated and accuracy of 99% has been achieved. We believe that the Agri-PAD framework enables all stakeholders in the agriculture cycle to connect and apply big data analytics at every step leading to a more efficient and smarter agriculture ecosystem.</span>
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Bhoyar, C. N. "Smart Agriculture System Using IoT Based." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2434–37. http://dx.doi.org/10.22214/ijraset.2023.50651.
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Abstract: Agriculture is becoming an important growing sector throughout the world due to increasing population. Major challenge in agriculture sector is to improve farm productivity and quality of farming without continuous manual monitoring to meet the rapidly growing demand for food. Apart from increasing population, the climate change is also a big concern in agricultural sector. The purpose of this research work is to purpose a smart farming method based on internet of things (IOT) to deal with the adverse situations. The smart farming can be adopted which offer high precision crop control, collection of useful data and automated farming technique. This works presents a smart agriculture system which monitors soil humidity and temperature. After processing the sensed data, it takes necessary action based on these values without human intervention. In IOT-based smart agriculture, a system is built for monitoring the crop field with the help of sensors (light, humidity, temperature, soil moisture, etc) and automating the irrigation system. IOT (internet of things) in an agricultural context refers to the use of sensors, cameras, and temperature and moisture of the soil measured and these sensed values are stored in things speak cloud for future data analysis.
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Et. al., M. R. Sundara Kumar,. "DESIGN AND DEVELOPMEENT OF AUTOMATIC ROBOTIC SYSTEM FOR VERTICAL HYDROPONIC FARMING USING IOT AND BIG DATA ANALYSIS." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 1597–607. http://dx.doi.org/10.17762/turcomat.v12i11.6090.
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In this digital world, all the developing countries' growth has improved elastically with the impact of farmers and their innovative farming processes. Generally, the farming process can be developed with ancient traditional methodologies for maintaining the quality of the crops and their yields. Their farming was developed and has given more profit only with the quality of the soil and the nutrition’s used on land. But the drawback is they were spending much time to get their yields from their land and the nutrition level was not maintained at all times. Moreover, more space was used for farming with huge manpower is required for maintaining the entire land. Most of the countries are moved to smart farming concepts with IoT platforms for optimizing the time and techniques. In that hydroponic the best innovative idea to produce more crops, vegetables, and fruits without soil. Rockwool is used for farming processes with water contaminants at regular intervals will provide huge productions as well as no need to wait for a long time for cultivation. This method was implemented in most of the countries that were doing smart farming with less manpower and low cost. The hydroponic farming methodology is implemented with IoT sensors for monitoring crop's status and health continuously. Once their nutrition level or water level has decreased it will provide all at constant time intervals to the entire system effectively. A few years ago hydroponic farming was horizontally implemented on smaller spaces for the regular water flow. But now a day it is implemented on a vertical surface to reduce the space and water flow is only at the time of need. This technology is used to increase the productivity of the crops with a small space of land and less manpower. Perhaps the cost of the entire system has been taken into the consideration by small-scale unit farmers vertical hydro farming provides better results when compared with previous classical methods. This research paper has given the design and implementation of automated vertical hydro farming techniques with IoT platform and their analytics will be done using big data analytics.
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Ding, Haohan, Jiawei Tian, Wei Yu, et al. "The Application of Artificial Intelligence and Big Data in the Food Industry." Foods 12, no. 24 (2023): 4511. http://dx.doi.org/10.3390/foods12244511.
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Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.
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Rabhi, Loubna, Noureddine Falih, Lekbir Afraites, and Belaid Bouikhalene. "Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 581–89. https://doi.org/10.11591/ijeecs.v24.i1.pp581-589.
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Due to the spead of objects connected to the internet and objects connected to each other, agriculture nowadays knows a huge volume of data exchanged called big data. Therefore, this paper discusses connected agriculture or agriculture 4.0 instead of a traditional one. As irrigation is one of the foremost challenges in agriculture, it is also moved from manual watering towards smart watering based on big data analytics where the farmer can water crops regularly and without wastage even remotely. The method used in this paper combines big data, remote sensing and data mining algorithms (neural network and support vector machine). In this paper, we are interfacing the databricks platform based on the apache Spark tool for using machine learning to predict the soil drought based on detecting the soil moisture and temperature.
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Rethnaraj, Jebakumar. "Future of Smart Farming Techniques: Significance of Urban Vertical Farming Systems Integrated with IoT and Machine Learning." Open Access Journal of Agricultural Research 8, no. 3 (2023): 1–11. http://dx.doi.org/10.23880/oajar-16000308.
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World population in recent decades has significant impacts on the traditional agricultural systems which has resulted in increased demand for food, land use and deforestation, water scarcity, climate changes but not limited to these impacts. In order to overcome all these issues, there is a need for advanced farming technologies for growing the most demand food crops. Smart farming also known as precision agriculture has evolved which uses the advanced technology to optimize the efficiency and productivity of the farming operations. It involves the integration of various technologies such as IoT sensors, drones, robotics and machine learning technologies, big data analytics to gather data on crop growth, environmental conditions and weather patterns. Vertical framing (VF) is one such precision framing efficient crop growth practices which adapts the integration of Internet of Things (IoT) and machine learning (ML) technologies in easier manner. Since, the vertical farming is completely an indoor farming technique, they do not depend on the particular geographical locations and outdoor growth parameters (like soil) for crop cultivation; hence, vertical farming is also known as controlled environment agriculture. This article explores the significance of different indoor vertical farming practices under controlled environment with the comparative analysis, efficiency, productivity, advantages and their potential benefits highlighting the need for sustainable agricultural practices that can meet the growing demand for food while minimizing the negative environmental impacts.
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Cahya, Waskita, Muhammad Febriansyah, Filda Angellia, and Tri Wahyu Widyaningsih. "Implementasi Arm Robot pada Smart Farming Berbasis Internet of Things." Techno.Com 21, no. 4 (2022): 927–34. http://dx.doi.org/10.33633/tc.v21i4.6928.
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Hidroponik merupakan metode penanaman tanpa media tanah yang mampu meningkatkan kuantitas hasil tanam dengan lahan minimal. Smart farming menerapkan teknologi dalam pertanian seperti big data, internet of things dan cloud computing. Dalam penelitian ini menerapkan teknologi IoT dengan menggunakan arm robot dan robot slider. Beberapa sensor yang digunakan untuk mengukur kualitas tanaman hidroponik antara lain sensor TDS untuk mendeteksi nutrisi, pH, dan DHT 11 untuk mendeteksi suhu dan kelembapan. Penelitian ini diawali dengan pembibitan, penanaman, pemantauan nutrisi, dan penuaian tanaman hidroponik. Perancangan sistem smart farming ini menggunakan diagram deployment, diagram objek dan diagram flowchart. Sistem smart farming ini mampu mendeteksi dan memonitoring tanaman hidroponik dengan menerapkan teknologi IoT, menerapkan arm robot untuk melakukan penanaman, penyiraman ,dan proses tuai secara otomatis, serta dapat membantu petani urban dalam mengelola pertanian meskipun dalam lahan yang terbatas.
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K., Vikranth, and Prasad K. Krishna. "An Implementation of IoT and Data Analytics in Smart Agricultural System – A Systematic Literature Review." International Journal of Management, Technology, and Social Sciences (IJMTS) 6, no. 1 (2021): 41–70. https://doi.org/10.5281/zenodo.4496828.
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India is a country that depends on agriculture, where about half the population relies heavilyon agriculture for their livelihood. However, most of the practices undertaken in the agriculturalprocess are not for profit and yield favorable. It should upgrade with current technologies toboost seed quality, check soil infertility, check the water level, environmental changes, andmarket price prediction, and achieve in agriculture sensitivity of faults and backgroundunderstanding. The advancement in technology and developments is seen as a significantaspect in their financial development and agricultural production growth. The Internet ofThings (IoT), Wireless Sensor Networks (WSN), and data analytics accomplish these upgrades.These technologies help in providing solutions to agricultural issues such as resourceoptimization, agricultural land monitoring, and decision-making support, awareness of thecrop, land, weather, and market conditions for farmers. Smart agriculture is based on data fromsensors, data from cloud platform storage and data from databases, all three concepts need tobe implemented. The data are collected from different sensors and stored in a cloud-based backend support, which is then analyzed using proper analytics techniques, and then the relevantinformation is transferred to a user interface, which naturally supported the decision toconclude. The IoT applications mainly use sensors to monitor the situation, which collects alarge size of data every time, so in the case of the Internet of Things (IoT) application, sensorscontribute more. Data analytics requires data storage, data aggregation, data processing anddata extraction. To retrieve data and information from database, we must use data miningtechniques. It acts a significant position in the selection-making process on several agriculturalissues. The eventual objective of data mining is to acquire information form data transform itfor some advanced use into a unique human-comprehensible format. Big data's role inAgriculture affords prospect to increase the farmers' economic gain by undergoing a digitalrevolution in this aspect that we examine with precision. This paper includes reviewing asummary of some of the conference papers, journals, and books that have been going in favorof smart agriculture. The type of data required for smart farming system are analyzed and thearchitecture and schematic diagram of a proposed intelligent farming system are included. Italso involves implementing different components of the smart farming system and integratingIoT and data analytics in the smart farming system. Based on the review, research gap, researchagendas to carry out further research are identified.
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Apoorva, Sharma H. K. Sharma Konga Upendar and Arun Kumar. "The role of GIS in smart farming and sustainable agriculture." Scientific Frontiers 02, no. 03 (2025): 6–8. https://doi.org/10.5281/zenodo.15206091.
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<strong>Abstract</strong> Geographic Information Systems (GIS) play a vital role in smart farming and sustainable agriculture by integrating spatial data with advanced analytics for precise decision-making. GIS enhances precision agriculture, soil and water management, crop monitoring, climate resilience, and sustainable land use planning. By leveraging satellite imagery, GPS, and remote sensing, GIS enables real-time monitoring, optimized resource allocation, and early detection of pests and diseases. Despite challenges like high costs and technical expertise requirements, future advancements in AI, IoT, and big data analytics will further enhance GIS applications, making agriculture more efficient, sustainable, and climate-resilient.
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Rabhi, Loubna, Noureddine Falih, Lekbir Afraites, and Belaid Bouikhalene. "Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 581. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp581-589.
Full textAbstract:
Due to the spead of objects connected to the internet and objects connected to each other, agriculture nowadays knows a huge volume of data exchanged called big data. Therefore, this paper discusses connected agriculture or agriculture 4.0 instead of a traditional one. As irrigation is one of the foremost challenges in agriculture, it is also moved from manual watering towards smart watering based on big data analytics where the farmer can water crops regularly and without wastage even remotely. The method used in this paper combines big data, remote sensing and data mining algorithms (neural network and support vector machine). In this paper, we are interfacing the databricks platform based on the apache Spark tool for using machine learning to predict the soil drought based on detecting the soil moisture and temperature.
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Yadav, Sargam, Abhishek Kaushik, Mahak Sharma, and Shubham Sharma. "Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis." AgriEngineering 4, no. 2 (2022): 424–60. http://dx.doi.org/10.3390/agriengineering4020029.
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Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies.
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Eremin, S. G. "Digitalization of agriculture: the role of big data in improving the efficiency and sustainability of the industry." Agrarian science 1, no. 4 (2025): 172–76. https://doi.org/10.32634/0869-8155-2025-393-04-172-176.
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The article investigates the impact of digitalization and big data technologies on the development of agriculture. Based on a literature review, key trends in the application of big data in the agricultural sector, including precision farming, smart farms, yield forecasting, and supply chain optimization, were identified. The empirical part of the study is based on survey data from Russian farming enterprises (n = 500) as well as an analysis of case studies on the implementation of digital solutions by large agricultural holdings. The main findings indicate a significant potential for big data to enhance the efficiency and sustainability of agriculture. It was found that the use of predictive analytics based on big data allows for a 15–20% increase in yield, a 10–15% reduction in storage losses, and a 20–25% optimization of resource costs. However, key barriers remain, such as a shortage of expertise in data science, high technology costs, and resistance to change. The conclusion highlights the need for state-level support for the digital transformation of agriculture, as well as the development of partnerships between science and business to create and transfer innovative solutions.
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Phasinam, Khongdet, Thanwamas Kassanuk, and Mohammad Shabaz. "Applicability of Internet of Things in Smart Farming." Journal of Food Quality 2022 (February 2, 2022): 1–7. http://dx.doi.org/10.1155/2022/7692922.
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Agriculture is critical to human life. Agriculture provides a means of subsistence for a sizable portion of the world’s population. Additionally, it provides a large number of work opportunities for inhabitants. Many farmers prefer traditional farming approaches, which result in low yields. Agriculture and related industries are vital to the economy’s long-term growth and development. The primary issues in agricultural production include decision-making, crop selection, and supporting systems for crop yield enhancement. Agriculture forecasting is influenced by natural variables such as temperature, soil fertility, water volume, water quality, season, and crop prices. Growing advancements in agricultural automation have resulted in a flood of tools and apps for rapid knowledge acquisition. Mobile devices are rapidly being used by everyone, including farmers. This paper presents a framework for smart crop tracking and monitoring. Sensors, Internet of Things cameras, mobile applications, and big data analytics are all covered. The hardware consists of an Arduino Uno, a variety of sensors, and a Wi-Fi module. This strategy would result in the most effective use of energy and the smallest amount of agricultural waste possible.
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Sarigiannidis, Panagiotis, Thomas Lagkas, Konstantinos Rantos, and Paolo Bellavista. "The Big Data era in IoT-enabled smart farming: Re-defining systems, tools, and techniques." Computer Networks 168 (February 2020): 107043. http://dx.doi.org/10.1016/j.comnet.2019.107043.
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Sharma, Santosh Kumar, and Bonomali Khuntia. "Integrated security for data transfer and access control using authentication and cryptography technique for Internet of things." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 4 (2021): 303–9. http://dx.doi.org/10.3233/kes-190116.
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The entire world is running behind the smart technology to accomplish the daily needs in a smart way such as smart farming, smart irrigation system, smart transportation system, smart medical management, handling of smart home appliances, smart security, etc. Smart technology is the soul property of internet services and accessing data from virtual servers, which raises the alarm of security vulnerability and threats. In recommended system we have focused on application layer security which are concerned with application interface and queue manager for service exchange. As application layer is the closest to end user and produces the big threat to the application platform it motivates us to recommend strong multilevel security system to identify the different activity of handlers and identify their roles to enroute of accessing confidential data services. Subsequently, our work is to assure that every user should have an authentication key with specific privileges to get the desired information. In focus, we see the security management by integrating the Kerberos authentication protocol with honey encryption technique to provide strong multilevel security system.
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Kumar, Ashok, S. R. Singh M. C.Yadav, B. D. Bhuj Shri Dhar, et al. "Information and Communication Technologies (ICTs) in Agriculture: A Review." International Journal of Current Microbiology and Applied Sciences 12, no. 2 (2023): 17–50. http://dx.doi.org/10.20546/ijcmas.2023.1202.003.
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Agricultural practices in India are facing many challenges such as change in climatic conditions, different geographical environment, conventional agricultural practices; economic and political scenario. Economic loss due to the lack of information on crop yield productivity is another major concern in the country. These hurdles can be overcome by the implementation of advanced technology in agriculture. Some of the trends observed are smart farming, digital agriculture and Big Data Analytics which provide useful information regarding various crop yields influencing factors and predicting the accurate amounts of crop yield. The exact prediction of crop yield helps formers to develop a suitable cultivation plan, crop health monitoring system, management of crop yield efficiently and also to establish the business strategy in order to decrease economic losses. This also makes the agricultural practices as one of the highly profitable ventures. This paper presents insights on the various applications of technology advancements in agriculture such as Digital Agriculture, Smart Farming or Internet of Agriculture Technology (IoAT), Crop Management, Weed and Pest control, Crop protection and Big data analytics.
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Singh, Ramendra, Jitender Kumar, and Avilash Nayak. "AGROY: creating value through smart farming." Emerald Emerging Markets Case Studies 9, no. 3 (2019): 1–31. http://dx.doi.org/10.1108/eemcs-10-2018-0214.
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Learning outcomes This case study outlines the marketing, strategic and organizational issues facing the ever-expanding agri-inputs market in India, through the perspective of Agroy – an agri-products company. This case can be used to assist in the teaching courses such as marketing management, rural marketing, business strategy, operations and logistics management, among others, for students of MBA or other specialized courses in management. The case has been developed to make students aware and to understand the arduous nature of setting up a company catering to the huge Indian agri-inputs market. This case delves into the complexities of marketing in rural India that is characterized by low technological awareness, low volumes of digital transactions and immense language barriers. The Indian agricultural market is huge and has undergone a considerable amount of change owing to competition among multinational companies and traditional local micro-retailers. This case discusses the various challenges faced by multinational companies in entering India and how they need to strategize to modify their Western model of a distribution channel which faces huge challenges when put to test in India. Specific learning outcomes include: the case study would help students to comprehend the new business strategies that an MNC could adopt in emerging markets. Some companies work on changing traditional and conventional value chains of activities to fit the emerging market customer’s best and hence companies needs to figure out a unique business model to compete in emerging markets. This case study gives readers the opportunity to think about strategy in an uncertain environment. The case illustrates the challenges associated with innovating new business ideas that would help the company serve a greater number of people from a diverse background. It highlights the importance of thinking about real options, a portfolio of projects and the type of organizational structure required to tackle the uncertainties associated with foreign companies aiming to enter the Indian market. It also explores marketing and distribution issues – which are the type of customers to target and which are the suitable geographic areas with suitable linguistic compatibility in which there shall be ease in doing business. Finally, it is an avenue for students to think about the changes necessary throughout the distribution channel to successfully implement and commercialize a project in rural India. The case is intended to work well as a learning tool for strategy implementation where uncertainty is inherent and as an application to lectures on real options and risk or for discussions related to marketing and distribution channels and its challenges. Case overview/synopsis The Indian agricultural market plays an important role in India’s economy having a staggering 58 per cent of rural households depending on it as the principal means of livelihood. However they have very small landholdings, and hence, they find it difficult to order either large quantities or in bulk, as a result of which the cost of agricultural inputs gets enhanced. Agroy, an MNC, is one of the many companies that have stepped in to bridge this gap by trying to tap into the huge agricultural market. Agroy aspires to be the “UBER of agriculture.” Agroy is a cloud-based buying platform for farmers to buy agri-inputs efficiently at scale and at the best price from around the world. With big data and smart farming, the company aims to enhance farm sustainability and productivity. Agroy’s competitors like Agro Star and Big Heart also have similar business models and hence the competition is stiff. The three debatable questions that the case poses are: Will Agroy be able to shatter the age-old loyalty that Indian farmers have toward local retailers and other Indian companies that have an existing strong foothold in the market? Will similar distribution models as practiced in developed Western countries work in India, given the distribution challenges in deep rural Indian hinterland? Will Agroy be able to create sustainable business models by marketing agri-inputs at low prices in India? Complexity academic level MBA in courses such as entrepreneurial marketing, strategic marketing, agricultural marketing. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 8: Marketing.
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Qiao, Yongliang, He Kong, Cameron Clark, et al. "Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review." Animals 11, no. 11 (2021): 3033. http://dx.doi.org/10.3390/ani11113033.
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The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.
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Hasan, Kamaruddin, Masriadi, Muchlis, and Asmaul Husna. "Digital Farming and Smart Farming from the Perspective of Agricultural Students at Malikussaleh University 2022." Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) 3 (January 27, 2023): 00065. http://dx.doi.org/10.29103/micoms.v3i.230.
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This study describes the views of agricultural students in seeing the opportunities and challenges of the era of digital farming and smart farming. To further grow their interest to be ready to become agricultural entrepreneurs who are creative, innovative, professional, competitive and able to absorb agricultural sector jobs. According to the Central Bureau of Statistics (BPS) national labor force survey, 20.62% of Indonesian youth work in the agricultural sector in August 2020, an increase compared to the previous period in 2019 which amounted to 18.43%, and will continue to increase until 2022. An increase in the number of young people in the sector agriculture can be a momentum to expand it. As many as 85.62% of them are internet users and have the opportunity to become early adopters of digital technology in the agricultural sector. So far, farmers' understanding of digital farming and smart farming is low because the majority of farmers have graduated from elementary and secondary schools. The average age is over 45 years, which makes it difficult to adapt to digital technology. Helplessness when dealing with digital media technology.
 The increasing number of young people interested in the agricultural sector is a hope as well as an opportunity to increase the development of a digital-based agricultural world. Of course this can be integrated into agricultural extension programs by millennials provided that agricultural students and alumni have adequate digital skills. That currently modernization of agriculture is a necessity. The agricultural sector continues to move towards digital farming and smart farming. Digitization of Agriculture facilitates monitoring, marketing, technology and helps accelerate the production process. Implementation of intelligent and critical use of digital media. The final results of this study are descriptive views and strategies of agricultural students regarding the phenomenon of digital farming and smart farming.
 Primary and secondary data were obtained through observation, interviews, Focus Group Discussion and literature review. Theoretical basis, concepts and models as well as scientific contributions are used by Digital Skills or Digital Literacy, Digital Farming, Smart Farming 4.0 and Agricultural Students. The research informants consisted of agricultural students and alumni, digital skills experts, agricultural academics and relevant stakeholders. Data analysis used snow ball informants and cross checks, synchronization, compression, reduction, data display and conclusion. The results of the study show that Malikussaleh University agricultural students and alumni as millennials are active internet users with various media that have the opportunity to become early adopters of digital technology in the agricultural sector towards Digital Farming and smart farming. Digitizing agriculture with the active involvement of millennials will facilitate monitoring, marketing, technology and help accelerate the production process by implementing the use of digital media intelligently and critically. The stigma of agriculture only for those with low education seems to still exist. Need to increase understanding of digital farming and smart farming among Millennials. Maximum implementation of digital farming and smart farming is a big hope in the hope of being able to realize a sustainable agricultural system.
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Ane, T., and S. Yasmin. "Agriculture in the Fourth Industrial Revolution." Annals of Bangladesh Agriculture 23, no. 2 (2019): 115–22. http://dx.doi.org/10.3329/aba.v23i2.50060.
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Agriculture and industry are tied up and both are complementary to each other. The fourth industrial revolution is an advanced digital technology, it focuses an opportunity that could change the environment in the way human think and work. The farms and factories must implement smart technology to move very fast and it should be an innovative applications to embrace the fourth industrial revolution robustly for Bangladesh. The fourth industrial revolution concept combines artificial intelligence and big data that have achieved significant attention and popularity in precision farming like in monitoring, diagnosing insect pests, measuring soil moisture, diagnosing harvest time and monitoring crop health status and reducing complicated monitoring by human. Industry that extend precision agriculture using artificial intelligence with robotic technology in fourth industrial revolution and its application is embedding into smart observation that retrieve real-time information from field level data with minor human interference. The fourth industrial revolution builds a smart farming technology which brings advanced and sustainable changes for both production and agroprocessing. The fourth industrial revolution extends farms production and also increase their value. This paper reviewed the past effects of industrial revolution, discussed expanded benefit into smart farming and predicted impacts of fourth industrial revolution in Bangladesh agriculture.
 Ann. Bangladesh Agric. (2019) 23(2) : 115-122
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Karunathilake, E. M. B. M., Anh Tuan Le, Seong Heo, Yong Suk Chung, and Sheikh Mansoor. "The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture." Agriculture 13, no. 8 (2023): 1593. http://dx.doi.org/10.3390/agriculture13081593.
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Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.
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Kresna, Andi B., and Arini P. Hanifa. "Evaluation of smart farm training for extension officers to support digitalizing era." IOP Conference Series: Earth and Environmental Science 1230, no. 1 (2023): 012149. http://dx.doi.org/10.1088/1755-1315/1230/1/012149.
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Abstract Smart farming is an agricultural concept based on precision agriculture. It utilizes technology automation supported by big data management, machine learning or artificial intelligence, and the Internet of Things to improve the quality and quantity of agricultural production. In its implementation, extension workers play a vital role as the vanguard of this program escort. Therefore, increasing the capacity of extension workers in smart farming is very necessary. This paper evaluated several aspects of smart farming training for extension workers. This paper uses secondary data from the evaluation form of 30 participants collected from Batangkaluku Agricultural Training Centre. Results showed that male extension officers dominated the training participants (70%). The subjects/training modules can be mastered by participants with scores>3.4 out of 5. The training can increase participants’ comprehension of the material up to 2.75x higher. The satisfaction level was above 4 out of 5. The findings can help the organizing committee in improving related aspects. Increased capacity building of agriculture extension officers by such training was expected to overcome the gap in the information of technology innovation from researchers to farmers.