Adoption Of Precision Agriculture Research Articles

Design and Development of an Intuitive Android Application for Smart Farming

Farmers face challenges in adapting to environmental changes, yet with decreasing implementation costs and a growing accessibility to technical expertise, progress is evident. This research introduces KisanVishwa, an Android app designed for smart farming. KisanVishwa optimizes precision agriculture systems, enabling farmers to monitor their fields in real time and address agricultural constraints. Tailored to meet the specific needs of today's farmers, the app provides user-friendly tools and the latest information on farm equipment, fertilizers, chemicals, technological advancements, and local distributors and retailers. By integrating KisanVishwa into their operations, farmers can overcome challenges associated with the adoption and maintenance of precision agriculture. KisanVishwa adopts a user-centric design for compatibility across diverse Android devices, integrating monitoring tools and collaborating with agricultural research institutions. Empowering farmers, the app addresses contemporary challenges, fostering informed decisions and efficiency. With its user-friendly interface, KisanVishwa promotes the adoption of precision agriculture, essential resources, and sustainable practices in the agricultural sector. Convincing traditional farmers to adopt advanced technologies may pose challenges. Functionality may depend on reliable internet, posing challenges in areas with limited connectivity.

Willingness to adopt precision agriculture: An analysis of Gombe and Bauchi States of Nigeria

Adoption of Precision Agriculture (PA) technologies has apparent benefits for agricultural production management, cost savings, and environmental sustainability. However, these technologies are also extremely complicated and influenced by various social and economic forces, which impact their adoption rate, particularly in developing nations. Demographic and agricultural characteristics also play an important role in determining willingness to adopt PA. This study is a survey aimed at establishing the willingness indices of farmers and other relevant personnel to embrace precision farming technologies in Gombe and Bauchi states of Nigeria. The survey included individuals of varied ages; but an average age of 28.5 years for Gombe and 36.5 years for Bauchi, educational levels, and employment statuses, as well as agricultural and operational features. The willingness to adopt PA is high in both states, with 92% in Gombe and 96% in Bauchi. The findings suggest that, while high costs and a lack of technical know-how had a negative impact on readiness to adopt PA in both states, the level of complexity was rated higher by the older generation in Bauchi state, which had a comparatively high mean farmer age. The size of the farm and the number of employees had no direct influence on the adoption of PA technology. Other criteria, such as age and number of years of operation, indicate overlap between the various stages of internet and digital technology adoption, mechanization, and PA. The high willingness rating indicates that farmers are willing to adopt PA technologies if made accessible. It is therefore advised that legislative tools, training programs, and necessary access to financing facilities be employed to make these technologies available and cheap for farmers.

Optimising Precision Agriculture Choices for Arable Farmers in Germany and the UK: the LINKDAPA Approach

SummaryFarmer adoption of so‐called Precision Agriculture (PA) or ‘smart’ technologies in the arable sector has grown in the last few decades with a focus on Global Navigation Satellite Systems (GNSS) and variable rate technologies (VRT). This has led to increased generation of large volumes of data about fields and their crop yields which could be used to increase the environmental and financial performance for farmers. However, survey results show that cost and adaptability have been issues for many farmers in the UK and Germany that have held back such adoption. The LINKDAPA (LINKing multi‐source Data for Adoption of Precision Agriculture) project's approach sought to minimise both concerns by creating a customisable web platform that incorporates both GNSS and VRT into one, easy to use, affordable option for farmers. The project developed an online cloud‐based decision support tool which takes into account different fertiliser strategies based on novel algorithms using soil, historic yield and satellite data. Co‐created by researchers, farmers and agricultural technology firms, the LINKDAPA approach offers both economical and easy to implement solutions for farm management to mitigate resource loss‐ratios such as in fertiliser use, provide financial performance analyses, and multi‐year graphical imagery for soil mapping.

Proposal of a Model of Irrigation Operations Management for Exploring the Factors That Can Affect the Adoption of Precision Agriculture in the Context of Agriculture 4.0

Agriculture is undergoing a profound change related to Agriculture 4.0 development and Precision Agriculture adoption, which is occurring at a slower pace than expected despite the abundant literature on the factors explaining this adoption. This work explores the factors related to agricultural Operations Management, farmer behavior, and the farmer mental model, topics little explored in the literature, by applying the Theory of Planned Behavior. Considering the exploratory nature of this work, an exploratory multi-method is applied, consisting of expert interviews, case studies, and modeling. This study’s contributions are a list of factors that can affect this adoption, which complements previous studies, theoretical propositions on the relationships between these factors and this adoption, and a model of irrigation Operations Management built based on these factors and these propositions. This model provides a theoretical framework to study the identified factors, the relationships between them, the theoretical propositions, and the adoption of Precision Agriculture. Furthermore, the results of case studies allow us to explore the relationships between adoption, educational level, and training. The identified factors and the model contribute to broadening the understanding of Precision Agriculture adoption, adding Operations Management and the farmer mental model to previous studies. A future research agenda is formulated to direct future studies.

Advanced strategies for optimization of primary nutrients requirement in rice-A review

The Green Revolution led to India's food independence mostly through the inclusion of supply-driven technologies, such as the introduction of high-yielding cultivars, improved access to water, agrochemicals, and mechanization. The present and future needs target agricultural sustainability without endangering the ecosystem. In this regard, the adoption of precision agriculture is required to meet this expected objective. In developed nations, precision farming has already experienced tremendous growth. However, precision farming methods have taken a while for emerging nations in Asia to comprehend, create, and embrace. Moreover, precision farming is frequently misunderstood as a sophisticated technological intervention intended for extensive agricultural fields. However, it is essentially a science that involves using the "right input" in the "right quantity," at the "right time," and in the "right place," to improve input use efficiency. In the case of primary nutrients such as nitrogen, phosphorus, and potassium, so-called recommendations for nutrient management based on soil tests have improved food grain output which increased the nutrient use efficiency up to a certain extent. Moreover, the recommendations are made for a given agroclimatic region and crops irrespective of site-specific soil fertility, cultivars, and agronomic management levels resulting in excess or scanty use to crop needs. At this juncture, assessing the nutritional requirements of plants proves to be a superior method, as it takes into account the cumulative impact of nutrient availability from various sources on plant growth at any specific stage, making it a reliable indicator of nutrient accessibility. Rice, the most important food crop, is grown in diverse agroclimatic regions at different management levels. Hence, there is an urgent need to adopt a precision nutrient management strategy to optimize the yield output. The article offers an overview of several precision instruments available for managing nutrients at specific sites and aids in choosing the most appropriate one for each circumstance.

Forecasting carrot yield with optimal timing of Sentinel 2 image acquisition

Accurate, non-destructive forecasting of carrot yield is difficult due to its subterranean growing habit. Furthermore, the timing of forecasting usually occurs when the crop is mature, limiting the opportunity to implement alternative management decisions to improve yield (during the growing season). This study aims to improve the accuracy of carrot yield forecasting by exploring time series and multivariate approaches. Using Sentinel-2 satellite imagery in three Australian vegetable regions, we established a time series of carrot phenological stages (PhS) from ‘days after sowing’ (DAS) to enhance prediction timing. Numerous vegetation indices (VIs) were analyzed to derive temporal growth patterns. Correlations with yield at different PhS were established. Although the average root yield (t ha−1) did not significantly differ across the regions, the temporal VI signatures, indicating different regional crop growth trends, did vary as well as the PhS at when the maximum correlation with yield occurred (PhSR2max)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PhS_{{R2_{max} }} )$$\\end{document} with two of the regions producing a delayed PhSR2max\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PhS_{{R2_{max} }}$$\\end{document} (i.e. 90–130 DAS). The best multivariate model was identified at 70 DAS, extending the forecasting window before harvest between 20 to 60 days. The performance of this model was validated with new crops producing an average error of 16.9 t ha−1 (27% of total yield). These results demonstrate the potential of the model at such early stage under varying growing conditions offering growers and stakeholders the chance to optimize farming practices, make informed decisions on selling, harvesting, and labor planning, and adopt precision agriculture methods.

A systematic review on the factors governing precision agriculture adoption among small-scale farmers

The global agricultural paradigm is witnessing a transformative shift with the advent of precision agriculture. While large-scale agricultural enterprises have been swift in their embrace of this innovation, its uptake among small-scale farmers remains nuanced and complex. This study elucidates the multi-faceted determinants that influence the adoption of precision agriculture within the small-scale farming sector. The study adopts a systematic literature review approach, meticulously selecting and analysing 29 relevant papers. Drawing upon an exhaustive literature review and empirical analyses, the research presents a composite framework weaving economic, technological, social, and environmental factors. Among these, the social dynamics emerge as the most significant factor, shaped by awareness levels, knowledge dissemination pathways, and entrenched cultural norms. These social elements often intertwine with ingrained traditional practices and perceptions, forming the most intricate layer shaping adoption attitudes. Notably, although economic factors like substantial initial investments and the calculus of Return on Investment are present, they are overshadowed by social considerations. The technological landscape is characterised by the challenges of digital literacy, infrastructural readiness, and technological interoperability. Lastly, environmental imperatives, underscored by resource scarcity, climate change resilience, and ecosystem services, offer both challenges and motivations. Together, these factors delineate the intricate roadmap guiding small-scale farmers in their journey toward precision agriculture adoption. This comprehensive exploration provides stakeholders with actionable insights, fostering informed decision-making and strategic interventions to augment precision agriculture's integration within the small-scale farming tapestry.

ADOPTION OF 4.0 TECHNOLOGIES IN AGRICULTURE: PROPOSITION OF A PROCESS BASED ON CASE STUDIES IN BRAZIL

4.0 technologies have been adopted in several sectors, such as the agricultural sector. In Brazil, recent research showed that around 50% of farmers adopt precision agriculture technologies, indicating opportunities for diffusion in the country. This work aimed to identify a process flow for the adoption of 4.0 technologies in agriculture, based on a case study carried out in 14 pilot projects of the Agro 4.0 Program of the Brazilian Agency for Industrial Development - ABDI, carried out in Brazil between 2020 and 2022. The study was carried out through the application of questionnaires and interviews with those responsible for the projects and in the consolidation of a flowchart. As a result, it was possible to understand elements that characterize the context of 4.0 technologies adoption, such as motivations, challenges and critical factors, in addition to identifying processes and sub-processes for this challenge.Â

Hungarian Farmers and the Adoption of Precision Farming

Abstract Definitions of precision farming emphasise that it is at the heart of the pursuit of economic, environmental and social sustainability. In our study, precision farming is understood as a form of farming that seeks to optimise and manage efficiently, where technology is the key factor, but is primarily based on human knowledge and willingness to use technology. We discuss precision farming as a social innovation of artificial intelligence. Primarily, the social conditions of the application were the focus of our research, which is characteristically different from economic, financial and productivity approaches. The potential for the application of precision farming varies widely from country to country. Our aim is to analyse the diffusion and limitations of precision farming in Hungary and the motivations of farmers. In our qualitative research, we interviewed 60 precision farmers and 10 experts about their perceptions of precision technology and their motivations and barriers to the use of AI. Our results show that the main barriers to the uptake of precision technologies are the low education level of farmers, their inexperience, low interest level and lack of commitment. They are not aware of the benefits of precision technologies and do not take the time to acquire information and knowledge. There is a lack of willingness to cooperate and a very low level of trust among Hungarian farmers.

Understanding farmer views of precision agriculture profitability in the U.S. Midwest

Precision Agriculture (PA) technologies are well known to be useful in addressing field heterogeneities and enabling informed site-specific management decisions. While profitability is the foremost factor considered by farmers when making PA adoption decisions, information in this regard is lacking from the farmers' perspective. This paper analyzed 1119 farmer responses from a 2021 survey conducted in four states along the western margins of the U.S. Midwest. Our findings show that while most (around 60%) non-adopters indicate that they are unaware of PA profit change, adopters are likely to rate a major profit increase. About two thirds of adopters rated at least a 5% increase in profitability towards variable rate (VR) fertilizer application (72%), VR seed application (68%), and automatic section control (66%). We modeled farmers' profit change subsequent to PA adoptions. Our regression results demonstrate that the profits from PA usage increase over time and that use of conservation practices likely influences PA profitability in a positive way. As soil quality and weather factors also affect profit ratings, it would be beneficial to compare and demonstrate profitability potential of various PA technologies on a regional basis and tailor the promotion efforts to farmers most likely to benefit from them.

Better richer than environmentally friendly? Describing preferences toward and factors affecting precision agriculture adoption in Italy

Precision agriculture is expected to support and strengthen the sustainability of food production. In spite of the demonstrated benefits of the application of Information Technology to improve agricultural practices, such as yield increase and input reduction, in Italy its adoption still lags behind. In order to understand limits of and perspectives on the adoption of such technologies, we conducted an explorative study. A survey with a choice experiment was carried out in Italy among 471 farmers and people interested in agricultural machinery and technologies. The results highlight how specific factors, such as excessive costs and lack of incentive policies, may limit the spread of precision agriculture. Conversely, the provision of adequate technical support would likely favor its adoption. Furthermore, latent class modeling was used to identify three segments of potential buyers: sustainability seekers; precision agriculture best features supporters; low emissions fans. Potential policy and market implications of this explorative study are discussed in the conclusion.

PRECISION AGRICULTURE: REVIEWING THE ADVANCEMENTS, TECHNOLOGIES, AND APPLICATIONS IN PRECISION AGRICULTURE FOR IMPROVED CROP PRODUCTIVITY AND RESOURCE MANAGEMENT

Precision agriculture is a rapidly advancing field that combines advanced technologies and data analytics to improve crop productivity and resource management. This article reviews the advancements, technologies, and applications in precision agriculture. It explores the role of sensing technologies, such as remote sensing, ground-based sensors, and GPS/GIS applications, in data collection and analysis for informed decision-making. The article also examines the impact of variable rate technologies, including variable rate seeding, nutrient application, and irrigation, on optimizing input usage and improving crop performance. Furthermore, it discusses the integration of precision crop management techniques, such as remote sensing, artificial intelligence, and the Internet of Things (IoT), in enhancing farming practices. While precision agriculture offers significant benefits, challenges related to cost, accessibility, data management, and education need to be addressed. It is crucial to overcome these challenges to fully harness the potential of precision agriculture for sustainable and efficient food production. By acknowledging and addressing these challenges, farmers and stakeholders can work towards the widespread adoption of precision agriculture, leading to improved crop productivity, resource management, and environmental sustainability.

Precision Agriculture Innovation in Agriculture

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

Impact of climate change on grape berry ripening: An assessment of adaptation strategies for the Australian vineyard.

Compressed vintages, high alcohol and low wine acidity are but a few repercussions of climate change effects on Australian viticulture. While warm and cool growing regions may have different practical concerns related to climate change, they both experience altered berry and must composition and potentially reduced desirable wine characteristics and market value. Storms, drought and uncertain water supplies combined with excessive heat not only depress vine productivity through altered physiology but can have direct consequences on the fruit. Sunburn, shrivelling and altered sugar-flavour-aroma balance are becoming more prevalent while bushfires can result in smoke taint. Moreover, distorted pest and disease cycles and changes in pathogen geographical distribution have altered biotic stress dynamics that require novel management strategies. A multipronged approach to address these challenges may include alternative cultivars and rootstocks or changing geographic location. In addition, modifying and incorporating novel irrigation regimes, vine architecture and canopy manipulation, vineyard floor management, soil amendments and foliar products such as antitranspirants and other film-forming barriers are potential levers that can be used to manage the effects of climate change. The adoption of technology into the vineyard including weather, plant and soil sensors are giving viticulturists extra tools to make quick decisions, while satellite and airborne remote sensing allow the adoption of precision farming. A coherent and comprehensive approach to climate risk management, with consideration of the environment, ensures that optimum production and exceptional fruit quality is maintained. We review the preliminary findings and feasibility of these new strategies in the Australian context.

Prospects for agribusiness in Ukraine over the next 5 years

ABSTRACT All-out war prompts an urgent search for food security. Looking ahead, we also seek ways and means of sustainable development for the agricultural sector. In reviewing promising activities for farmers that can be implemented within the next 5 years, each individual approach is considered from technological, financial, and legal angles, and guidelines are developed for the agricultural enterprises of the near future. Despite the daunting challenges, agribusiness in Ukraine has great prospects through adoption of precision agriculture, zero tillage, and diverse cropping systems; high-quality products in tune with international standards, including substantially more organically grown products; digital literacy and new technical solutions; and more resilient logistics. Of course, all depend on a secure knowledge base and proper financing.

Precision Agriculture Implementation Factors and Adoption Potential: The Case Study of Polish Agriculture

Some of the current economic, social and environmental challenges could potentially be addressed by Precision Agriculture (PA) introduction. However, the pace of PA introduction is found to be slower than expected in developed, transitioning and developing countries, with the PA adoption literature is predominantly been focused on research on PA adoption in developed world. This paper addresses these shortcomings by identification and explanation of PA adoption factors and compilation of the regional ranking of PA adoption potential for 16 Polish voivodships. It contributes to the PA adoption factors’ literature by filling the gap on under-researched transitioning economies using Poland as a case-study. The key PA adoption factors were identified by Systematic Literature Review (SLR) based on the final sample of 21 papers from journals indexed in Scopus or Web of Science databases and were organized in 5 groups (socio-economic, agro-technological, financial, technological, and informational factors). These factors formed the conceptual framework for the ranking of PA adoption potential of 16 Polish voivodships based on the application of Sturgess rule. The analysis of PA adoption potential of 16 Polish voivodships shows the clear “core-periphery” divergence: i.e., well-developed metropolitan areas got the highest ranking and less developed peripheral regions were ranked low.

Adoption of digital technologies in agriculture—an inventory in a european small-scale farming region

As 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.

Learning Space-Time Crop Yield Patterns with Zigzag Persistence-Based LSTM: Toward More Reliable Digital Agriculture Insurance

More than US$ 27 billion is estimated to have been paid-out in farm support in USA alone since 1991 in response to climate change impacts on agriculture, with costs likely continuing to rise. With the wider adoption of precision agriculture - an agriculture management strategy that involves gathering, processing and analyzing temporal, spatial and individual data - in both developed and developing countries, there is an increasing opportunity to harness accumulating, shareable, big data using artificial intelligence (AI) methods, collected from weather stations, field sensor networks, Internet-of-Things devices, unmanned aerial vehicles, and earth observational satellites. This requires smart algorithms tailored to agricultural data types, integrated into digital solutions that are viable, flexible, and scalable for wide deployment for a wide variety of agricultural users and decision-makers. We discuss a novel AI approach that addresses the real-world problem of developing a viable solution for reliably, timely, and cost-effectively forecasting crop status across large agricultural regions using Earth observational information in near-real-time. Our approach is based on extracting time-conditioned topological features which characterize complex spatio-temporal dependencies between crop production regions and integrating such topological signatures into Long Short Term Memory (LSTM). We discuss utility and limitations of the resulting zigzag persistence-based LSTM (ZZTop-LSTM) as a new tool for developing more informed crop insurance rate-making and accurate tracking of changing risk exposures and vulnerabilities within insurance risk areas.

Electrochemical Sensors for Sustainable Precision Agriculture-A Review.

Greenhouse gases released by agriculture account for 19% of global greenhouse gas emission. Moreover, the abuse of pesticides and fertilizers is a fundamental cause of soil and water pollution. Finding sustainable countermeasures for these problems requires completely new approaches and the integration of knowledge. Precision agriculture (PA) is a technology that reduces environmental pollution with minimal input (e.g., fertilizer, herbicides, and pesticides) and maximize the production of high-quality crops by monitoring the conditions and environment of farmland and crops. However, the lack of data—a key technology for realizing PA—remains a major obstacle to the large-scale adoption of PA. Herein, we discuss important research issues, such as data managements and analysis for accurate decision-making, and specific data acquisition strategies. Moreover, we systematically review and discuss electrochemical sensors, including sensors that monitor the plant, soil, and environmental conditions that directly affect plant growth.

Developing precision agriculture through creating information processing capability in rural China

Previous research has suggested giving more attention to the quality of precision agricultural technology adoption. By taking the information processing view as the theoretical lens, this study investigates precision agriculture adoption cases from two Chinese agricultural organizations to examine how they achieve high-quality adoption. Interviews, secondary data collection and coding strategies were utilized to collect and analyze the data to identify the development of information processing capabilities. The research findings reveal two pathways for developing information processing capabilities through different information processing controls and networks appropriate for cooperatives (co-ops) and agricultural firms. This study contributes to the agricultural technology adoption literature by extending precision agriculture technology adoption from the adoption rate to adoption quality.