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Artykuły w czasopismach na temat „Precision farming”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 29 lipca 2024

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1

Bhatia, Kartikeya, i Devendra Duda. "Precision Farming". International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (30.04.2019): 403–6. http://dx.doi.org/10.31142/ijtsrd22793.

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Yasam, Mr Srinath, i Dr S. Anu H. Nair. "ecision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Precision Farming and Predictive Analytics in Agriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture Context Agriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture ContextAgriculture". International Journal of Engineering and Advanced Technology 9, nr 1s5 (30.12.2019): 74–80. http://dx.doi.org/10.35940/ijeat.a1023.1291s519.

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The scope of sensor networks and the Internet of Things spanning rapidly to diversified domains but not limited to sports, health, and business trading. In recent past, the sensors and MEMS integrated Internet of Things are playing crucial role in diversified farming strategies like dairy farming, animal farming, and agriculture farming. The usage of sensors and IoT technologies in farming are coined in contemporary literature as smart farming or precision farming. At its early stage of smart farming, the practices applying in agriculture farming are limited to collect the data related to the context of farming, such as soil state, weather state, weed state, crop quality, and seed quality. These collections are to help the farmers, scientists to conclude the positive and negative factors of crop to initiate the required agricultural practices. However, the impact of these practices taken by the agriculturists depends on their experience. In this regard, the computer-aided predictive analytics by machine learning and big data strategies are having inevitable scope. The emphasis of this manuscript is reviewing the existing set of computer-aided methods of predictive analytics defined in related to precision farming, gaining insights into how distinct set of precision farming inputs are supporting the predictive analytics to help farming communities towards improvisation. It is imperative from the review of the literature that right from the farming process and techniques to usage of distinct sets of farming precision models like the machine learning solutions and other such factors indicate that there are potential ways in which the precision farming solutions can be resourceful for the farming groups. Optical sensing, soil analysis, imagery processing based analysis, machine learning models that can support in effective prediction are some of the key areas wherein the numbers of solutions that have offered from the market are high. From the compiled sources of literature in the study, there must be many techniques, tools, and available solutions, but one of the key areas wherein the solutions are turning complex for the companies is about usage of the comprehensive kind of machine learning models used in the precision farming which is currently a major gap and is potential scope for the future research process. This contemporary review indicating that both supervised and unsupervised machine learning models are yielding results, still in terms of improvements that are essential in precision farming. The overall efforts of this review portraying that, there is a need for developing a system that can self-train on the critical features based on the loop model of features gathered from the process and make use of such inputs for analysis. If such clustered solution is gathered, it can help in improving the quality of analysis based on the learning practices and the historical data captured from the systems aligned.

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Norton, T., i D. Berckmans. "Developing precision livestock farming tools for precision dairy farming". Animal Frontiers 7, nr 1 (1.01.2017): 18–23. http://dx.doi.org/10.2527/af.2017.0104.

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Hamrita, Takoi K., Jeffrey S. Durrence i George Vellidis. "Precision farming practices". IEEE Industry Applications Magazine 15, nr 2 (marzec 2009): 34–42. http://dx.doi.org/10.1109/mias.2009.931816.

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Tutkun, Muhittin. "PRECISION DAIRY FARMING". Journal of Agricultural, Food and Environmental Sciences 77, nr 1 (2023): 12–19. http://dx.doi.org/10.55302/jafes23771012t.

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Gnip, P., i K. Charvát. "Management of zones in precision farming". Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2.03.2012): 416–18. http://dx.doi.org/10.17221/5425-agricecon.

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Precision farming is a very fast developing form of the Farm Management System, especially in crop production, in whole world and in our country as well. There, it is adopted since the second half of the 90s of the 20th century. The system of data collection, analysis, presentation and application of information in precision farming is reaching over the possibilities of their use by common farmers or agricultural companies. Service companies in this case play a very important role as an executor of exacting analysis, data collection and their presentation. Management zones present simplification of the difficult operations and recommendations including economic calculations for the common user involved in the precision farming management.

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Győrffy, Béla. "From Organic to Precision Farming (Contemporary Publication)". Acta Agraria Debreceniensis, nr 9 (10.12.2002): 81–86. http://dx.doi.org/10.34101/actaagrar/9/3565.

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The paper presents a short review of the different types of farming systems:Biofarming, Organic farming, Alternatíve farming, Biodynamic farming, Low input sustainable agriculture (LISA)Mid-tech farming, Sustainable agriculture, Soil conservation farming, No till farming, Environmentally sound, Environmentally friendly, Diversity farmingCrop production system, Integrated pest management (IPM), Integrated farming, High-tech farmingSite specific production (SSP), Site specific technology (SST), Spatial variable technology, Satellite farming.Precision farmingIt concludes that the various systems are applicable in different ratios and combinations depending on the natural and economic conditions.The author predicts an increase in precision technologies , the first step being the construction of yield maps compared with soil maps and their agronomic analysis. Based on this information, it will be necessary to elaborate the variable technology within the field, especially for plant density, fertilization and weed control.The changes in weed flora during the past fifty years based on 10.000 samples within the same fields using the weed cover method are presented.

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Mandal, Manas, Bappa Paramanik, Anamay Sarkar i Debasis Mahata. "PRECISION FARMING IN FLORICULTURE". International Journal of Research -GRANTHAALAYAH 9, nr 1 (26.01.2021): 75–77. http://dx.doi.org/10.29121/granthaalayah.v9.i1.2021.2871.

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Precision farming is a science base modern technology which provided management concept based on observation and response to intra-field variations. New technologies such as Global Positioning Systems (GPS), sensors, satellites or aerial images and Geographical Information Systems (GIS) are utilized to assess and analyse variations in agricultural and horticultural production. In this technology have two primary goals that are (i) optimum return (ii) preserving resource. Wireless Sensor Networks has crucial role to management of water resources, to assess the optimum point of harvesting, to estimate fertilizer requirements and to predict crop performance more accurately, disease and pest hazard also. Sensors use to precision farming technology in horticulture, which increasing productivity, decreasing production costs and minimizing the environmental impact of farming. Though precision farming has vital role in Agriculture and Horticulture sector but, no so popular due to high cost of technology and need high speed internet facility.

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István Komlósi. "The precision livestock farming". Acta Agraria Debreceniensis, nr 49 (13.11.2012): 201–2. http://dx.doi.org/10.34101/actaagrar/49/2525.

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The application of information technology is the response of the livestock farming to the demand of customer, legal and economical expectations. This technology is the socalled precision livestock farming (PLF). The elements of the PLF are: continuous monitoring of inputs, animal behaviour by sensors, an algorithm which converts these signals into a figure, this figure is compared to an optimum then adjustment of the input is followed, if it is necesary.

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Zhuravleva, Larisa Anatolyevna. "Precision farming. Soil scanners". Agrarian Scientific Journal, nr 10 (27.10.2020): 100–106. http://dx.doi.org/10.28983/asj.y2020i10pp100-106.

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The article provides an analysis of devices, installations and sensors that allow you to capture various characteristics of soils, including in real time. A description of a mobile device for measuring the characteristics of soil and crops, which allows you to visualize the information obtained in real time. High performance is provided by installing sensors that determine the parameters of crops and soil moisture on a mobile trailer device and transmit information to a microcontroller, WiFi module, with Internet access.

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Deininger, Olaf. "Precision Farming für alle". agrarzeitung 77, nr 17 (2022): 16. http://dx.doi.org/10.51202/1869-9707-2022-17-016.

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„Unser Ziel ist es, eine offene und zugängliche Technologie zu schaffen, die jedem hilft, Gemüse, Salate oder Kräuter anzubauen", erklärt Gründer Rory Aronson. Sein Geschäftspartner und Co-Gründer Rick Carlino nickt euphorisch.

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Umbetkaliev, Nurlan. "Precision farming in Kazakhstan". Zemleustrojstvo, kadastr i monitoring zemel' (Land management, cadastre and land monitoring), nr 12 (2.12.2021): 898–903. http://dx.doi.org/10.33920/sel-04-2112-03.

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The article deals with the problems of modern land management, and investigates the historical background and the emergence of precision farming in the world. The author analyzes the advantage of this technology over traditional land use, and the prerequisites for the emergence of precision farming in Kazakhstan. The paper also considers legislative acts, programs and the prospects for development of this type of land use in modern Kazakhstan. English version of the article is available at URL:https://panor.ru/articles/issue-of-precision-farming-in-kazakhstan/77065.html

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Abramov, N. V., S. A. Semizorov, S. V. Sherstobitov, M. V. Gunger i A. E. Ufimtsev. "Digital precision farming technologies". IOP Conference Series: Earth and Environmental Science 949, nr 1 (1.01.2022): 012011. http://dx.doi.org/10.1088/1755-1315/949/1/012011.

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Abstract A systematic approach to the use of digital technologies in precision farming is proposed. The scientifically grounded transition to digitalization of production processes in crop production includes two stages. The first is the formation of comprehensive data on terrestrial and space factors in the production of agroecosystems. The natural potential of the Northern Trans-Urals (1.6 - 2.2) has been established, which indicates satisfactory soil and climatic conditions for growing crops. The biopotential of agrocenoses was calculated for photosynthetic active radiation (PAR). The potential yield of spring wheat is 13.75 t / ha. In long-term experiments (1977-2020), the really possible crop yield was determined. The mathematical model of the formation of soil fertility parameters shows the intensity of growth or decline in the productivity of agrocenoses. At the second stage, in order to optimize life-supporting factors, innovative technologies for growing crops using space systems are being developed. The practical implementation of digital technologies begins with the digitization of fields, monitoring of soil fertility in elementary areas with reference to geographic coordinates. The use of precise technologies for sowing, differentiated application of fertilizers, pesticides, and yield mapping made it possible to reduce the cost of grain by 140 rubles / c and increase the profitability of its production by 24%.

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Blackmore, Simon. "Precision Farming: An Introduction". Outlook on Agriculture 23, nr 4 (grudzień 1994): 275–80. http://dx.doi.org/10.1177/003072709402300407.

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Traditional arable management practice has tended to manage fields uniformly and has tended to ignore the inherent spatial variability found on most farms. This has been exacerbated by the increase of field size due to pressures from mechanization. Precision farming is a management practice that has been made possible by the advent of suitable information technologies, and it provides a framework within which arable managers can more accurately understand and control what happens on their farms.

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Ivanov, Stepan, Kriti Bhargava i William Donnelly. "Precision Farming: Sensor Analytics". IEEE Intelligent Systems 30, nr 4 (lipiec 2015): 76–80. http://dx.doi.org/10.1109/mis.2015.67.

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Webber, Henry, Volker Heyd, Mark Horton, Martin Bell, Wendy Matthews i Amanda Chadburn. "Precision farming and archaeology". Archaeological and Anthropological Sciences 11, nr 2 (22.11.2017): 727–34. http://dx.doi.org/10.1007/s12520-017-0564-8.

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Berckmans, Daniel. "Precision livestock farming (PLF)". Computers and Electronics in Agriculture 62, nr 1 (czerwiec 2008): 1. http://dx.doi.org/10.1016/j.compag.2007.09.002.

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Shibusawa, Sakae. "Precision Farming Japan Model". Agricultural Information Research 12, nr 2 (2003): 125–32. http://dx.doi.org/10.3173/air.12.125.

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Vadali, Ramkrishna, Swati Sakhare, Jayshri Patil i Sonali Nale. "Technical Advances in Precision Farming". International Journal of Computer Applications 180, nr 6 (15.12.2017): 5–7. http://dx.doi.org/10.5120/ijca2017916039.

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Kumar, Shiv, i Shrawan Kumar Sharma. "Precision Farming India and Abroad". International Journal of Computer Sciences and Engineering 7, nr 4 (30.04.2019): 662–66. http://dx.doi.org/10.26438/ijcse/v7i4.662666.

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Beluhova-Uzunova, Rositsa, i Dobri Dunchev. "PRECISION FARMING – CONCEPTS AND PERSPECTIVES". Problems of Agricultural Economics 360, nr 3 (25.09.2019): 142–55. http://dx.doi.org/10.30858/zer/112132.

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Mazirov, M. A., A. O. Ragimov, A. A. Korchagin, E. M. Shenterova i S. D. Malakhova. "Development of precision farming technologies". IOP Conference Series: Earth and Environmental Science 843, nr 1 (1.11.2021): 012031. http://dx.doi.org/10.1088/1755-1315/843/1/012031.

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Abstract The research was carried out on the territory of the Vladimir Opolye (Suzdal, Russian Federation). For the experimental site, in the watershed part of the plakor terrain with a slope of < 1°, a soil map was compiled. Field studies were carried out in a long-term stationary field experiment measuring 86 m by 280 m with the total area of 2.41 hectares. Four tillage systems were studied in the experiment: 1. generally accepted dump-annual dump plowing; 2. combined-energy-saving-alternation of small non-fall treatments with dump plowing of perennial grasses; 3. combined-tier-alternation of small non-fall treatments with plowing with a long-line plow of perennial grasses; 4. anti-erosion-alternation of deep non-fall treatments with plowing of perennial grasses. The results showed a different reaction of soil to agrotechnological techniques. Gray forest soils with a second humus horizon have a higher potential fertility, the advantage of which when applying moderate doses of mineral fertilizers was 3.3-4.4 centners per hectare of grain units compared to the background gray forest soils. A further increase in the dose negates these differences. Deep soil loosening by 25-27 cm also shows the advantage of soils with a second humus horizon, the yield increases were 4.4-5.2 centners per hectare.

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Huber, Daphne. "Umweltauflagen leisten Precision Farming Vorschub". agrarzeitung 76, nr 30 (2021): 40. http://dx.doi.org/10.51202/1869-9707-2021-30-040.

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Die Tage der konventionellen Düngung sind gezählt. Die EU-Kommission macht beim Klima- und Umweltschutz mächtig Druck. Wissenschaft und Hersteller forschen an nachhaltigen Produkten und umwelteffizienter Technik.

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Deininger, Olaf. "So geht Precision Farming weiter". agrarzeitung 77, nr 37 (2022): 10. http://dx.doi.org/10.51202/1869-9707-2022-37-010.

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Präzise Aussaat, Teilflächenbehandlung, Einzeltiermanagement, Farm-Management-Systeme. Wer diese Methoden oder Lösungen nicht bereits nutzt, der führt sie gerade ein. Wer sie nicht gerade einführt, der spricht aktuell mit Lösungsanbietern. Wer auch das nicht tut, der verschafft sich einen Überblick. Oder überlegt sich, wann er das einführt. Precision Farming ist also gesetzt – und wird umgesetzt.

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Vranken, Erik, i Dries Berckmans. "Precision livestock farming for pigs". Animal Frontiers 7, nr 1 (1.01.2017): 32–37. http://dx.doi.org/10.2527/af.2017.0106.

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Auernhammer, Hermann. "Precision farming — the environmental challenge". Computers and Electronics in Agriculture 30, nr 1-3 (luty 2001): 31–43. http://dx.doi.org/10.1016/s0168-1699(00)00153-8.

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Oke, Satoshi, Masaharu Ookado, Takuo Kokuryu i Sakae Shibusawa. "Soil Maps in Precision Farming". Agricultural Information Research 13, nr 1 (2004): 69–78. http://dx.doi.org/10.3173/air.13.69.

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Komlatskiy, Vasily, i Roman Smolkin. "Precision technologies in pig farming". E3S Web of Conferences 371 (2023): 03057. http://dx.doi.org/10.1051/e3sconf/202337103057.

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The transition to digital, intelligent production technologies and robot-based systems is one of the priorities of the scientific and technological development of Russia. The extensive use of information technology and electronic automated systems is the basis of an intelligent manufacturing execution system that replaces a person, especially for objective and precise control over the quantity and quality of feed given to animals. Modern genetics has achieved a substantial increase in the number of piglets per litter, which does not allow a sow to fully meet their biological needs with her own milk. To preserve the health and high performance of all piglets born, effective and economically feasible methods and technologies are needed. The objective of the research was to analyze the efficiency of applying the components of precision pig farming in piglet feeding when using the feeding systems for suckling piglets CulinaCup and CulinaFlexpro. Their application has a positive effect on the growth and survival rate of suckling piglets due to proper and timely feeding. The systems meet all sanitary and hygienic standards and ensure efficient raising of piglets in large litters.

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Rink, Ann-Kristin. "Nachhaltig ackern mit Precision Farming". VDI nachrichten 77, nr 26 (2023): 21. http://dx.doi.org/10.51202/0042-1758-2023-26-21-4.

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Bhadale, Siddhesh, Rushikesh Jagadale i Prof Chaudhari V.S. "AgriAI: Smart Precision Farming App". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, nr 11 (1.11.2023): 1–11. http://dx.doi.org/10.55041/ijsrem26793.

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In our big world with over 8 billion people, we all share one thing in common – the need to eat. Sadly, we often hear about the tough times farmers in India face, despite being more than half of the working population. Every day, 28 farmers, dependent on agriculture, feel so burdened that they resort to suicide. While many of us express frustration and sympathy, we often don't truly understand the problems these farmers face on the ground. To help them out, we've come up with a Precision Farming App, a tool that we hope will bring a positive change to farming. This app is like a bundle of helpful features for modern farming. For instance, it can track how your crops are growing in real-time, like a virtual map for your plants. It also acts like a health check for your crops, spotting diseases early and suggesting solutions. The app even recommends what crops are best for your farm based on where you are, the type of soil you have, and the local weather conditions. But it's not just about your crops; the app also connects farmers with each other, creating a community where they can share experiences and learn from one another. And when it's time to sell crops, the app makes it easier by connecting farmers directly to buyers. Looking at how farming in India has changed over time – from old ways to the Green Revolution and now, technology playing a big role – our app fits right into this journey. It addresses the challenges farmers deal with, from unpredictable weather to soil problems, by providing them with smart tools for better and more sustainable farming. In a nutshell, this app is like a ray of hope for Indian farmers, bringing together technology and traditional farming practices. It wants to create a future where farming is not just smart and sustainable but also brings success and prosperity to every farmer. So, as we embark on this exciting farming journey, the goal is clear: to make farming smart, sustainable, and successful for years to come. Key Words: Precision Farming, Advance Farming, Future of Farming, Machine

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Rimpika, Anushi, S. Manasa, Anusha K. N., Sakshi Sharma, Abhishek Thakur, Shilpa i Ankita Sood. "An Overview of Precision Farming". International Journal of Environment and Climate Change 13, nr 12 (21.12.2023): 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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Gailums, A. "Precision Agriculture in Latvia in the Last 20 Years". Advances in Animal Biosciences 8, nr 2 (1.06.2017): 698–702. http://dx.doi.org/10.1017/s2040470017000681.

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The establishment of the system Soil – Yield that occured in Latvia during the 1970–80s could be considered as the beginning of precision farming with the available technologies. The first precision farming technologies have been associated with harvesting where combines and tractors with the automatic steering were used. The precision agriculture in Latvia includes various branches. Latvia farmers are using precision crop farming, precision livestock farming, precision fruit growing, precision bee keeping, precision farming greenhouse and precision growing berries. Precision farming technologies in Latvia are introduced mainly in large scale farms, with more than 1000 ha. The most important researches in precision farming in Latvia were done in 2000s.

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Péter Boldizsár. "Precision dairy farming in the practice – DeLaval „Smart Farming”". Acta Agraria Debreceniensis, nr 49 (13.11.2012): 119–22. http://dx.doi.org/10.34101/actaagrar/49/2508.

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Vaghamashi, G., G. P. Sabapara, D. D. Garg, H. H. Savsani, M. R. Chavda, A. Chavda, N. K. Ribadiya, V. K. Karangiya i R. B. Makwana. "Precision Dairy Farming: The New Era in Dairy Farming". International Journal of Current Microbiology and Applied Sciences 11, nr 5 (10.05.2022): 20–28. http://dx.doi.org/10.20546/ijcmas.2022.1105.004.

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India has world’s largest livestock population and 1st rank in milk production with 187 million tons in 2018-19. Dairy farming is the single largest contributors to Indian GDP with 5.1 % in 2018-19 and annual growth rate in Livestock sector is 4.6%. Indian dairy production is characterized as a low input/low output system. In general, milk productivity of dairy animals is very low in comparison to global standards. The lower milk yield is mainly due to low genetic potential, lack of nutritional feeds and inadequate veterinary services. So, with suitable and scientific approach production efficiency can raise. In this context, Precision dairy farming (PDF) aims to improve individual animal performance, well being and socio-economic status of dairy farmer. Today, traditional dairy farming becomes organized commercial business with technological specializations in every part of the process. Thus, farmers are shifting towards adopting modern dairy farming practices for increase their production. PDF is the use of information and technology based farm management system to record physiological, behavioral and production parameters of individual animals to improve management strategies, profitability and production performance. There are so many important precision dairy farming technologies available globally, which are routinely useful for large and commercial dairy farm. In this direction, the authors have also highlighted the status of adoption in Indian scenario, benefits, challenges and limitations of precision dairy farming technologies. Many developing countries including India are in initial stage in these advance technology, but there are tremendous opportunities for betterment of animal and upliftment of animal husbandry profession.

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Nasritdinova, Umida, Xaitboy Sultonov, Husan Muratov, Rahim Ankabaev, Ulfat Ismatov i Avazjon Abdumajitov. "Application of geoinformation systems in the agricultural complex". BIO Web of Conferences 105 (2024): 03005. http://dx.doi.org/10.1051/bioconf/202410503005.

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Precision farming, encompassing coordinated farming, adaptive landscape farming, and precision farming, represents a scientific paradigm in agricultural production that capitalizes on the inherent variability in soil fertility within localized areas. By tailoring practices to specific soil characteristics, precision farming optimizes profitability through the targeted application of fertilizers and plant protection products. Serving as the epitome of flexible landscape farming, precision farming integrates advanced agricultural technologies to achieve heightened productivity levels. Data collected through precision farming practices informs critical aspects of agricultural management, including strategic planting, precise calculation of input quantities, improved crop forecasting, and enhanced financial planning. Successful implementation of precision farming hinges on a thorough consideration of local soil attributes and climatic conditions, underscoring the importance of site-specific adaptation in modern agricultural strategies.

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Bobade, MD, VN Khune, Sharad Mishra, Ashutosh Dubey, Aayush Yadav, Anupam Soni, Sudheer Bhagat, Subhrajit Das i Kundan Krishnan. "New age dairy farming: Precision dairy farming (PDF): A review". International Journal of Chemical Studies 8, nr 1 (1.01.2020): 1041–46. http://dx.doi.org/10.22271/chemi.2020.v8.i1n.8386.

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Singh, Harmandeep, i Nitika Sharma. "DECISION SUPPORT SYSTEM FOR PRECISION FARMING". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, nr 1 (1.02.2013): 76–81. http://dx.doi.org/10.24297/ijct.v4i1b.3065.

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A decision support system for precision farming is designed to assist farmers, agricultural experts, research workers or any intellectuals with guidance in making various farming related decisions and help them to access, display and analyze data that have geographic content and meaning. The concept of precision farming is not only related with the use of technologies but it is also about the five Râ€™s that is use of right input (nutrients, water, fertilizer, money, machinery etc.), at the right time, at the right place, in the right amount and in the right manner. There is need to have accurate information and suitable decisions regarding the right inputs required for the farming practices and to initiate the step towards the precision farming. DSS calculates irrigation requirement of crops. In this paper, Maps that are shown generated with the help of ArcGIS software (ArcMap tool). The system has been developed using Hypertext Pre Processor (PHP) at front end and MySQL at back end.

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Shibusawa, Sakae. "Trials for Precision Farming Japan Model". TRENDS IN THE SCIENCES 7, nr 9 (2002): 76–77. http://dx.doi.org/10.5363/tits.7.9_76.

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Korchagin, A., L. I. Ilyin, R. D. Petrosyan, I. Yu Vinokurov i S. G. Baranov. "Carbon-saving technologies in precision farming". IOP Conference Series: Earth and Environmental Science 862, nr 1 (1.10.2021): 012081. http://dx.doi.org/10.1088/1755-1315/862/1/012081.

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Klingström, Tomas, Karl-Johan Petersson, Natalie Von der Lehr, Hans Persson i Dirk-Jan De Koning. "Precision dairy farming – A Phenomenal opportunity". EMBnet.journal 26, A (8.07.2021): e966. http://dx.doi.org/10.14806/ej.26.a.966.

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Nonaka, Kunihiko. "Outline of Precision Tea Farming Project". Chagyo Kenkyu Hokoku (Tea Research Journal) 2009, nr 108 (2009): 108_61–108_62. http://dx.doi.org/10.5979/cha.2009.108_61.

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Tiwari, Akhilesh, i Praveen Kumar Jaga. "Precision Farming in India – A Review". Outlook on Agriculture 41, nr 2 (czerwiec 2012): 139–43. http://dx.doi.org/10.5367/oa.2012.0082.

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Wolf, Steven A., i Frederick H. Buttel. "The Political Economy of Precision Farming". American Journal of Agricultural Economics 78, nr 5 (grudzień 1996): 1269–74. http://dx.doi.org/10.2307/1243505.

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Lück, E., R. Gebbers, J. Ruehlmann i U. Spangenberg. "Electrical conductivity mapping for precision farming". Near Surface Geophysics 7, nr 1 (1.09.2008): 15–26. http://dx.doi.org/10.3997/1873-0604.2008031.

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Padma, SR, i T. Rathakrishnan. "SWOC Analysis on Precision Farming System". Journal of Global Communication 6, nr 2 (2013): 96. http://dx.doi.org/10.5958/j.0976-2442.6.2.015.

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J. D. Whitney, W. M. Miller, T. A. Wheaton, M. Salyani i J. K. Schueller. "PRECISION FARMING APPLICATIONS IN FLORIDA CITRUS". Applied Engineering in Agriculture 15, nr 5 (1999): 399–403. http://dx.doi.org/10.13031/2013.5795.

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Berckmans, D. "General introduction to precision livestock farming". Animal Frontiers 7, nr 1 (1.01.2017): 6–11. http://dx.doi.org/10.2527/af.2017.0102.

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Xin, Hongwei, i Kai Liu. "Precision livestock farming in egg production". Animal Frontiers 7, nr 1 (1.01.2017): 24–31. http://dx.doi.org/10.2527/af.2017.0105.

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Lu, Yao‐Chi, Craig Daughtry, Galen Hart i Bradley Watkins. "The current state of precision farming". Food Reviews International 13, nr 2 (maj 1997): 141–62. http://dx.doi.org/10.1080/87559129709541104.

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Weis, Martin, Christoph Gutjahr, Victor Rueda Ayala, Roland Gerhards, Carina Ritter i Florian Schölderle. "Precision farming for weed management: techniques". Gesunde Pflanzen 60, nr 4 (8.11.2008): 171–81. http://dx.doi.org/10.1007/s10343-008-0195-1.

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