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Relevant bibliographies by topics / Precision agriculture

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Academic literature on the topic 'Precision agriculture'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 May 2025

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Journal articles on the topic "Precision agriculture"

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Kalbhor, Atharva. "AI and Machine Learning in Precision Agriculture: The Future of Agricultural Precision Agriculture." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (February 28, 2025): 648–54. https://doi.org/10.22214/ijraset.2025.66920.

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Agriculture is rapidly transforming with the integration of technologies such as machine learning (ML) and artificial intelligence (AI) to solve critical issues such as food security, climate change, and sustainable agriculture. Precision agriculture uses these technologies to increase yields, improve resource utilization, and reduce environmental impact. Machine learning techniques, particularly deep learning models such as convolutional neural networks (CNNs), have been successful in studying plant diseases, enabling early detection and reduction of crop losses. AI models improve decision-making by analyzing a wide range of agricultural data to predict crop yields, optimize irrigation schedules, and manage fertilization. These intelligent systems provide rapid insights, helping farmers make informed decisions and increase productivity and sustainability. While the Internet of Things enables machine learning and artificial intelligence by collecting real-time data from operations, the real breakthrough will come from machine learning algorithms that can predict outcomes, maintain standards, and work on the farm. Challenges such as high technology costs, complex data management, and implementation processes are only limited by time, but continuous advances in technology and research have the potential to transform agriculture by providing simple, effective, and practical solutions to today’s agricultural sector.

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Bujdos, Ágnes. "Precision Agriculture." Hungarian Yearbook of International Law and European Law 6, no. 1 (December 2018): 371–88. http://dx.doi.org/10.5553/hyiel/266627012018006001022.

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Goss, Michael J. "Precision agriculture." Field Crops Research 55, no. 3 (February 1998): 285–87. http://dx.doi.org/10.1016/s0378-4290(97)00082-8.

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Branzova, Petia. "PRECISION AGRICULTURE: TECHNOLOGICAL INNOVATIONS FOR SUSTAINABLE AGRICULTURE." Economic Thought journal 69, no. 1 (May 14, 2024): 24–36. http://dx.doi.org/10.56497/etj2469102.

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Precision agriculture represents an innovative approach utilizing technologies and scientific methods to enhance the efficiency and sustainability of agricultural oper-ations and their application in modern agriculture. Various technological innovations are analyzed, including the use of sensors, GPS systems, remote sensing, and software solutions that aid in optimizing agricultural operations. The article discusses the chal-lenges of implementing precision agriculture, as well as future development opportuni-ties in the sector and the potential benefits for farmers, rural communities, and the en-vironment from implementing this approach. The importance of precision agriculture as an innovative strategy for addressing challenges and achieving sustainable develop-ment in agriculture is emphasized. The goal of this article is to assist agricultural pro-ducers, agricultural specialists, and decision-makers in the sector in making informed decisions and strategies for implementing precision agriculture in their practices. Im-plementing precision agriculture will lead to improved efficiency and sustainability by reducing the use of resources such as water, fertilizers, and pesticides, increasing the productivity of agricultural crops, and reducing the adverse environmental impacts of agriculture.

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Šilha, J., P. Hamouz, V. Táborský, K. Štípek, J. Šnobl, K. Voříšek, L. Růžek, L. Brodský, and K. Švec. "Case studies for precision agriculture." Plant Protection Science 38, SI 2 - 6th Conf EFPP 2002 (December 31, 2017): 704–10. http://dx.doi.org/10.17221/10595-pps.

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The results of spatial variability of plant-available soil nutrients (P, K, Mg) and soil pH are described in this paper. Experiment was realized on the field of area 72 ha (orthic luvisol), located in the area of Český Brod. The use of coefficient of variation as a criterion of variability of soil agrochemical properties and yield on the field showed the following: the highest variability was observed in available P, the second highest variability was in available K, and the lowest variability of main non-mobile nutrients was in the available Mg. Soil pH was the lowest of all measured soil properties. Although the highest correlation coefficient between the soil available P content and soil pH was established, the process of spatial dependence was not detected. Detailed field scouting and others data can be important elements, as can complex decision rules, taking into account additional factors such as the characteristics of crop protection agents and preferences of the farm manager. This paper illustrates, how to plant nutritions, crop protection, crop production might be integrated to support these diseases and weeds management decisions.

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Loveleen, L., and S. Pillai. "Precision Agriculture Innovation in Agriculture." CARDIOMETRY, no. 25 (February 14, 2023): 678–84. http://dx.doi.org/10.18137/cardiometry.2022.25.678684.

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Precision farming refers to the latest trends in agriculture that use technology to improve quality, quantity, and productivity, thereby ensuring profitability, sustainability, betterment, and preservation of the environment. The paper discusses the development and needs for precision agriculture in India with its existing problems and opportunities. The challenges in the future cannot be resolved with ancient methods. In order to make agriculture efficient and sustainable, investment in new technologies accompanied by research and development is required. Agronomics is the highest contributor to national income. More than 70% of the total workforce is dependent on it. The agriculture industry needs top priority because the government and the nation both would fail to succeed in this sector. The paper identifies various challenges associated with the adoption of precision farming in India and the technologies that could be used for better results and the betterment of both farmers and the Agri industry of India.

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Dr. V. B. Kirubanand, Dr Rohini v,. "Environment based Precision Agriculture." Psychology and Education Journal 58, no. 2 (February 17, 2021): 6157–64. http://dx.doi.org/10.17762/pae.v58i2.3133.

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Agriculture, farming or animal husbandry is a vital occupation, since the history of mankind. The name agriculture represents all entities that came under the linear sequence of links of food chain for human beings. India is in an agricultural era, which is earning fame to it. In the fast moving world, agriculture should also run in the same pace along with the existing nature. This paper analyses the different methodologies for environment friendly precision agriculture. It also comparesthevariousmethodsavailablefortheusageofmoderntoolsandtechniquesinagriculture in the digital world. It discusses an insight to dwell into the different techniques for intelligent farming in the digital world. It acts as a decision support system for the farmers to perform environment friendly smartarming.

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Rimpika, Anushi, S. Manasa, Anusha K. N., Sakshi Sharma, Abhishek Thakur, Shilpa, and Ankita Sood. "An Overview of Precision Farming." International Journal of Environment and Climate Change 13, no. 12 (December 21, 2023): 441–56. http://dx.doi.org/10.9734/ijecc/2023/v13i123701.

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With respect to conventional farming precision agriculture increases average yields by limiting the wastage by calculating the exact required quantities of inputs. One major issue in India is the relatively small and scattered landholdings. In India 58% of the cultivable land is less than 1ha under single owner. The agricultural production system is the result of a complex interplay between seed, soil, water, and agrochemicals (including fertilizers). As a result, judicious control of all inputs is critical for the long-term viability of such a complex system. Precision agriculture is the use of technology and techniques to control the geographical and temporal variability associated with all aspects of agricultural production to improve output and environmental quality. Precision agricultural success is dependent on an accurate assessment of variability, its management, and evaluation in the space-time continuum of crop production. Precision agriculture's agronomic performance has been highly impressive in sugar beet, sugarcane, tea, and coffee crops. Due to lack of knowledge of space-time continuum the economic benefits environmental and social advantages are not explored yet. Precision agriculture is a relatively new field that integrates cutting-edge geographic technology with farming scenarios to optimize inputs, eliminate waste, and maximize returns. Precision farming systems are intended for use in many sorts of agricultural systems, ranging from row crops to dairy, and the technology has experienced extensive acceptance in the United States and across the globe.

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McClure, Julie. "Deconstructing Precision Agriculture." CSA News 60, no. 4 (April 2015): 26. http://dx.doi.org/10.2134/csa2015-60-4-15.

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Bruce, D. M., J. W. Farrent, C. L. Morgan, and R. D. Child. "PA—Precision Agriculture." Biosystems Engineering 81, no. 2 (February 2002): 179–84. http://dx.doi.org/10.1006/bioe.2001.0002.

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Dissertations / Theses on the topic "Precision agriculture"

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Window, Marc. "Security in Precision Agriculture : Vulnerabilities and risks of agricultural systems." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74309.

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BOTTA, ANDREA. "Agri.Q - Sustainable Rover for Precision Agriculture." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2963950.

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Diaz, John Faber Archila. "Design of a Rover to precision agriculture applications." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-21112017-160424/.

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The population growth associated with natural resources shortage, food demand increasing and the lack of manpower for agricultural activities generate important challenges to agriculture and engineering. The challenges imply the improvement of productivity with fewer resources, This scenario is consisted parameters that generally are in opposite directions. This work presents the development of a rover to agriculture (R2A) to support agriculturescouting tasks, the tasks will be in the future important tools to improve the productivity and enable the use of less agricultural supplies. The research presents the development of a rover for agriculture (R2A) to support scouting tasks; tasks that in the future will improve productivity and allowed the use of less agricultural supplies. The study begins with a bibliographic review of Robots for agriculture, Rovers, and agricultural Rovers developed by different research institutions. After the review is presented the work methodology based on mechanic and mechatronic design methodologies; in the development of the methodology are presented the general crop characteristics, the proposed of scouting tasks, the benchmarking developing mathematical models, CAD (Computer Aided Design ) models, simulations and tests in order to know the different features of the Rovers and agricultural robots. Using the knowledge gained in the course of work is proposed the concept of a rover for agriculture R2A, the concept is compared in simulations, and developed the detailed design using CAE tools (Computer Aided Engineering) after it built a prototype and tested. As results are presented comparative simulations of R2A, their mathematical modeling, R2A simulations in ideally conditions highlighting their skills, and finally R2A tests and comparison are presented.
O crescimento populacional associado à escassez de recursos naturais, a crescente demanda alimentar e a falta de mão de obra para as actividades agrícolas geram importantes desafios para a agricultura e a engenharia. Os desafios implicam a melhoria da produtividade com menos recursos. O cenário é constituido por parâmetros que geralmente estão em direções opostas. O trabalho apresenta o desenvolvimento de um rover para agricultura (R2A) para suportar tarefas de Scouting, tarefas que no futuro melhorarão a produtividade e permitirão o uso de menos subministros agrícolas. O estudo começa pela revisão bibliográfica de Robôs para agricultura, Rovers e Rovers agrícolas desenvolvidos por diferentes instituições de pesquisa. Apos a revisão é apresentada a metodologia do trabalho baseada nas metodologias de projeto mecânico e mecatrônico; no desenvolvimento da metodologia são apresentadas as caraterísticas das culturas de maneira geral, a proposta de tarefas de Scouting, o benchmarking desenvolvendo modelos matemáticos, modelos CAD (Computer Aided Design) simulações e testes com o intuito de conhecer as diferentes caraterísticas dos Rovers e Robôs agrícolas. Usando o conhecimento no decorrer do trabalho é proposto o conceito do rover para agricultura R2A, o conceito é comparado em simulações, e feito o projeto detalhado usando ferramentas CAE (Computer Aided Enginnerring), após é construído o protótipo, e testado. Como resultados são apresentadas simulações comparativas do R2A, a sua modelagem matemática, simulações do R2A em condições ideais, destacando as suas capacidades, e finalmente são apresentados os testes e comparações do R2A.

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Lundblad, Lowe, and Anna-Liisa Rissanen. "Precision Agriculture and Access to Agri-Finance : How precision technology can make farmers better applicants." Thesis, Umeå universitet, Företagsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149677.

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The World Bank has estimated that an additional $80 billion in financing are needed annually to achieve the 70 % increase in food supply required to feed the world in 2050. One of the cornerstones in achieving this increase in production is expected to be improved agricultural technology, where one of the latest additions is precision agriculture. It is believed that the money for investing in this technology must come from the private sector, but financial institutions are hesitant in lending money to farmers. This, in part, comes down to a high perceived riskiness in agricultural lending stemming from the risk composition in agriculture compared to other industries as well as from weak collaterals provided by farmers. This thesis aims to find what factors are most prominent in banks´ risk assessment of agricultural firms during the lending process and look at how precision agriculture could help mitigate these risks. We have gathered aggregated quantitative data from FAOSTAT and the Swedish Board of Agriculture on farm income and hectare yield (productivity) at Swedish farms. These variables were found to be two of the most important factors in agricultural lending based on previous research. In addition to this data, information on e.g. weather, ecological farming and expenditure related to pesticides, fertilizer, and machinery were collected to further the analysis. Precision agriculture is made up from a myriad of different technologies. We have opted to not separate the technologies in this study as the adoption of each technology included in the term is currently not sufficiently well understood. This aggregation of technologies allowed for us to use the dynamic AAGE-model to estimate the adoption based on the minimum hectare size where precision agriculture should be profitable at each point in time. The study finds that precision agriculture does have a positive impact on farm productivity and income volatility. Hence, precision agriculture should reduce the risk of agricultural financing given to adopting farmer which would increase the access to credit and, in continuation, lead to an increase in aggregated food production. In addition, we conclude that financial institutions should gain a better knowledge of precision agriculture technologies and use this information to improve the credit evaluation process in agricultural lending. Lastly, banks should understand how the risks related to information asymmetry and moral hazard could be reduced by utilizing the data available through farmers use of precision agriculture technology.

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

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

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Shockley, Jordan Murphy. "WHOLE FARM MODELING OF PRECISION AGRICULTURE TECHNOLOGIES." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_diss/105.

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This dissertation investigated farm management concerns faced by grain producers due to the acquisition of various precision agriculture technologies. The technologies evaluated in the three manuscripts included 1) auto-steer navigation, 2) automatic section control, and 3) autonomous machinery. Each manuscript utilized a multifaceted economic model in a whole-farm decision-making framework to determine the impact of precision agriculture technology on machinery management, production management, and risk management. This approach allowed for a thorough investigation into various precision agriculture technologies which helped address the relative dearth of economic studies of precision agriculture and farm management. Moreover, the research conducted on the above technologies provided a wide array of economic insight and information for researchers and developers to aid in the advancement of precision agriculture technologies. Such information included the risk management potential of auto-steer navigation and automatic section control, and the impact the technologies had on optimal production strategies. This dissertation was also able to provided information to guide engineers in the development of autonomous machinery by identifying critical characteristics and isolating the most influential operating machine. The inferences from this dissertation intend to be employed in an extension setting with the purpose of educating grain producers on the impacts of implementing such technologies.

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Russell, David C. "DEM creation for application in precision agriculture." Thesis, University of Nottingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366365.

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Faiçal, Bruno Squizato. "The Use of Computational Intelligence for Precision Spraying of Plant Protection Products." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-02032017-155603/.

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Protection management with the aid of plant protection products makes it possible to carry out pest control programs in agricultural environments and make them less hazardous for the cultivation of products on a large scale. However, when these programs are put into effect, only a small proportion of the sprayed products is really deposited on the target area while much of it is carried to neighboring regions. The scientific literature includes studies on the use of mathematical techniques to calculate the physical transformation and movement and provide a deposition estimate of the product. On the basis of this prediction, it is possible to configure a system which can allow the spraying to be carried out in normal weather conditions in the region for a satisfactory performance, although these conditions can undergo changes and make any statistical configuration unreliable. An alternative way of overcoming this problem, is to adapt the spray elements to the meteorological conditions while the protection management is being undertaken. However, the current techniques are operationally expensive in computational terms, which makes them unsuitable for situations where a short operational time is required. This thesis can be characterized as descriptive and seeks to allow deposition predictions to be made in a rapid and precise way. Thus it is hoped that the new approaches can enable the spray element to be adapted to the weather conditions while the protection management is being carried out. The study begins by attempting to reduce costs through a computational model of the environment that can speed up its execution. Subsequently, this computational model is used for predicting the rate of deposition as a fitness function in meta-heuristic algorithms and ensure that the mechanical behavior of the spray element can be adapted to the weather conditions while the management is put into effect. The results of this approach show that it can be adapted to environments with low variability. At the same time, it has a poor performance in environments with a high variability of weather conditions. A second approach is investigated and analyzed for this scenario, where the adaptation requires a reduced execution time. In this second approach, a trained machine learning technique is employed together with the results obtained from the first approach in different scenarios. These results show that this approach allows the spray element to be adapted in a way that is compatible with what was provided by the previous approach in less space of time.
O manejo de proteção com uso de produtos fitofarmacêuticos possibilita o controle de pragas em ambientes agrícolas, tornando-o menos nocivo para o desenvolvimento da cultura e com produção em grande escala. Porém, apenas uma pequena parte do produto pulverizado realmente é depositado na área alvo enquanto a maior parte do produto sofre deriva para regiões vizinhas. A literatura científica possui trabalhos com o uso de técnicas matemáticas para calcular a transformação física e movimento para estimar a deposição do produto. Com base nessa predição é possível configurar o sistema de pulverização para realizar a pulverização sob uma condição meteorológica comum na região para um desempenho satisfatório, mas as condições meteorológicas podem sofrer alterações e tornar qualquer configuração estática ineficiente. Uma alternativa para esse problema é realizar a adaptação da atuação do elemento pulverizador às condições meteorológicas durante a execução do manejo de proteção. Contudo, as técnicas existentes são computacionalmente custosas para serem executadas, tornando-as inadequadas para situações em que é requerido baixo tempo de execução. Esta tese se concentra no contexto descrito com objetivo de permitir a predição da deposição de forma rápida e precisa. Assim, espera-se que as novas abordagens sejam capazes de possibilitar a adaptação do elemento pulverizador às condições meteorológicas durante a realização do manejo de proteção. Este trabalho inicia com o processo de redução do custo de execução de um modelo computacional do ambiente, tornando sua execução mais rápida. Posteriormente, utiliza-se este modelo computacional para predição da deposição como função Fitness em algoritmos de meta-heurística para adaptar o comportamento do elemento pulverizador às condições meteorológicas durante a realização do manejo. Os resultados desta abordagem demonstram que é possível utilizá-la para realizar a adaptação em ambientes com baixa variabilidade. Por outro lado, pode apresentar baixo desempenho em ambientes com alta variabilidade nas condições meteorológicas. Uma segunda abordagem é investigada e analisada para este cenário, onde o processo de adaptação requer um tempo de execução reduzido. Nesta segunda abordagem é utilizado uma técnica de Aprendizado de Máquina treinada com os resultados gerados pela primeira abordagem em diferentes cenários. Os resultados obtidos demonstram que essa abordagem possibilita realizar a adaptação do elemento pulverizador compatível com a proporcionada pela abordagem anterior em um menor espaço de tempo.

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Colaizzi, Paul Dominic. "Ground based remote sensing for irrigation management in precision agriculture." Diss., The University of Arizona, 2001. http://hdl.handle.net/10150/280497.

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The relationship between remotely sensed canopy temperature and soil moisture was studied. The objectives were to relate two remotely sensed canopy temperature-based indices, the Crop Water Stress Index (CWSI) and the Water Deficit Index (WDI), to soil moisture through the water stress coefficient, to estimate soil moisture depletion with the CWSI and the WDI, and to develop a remote sensing system aboard a linear move irrigation system that would provide field images of the WDI at one-meter spatial resolution. Studies were conducted in Maricopa, Arizona during the 1998 and 1999 seasons with cotton (Gossypium hirsutum, Delta Pine 90b). In 1998, the field was surface irrigated (low frequency irrigation), and the CWSI was calculated from canopy temperature measurements using stationary infrared thermometers. In 1999, the field was irrigated with a linear move system (high frequency irrigation), and the WDI was calculated using measurements made by the on board remote sensing system. Both the CWSI and the WDI were correlated to soil moisture through the water stress coefficient. Soil moisture depletion could be estimated using the CWSI under low frequency irrigation, but could not be estimated using the WDI under high frequency irrigation. These differences were attributed to the range of soil moisture resulting from infrequent surface irrigation vs. frequent irrigation using the linear move. High spatial resolution images of the WDI could nonetheless monitor water stress throughout the field from partial to full canopy cover, which demonstrated that ground-based remote sensing is feasible for irrigation management in precision agriculture. This application of remote sensing provides an opportunity to improve water use efficiency.

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Brown, Rachael M. "Economic Optimization and Precision Agriculture: A Carbon Footprint Story." UKnowledge, 2013. http://uknowledge.uky.edu/agecon_etds/11.

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This thesis examines the economic and environmental impacts that precision agriculture technologies (PATs) can have on the carbon footprint of a grain farm. An analysis is offered using two manuscripts. The first examines the impacts of three PATs and compares the findings to a conventional farming method. It was found that all three PATs investigated showed a potential Pareto improvement over conventional farming. The second manuscript expanded the model used previously to in order to develop a process to construct a carbon efficient frontier (CEF). The model employed examined uniform and variable rate technologies. In addition to the CEF, a marginal abatement cost curve was constructed. Using these curves in a complementary fashion, more accurate information on the adaptive behavior of farmer technology adoption can be gleaned. the information gleaned for the two manuscripts can give both producers and policy makers the analytical tools needed to make more information decisions with regard to economic and environmental feasibility of PATs.

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Books on the topic "Precision agriculture"

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Stafford, J., and A. Werner, eds. Precision Agriculture. The Netherlands: Wageningen Academic Publishers, 2003. http://dx.doi.org/10.3920/978-90-8686-514-7.

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Brase, Terry A. Precision agriculture. Clifton Park, NY: Thomson Delmar Learning, 2006.

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V, Lake J., Bock Gregory, Goode Jamie, European Environmental Research Organisation, Ciba Foundation, and Symposium on Precision Agriculture (1997 : Wageningen, Netherlands), eds. Precision agriculture. Chichester: Wiley, 1997.

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Stafford, J. V., ed. Precision Agriculture '05. The Netherlands: Wageningen Academic Publishers, 2005. http://dx.doi.org/10.3920/978-90-8686-549-9.

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Stafford, J. V., ed. Precision agriculture ‘07. The Netherlands: Wageningen Academic Publishers, 2007. http://dx.doi.org/10.3920/978-90-8686-603-8.

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van Henten, E. J., D. Goense, and C. Lokhorst, eds. Precision agriculture '09. The Netherlands: Wageningen Academic Publishers, 2009. http://dx.doi.org/10.3920/978-90-8686-664-9.

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Stafford, John V., ed. Precision agriculture '13. The Netherlands: Wageningen Academic Publishers, 2013. http://dx.doi.org/10.3920/978-90-8686-778-3.

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Stafford, John V., ed. Precision agriculture '15. The Netherlands: Wageningen Academic Publishers, 2015. http://dx.doi.org/10.3920/978-90-8686-814-8.

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Kent Shannon, D., David E. Clay, and Newell R. Kitchen, eds. Precision Agriculture Basics. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America, 2018. http://dx.doi.org/10.2134/precisionagbasics.

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Cammarano, Davide, Frits K. van Evert, and Corné Kempenaar, eds. Precision Agriculture: Modelling. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15258-0.

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Book chapters on the topic "Precision agriculture"

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Thorp, Kelly. "Precision Agriculture." In Encyclopedia of Remote Sensing, 515–17. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_132.

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Reddy, P. Parvatha. "Precision Agriculture." In Agro-ecological Approaches to Pest Management for Sustainable Agriculture, 295–309. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4325-3_19.

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Naresh, R., S. Sakthipriya, C. N. S. Vinoth Kumar, and S. Senthilkumar. "Precision Agriculture." In Cybersecurity and Data Science Innovations for Sustainable Development of HEICC, 450–58. Boca Raton: CRC Press, 2024. https://doi.org/10.1201/9781032711300-31.

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Ozguven, Mehmet Metin. "Precision Agriculture." In The Digital Age in Agriculture, 1–28. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/b23229-1.

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Fountas, Spyros, Katerina Aggelopoulou, and Theofanis A. Gemtos. "Precision Agriculture." In Supply Chain Management for Sustainable Food Networks, 41–65. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118937495.ch2.

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Tarabella, Angela, Leonello Trivelli, and Andrea Apicella. "Precision Agriculture." In SpringerBriefs in Food, Health, and Nutrition, 79–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-23811-1_6.

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Ahmad, Latief, Gazi Mohammad Shoaib Shah, and Asim Biswas. "Precision Agriculture." In Fundamentals and Applications of Crop and Climate Science, 151–61. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61459-0_7.

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Falola, Peace Busola, Joseph Bamidele Awotunde, Abidemi Emmanuel Adeniyi, and Folashade Titilope Ogunajo. "Precision Agriculture." In Blockchain and Digital Twin Applications in Smart Agriculture, 178–91. New York: Auerbach Publications, 2025. https://doi.org/10.1201/9781003507390-11.

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Bishnoi, Aaskaran, Gurwinder Singh, Ranjit Singh, and Simrat Waila. "Precision Agriculture." In Digital Technologies and Tools for Smart Agriculture, 29–40. New York: CRC Press, 2025. https://doi.org/10.1201/9781003487005-3.

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Abobatta, Waleed Fouad. "Precision Agriculture." In Precision Agriculture Technologies for Food Security and Sustainability, 23–45. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5000-7.ch002.

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Precision agriculture is a management system that aims to reduce inputs like seeds, water, and energy; protect the environment; and maximize profitability. Precision agriculture uses advanced technology like positioning technology, geographical information systems, satellite navigation, and remote sensing. There are different factors affect the adoption of precision agriculture like farm size, legal affairs, and social interaction. Under climate change and increases in world population, adoption of precision agriculture could assist farmers to face various challenges to achieve ideal production and maximizing profitability. Information, technology, and management are considered the backbone of the precision agriculture system, and combining these elements reduces inputs and maximizes productivity. Different threats attacked precision agriculture including threats to confidentiality, threats to integrity, threats to availability, and crowding of the spectrum signal. This chapter explains the different roles of precision agriculture in developing agricultural production.

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Conference papers on the topic "Precision agriculture"

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Singh, Sujay, Suhasi Sethi, Raghav Sharma, Dikshita Vaibhavi, and Abhishek Tiwari. "Precision Agriculture Monitoring System." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724188.

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Akter, Tahmina, Tanjim Mahmud, Rishita Chakma, Nippon Datta, Mohammad Shahadat Hossain, and Karl Andersson. "IoT-based Precision Agriculture Monitoring System: Enhancing Agricultural Efficiency." In 2024 Second International Conference on Inventive Computing and Informatics (ICICI), 749–54. IEEE, 2024. http://dx.doi.org/10.1109/icici62254.2024.00126.

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Alakuş, Dilan Onat, and İbrahim Türkoğlu. "Smart Agriculture, Precision Agriculture, Digital Twins in Agriculture: Similarities and Differences." In 2024 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–5. IEEE, 2024. https://doi.org/10.1109/asyu62119.2024.10757158.

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Tirumalasetti, Gnana Kartheek, Ajay Kumar Kandula, Abdul Basheer Shaik, Baladithya Yendluri, and Hemantha Kumar Kalluri. "An Empirical Study of Precision Agriculture." In 2024 IEEE Students Conference on Engineering and Systems (SCES), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/sces61914.2024.10652361.

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Manwatkar, Ashish Bapurao, Rajesh Keshavrao Deshmukh, Aparna Atul Junnarkar, Shubhangi Jagdish Kamble, Pranita Kishor Kachare, and Snehal Prashant Latkar. "Securing Industry 4.0-Based Precision Agriculture." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG), 1–7. IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911640.

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Ifty, Rashedul Arefin. "Project AgriSage Tech: Federated Learning-Driven Agricultural Innovations for Precision Agriculture." In 2024 IEEE 12th Region 10 Humanitarian Technology Conference (R10-HTC), 1–6. IEEE, 2024. https://doi.org/10.1109/r10-htc59322.2024.10778905.

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V, Akhilesh, Boobesh K. S, Danush S. V, and Madhumathi R. "Revolutionizing Agriculture: Empowering Farmers through Drone Training Simulations for Precision Agriculture." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 822–26. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717278.

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Gaines E. Miles, Daniel R. Ess, R. Mack Strickland, and Mark T. Morgan. "Agricultural Systems Management Technologies for Precision Agriculture." In 2002 Chicago, IL July 28-31, 2002. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2002. http://dx.doi.org/10.13031/2013.10370.

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"5 Precision Agriculture." In CIGR Handbook of Agricultural Engineering Volume VI: Information Technology . St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2006. http://dx.doi.org/10.13031/2013.21676.

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SUÁREZ BARÓN, Marco Javier, Angie Lizeth GOMEZ AGUDELO, and Juana Valentina GARCIA ARIZA. "PRECISION AGRICULTURE (PA) SUPPORT OF INCREASING AGRICULTURAL PRODUCTIVITY." In 10th International Conference on Management. Mendelova univerzita v Brně, 2021. http://dx.doi.org/10.11118/978-80-7509-820-7-0356.

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Reports on the topic "Precision agriculture"

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DeWitt, Jerald R., William Lotz, George Cummins, and Kenneth T. Pecinovsky. Precision Agriculture Demonstration Project. Ames: Iowa State University, Digital Repository, 2002. http://dx.doi.org/10.31274/farmprogressreports-180814-2228.

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Brown, R. J., K. Staenz, H. McNairn, B. Hopp, and R. van Acker. Application of High Resolution Optical Imagery to Precision Agriculture. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1997. http://dx.doi.org/10.4095/218969.

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

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Skorbiansky, Sharon Raszap, Jonathan McFadden, and Monica Saavoss. The economics of cellular agriculture. [Washington, D.C.]: U.S. Department of Agriculture, Economic Research Service, 2024. https://doi.org/10.32747/2024.8754557.ers.

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Cellular agriculture is the production of animal products, such as meat, seafood, milk, and eggs, with no or minimal use of animals. This report introduces the economics of cell-cultured and precision fermentation foods and documents the growth in the sector. Areas of emphasis are market drivers, structural aspects of the industry, the U.S. regulatory environment, government research funding, and market challenges as of 2023.--

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Brisco, B., R. J. Brown, T. Hirose, H. McNairn, and K. Staenz. Precision Agriculture and the Role of Remote Sensing: A Review. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219370.

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Whiting, Gregory, Raj Khosla, and Ana Claudia Arias. Final Technical Report: Precision Agriculture using Networks of Degradable Analytical Sensors (PANDAS). Office of Scientific and Technical Information (OSTI), December 2024. https://doi.org/10.2172/2516744.

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Dhal, Sambandh. Precision Plant Biomass Characterization in Agriculture: Harnessing Machine Learning and Hyperspectral Imaging. Office of Scientific and Technical Information (OSTI), October 2024. https://doi.org/10.2172/2479703.

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van Boheemen, K., J. Riepma, and J. F. M. Huijsmans. Precision Agriculture and Crop Protection = (Precisielandbouw en Gewasbescherming) : Definitions and the relation between precision-applications and the authorisation procedure of PPPs. Wageningen: Stichting Wageningen Research, Wageningen Plant Research, Business Unit Agrosystems Research, 2022. http://dx.doi.org/10.18174/566499.

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Research Institute (IFPRI), International Food Policy. Protected agriculture, precision agriculture, and vertical farming: Brief reviews of issues in the literature focusing on the developing region in Asia. Washington, DC: International Food Policy Research Institute, 2019. http://dx.doi.org/10.2499/p15738coll2.133152.

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Mishra, Aditi. Cloud infrastructure for multi-sensor remote data acquisition system for precision agriculture (CSR-DAQ). Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-369.

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