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1
Triantafyllou, Anna, Panagiotis Sarigiannidis, and Stamatia Bibi. "Precision Agriculture: A Remote Sensing Monitoring System Architecture †." Information 10, no. 11 (November 9, 2019): 348. http://dx.doi.org/10.3390/info10110348.
Повний текст джерелаАнотація:
Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece.
2
Sarri, Daniele, Stefania Lombardo, Andrea Pagliai, Carolina Perna, Riccardo Lisci, Valentina De Pascale, Marco Rimediotti, Guido Cencini, and Marco Vieri. "Smart Farming Introduction in Wine Farms: A Systematic Review and a New Proposal." Sustainability 12, no. 17 (September 3, 2020): 7191. http://dx.doi.org/10.3390/su12177191.
Повний текст джерелаАнотація:
This study shows a new methodological proposal for wine farm management, as a result of the progressive development of the technological innovations and their adoption. The study was carried out in Italy involving farmers, workers, or owners of wine farms who are progressively introducing or using precision agriculture technologies on their farm. The methodology proposed was divided in four stages (1. understanding the changes in action; 2. identifying the added value of Smart Farming processes; 3. verifying the reliability of new technologies; 4. adjusting production processes) that can be applied at different levels in vine farms to make the adoption of precision agriculture techniques and technologies harmonious and profitable. Data collection was carried out using a participant-observer method in brainstorming sessions, where the authors reflected on the significance of technology adoption means and how to put them in practice, and interviews, questionnaire surveys, diaries, and observations. Moreover, project activities and reports provided auxiliary data. The findings highlighted the issues of a sector which, although with broad investment and finance options, lacks a structure of human, territorial, and organizational resources for the successful adoption of technological innovations. The work represents a basis for the future development of models for strategic scenario planning and risk assessments for farmers, policymakers, and scientists.
3
Hrynevych, Oksana, Miguel Blanco Canto, and Mercedes Jiménez García. "Tendencies of Precision Agriculture in Ukraine: Disruptive Smart Farming Tools as Cooperation Drivers." Agriculture 12, no. 5 (May 16, 2022): 698. http://dx.doi.org/10.3390/agriculture12050698.
Повний текст джерелаАнотація:
Precision farming innovations are designed to improve the efficiency of agricultural activities via minimal initial input of material and human resources and avoiding harmful effects on the environment on one hand and automatizing the production on another hand, thus providing environmental, social and economic benefits. In the article, the tendencies in the adoption of precision agriculture technologies (PAT) in Ukraine were observed, with a specific focus on cooperatives as a valuable tool of social and solidarity economy helping to achieve progress in local rural development. On the example of cooperatives, applying a technology acceptance model (TAM) has identified how the adoption of new smart farming tools influence their behavior in implementing technological innovations. The results of the study will be of particular interest to representatives of other cooperatives and to agribusiness players engaged in agriculture or software development. In addition, the outputs will be useful for researchers in the field of the socio-economic development of territories and the impact of new technologies on it, as well as for local governments and higher-level government officials, which can contribute to the implementation of better rural development strategies.
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Strizhkova, Alla, Kateryna Tokarieva, Anna Liubchych, and Svitlana Pavlyshyn. "Digital Farming as Direct of Digital Transformation State Policy." European Journal of Sustainable Development 9, no. 3 (October 1, 2020): 597. http://dx.doi.org/10.14207/ejsd.2020.v9n3p597.
Повний текст джерелаАнотація:
Article is devoted considerations of digital agriculture as directly public policy. By authors it is considered terminological a variety of the studied phenomenon with which scientists of different specialties allocate an object of research. If at first among landowners arose and became settled the term "exact agriculture", then the names "E-Agriculture" and "digital farming" become even more relevant now, however in their work it is considered as synonyms. The essence and advantages of electronic agriculture, prospect revival of economic activity and efficiency of using technologies of digital agriculture and also a condition of legal regulation of digital agriculture in Ukraine are analyzed. Special relevance the idea of use of electronic agriculture in Ukraine enters in connection with plans of the Verkhovna Rada of Ukraine and the government to finish a land reform - to open the market of the land. Offers on further development of state regulation of digital agriculture are formulated.
Keywords: computer aided farming, digital farming, digital transformation, digitalization, e-agriculture, precision agricuture, smart farming, state regulation
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Baurina, S. B., S. V. Khudyakov, and M. Y. Uchirova. "Digital Mainstream in the Promotion of Food Industry Products." IOP Conference Series: Earth and Environmental Science 988, no. 5 (February 1, 2022): 052041. http://dx.doi.org/10.1088/1755-1315/988/5/052041.
Повний текст джерелаАнотація:
Abstract Digitalization of the food industry involves the use of modern digital information technologies at all stages of agro-industrial production, i.e. digital transformation of the industry, as well as other high technologies formed at the intersection of electronics and robotics. Information technologies, digital technologies, science-intensive technologies are currently closely intertwined, and their implementation in the industry depends not only on the degree of computerization, but also on the level of use of systems, devices and mechanisms that allow for the possibility of autonomous use (without human intervention). The article outlines well-known advanced innovative technologies, including “smart”, “precision” farming, artificial intelligence, nanotechnology, biotechnology, off-ground growing of plants (hydroponics) and vertical farming, satellite navigation systems for combines and other equipment, autonomous robots, unmanned aerial vehicles, “Internet of Things”, blockchain technologies. The characteristics of existing digital technologies are given and the options for using artificial intelligence in the food industry are described. In addition to artificial intelligence, complex agricultural technologies are: “smart” agriculture, “smart farming”, “precision farming”, etc.
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Sigalingging, Xanno, Setya Widyawan Prakosa, Jenq-Shiou Leu, He-Yen Hsieh, Cries Avian, and Muhamad Faisal. "SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications." Sensors 23, no. 3 (January 25, 2023): 1358. http://dx.doi.org/10.3390/s23031358.
Повний текст джерелаАнотація:
In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves 88.72% accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia.
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Shevtsova, Hanna, Nataliia Shvets, Maiia Kramchaninova, and Hanna Pchelynska. "In Search of Smart Specialization to Ensure the Sustainable Development of the Post-Conflict Territory: the Case of the Luhansk Region in Ukraine." European Journal of Sustainable Development 9, no. 2 (June 1, 2020): 512–24. http://dx.doi.org/10.14207/ejsd.2020.v9n2p512.
Повний текст джерелаАнотація:
This work highlights a range of problems related to ensuring sustainable development of post-conflict territories, as well as ways to overcome those through smart specialization approach. The influence of military conflict on Luhansk region's economy structure and economy's pace of development had been determined. Capacities of smart specialization as a contemporary tool meant for intensifying innovative development, structural modernization and improving competitiveness of regions had been studied. While researching regional context the current state and structure of the chemical sector, which is region's traditional specialty, had been analyzed and also its SWOT analysis had been conducted. Potential for diversification, innovative and cross-sectoral evolution of the existing chemical business was discovered. We pay attention to chemical production's digitalization horizons and its integration into rapidly growing region's agricultural sector as part of precision farming concept. The suggested agrochemical ecosystem's model facilitates communications between leading stakeholders, increases efficiency of entrepreneurial discovery process, creates new areas of specialization and new leverage for ensuring region's sustainable development. Keywords: sustainable development, smart specialization, chemical industry, agriculture, ecosystem, precision farming, Luhansk region
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Groher, Tanja, Katja Heitkämper, Achim Walter, Frank Liebisch, and Christina Umstätter. "Status quo of adoption of precision agriculture enabling technologies in Swiss plant production." Precision Agriculture 21, no. 6 (May 8, 2020): 1327–50. http://dx.doi.org/10.1007/s11119-020-09723-5.
Повний текст джерелаАнотація:
Abstract This paper presents the state of application of Precision Agricultural enabling Technology (PAT) in Swiss farms as an example for small-scale, highly mechanised Central European agriculture. Furthermore, correlations between farm and farmers’ characteristics and technology adoption were evaluated. Being part of a comprehensive and representative study assessing the state of mechanisation and automation in Swiss agriculture, this paper focuses on the adoption of Driver Assistance Systems (DAS) and activities in which Electronic Measuring Systems (EMS) are used. The adoption rate of DAS was markedly higher compared to EMS in all agricultural enterprises. The adoption rate was highest for high-value enterprise vegetables and surprisingly low for the high-value enterprise grapes. The results of a binary logistic regression showed that farmers located in the mountain zone were less likely to adopt PAT compared to farmers in the valley. Small farm size correlated with low adoption rates and vice versa showing adoption happens country-specific in the upper farm size distribution. The results show the potential for novel technologies to be adopted by farmers of high-value products. Furthermore, technologies have been partially used to reduce physical workload but not yet to evaluate crop or management performance to support decisions. However, automatic collection and forwarding of data is a fundamental step towards Smart Farming realizing its full potential in the future.
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Caria, Sara, Todde, Polese, and Pazzona. "Exploring Smart Glasses for Augmented Reality: A Valuable and Integrative Tool in Precision Livestock Farming." Animals 9, no. 11 (November 1, 2019): 903. http://dx.doi.org/10.3390/ani9110903.
Повний текст джерелаАнотація:
The growing interest in Augmented Reality (AR) systems is becoming increasingly evident in all production sectors. However, to the authors’ knowledge, a literature gap has been found with regard to the application of smart glasses for AR in the agriculture and livestock sector. In fact, this technology allows farmers to manage animal husbandry in line with precision agriculture principles. The aim of this study was to evaluate the performances of an AR head-wearable device as a valuable and integrative tool in precision livestock farming. In this study, the GlassUp F4 Smart Glasses (F4SG) for AR were explored. Laboratory and farm tests were performed to evaluate the implementation of this new technology in livestock farms. The results highlighted several advantages of F4SG applications in farm activities. The clear and fast readability of the information related to a single issue, combined with the large number of readings that SG performed, allowed F4SG adoption even in large farms. In addition, the 7 h of battery life and the good quality of audio-video features highlighted their valuable attitude in remote assistance, supporting farmers on the field. Nevertheless, other studies are required to provide more findings for future development of software applications specifically designed for agricultural purposes.
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Kovalyev, I. L., and M. N. Kostomakhin. "Development vectors and foreign experience of information technologies in the agro-industrial complex of Russia and Belarus." Glavnyj zootehnik (Head of Animal Breeding), no. 1 (January 1, 2021): 49–61. http://dx.doi.org/10.33920/sel-03-2101-06.
Повний текст джерелаАнотація:
The current stage of information technology development is characterized as digital called BCG (Boston Consulting Group) digitalization, while the analog period in agriculture is over, the industry has entered the digital era, which means that by 2050 the use of new generation technologies will be able to increase the productivity of world agriculture by 70 %. The main stages of information technology development in the world considers some of the most important areas of it technology development and global trends in the digital transformation of the agro-industrial complex based on the analysis of global scientific achievements, research reports, articles by well-known scientists, scientific and expert organizations have been investigated in the article. The main trends that determine the conceptual development of the so-called “Smart (digital) agriculture” are identified, the active use of elements of which contributes in every possible way to the highly rational social, economic, technical and technological development of the agricultural sector. A promising area is Precision Animal Husbandry (similar to Precision Farming). Among the elements of Precision Animal Husbandry the most widely used are identification and monitoring of individual animals using modern information technologies (feeding ration, milk yield, growth, body temperature, activity), meeting their individual needs; automatic regulation of the microclimate and control of harmful gases; monitoring the health of the herd, product quality; electronic database of the production process; robotization of the milking process.
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Maraveas, Chrysanthos, and Thomas Bartzanas. "Application of Internet of Things (IoT) for Optimized Greenhouse Environments." AgriEngineering 3, no. 4 (November 29, 2021): 954–70. http://dx.doi.org/10.3390/agriengineering3040060.
Повний текст джерелаАнотація:
This review presents the state-of-the-art research on IoT systems for optimized greenhouse environments. The data were analyzed using descriptive and statistical methods to infer relationships between the Internet of Things (IoT), emerging technologies, precision agriculture, agriculture 4.0, and improvements in commercial farming. The discussion is situated in the broader context of IoT in mitigating the adverse effects of climate change and global warming in agriculture through the optimization of critical parameters such as temperature and humidity, intelligent data acquisition, rule-based control, and resolving the barriers to the commercial adoption of IoT systems in agriculture. The recent unexpected and severe weather events have contributed to low agricultural yields and losses; this is a challenge that can be resolved through technology-mediated precision agriculture. Advances in technology have over time contributed to the development of sensors for frost prevention, remote crop monitoring, fire hazard prevention, precise control of nutrients in soilless greenhouse cultivation, power autonomy through the use of solar energy, and intelligent feeding, shading, and lighting control to improve yields and reduce operational costs. However, particular challenges abound, including the limited uptake of smart technologies in commercial agriculture, price, and accuracy of the sensors. The barriers and challenges should help guide future Research & Development projects and commercial applications.
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Symeonaki, Eleni, Konstantinos Arvanitis, and Dimitrios Piromalis. "A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0." Applied Sciences 10, no. 3 (January 23, 2020): 813. http://dx.doi.org/10.3390/app10030813.
Повний текст джерелаАнотація:
The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.
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Murugan, Reshma J., B. N. Bindhya, and G. S. Sreedaya. "Artificial intelligence - The promise for an agricultural revolution in new era." AGRICULTURE UPDATE 15, no. 4 (November 15, 2020): 435–37. http://dx.doi.org/10.15740/has/au/15.4/435-437.
Повний текст джерелаАнотація:
Agriculture is slowly becoming digital. The adoption of Artificial Intelligence (AI) and Machine Learning (ML) both in terms of agricultural products and in-field farming techniques are increasing. Artificial Intelligence in agriculture is emerging in three major areas, namely agricultural robotics, soil and crop monitoring and predictive analytics. The use of sensors and soil sampling techniques are increasing day by day which helps in gathering of data. In turn, this data is stored in farm management system which is better processed and analysed. Thus, the data available along with other related data paves a way to successfully deploy AI in agriculture. AI in agriculture is emergingin cognitive computing and it has all the scope to become the most disruptive technology in agriculture services as it is able to understand, learn and respond to different situations (based on learning) to increase efficiency. The areas where the use of cognitive solutions can benefit agriculture are growth driven by IOT, image-based insight generation, identification of optimal mix for agronomic products, health monitoring of crops and automation techniques in irrigation and enabling farmers. In addition, the drone based solutions have significant impact in terms of productivity gains, coping with adverse weather conditions, yield management and precision farming.The emergence of new age technologies like Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are creating an ecosystem for smart farming. Fusion of all this technology is enabling farmers achieve higher average yield and better price control.
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Campuzano, Laura Restrepo, Gustavo Adolfo Hincapié Llanos, Jhon Wilder Zartha Sossa, Gina Lía Orozco Mendoza, Juan Carlos Palacio, and Mariana Herrera. "Barriers to the Adoption of Innovations for Sustainable Development in the Agricultural Sector—Systematic Literature Review (SLR)." Sustainability 15, no. 5 (March 1, 2023): 4374. http://dx.doi.org/10.3390/su15054374.
Повний текст джерелаАнотація:
In this article, we focused on studying the current barriers to implementing innovations in order for the agricultural sector to become more sustainable. Through a systematic literature review (SLR), 73 scientific articles were obtained with a search equation in SCOPUS. Of these, 48 were analyzed because of the mention of an obstacle preventing the sector from implementing innovations towards sustainability. Information related to the publication year, abstract, authors, keywords, innovation, innovation type, relationship with Fourth Industrial Revolution (4IR), identified barrier, nature of the barrier (internal/external), agricultural subsector, country, and methodology of each article was identified, and with VantagePoint software, a technological surveillance technique was applied as a quantitative analysis of the information. The United States is the country with the most publications related to the subject. The most mentioned keywords were “Sustainable Agriculture”, “Agroecology”, “Climate Change”, “Innovation”, and “Organic Farming”. Additionally, a qualitative analysis showed 43 types of innovations, 16 of them related to technology. “Organic Agriculture” is the most mentioned innovation, followed by “Genetic Engineering” and “Precision Agriculture”. In addition, 51 barriers were identified, 28 external to farmers and 23 internal. “Lack of policies that promote that innovation Innovative Practices” is the most mentioned barrier, followed by “Epistemic Closure”, “Unfavorable Regulation”, Climate-Smart Agriculture, and “Unskilled Labor”. This article is intended not only to show trends in the barriers to innovation that prevents the achievement of sustainability that the agricultural sector needs, but also to serve as an input for the development of policies that provide solutions to these impediments. It was shown that 17 out of the 28 external barriers are related to topics that could be solved by formulating policies, laws, incentives, guidelines, and regulations.
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Belcore, Elena, Stefano Angeli, Elisabetta Colucci, Maria Angela Musci, and Irene Aicardi. "Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard." ISPRS International Journal of Geo-Information 10, no. 4 (April 7, 2021): 236. http://dx.doi.org/10.3390/ijgi10040236.
Повний текст джерелаАнотація:
In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales.
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Osovin, M. N. "DIGITAL DIFFERENTIATION OF REGIONS OF AGRICULTURAL SPECIALIZATION: PROBLEMS AND WAYS OF THEIR SOLUTION." Scientific Review Theory and Practice 11, no. 7 (2021): 2149–59. http://dx.doi.org/10.35679/2226-0226-2021-11-7-2149-2159.
Повний текст джерелаАнотація:
According to global trends, the growing level of technological equipment of agricultural production requires a reduction in the chain of introduction of all newly created or improved technologies by reducing the territorial imbalances of digital development. An analysis of the regulatory framework of the EU member states confirms that for most regions of the EU, the transition from support to large agricultural organizations to the priority development of small and medium-sized businesses is seen as a key area of economic development. The Russian agrifood complex is characterized by technological heterogeneity and high differentiation of regions of agricultural specialization in terms of the level of innovation activity. Based on data from open sources, the subjects of the Russian Federation were compared in terms of the size and specific weight of the sown (planting) area, on which elements of precision farming are used. It was revealed that at the moment, in most constituent entities of the Russian Federation, digitalization means are used disproportionately to the size of their sown areas. It has been confirmed that the regional imbalance in the use of digital technologies is due to the predominance of small businesses in the structure of crop production. Proposals for reducing the digital inequality between the regions of the Russian Federation of agricultural specialization are substantiated. The analysis of the principles of building a European thematic platform for smart specialization, its impact on strengthening interregional cooperation in the field of digitalization of agriculture. The necessity of transferring the experience of the EU member states to reduce the digital divide between the regions of the Russian Federation and increase the innovative activity of enterprises of the national agri-food complex has been substantiated. It is concluded that the creation of an interregional innovation ecosystem will clearly identify the gaps in the value chains of each participant, identify the general directions of technical modernization of agricultural pro- duction and concretize areas for joint investment in projects with high potential returns.
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Pudeyan, Lyubov, Elena Zaporozceva, and Tatiana Medvedskaya. "Innovation as a strategic direction for increasing the economic efficiency of the agro-industrial complex." E3S Web of Conferences 371 (2023): 01063. http://dx.doi.org/10.1051/e3sconf/202337101063.
Повний текст джерелаАнотація:
The scientific article is devoted to the study of the processes of innovatization and informatization of the agro-industrial complex. Critical review of publications of academic and business circles and identification of strategic directions of innovative development of the agro-industrial complex; formation of a statistical database and quantitative analysis of indicators of innovative development and digital maturity of the agro-industrial complex; identification of key milestones in the development of the agro-industrial complex in the context of ongoing digital reforms. In the process of writing a scientific article, general scientific (observation, comparison, measurement, analysis and synthesis, the method of logical reasoning) and specifically scientific (static analysis, expert assessments, graphical method) methods of scientific cognition were used. The main directions of innovation and digitalization of the agro-industrial complex are: the development of precision agriculture, the formation of a network of smart farms and greenhouse complexes, the implementation of the federal program of technological cooperation of the agro-industrial complex and the IT sphere "Industrial FoodNet". The main obstacles to the innovative development of the agro-industrial complex are: the dominance of state financing of projects in the field of digital reform; lack of educational programs in the field of cooperation of the agroindustry and IT; insufficient dissemination of ideas and requirements of responsible business policy, expressed in the adoption and compliance with ESG principles; internal resistance of agribusiness management to deep reforms due to the fear of disclosure of trade secrets and the need for admission of partners from the IT sphere to sensitive economic information.
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Gabriel, Andreas, and Markus Gandorfer. "Adoption of digital technologies in agriculture—an inventory in a european small-scale farming region." Precision Agriculture, July 26, 2022. http://dx.doi.org/10.1007/s11119-022-09931-1.
Повний текст джерелаАнотація:
AbstractAs digitalization in the agricultural sector has intensified, the number of studies addressing adoption and use of digital technologies in crop production and livestock farming has also increased. However, digitalization trends in the context of small-scale farming have mainly been excluded from such studies. The focus of this paper is on investigating the sequential adoption of precision agriculture (PA) and other digital technologies, and the use of multiple technologies in a small-scale agricultural region in southern Germany. An online survey of farmers yielded a total of 2,390 observations, of which 1,820 operate in field farming, and 1,376 were livestock farmers. A heuristic approach was deployed to identify adoption patterns. Probable multiple uses of 30 digital farming technologies and decision-support applications, as well as potential trends of sequential technology adoption were analyzed for four sequential points of adoption (entry technology, currently used technologies, and planned short-term and mid-term investments). Results show that Bavarian farmers cannot be described as exceedingly digitalized but show potential adoption rates of 15–20% within the next five years for technologies such as barn robotics, section control, variable-rate applications, and maps from satellite data. Established use of entry technologies (e.g., automatic milking systems, digital field records, automatic steering systems) increased the probability of adoption of additional technologies. Among the most used technologies, the current focus is on user-friendly automation solutions that reduce farmers’ workload. Identifying current equipment and technology trends in small-scale agriculture is essential to strengthen policy efforts to promote digitalization.
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"A Study on Smart Farming Agriculture." Recent trends in Management and Commerce 1, no. 2 (November 1, 2020): 45–48. http://dx.doi.org/10.46632/rmc/1/2/6.
Повний текст джерелаАнотація:
There is a lot of literature on various forms of digitization in agriculture (big data, internet of things, augmented reality, robotics, sensors, 3D printing, computer integration, ubiquitous connectivity, artificial intelligence, digital twins and block chain among others), social science researchers have recently begun to explore various aspects of digital agriculture related to production systems, value Chains and food systems. This led to a growing but fragmented physical social science literature. So, no overview of how and about this field of study is growing. Where is it article purpose contribute this special issue seventeen articles then precision agriculture, digital agriculture, smart agriculture or the dynamics, economic and organizational issues of agriculture in society. According to an assessment of the literature, social science literature on the digitization of agriculture can be grouped into five themes: The adoption, use, and adaptation of digital technologies in agriculture; the effects of digitization on farmer identity, skill sets, and labor; the powerful, ethical, and private digitization of agricultural production systems and value chains; the adoption, use, and adaptation of agricultural knowledge and innovation systems (AKIS); and the economics and management of digital agriculture production systems and value chains. Network of things a promising technology exists that offers effective and dependable solutions for upgrading numerous domains. Web based solutions are being created primarily to monitor and autonomously maintain agricultural farms with the least amount of human intervention. The article discusses a number of Internet of Things applications in agriculture. This explains it key elements are smart farming. It is only through information management that crops can be profitably converted modern agricultural advances are causing smart agriculture to expand tremendously and become a vital component, which will be important for producers' decision-making. Objective data collected by sensors with the intention of boosting productivity and sustainability yields significant advantages. This kind of data-based management farms can boost reliance on data by preventing resource. Given its enormous potential to help both producers, the push for the widespread use of information and communication technology (ICT) in the digitization of the agricultural industry is currently gathering speed. On the other hand, introducing technological solutions into rural settings presents a number of difficulties
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"Smart Farming: IoT Based Plant Leaf Disease Detection and Prediction using Deep Neural Network with Image Processing." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (July 10, 2019): 3081–83. http://dx.doi.org/10.35940/ijitee.i7707.078919.
Повний текст джерелаАнотація:
Agriculture plays a major role in human life. Almost 60% of the population is involved directly or indirectly in some agriculture activity. But Nowadays, farmers have quit agriculture and shifted to other sectors due to less adoption of automation and other reasons like increase in the requirement of agricultural laborers. So, Farmers now largely depend on adoption of cognitive solutions with technological advancements to acquire the benefits. Image processing and Internet of Things jointly produces new dimensions in the field of smart precision farming. This proposed methodology aims to create an approach for plant leaf disease detection based on deep neural network. This approach combines IoT and image processing which runs preprocessing and feature extraction techniques by considering different features such as color, texture, size and performs classification using deep learning model that expands to help identification of plant leaf disease
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Maritim, F. C., V. Kuto, F. Njoroge, and E. Kashara. "Effects of Communication Barriers on Adoption of Climate Smart Agriculture Technologies in Kenya: A Case of Agro-pastoralists in West Pokot County." South Asian Journal of Social Studies and Economics, September 3, 2022, 43–53. http://dx.doi.org/10.9734/sajsse/2022/v15i230403.
Повний текст джерелаАнотація:
Over the last two decades, agricultural researchers have been developing technology-based systems to aid farmers in various aspects of farming. However, information about these agricultural research technologies has not been effectively disseminated to farmers, thus, low uptake of agricultural technologies among farmers. In Kenya, one of the major factors identified to contribute to the low uptake of agricultural technologies among farmers is communication barriers among agricultural researchers, policy makers, value chain actors, and farmers concerning the availability, applicability, and how to adopt the agricultural technologies for high production. The general objective of this study, therefore, was to explore how barriers to communication influenced the uptake of climate-smart technologies among farmers in West Pokot County, Kenya. The study employed a Pragmatism approach, specifically sequential QUAN→QUAL mixed method. The target population of the study looked at the entire group of objects having common observable characteristics and a population that tends to have a wide geographical spread but not the total or universal population. The population sample was therefore based on practice, the expense of data collection, and the need to have sufficient statistical power, precision level, the level of confidence of risk, and the variability degree in the attributes being measured. This sample size of farmers from West Pokot who participated in this study, therefore, was 494 farmers and 29 selected key informants from various agricultural institutions. Procedures of sampling were used at a characteristic level of a material specification or task list. Cluster random sampling and purposive sampling methods were used to select the respondents for the study. Farmers were grouped into four clusters based on the four Sub-Counties of West Pokot County. The selected key informants were assumed to have adequate experience in matters communication of agricultural information towards successful uptake of climate-smart agriculture in West Pokot County. The administration of questions guided by questionnaires through an online data kit app and conducting of in-depth interviews guides. Data collected through questionnaires was quantitative (closed-ended) with a few qualitative (open-ended) questions. One of the results showed that major barriers are the language barrier, poor road network, and poor telecommunications infrastructure.
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Ториков, В. Е., В. А. Погонышев, and Д. А. Погонышева. "STATE AND PROSPECTS OF DIGITAL TRANSFORMATION OF AGRICULTURE." VESTNIK RIAZANSKOGO GOSUDARSTVENNOGO AGROTEHNOLOGICHESKOGO UNIVERSITETA IM P A KOSTYCHEVA, no. 2(54) (June 30, 2022). http://dx.doi.org/10.36508/rsatu.2022.54.2.013.
Повний текст джерелаАнотація:
Проблема и цель. Целью настоящего исследования является анализ состояния и перспектив цифровой трансформации сельского хозяйства. Методология. Материалы и методы исследования представляют собой аналитический обзор цифровых решений, агроинноваций в растениеводстве, опирающихся на достижения науки и техники. Результаты. В аграрной индустрии в бизнес-процессах участвуют территориально распределенные субъекты и объекты: сотрудники, сельхозтехника, живые организмы, присутствуют ситуации риска и неопределенности. Благодаря современному научно-техническому прогрессу аграрии на основе использования цифровых инноваций преобразуют сельское хозяйство в высокотехнологичную отрасль. В статье рассмотрена сущность «умного» сельского хозяйства как перспективного вектора развития аграрного сектора. Выявлено, что умные технологии (квантовые технологии, технологии дополненной и виртуальной реальности, искусственный интеллект, блокчейн, робототехника, Интернет вещей и др.) радикальным образом оказывают влияние на достижение высокой производительности труда в аграрной сфере. Использование отечественных цифровых платформ инициирует создание и внедрение новых моделей экономического поведения сельскохозяйственных организаций. Высокотехнологичное сельское хозяйство эффективно разрешает современные проблемы продовольственной безопасности, изменения погодно-климатических условий. Установлено, что цифровая трансформация отрасли, внедрение точного земледелия обеспечат прирост урожайности культур, позволят аграриям получить финансовую выгоду, снизят нагрузку на природу. Рассмотрено состояние цифровизации точного земледелия в Брянской области. По промышленному производству картофеля регион является лидером в РФ, по урожайности рапса область – мировой лидер. Высоки результаты в зерновом хозяйстве. Крупные предприятия области используют спутниковые технологии при обработке полей, проводится космический мониторинг сельхозугодий, сельхозтехника оснащена системами мониторинга, датчиками и пр. Выявлены причины и факторы, «тормозящие» внедрение цифровых технологий в аграрной сфере. Заключение. Высокотехнологичные решения кардинальным образом трансформируют аграрную сферу экономики, способствуют росту производительности труда в отрасли. Использование российских цифровых платформ формирует новые модели экономического поведения сельскохозяйственных организаций. Цифровая трансформация аграрной индустрии имеет положительные как экономические, так и косвенные и социальные эффекты. Problem and purpose. The purpose of this study is to analyse the state and prospects of the digital transformation of agriculture. Methodology. The research materials and methods are an analytical overview of digital solutions, agroinnovations in crop production, based on the achievements of science and technology. Results. In the agricultural industry, geographically distributed subjects and objects participate in business processes: employees, agricultural machinery, living organisms, there are situations of risk and uncertainty. Thanks to modern scientific and technological progress, farmers, based on the use of digital innovations, are transforming agriculture into a high-tech industry. The article considers the essence of smart agriculture as a promising vector for the development of the agricultural sector. It was revealed that smart technologies (quantum technologies, augmented and virtual reality technologies, artificial intelligence, blockchain, robotics, In-ternet of things, etc.) radically affect the achievement of high productivity in the agricultural sphere. The use of domestic digital platforms initiated the creation and introduction of new modes of economic behavior of agricultural organizations. High-tech agriculture effectively addresses the current problems of food security and climate change. It was established that the digital transformation of the industry, the introduction of precision agriculture would provide an increase in crop yields, allow farmers to receive financial benefits, and reduce the burden on nature. The state of digitalization of precision agriculture in the Bryansk region was considered. By industrial production of potatoes, the region is the leader-rum in the Russian Federation, by rapeseed yield, the region is a world leader. The results in grain farming are high. Large enterprises of the region use satellite technologies in field processing, space monitoring of farmland is carried out, agricultural machinery is equipped with monitoring systems, sensors, etc. Reasons and factors have been identified that "inhibit" the introduction of digital technologies in the agricultural sector. Conclusion. High-tech solutions radically transform the agricultural sphere of the economy, contribute to the growth of labor productivity in the industry. The use of Russian digital platforms was shaping new models of economic behaviour among agricultural organizations. The digital transformation of the agricultural industry has positive both economic and indirect and social effects.
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Subramanian, K. S., S. Pazhanivelan, G. Srinivasan, R. Santhi, and N. Sathiah. "Drones in Insect Pest Management." Frontiers in Agronomy 3 (December 9, 2021). http://dx.doi.org/10.3389/fagro.2021.640885.
Повний текст джерелаАнотація:
One of the major components in precision agriculture is crop health monitoring, which includes irrigation, fertilization, pesticide sprays, and timely harvest of the crop. Further, the progressive change in growth and development is critical in crop monitoring and taking suitable decisions to maintain health status. In order to accomplish the task, drones are highly useful for on site detection of problems so as to undertake corrective measures instantly. Although it is expensive to build algorithms and establish relationships between ground truth and spectral signatures, it is a user-friendly technique once the basics studies are done. As labor availability and technical manpower are extremely limited, particularly in India, drones are gaining popularity in the context of smart farming. Insect pests are known to cause catastrophe and drastic reduction in food grain production across the globe. The losses that have been predicted by FAO is over 37% due to pests and diseases. Recently, crops cultivated in India have been threatened by invasive pests like fall army worm (Spodoptera frugiperda) in corn and Rugose spiraling whitefly in coconut (Aleurodicus rugiperculatous Martin); these pests caused extensive damage during the years 2018 and 2019. The plant protection measures are to be taken on a community basis so as to ensure effective management of pests. In India, more than 80% of farmlands are in the category of small and marginal (<1 ha), so it is very difficult to manage the invasive pests. If one field is sprayed, the pests simply shift their feeding to the neighboring fields. To address this, drones become essential. Drones are unmanned aerial vehicles exploited in a wide array of disciplines such as defense, monitoring systems, and disaster management but are only beginning to be utilized in agricultural sciences. There are three major types of drones, namely fixed wing, multi-rotor, and hybrid type, and the usage depends on specific applications. The other types depend on degree of automation, size, weight, and power source. The set operational parameters such as flight speed, height, and endurance need to be optimized to use drones appropriately in agriculture and allied sectors. In addition, parameters related to drone-based spraying such as droplet size, spread, density, uniformity, deposition, and penetrability should also be factored in when implementing drone-based mitigation strategies. Despite the fact that drone technology is highly relevant and appropriate for pest management, the adoption of the technology is restricted. Regulatory guidelines have been set across the globe to perform site-specific farm management with higher precision at a very high resolution. Overall, drones can be employed in almost all agricultural field operations and are considered excellent tools for rapid, reliable, and non-destructive detection of field problems. This review provides panoramic views of drone technology and its application in the management of pests in a digital agriculture era.