BASF: Precision Farming with Lark Bread Initiative

During the last decades and years of transition, Bulgaria has seen extremely unfavorable and negative trends in its natural growth. It is entirely negative and reinforced by the rate of net migration and the widespread aging of the country's population leads to very unfavorable results for the population in the rural areas of the country. At the same time, the prerequisites for implementing precision and digital agriculture are being successfully developed in the world, Europe and Bulgaria.The purpose of the present study is to analyze the situation in the country from a demographic point of view, to determine whether the declining population in rural areas is not a prerequisite for the faster emergence of the process of digitization and precision agriculture in rural areas of Bulgaria.For the purposes of our research, scientific, statistical methods, as well as methods of induction, deduction, comparative analysis, GAP analysis were used, and its results can be successfully used and applied by all interested parties.

The yield and quality of cut flowers (Chrysanthemum) in a conventional greenhouse and a smart farm were assessed. The two sites were compared in terms of facilities, soils, and growth conditions. The three cultivars of cut spray chrysanthemums used in this study were ‘Abishag’, ‘Ford’, and ‘Henna’. The smart farm was equipped with 16 automatic sensor channels for monitoring temperature, humidity, CO2, nutri-culture solution, air flow, and light. Fog spray devices were used in the smart farm but not in the greenhouse, where temperature was lowered by opening side windows for ventilation. We found that cut chrysanthemums of excellent quality are produced in the smart farm, due to the uniform environment year-round. The chrysanthemum crop was harvested at the smart farm 3.5 times per year and produced an average of 129 plants/m2 annually, in one year, which is an improvement over the conventional greenhouse, in which the crop was harvested 3 times per year, yielding 91 crops/m2 annuallyin 1 year. The smart farm has an appropriate amount of organic matter and pH; however, the greenhouse has high organic matter. The electrical conductivity in the smart farm was 3.8 times higher than the corresponding value in the greenhouse. The three varieties grown on the smart farm were all superior to the greenhouse plants in terms of plant height, number of leaves, and stem diameter. On the other hand, chlorophyll content tended to be higher in the greenhouse plants. Through these results, we found that cut chrysanthemums of commercially excellent quality are produced in the smart farm, due to the uniform environment year-round using smart-farming system.

Purpose Effectively transferring knowledge of digital innovations, such as precision agriculture, is critical for farmers to make adoption decisions to meet current and future farming challenges. Stronger knowledge flows from Extension systems to stakeholders benefit adoption of precision agriculture. Methods This study used a three-round Delphi technique with a panel consisting of 15 precision agriculture experts who are academic extension faculty at land-grant universities in the Southeastern United States to identify the key knowledge, skills, and abilities needed by Extension agents to facilitate effective precision agriculture interactions. Results A total of 13 key competencies were identified. The findings revealed that the majority category identified in the 13 key competencies was the skill component, followed by the knowledge component, indicating the need to expand Extension agents' skills and knowledge related to precision agriculture. Conclusion The top key competency of basic precision agriculture awareness underscores the importance of ensuring Extension agents are aware of precision agriculture applications in their region. While all items were considered relatively important to the panelists based on the rate data, sustainability awareness, and equipment operation skill competencies were ranked as a lower priority, indicating Extension agents do not necessarily need to understand how to use the precision agriculture technology or manage the data but should function as change agents who introduce farmers to precision agriculture and connect them with relevant experts. The findings inform future tailored professional development programs and educational resources for equipping Extension agents with the needed competencies and guide future assessment of precision agriculture competencies among Extension agents.

Agriculture serves as a linchpin for the national economy. With the accelerated evolution of information technology and emergent high-tech innovations, the digitization of agriculture has emerged as a pivotal trajectory for contemporary agricultural advancement. Conventional practices encompassing planting, fertilization, and harvesting are transitioning towards more quantitative, localized, and technologically-driven precision agriculture. Concurrently, with the global surge in "precision agriculture", Chinese academicians have progressively delved into its study. Over the past three decades, the "3S" technology-centric "precision agriculture" in China has witnessed commendable advancements, exemplified by innovations such as the Global Agricultural Remote Sensing Quick Report System and the Beidou Satellite Navigation System-based autonomic agricultural machinery operation. However, the modernization of Chinese agriculture exhibits certain limitations, including limited adoption, suboptimal integration, and a scarcity of high-caliber experts. Given the swift progression of Chinese scientific and economic domains, the "3S" technology is poised to surmount these challenges, enhancing the modernization of Chinese agriculture. Hence, "precision agriculture" anchored in "3S" technology retains significant growth prospects within China.

Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges

Two mail surveys were carried out in Denmark and the Eastern Corn Belt, USA in 2002. Questionnaires were sent to 580 farmers who had used precision agriculture (PA) and 198 responses were received. The surveys focused on the current status of use of PA in both countries, including: PA practices, equipment and software, Internet and e-mail use, information sources for PA, satisfaction level from service providers, data handling, interpretation, storage and ownership, value of data for decision making, changes in management practices, desired information and services, and the next planned step in the practice of PA. The survey results showed more similarities in practicing PA between the two countries than differences. Time requirement and high cost of data handling were cited as the main problems. Survey respondents found soil maps to be more valuable than yield maps in management decisions. About 80% of the respondents would like to store the PA data themselves. The majority of the respondents indicated that they have changed their management practices due to PA, but not substantially. Some 90 of the respondents used the Internet and e-mail for agricultural purposes, but only a small number for PA websites.

Recent technological and methodological changes in farming have led to an emerging set of claims about the role of digital technology in food production. Known as precision agriculture, the integration of digital management and surveillance technologies in farming is normatively presented as a revolutionary transformation. Proponents contend that machine learning, Big Data, and automation will create more accurate, efficient, transparent, and environmentally friendly food production, staving off both food insecurity and ecological ruin. This article contributes a critique of these rhetorical and discursive claims to a growing body of critical literature on precision agriculture. It argues precision agriculture is less a revolution than an evolution, an effort to shore up and intensify the conventional farming system responsible for generating many of the social and environmental problems precision agriculture is presented as solving. While precision agriculture advocates portray it as a radical, even democratic epistemological break with the past, this paper locates truth claims surrounding datafication and algorithmic control in farming within deeper historical contexts of the capitalist rationalization of production and efforts to quantify and automate physical and mental labor. Abjuring the growing cultural tendency to treat algorithmic systems as revolutionary in favor of social and historical dimensions of precision agriculture, can help re-frame the discussion about its design and use around real, socially and ecologically oriented change in farming, and so ensure that the possibilities and benefits of precision agriculture are as evenly and effectively shared as possible.

Over the past decade, the government has actively supported smart farm investment projects, and with the implementation of the “Smart Agriculture Act” in 2024, further expansion of support for smart farms is anticipated. Despite the recognized need, there has been limited quantitative post-evaluation of these existing smart farm investment projects. This study employs propensity score matching to quantitatively estimate the impact of smart farm adoption on farm income. The analysis utilizes data from the Agricultural and Fisheries Census, spanning from 2011 to 2022, comparing the income differences between farms that have adopted smart farming technologies and those that have not. Following the estimation of the impact of smart farm adoption on farm income, the study projects future income effects and conducts a benefit-cost analysis, incorporating the government’s budget for smart farming. The findings indicate that the adoption of smart farming has significantly increased the income of farms engaged in horticulture and livestock production. Moreover, the benefit-cost analysis reveals that the accumulated benefits are projected to grow in the future, enhancing the economic viability of smart farming. This study contributes to the assessment of the economic viability of smart farm investment projects and is expected to serve as a valuable reference for the formulation of future smart farm policies.

A survey on smart farming data, applications and techniques

The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims to contribute to the comprehensive understanding of the application of image recognition technology in smart agriculture by investigating the scientific literature related to this technology in the last few years. We discussed and analyzed the applications of plant disease and pest detection, crop species identification, crop yield prediction, and quality assessment. Then, we made a brief introduction to its applications in soil testing and nutrient management, as well as in agricultural machinery operation quality assessment and agricultural product grading. At last, the challenges and the emerging trends of image recognition technology were summarized. The results indicated that the models used in image recognition technology face challenges such as limited generalization, real-time processing, and insufficient dataset diversity. Transfer learning and green Artificial Intelligence (AI) offer promising solutions to these issues by reducing the reliance on large datasets and minimizing computational resource consumption. Advanced technologies like transformers further enhance the adaptability and accuracy of image recognition in smart agriculture. This comprehensive review provides valuable information on the current state of image recognition technology in smart agriculture and prospective future opportunities.

This study aims to clarify the Japanese characteristics of the spread of smart agriculture utilizing digital technology, which is expected to spread worldwide, and to provide policy implications for further dissemination of the technology. We conducted a questionnaire survey on actual conditions related to smart agriculture on Japanese farms. We have also proposed creation of a Smart Agricultural Kaizen Level (SAKL) technology map by applying the evaluation method used in management technology theory for the manufacturing industry. Using the results of the questionnaire survey and the proposed SAKL technology map, we analyzed the current pattern of expansion of smart agricultural technologies in Japan. Our results suggest that production efficiency in Japanese agriculture could be improved by raising the data visualization level and introducing smart agricultural technology. We also found that Japanese agriculture efficiency can be improved by introducing smart agricultural technology even if the data visualization level remains low. Smart agricultural technology automatically visualizes information and optimizes conditions without relying on the farmer's information literacy. At Japanese agriculture sites, the current smart agricultural technology introduction rate is less than 50%. To effectively disseminate smart agricultural technologies in the future, a policy should be implemented that promotes the development of a standardized package of smart agricultural technologies that can improve efficiency to some extent through default operation. With such a package, smart agriculture could be expanded without resorting to improving farmers' information literacy. Agricultural sites in Japan are thought to be currently engaged in developing such a standardized package of smart agricultural technologies.

Smart agriculture integrates key information communication technologies with sensing technologies to provide effective and cost-efficient agricultural services. Smart agriculture leverages a wide range of advanced technologies, such as wireless sensor networks, Internet of Things, robotics, agricultural bots, drones, artificial intelligence, and cloud computing. The adoption of these technologies in smart agriculture enables all stakeholders in the agricultural sector to develop better managerial decisions to get more yield. We differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture. We present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture. We also review the integration of various sensing technologies with emerging technologies and computing infrastructures to make agriculture smarter. Finally, we discuss open research challenges that must be addressed to improve the adoption and deployment of smart agriculture in the future.

The relentless growth of the global population, compounded by the adversities posed by climate change, has triggered an unprecedented demand for sustenance. This burgeoning need places an imperative on the agricultural sector to adopt practices that are both sustainable and efficient. Traditional farming methods, marked by inefficiencies and environmental ramifications, prove inadequate in meeting this escalating demand. Inresponse, precision farming emerges as a transformative solution, centering on data-driven decision-making and the optimization of resources. This groundbreaking whitepaper meticulously navigates the intersection of precision farming and augmented reality (AR) technology, showcasing a paradigm shift in agriculturalpractices. AR, a revolutionary tool overlaying digital insights onto the physical world,empowers farmers with real-time information on soil conditions, crop health, and moisture Levels. Harnessing AR's capabilities, farmers gain unprecedented control over irrigation and fertilizer applications, minimizing waste while maximizing yields. The paper dives deep into the modern challenges of agriculture, articulates the substantial benefits of precision farming, and underscores AR's transformative potential in surmounting these challenges. Precision farming, elucidated as the fusion of technology to gather and analyze data on parameters like soil moisture and weather conditions, is detailed with utmost clarity. In contrast to the antiquated one-size-fits-all approach of traditional farming, precision farming, facilitated by real-time data and analytics, enables bespoke crop management. This optimization minimizes resource usage, curtails waste, and augments productivity to Unparalleled levels. The whitepaper seamlessly dissects precision farming into two indispensable components:data capture and data visualization. The Internet of Things (IoT) emerges as the linchpin for data capture, employing sensors that collect real-time data transmitted to a centralized platform for astute analysis. Concurrently, AR, the second component, emerges as the pivotal interface, empowering farmers to seamlessly visualize and interpret data for judicious decision-making. In the current corporate panorama, stalwarts like Augmenta and Grow Glide, coupled with potential contributors like Rams Creative Technologies Pvt. Ltd. and emerging entities likeInfosys Limited, stand as vanguards propelling technological advancements to bolster precision farming. However, the potential for widespread AR adoption in precision farming faces impediments. Concerns regarding the safety and health risks associated with AR hardware, coupled with formidable development costs, and a prevailing lack of awareness pose substantial challenges.

The purpose of this research is to study the experience of introducing precision farming technologies abroad and to substantiate the directions of development and practical implementation of these technologies in Belarus. The article discuss-es the leading countries in the field of practical testing of modern technologies of precision farming, analyzes the experience of their use in the EAEU countries, studies the developments in the main directions of the introduction of precision farming in Belarus: the use of GPS for determining field boundaries, remote sensing of the earth, mapping and monitoring the state of crops and productivity; technologies of differentiated application of seeds, fertilizers and pesticides, differentiated pre-sowing soil treatment; technology of parallel driving and automatic control over the work and movement of technical equipment. Based on the results of the analysis, such directions of development and practical use of precision farming tech-nologies in Belarus have been identified, such as the implementation of domestic industrial developments; development of domestic software and hardware; expanding scientific research on precision farming technologies; generalization and dis-semination of scientific and production experience in the introduction of precision farming; development of government measures to support commodity producers, including farms, mastering precision farming technologies. The scientific novelty lies in the study of the problems of introducing precision farming technologies in Belarus, tak-ing into account world experience, as well as in developing priority areas for the further development of precision farming in Belarus. The practical significance lies in the need to introduce precision farming technologies in agricultural organizations of Belarus. It is advisable to continue the research in the direction of developing a methodology for assessing the degree of intro-duction of precision farming technologies, as well as a comprehensive analysis of the results of the practical use of these technologies at micro- and mesoscale levels. Keywords: agriculture, efficiency, sustainability, innovative technologies, precision farming, world experience, de-velopment directions in the Republic of Belarus.

Using data from a survey of cotton producers in 14 US states, and a bivariate probit regression, this study examined the effects of the following measured parameters on the adoption of Automatic Section Control (ASC) technologies and GPS Auto-Guidance (AG) systems: age, education, farm size, field geometry, information sources, as well as the use of specific production practices and other Precision Agriculture (PA) technologies. Results suggest that younger, more educated producers, consulting farm dealers for information about PA technologies, using other PA technologies, and managing larger farming operations located in counties with more irregularly shaped fields are more likely to adopt ASC technologies and AG systems. Predicted adoption probabilities estimated using regression results suggest the use of other PA technologies and farm dealers as a source of precision farming information have the largest impact on the probability of adopting ASC by cotton farmers. Additionally, these results suggest farmers with operations in eastern Arkansas, western Tennessee, and a couple of counties in middle Tennessee are more likely to adopt ASC technologies. Producers in these regions had the highest percentages of users of other PA technologies and farm dealers to obtain PA information.