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Relevant bibliographies by topics / Precision farming Agricultural engineering

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Academic literature on the topic 'Precision farming Agricultural engineering'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Precision farming Agricultural engineering"

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Fahad, Muhammad, Tariq Javid, Hira Beenish, Adnan Ahmed Siddiqui, and Ghufran Ahmed. "Extending ONTAgri with Service-Oriented Architecture towards Precision Farming Application." Sustainability 13, no. 17 (August 31, 2021): 9801. http://dx.doi.org/10.3390/su13179801.

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The computer science perspective of ontology refers to ontology as a technology, however, with a different perspective in terms of interrogations and concentrations to construct engineering models of reality. Agriculture-centered architectures are among rich sources of knowledge that are developed, preserved, and released for farmers and agro professionals. Many researchers have developed different variants of existing ontology-based information systems. These systems are primarily picked agriculture-related ontological strategies based on activities such as crops, weeds, implantation, irrigation, and planting, to name a few. By considering the limitations on agricultural resources in the ONTAgri scenario, in this paper, an extension of ontology is proposed. The extended ONTAgri is a service-oriented architecture that connects precision farming with both local and global decision-making methods. These decision-making methods are connected with the Internet of Things systems in parallel for the input processing of system ontology. The proposed architecture fulfills the requirements of Agriculture 4.0. The significance of the proposed approach aiming to solve a multitude of agricultural problems being faced by the farmers is successfully demonstrated through SPARQL queries.

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Perz, Rafal, and Kacper Wronowski. "UAV application for precision agriculture." Aircraft Engineering and Aerospace Technology 91, no. 2 (February 4, 2019): 257–63. http://dx.doi.org/10.1108/aeat-01-2018-0056.

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Purpose The purpose of this study is to show the potentials of a cost-effective unmanned aerial vehicles (UAV) system for agriculture industry. The current population growth rate is so vast that farming industry must be highly efficient and optimized. As a response for high quality food demands, the new branch of the agriculture industry has been formed – the precision agriculture. It supports farming process with sensors, automation and innovative technologies. The UAV advantages over regular aviation are withering. Not only they can fly at lower altitude and are more precise but also offer same high quality and are much cheaper. Design/methodology/approach The main objective of this project was to implement an exemplary cost-effective UAV system with electronic camera stabilizer for gaining useful data for agriculture. The system was based on small, unmanned flying wing able to perform fully autonomous missions, a commercially available camera and an own-design camera stabilizer. The research plan was to integrate the platform and run numerous experimental flights over farms, fields and woods collecting aerial pictures. All the missions have been planned to serve for local farming and forest industries and cooperated with local business authorities. Findings In preliminary flight tests, the variety of geodetic, forest and agriculture data have been acquired, placed for post processing and applied for the farming processes. The results of the research were high quality orthophoto maps, 3D maps, digital surface models and images mosaics with normalized difference vegetation index. The end users were astonished with the high-quality results and claimed the high importance for their business. Originality/value The case study results proved that this kind of a small UAV system is exceptional to manage and optimize processes at innovative farms. So far only professional, high-cost UAV platforms or traditional airships have been applied for agriculture industry. This paper shows that even simple, commercially available equipment could be used for professional applications.

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Zecha, C. W., J. Link, and W. Claupein. "Mobile sensor platforms: categorisation and research applications in precision farming." Journal of Sensors and Sensor Systems 2, no. 1 (May 14, 2013): 51–72. http://dx.doi.org/10.5194/jsss-2-51-2013.

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Abstract. The usage of mobile sensor platforms arose in research a few decades ago. Since the beginning of satellite sensing, measurement principles and analysing methods have become widely implemented for aerial and ground vehicles. Mainly in Europe, the United States and Australia, sensor platforms in precision farming are used for surveying, monitoring and scouting tasks. This review gives an overview of available sensor platforms used in recent agricultural and related research projects. A general categorisation tree for platforms is outlined in this work. Working in manual, automatic or autonomous ways, these ground platforms and unmanned aircraft systems (UAS) with an agricultural scope are presented with their sensor equipment and the possible architectural models. Thanks to advances in highly powerful electronics, smaller devices mounted on platforms have become economically feasible for many applications. Designed to work automatically or autonomously, they will be able to interact in intelligent swarms. Sensor platforms can fulfil the need for developing, testing and optimising new applications in precision farming like weed control or pest management. Furthermore, commercial suppliers of platform hardware used in sensing tasks are listed.

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Polishchuk, Yurij Vladimirovich, Vladimir Leonidovich Astafyev, Alexey Ivanovich Derepaskin, Nikolay Vladimirovich Kostyuchenkov, Nikolay Vladimirovich Laptev, and Artem Pavlovich Komarov. "Impacts of Automatic and Parallel Driving Systems on the Productivity of Machine-Tractor Units in the Northern Region of the Republic of Kazachstan." Acta Technologica Agriculturae 24, no. 3 (August 10, 2021): 143–49. http://dx.doi.org/10.2478/ata-2021-0024.

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Abstract Precision farming systems are being intensively introduced into the agricultural production of the Republic of Kazakhstan. According to developers and dealers, precision farming can reduce the cost of fertilizers, seeds, PPA, fuel and lubricants by 20% on average. At the same time, the efficiency possibilities resulting from the utilization of precision farming systems under certain conditions have not been fully studied. The aim of this work was to assess the influence of parallel and automatic driving systems on the technical, operational and economic indicators of units for sowing, chemical processing, harvesting, and autumn deep tillage using comparative tests in the northern region of the Republic of Kazakhstan. For these purposes, comparative tests were conducted for a seeder for sowing wheat; self-propelled sprayer for chemical weeding of wheat and flax; combine harvester for harvesting wheat; and unit for deep, subsurface tillage in Northern Kazakhstan. The comparative tests determined the impacts of GPS navigation systems, automatic and parallel control systems, and seeding control systems on agricultural, energy, operational, technological and economic performance of units.

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Loret, Niccolò, Antonio Affinito, and Giuliano Bonanomi. "Introducing Evja - "Rugged" Intelligent Support System for precision farming." ACTA IMEKO 9, no. 2 (June 30, 2020): 83. http://dx.doi.org/10.21014/acta_imeko.v9i2.795.

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<p>Precision agriculture is a farming system based on the combination of detailed observations, measuring and rapid-response to optimize energetic input to maximize crops production. Precision agriculture use decision support system (DSS) for optimize farm management. In this context, <em>EVJA Observe Prevent Improve</em> (or just EVJA) is an Intelligent Support System for precision agriculture. A vast set of data (i.e. temperature, relative humidity, deficit of vapour pressure, leaf wetness, solar radiation, carbon dioxide concentration, soil moisture etc.) is continuously collected, submitted to a local control unit, and processed through algorithms specifically developed for different crops. On the other hands, farmers can access EVJA from their pc and mobile devices, and monitor complex agronomic data analysis presented in a user-friendly interface.</p>In this article, we will show how EVJA works, and how its output can be used to assess the health state of plants through a specific set of functions. Moreover, we will show the methodology to develop useful predictive models based on this information.<p>Specifically, we will describe a predictive algorithms capable to predict the infection risks of downy mildew for baby leaves plantations and for Fusarium ear blight of wheat.</p>

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Schreiner, Simon, Dubravko Culibrk, Michele Bandecchi, Wolfgang Gross, and Wolfgang Middelmann. "Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors." at - Automatisierungstechnik 69, no. 4 (April 1, 2021): 325–35. http://dx.doi.org/10.1515/auto-2020-0042.

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Abstract This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.

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Beloev, Ivan, Diyana Kinaneva, Georgi Georgiev, Georgi Hristov, and Plamen Zahariev. "Artificial Intelligence-Driven Autonomous Robot for Precision Agriculture." Acta Technologica Agriculturae 24, no. 1 (January 29, 2021): 48–54. http://dx.doi.org/10.2478/ata-2021-0008.

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Abstract In the recent years, robotic systems became more advanced and more accessible. This has led to their slow, but stable integration and use in different processes and applications, including in the agricultural domain. Nowadays, agricultural robots are developed with the aim to replace the human labour in the otherwise exhausting, time-consuming or dangerous activities. Agricultural robotic systems provide many advantages, which can differ based on the type of the robot and its sensors, actuators and communication systems. This paper presents the design, the construction process, the main characteristics and the evaluation of a prototype of a small-scale agricultural robot that can be used for some of the simplest activities in agricultural enterprises. The robot is designed as an end-user autonomous mobile system, which is capable of self-localization and can map or inspect a specific farming area. The decision-making capabilities of the robot are based on artificial intelligence (AI) algorithms, which allow it to perform specific actions in accordance to the situation and the surrounding environment. The presented prototype is in its early development and evaluation stages and the paper concludes with discussions on the possible further improvements of the platform.

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Hossain, Md Selim, Md Habibur Rahman, Md Sazzadur Rahman, A. S. M. Sanwar Hosen, Changho Seo, and Gi Hwan Cho. "Intellectual Property Theft Protection in IoT Based Precision Agriculture Using SDN." Electronics 10, no. 16 (August 18, 2021): 1987. http://dx.doi.org/10.3390/electronics10161987.

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In this work, we examine the privacy and safety issues of Internet of Things (IoT)-based Precision Agriculture (PA), which could lead to the problem that industry is currently experiencing as a result of Intellectual Property Theft (IPT). Increasing IoT-based information flow in PA will make a system less secure if a proper security mechanism is not ensured. Shortly, IoT will transform everyday lives with its applications. Intellectual Property (IP) is another important concept of an intelligent farming system. If the IP of a wise farming system leaks, it damages all intellectual ideas like cultivation patterns, plant variety rights, and IoT generated information of IoT-based PA. Thus, we proposed an IoT enabled SDN gateway regulatory system that ensures control of a foreign device without having access to sensitive farm information. Most of the farm uses its devices without the use of its integrated management and memory unit. An SDN-based structure to solve IP theft in precision farming has been proposed. In our proposed concept, a control system integrates with the cloud server, which is called the control hub. This hub will carry out the overall PA monitoring system. By hiring the farm devices in the agricultural system, these devices must be tailored according to our systems. Therefore, our proposed PA is a management system for all controllable inputs. The overall goal is to increase the probability of profit and reduce the likelihood of IPT. It does not only give more information but also improves information securely by enhancing the overall performance of PA. Our proposed PA architecture has been measured based on the throughput, round trip time, jitter, packet error rate, and the cumulative distribution function. Our achieved results reduced around (1.66–6.46)% compared to the previous research. In the future, blockchain will be integrated with this proposed architecture for further implementation.

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Poblete-Echeverría, Carlos, and Sigfredo Fuentes. "Editorial: Special Issue “Emerging Sensor Technology in Agriculture”." Sensors 20, no. 14 (July 9, 2020): 3827. http://dx.doi.org/10.3390/s20143827.

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Research and innovation activities in the area of sensor technology can accelerate the adoption of new and emerging digital tools in the agricultural sector by the implementation of precision farming practices such as remote sensing, operations, and real-time monitoring [...]

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Fang, Li Gang, Zhao Bin Liu, Hong Li Li, Cai Dong Gu, and Min Li Dai. "Application of Wireless Sensor Network for M2M in Precision Fruits." Advanced Materials Research 267 (June 2011): 482–87. http://dx.doi.org/10.4028/www.scientific.net/amr.267.482.

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The study introduced foreign and domestic situation of wireless sensor network technology in precision agriculture in detail. Applications of wireless sensor network are universal in foreign precision agriculture, however in its beginning stage in domestic agriculture. The function of domestic system based on wireless sensor network is usually positioning measurement and processing of agriculture elements, which does not meet final requirement of precision agriculture. The study designed a universal wireless sensor network for M2M combined intelligent communication technology and big agriculture machinery. The key technologies of wireless sensor network for M2M include development plan of ISA SP100.11a, spectrum technology based on DSSS, network technology based on net routing and low power radio frequency design, which can meet the real-time character, reliability, robustness and low energy consumption requirement of wireless communication in precision fruits. Moreover, the study presented several typical applications in precision fruits (including farming machine, water-saving irrigation machine and picking machine). With development of precision fruits in breadth and depth, integration application of wireless sensor network would have a widen prospect in the future.

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Dissertations / Theses on the topic "Precision farming Agricultural engineering"

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Rusch, Peter C. "Precision farming in South Africa." Diss., Pretoria : [s.n.], 2001. http://upetd.up.ac.za/thesis/available/etd-01072004-153302.

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Dube, Elias Edo. "Wireless Farming: a mobile and Wireless Sensor Network based application to create farm field monitoring and plant protection for sustainable crop production and poverty reduction." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20488.

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

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Chen, Yu. "Development of an Intelligent Sprayer to Optimize Pesticide Applications in Nurseries and Orchards." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1290526778.

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Adrian, Anne Mims Rainer R. Kelly. "Factors influencing adoption and use of precision agriculture." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Summer/Dissertations/ADRIAN_ANNE_27.pdf.

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Addicott, James Edward. "Farming by satellites : how West Country farmers were being driven to, and by, precision agricultural systems." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278696.

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Precision farming integrates satellite coordination and information communication technologies into farming practices to deliver self-driving and auto-regulating machinery and equipment to farmers, who can afford to invest, right across the globe. It is often sold on the basis that it can help clean up or ‘ecologically modernise’ conventional, industrial agriculture. It should also increase production rates in industrial agriculture to help to ‘feed the world’ as well as being cost effective in ways that could make farmers more money – miracle-grow formula and win-win technology. There are critical concerns that precision farming facilitates a continuing trend of transnational firms appropriating control over agricultural industries. Many neo-Marxist or neo-Weberian critics contend that any ‘green’ benefits fall secondary to the more dominant social and economic trend of ongoing capital accumulation, increasing rationalisation and industrial progress that has been deemed detrimental to natural environments and human populations. These social and economic pressures are actually the real drivers in change. Rather than greening industrial agriculture, precision farming is another way of masking over and profiting from the risks caused by ongoing capitalist accumulation and industrial agriculture. The other set of concerns are to do with human culture and labour. Farming is the grass roots of modern civilisation and dependent upon human labour, knowledge and cultural methods. With the introduction of data over knowledge, and auto-steering tractors over human labour and skills, what kinds of impacts will this have on farm families, rural cultures within countryside landscapes in Britain or other countries where precision farming is being adopted? As a farmer’s son, I was concerned about the impact the computerisation of agriculture will have on family farms, nature and rural communities. I spent four years interviewing and working with a cooperative group of Duchy of Cornwall farmers in the West Country of England. I wanted to know why they were using these new technologies and the kinds of benefits, impacts or outcomes that they experienced following adoption. The results tend to confirm critics’ concerns, unfortunately. Precision farming has much more to do with the organising of agricultural production. The restructuring of farming by way of precision farming greater empowers transnational agribusinesses and Agri-Food supply chains, rather than protecting the environment, feeding hungry people or making family farming more sustainable. I conclude my research by suggesting that it is not technology, or agricultural technologies such as precision farming that will deliver these end goals in and of them selves. There could be room to improve precision farming systems if they are coupled with well-managed policy designs and agri-environmental schemes.

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Rahman, Baishali. "Estimating the Economic Benefits of Automatic Section Control in the North Dakota Prairie Pothole Region." Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28870.

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The impact of Automatic Section Control (ASC) as a tool of Precision Agricultural Technology as considered in the more efficient application of inputs to produce the four major crops, corn, soybean, HRSW, and canola in the North Dakota Prairie Pothole Region. Reduction in machinery overlap in the sample 105 fields was calculated by simulating the routing paths of a 60-feet wide planter with 24 sections controlled and a 120-feet wide boom sprayer with individual nozzle control. The dollar and percentage seed and chemical costs that a farm can save by reducing overlapping area were calculated. Impact of field parameters on net savings were estimated by developing and estimating an econometric model. Results show that ASC can save substantial cost in the sample fields while field shape had the highest significant impact on net cost savings.
North Dakota State University. Department of Agribusiness and Applied Economics

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Shelley, Anthony N. "INCORPORATING MACHINE VISION IN PRECISION DAIRY FARMING TECHNOLOGIES." UKnowledge, 2016. http://uknowledge.uky.edu/ece_etds/86.

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The inclusion of precision dairy farming technologies in dairy operations is an area of increasing research and industry direction. Machine vision based systems are suitable for the dairy environment as they do not inhibit workflow, are capable of continuous operation, and can be fully automated. The research of this dissertation developed and tested 3 machine vision based precision dairy farming technologies tailored to the latest generation of RGB+D cameras. The first system focused on testing various imaging approaches for the potential use of machine vision for automated dairy cow feed intake monitoring. The second system focused on monitoring the gradual change in body condition score (BCS) for 116 cows over a nearly 7 month period. Several proposed automated BCS systems have been previously developed by researchers, but none have monitored the gradual change in BCS for a duration of this magnitude. These gradual changes infer a great deal of beneficial and immediate information on the health condition of every individual cow being monitored. The third system focused on automated dairy cow feature detection using Haar cascade classifiers to detect anatomical features. These features included the tailhead, hips, and rear regions of the cow body. The features chosen were done so in order to aid machine vision applications in determining if and where a cow is present in an image or video frame. Once the cow has been detected, it must then be automatically identified in order to keep the system fully automated, which was also studied in a machine vision based approach in this research as a complimentary aspect to incorporate along with cow detection. Such systems have the potential to catch poor health conditions developing early on, aid in balancing the diet of the individual cow, and help farm management to better facilitate resources, monetary and otherwise, in an appropriate and efficient manner. Several different applications of this research are also discussed along with future directions for research, including the potential for additional automated precision dairy farming technologies, integrating many of these technologies into a unified system, and the use of alternative, potentially more robust machine vision cameras.

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Siemens, Mark Cornelius 1965. "Cable-drawn farming system analysis and control development." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/282232.

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Four types of cable drawn farming systems, a single engine system, a double engine system, a perimeter system, and a double implement system, were analyzed to determine which was best suited for Arizona. The systems were compared in terms of relative cost, reliability/simplicity and field capacity. Field capacity computation variables were implement width, implement speed, tower travel speed, implement carrier travel speed, and implement rotation time. The analysis showed the single engine system was the least expensive, simplest system with a field capacity identical to that of the double engine system, eight percent lower than the double implement system, and approximately thirteen percent higher than the perimeter system. Based on these results, the single implement system was judged superior to the others. The parameters affecting single implement system performance were then examined to optimize performance. The evaluation yielded a recommendation that the system be designed to have a tower speed of 48 ft/min, and a rotation time of 7.5 seconds. A positioning system for the mobile truss of a cable drawn farming system was also developed and tested. The system used a linear move irrigation system's above ground cable guidance system for steering, a wicket positioning system for stopping the machine at the indexing locations, and a wire-alignment system to control inner tower alignment. The system was tested over a length of 280 ft using a five tower, 575 ft long, linear move irrigation system. It was found that the above ground cable guidance system provided ±0.5 ft steering accuracy, the wicket positioning system controlled the power unit and end tower position within ±0.2 ft of the target destination, and that the wire alignment system controlled inner tower position within ±0.3 ft of the target destination. Statistical analysis of the test results showed the probability of position error being controlled to within ±0.4 ft and ±0.8 ft to be at the 99.7% and 99.99% confidence levels, respectively.

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Prigg, David B. "DESIGN OF A HYDRAULICALLY-ACTUATED HEXAPOD ROBOT FOR ORGANIC FARMING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1588002562729248.

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Whitlow, John Richard. "Gullying within dambos, with particular reference to the communal farming areas of Zimbabwe." Thesis, University of London, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320320.

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Books on the topic "Precision farming Agricultural engineering"

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Oerke, E. C. Precision Crop Protection - the Challenge and Use of Heterogeneity. Dordrecht: Springer Science+Business Media B.V., 2010.

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Munson, Robert D. Improving fertilizer and chemical efficiency through "high precision farming". St. Paul, Minn: Center for International Food and Agricultural Policy, University of Minnesota, 1990.

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The common agricultural policy and organic farming. Wallingford, UK: CABI Pub., 2006.

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Convention, Indian Society of Agricultural Engineers. Role of agricultural engineering in dryland agriculture: Proceedings of the XXIII Annual Convention, March 9-11, 1987, Indian Society of Agricultural Engineers, held at Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, M.P. New Delhi: The Society, 1987.

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Enric, Guinot Rodríguez, ed. Hidráulica agraria y sociedad feudal: Prácticas, técnicas, espacio. Valencia: Universitat de Valencia, 2012.

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Bernardo, Sorj, and Wilkinson John 1946-, eds. From farming to biotechnology: A theory of agro-industrial development. Oxford: Basil Blackwell, 1987.

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Vieri, Marco, ed. SPARKLE - Entrepreneurship for Sustainable Precision Agriculture. Florence: Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-044-3.

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SPARKLE - Entrepreneurship for Sustainable Precision Agriculture (SPA) is a course aiming to increase 'agripreneurship' among students, entrepreneurs and academics, enhancing knowledge and skills on technologies, innovations, entrepreneurial thinking and problem-solving skills into the farming sector. It also aims to transform the agricultural sector into a SPA-oriented system that could build an innovative ecosystem of agripreuners and agritechnicians around agriculture and entrepreneurship. The course is divided into four areas (SPA Overview, Tecnologies, Social and economic aspects and entrepreneurship in agriculture), 12 lessons and 55 topics leads students on a path for deepening the knowledge in a comprehensive system where technologies are a piece of the whole structure.

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Practices of irrigation & on-farm water management. New York: Springer, 2011.

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Bebee, Charles N. Chemigation, January 1970-August 1986. Beltsville, MD: U.S. Dept. of Agriculture, National Agricultural Library, 1987.

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Bebee, Charles N. Chemigation, January 1970-August 1986. Beltsville, MD: U.S. Dept. of Agriculture, National Agricultural Library, 1987.

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Book chapters on the topic "Precision farming Agricultural engineering"

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Sivanappan, R. K. "Water Conservation and Increased Crop Production Under Precision Farming." In Emerging Technologies in Agricultural Engineering, 305–14. Waretown, NJ : Apple Academic Press, 2017.: Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315366364-12.

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Macy, Ted S. "Field Experiences Supporting Precision Farming Technology." In Site-Specific Management for Agricultural Systems, 953. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/1995.site-specificmanagement.c71.

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Patel, Neelam, Rohitashw Kumar, and Dinesh Kumar Vishwakarma. "Status and Prospect of Precision Farming in India." In Agricultural Impacts of Climate Change, 119–59. Boca Raton : CRC Press, 2019-: CRC Press, 2019. http://dx.doi.org/10.1201/9780429326349-8.

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Fairchild, Dean. "Precision Farming Concepts: An Industry's Perspective and Experience Since 1986." In Site-Specific Management for Agricultural Systems, 753. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/1995.site-specificmanagement.c56.

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Blackmore, B. S., P. N. Wheeler, J. Morris, R. M. Morris, and R. J. A. Jones. "The Role of Precision Farming in Sustainable Agriculture: A European Perspective." In Site-Specific Management for Agricultural Systems, 777–93. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/1995.site-specificmanagement.c60.

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Ajit K. Srivastava, Carroll E. Goering, Roger P. Rohrbach, and Dennis R. Buckmaster. "Chapter 6 Precision agriculture." In Engineering Principles of Agricultural Machines, Second Edition, 123–38. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2006. http://dx.doi.org/10.13031/2013.41468.

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Bhavani, D. Durga, Mounika Kamatam, and R. Bhashya Sri Bharati. "Proposal for Economic Implementation of Precision Farming in India." In Electronics and Communications Engineering, 267–72. Description: This book reports the proceedings of the National Conference on Electronics and: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9781351136822-27.

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Huggins, D. R., and R. D. Alderfer. "Yield Variability Within a Long-Term Corn Management Study: Implications for Precision Farming." In Site-Specific Management for Agricultural Systems, 417–26. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/1995.site-specificmanagement.c28.

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Athanasiadis, Ioannis N., Sander Janssen, David Huber, Andrea Emilio Rizzoli, and Martin van Ittersum. "Semantic Modeling in Farming Systems Research - The Case of the Agricultural Management Definition Module." In Information Technologies in Environmental Engineering, 417–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71335-7_43.

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Chiappini, S., A. Galli, E. S. Malinverni, P. Zingaretti, R. Orsini, M. Fiorentini, and S. Zenobi. "An Ontology-Based Study for the Design of a Database for Data Management in Precision Farming." In Lecture Notes in Civil Engineering, 811–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39299-4_87.

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Conference papers on the topic "Precision farming Agricultural engineering"

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Vidoni, Renato, Raimondo Gallo, Gianluca Ristorto, Giovanni Carabin, Fabrizio Mazzetto, Lorenzo Scalera, and Alessandro Gasparetto. "ByeLab: An Agricultural Mobile Robot Prototype for Proximal Sensing and Precision Farming." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71216.

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At today, available mechatronics technology allows exploiting smart and precise sensors as well as embedded and effective mechatronic systems for developing (semi-)autonomous robotic platforms able to both navigate in different outdoor environments and implementing Precision Farming techniques. In this work, the experimental outdoor assessment of the performance of a mobile robotic lab, the ByeLab — Bionic eYe Laboratory — is presented and discussed. The ByeLab, developed at the Faculty of Science and Technology of the Free University of Bolzano (I), has been conceived with the aim of creating a (semi-)autonomous robotic system able to sense and monitor the health status of orchards and vineyards. For assessing and measuring the shape and the volume of the canopy, LIDAR technology coupled with ad-hoc developed algorithms have been exploited. To validate the ByeLab different experimental tests have been carried out. In addition to the in-lab and structured environments experimental tests that allowed to tune the algorithms, in this work the assessment of its capabilities — in particular the sensoric system — has been made outdoor controlled environment tests.

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"CIGR Handbook of Agricultural Engineering, Volume III Plant Production Engineering, Chapter 3 Trends for the Future, Part 3.2 Precision Farming." In CIGR Handbook of Agricultural Engineering Volume III Plant Production Engineering . St. Joseph, MI: American Society of Agricultural and Biological Engineers, 1999. http://dx.doi.org/10.13031/2013.36361.

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Idris, Harun Dzulquornain, Muhammad Aizzat Zakaria, and Ahmad Najmuddin Ibrahim. "Outdoor Position Estimation of a Mobile Platform for Precision Farming and Agriculture Automation." In 2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2020. http://dx.doi.org/10.1109/icetas51660.2020.9484294.

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Muth, David, Joshua Koch, Douglas McCorkle, and Kenneth Bryden. "A Computational Strategy for Design and Implementation of Equipment That Addresses Sustainable Agricultural Residue Removal at the Subfield Scale." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71430.

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Agricultural residues are the largest potential near term source of biomass for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in maintaining soil health and productivity. Innovation equipment designs for residue harvesting systems can help economically collect agricultural residues while mitigating sustainability concerns. A key challenge in developing these equipment designs is establishing sustainable reside removal rates at the sub-field scale. Several previous analysis studies have developed methodologies and tools to estimate sustainable agricultural residue removal by considering environmental constraints including soil loss from wind and water erosion and soil organic carbon at field scale or larger but have not considered variation at the sub-field scale. This paper introduces a computational strategy to integrate data and models from multiple spatial scales to investigate how variability of soil, grade, and yield within an individual cornfield can impact sustainable residue removal for bioenergy production. This strategy includes the current modeling tools (i.e., RUSLE2, WEPS, and SCI), the existing data sources (i.e., SSURGO soils, CLIGEN, WINDGEN, and NRCS managements), and the available high fidelity spatial information (i.e., LiDAR slope and crop yield monitor output). Rather than using average or representative values for crop yields, soil characteristics, and slope for a field, county, or larger area, the modeling inputs are based on the same spatial scale as the precision farming data available. There are three challenges for developing an integrated model for sub-field variability of sustainable agricultural residue removal—the computational challenge of iteratively computing with 400 or more spatial points per hectare, the inclusion of geoprocessing tools, and the integration of data from different spatial scales. Using a representative field in Iowa, this paper demonstrates the computational algorithms used and establishes key design parameters for an innovative residue removal equipment design concept.

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Nagchaudhuri, Abhijit, Travis Ford, and Christopher Hartman. "Overview of Remote Sensing Efforts at University of Maryland Eastern Shore." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98457.

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Abstract Remote sensing and aerial imaging efforts at University of Maryland Eastern Shore (UMES) have been ongoing for over a decade. It was initiated with the UMESAIR (Undergraduate Multidisciplinary Earth Science Airborne Instrumentation Research) project in early part of the century as an exploratory experiential learning project as means to foster collaboration and provide exposure to science and engineering students to scientists and engineers at NASA’s Wallops Flight Facility which is within 50 miles of campus. Subsequently, with significant support from USDA’s National Institute of Food and Agriculture (NIFA) the remote sensing endeavors have been integrated with the smart farming and precision agriculture efforts closely aligned with the land grant mission of UMES and the regional emphasis in the Delmarva Peninsula. Maryland Space Grant Consortium (MDSGC) have also supported a synergistic project titled Aerial Imaging and Remote Sensing for Precision Agriculture and Environmental Stewardship (AIRSPACES) on an annual basis which has allowed continued involvement of multidisciplinary undergraduate students from the STEM fields to remain involved with the efforts.

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Shchegrin, M. N. "YIELD MAPPING IN THE PRECISION FARMING SYSTEM." In TOPICAL ISSUES OF AGRICULTURAL DEVELOPMENT. Komi Republican Academy of Public Service and Management, 2021. http://dx.doi.org/10.19110/93206-022-33.

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Eaton, R., J. Katupitiya, K. W. Siew, and K. S. Dang. "Precision Guidance of Agricultural Tractors for Autonomous Farming." In 2008 2nd Annual IEEE Systems Conference. IEEE, 2008. http://dx.doi.org/10.1109/systems.2008.4519026.

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Jayantha Katupitiya and Ray Eaton. "Precision Autonomous Guidance of Agricultural Vehicles for Future Autonomous Farming." In 2008 Providence, Rhode Island, June 29 - July 2, 2008. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2008. http://dx.doi.org/10.13031/2013.24843.

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Castineira, Samir, Tamim Delwar, Rodolfo Duran, and Nezih Pala. "UAV-based agricultural monitoring and data acquisition system for precision farming." In Sensing for Agriculture and Food Quality and Safety XIII, edited by Moon S. Kim and Byoung-Kwan Cho. SPIE, 2021. http://dx.doi.org/10.1117/12.2587914.

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Lopes, S. I., R. Bexiga, J. P. Araújo, J. L. Cerqueira, C. Abreu, C. Paredes, and J. M. Alonso. "90. Precision livestock farming for reproductive performance optimization: a survey." In 13th Congress of the European Society for Agricultural and Food Ethics. The Netherlands: Wageningen Academic Publishers, 2016. http://dx.doi.org/10.3920/978-90-8686-834-6_90.

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