A survey on smart farming data, applications and techniques

Innovation of agricultural economic management in the process of constructing smart agriculture by big data

The role of emerging technologies has gained huge attention in recent years. With the advancements in sensor technologies, internet of things (IoT), and remote sensing, large amounts of data are being generated in agriculture. However, making sense of this data and extracting valuable information offers major/ important challenges. Data visualization techniques offer a solution by providing intuitive and interactive ways to represent complex agricultural data. Furthermore, big data analytics techniques enable the extraction of valuable information and patterns from large datasets, contributing to informed decision-making in smart agriculture. This chapter discusses the importance/benefits of data visualization and big data analytics in smart agriculture. It discusses various data sources in agriculture, including sensor technologies, IoT, and remote sensing. This work also provides an overview of the tools and technologies available for data visualization in smart agriculture, including data visualization software, etc., in detail.

This paper discusses the role of technology and big data in the agri-food industry. With the world population growing rapidly, there is a need to produce more food to feed everyone. Agriculture is changing rapidly, and technology is playing a crucial role in this change. The concept of Agriculture 4.0 seeks to optimize every factor to minimize inputs and maximize efficiency. Big data is being collected to improve management and farm management. Precision farming is being used to predict crop development phases and yields. The European Union is lagging behind the US and China in terms of innovation and market evaluation. Innovation drives economic growth, and it goes beyond invention. Small and medium-sized enterprises are playing an essential role in driving economic growth, particularly in the agri-food business. To improve resilience and production efficiency in the agri-food business, there is a need to boost SMEs' competitiveness. The European agri-food business is lagging in terms of digitalization, but there is a significant potential for growth. Finally, technology and big data are playing a crucial role in the agri-food industry, and there is a need to evaluate contextual elements to improve farmers' technology use.

This paper discusses a crucial component of contemporary agriculture: using Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) to monitor and reduce carbon emissions from agriculture. We concentrate on how these cutting-edge technologies augment Climate-Smart Agriculture (CSA) and advance sustainable farming methods. A thorough analysis of the ways in which IoT, Big Data, and AI can be integrated to track carbon footprints and promote more general sustainability goals in agriculture is given in this research. As a significant contribution, we provide a practical, all-encompassing system architecture that integrates real-time data analytics, predictive modeling, and IoT-enabled sensors created especially for carbon footprint assessment. This paper assesses how well these technologies work to enhance emission monitoring, operational effectiveness, and environmental compliance while highlighting their observable advantages through real-world case studies. The rigorous analysis of issues including data interoperability, device energy efficiency, and implementation costs also sheds light on current research gaps. The report also outlines potential future paths, such as blockchain-based carbon credit trading platforms, scalable IoT-based carbon markets, and machine learning (ML) algorithms for precision farming. The goal is to offer helpful guidance on how to apply cutting-edge technology in the agricultural sector to achieve carbon neutrality and environmental sustainability.

Advances in Smart Farming and Big Data applications have the potential to help agricultural industries meet productivity and sustainability challenges. However, these benefits are unlikely to be realised if the social implications of these technological innovations are not adequately considered by those who promote them. Big Data applications are intrinsically socio-technical; their development and deployment are a product of social interactions between people, institutional and regulatory settings, as well as the technology itself. This paper explores the socio-technical factors and conditions that influence the development of Smart Farming and Big Data applications, using a multi-level perspective on transitions combined with social practice theory. We conducted semi-structured interviews with 26 Australian grain farmers and industry stakeholders to elicit their perspectives on benefits and risks of these changes. The analysis shows that issues related to trust are central concerns for many participants. These include procedural concerns about transparency and distributional concerns about who will benefit from access to and use of “farmers’ data”. These concerns create scepticism about the value of ‘smart’ technologies amongst some industry stakeholders, especially farmers. It also points to a divergence of expectations and norms between actors and institutions at the regime and niche levels in the emerging transition towards Smart Farming. Bridging this divide will require niche level interventions to enhance the agency of farmers and their local networks in these transactions, and, the cooperative design of new institutions at regime level to facilitate the fair and transparent allocation of risk and benefit in farming data information chains.

With the continuous advancement of digital informationization, the agricultural field has naturally entered the era of big data. At present, big data technology has achieved remarkable results in the Internet industry, but it is still in its infancy in the agricultural field. Due to the complexity of the agricultural field itself and the obvious diversity and heterogeneity of data, the agricultural Internet of Things in the big data environment is facing many difficulties and challenges, especially the collection and storage of massive data. The purpose of this article is to study the application of agricultural big data. This article briefly introduces the background of the development of agricultural big data and the significance of developing agricultural big data, and summarizes the meaning of big data. At the same time, this article summarizes and analyzes the major applications of agricultural big data in production process management, agricultural resource management, and agricultural ecology environmental management, agricultural products and food safety management. In the experimental part, this paper designed and implemented an agricultural big data system platform based on Hadoop. This article simulated the query of 10, 100 and 1000 records respectively for 10,000, 100,000, 1 million and 10 million records, and the average response time was 56ms. The experimental results show that the performance of the agricultural big data platform designed in this paper can meet the needs.

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

Precision agriculture (PA) is an approach that uses information technology (IT) to ensure agricultural production and increases profitability. The Internet of Things (IoT) has important applications and the potential to be used in agriculture. For instance, it can manage soil, water, and weather information for the farmers. Moreover, it can be used to increase the productivity of crops, farmers looking for real-time data, and strive to ensure enough agricultural inputs to meet the crop requirements while taking advantage of the data already present on the farm. The proposed study will enable farmers to obtain necessary information about their land and, hence, decide how to manage the limited water resources, soil nutrients, and weather forecasting on their farms. Artificial intelligence and machine learning techniques will be used to learn from the historical data and show any pattern that might increase the farm’s efficiency and productivity. To ensure the benefit of smart farming, it is of utmost importance to commit field-specific research, demonstrate trials, and bring in governmental policies.The study refers to the challenges of using IoT in smart farms, such as strong internet connection without interruption and provide fast transmission speeds. However, Uganda has already taken advantage of IoT technology, applied it widely, and has rapidly strengthened the capability. This system presents obvious benefits to agriculture and can reduce costs and increase the yield up to 50% because Smart Farms is the leading solution for increasing agricultural production.

Risk monitoring model of intelligent agriculture Internet of Things based on big data

Because of continuous innovation and the great importance placed on automation, digital information has become an increasingly valuable driver transitioning all domains. The fundamental focus of this chapter stems from the dynamic interaction between information progression and trending technologies, as well as the growth of real-world concerns, which has been speeding the progress of the digital economy. Those who can acquire, keep, transform, and use data are in control. As a result, intelligence gained through data analysis and learning improves our understanding via computational intelligence, big data analytics and Internet of things. This chapter provides a systematic literature review on computational intelligence, big data analytics, and the Internet of things, focusing on the problems and opportunities presented by domains such as healthcare, smart farming, industry 4.0/5.0, smart cities.

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.

This paper presents a comprehensive review of emerging technologies for the internet of things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories: including smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the aforementioned technologies, which demonstrate their great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs.

Nowadays, extensive research is going on internet of things (IoT) in agriculture field to enhance production. IoT systems use vast sensors to collect data and investigate different conditions such as soil quality, drip irrigation, seeds and crop quality, pest and insect detection etc. which is called smart agriculture. Machine learning and deep learning techniques facilitate to apply analytics over collected sensor data streams to find out new information and anticipate future insights. Whenever working with huge data, optimization methods become very important to design algorithms for extracting essential information from the huge amount of data for obtaining better predictive results. Various bio-inspired optimization algorithms used in machine learning and deep learning techniques to analyze the sensor data and to predict future issues in the field of agriculture are discussed in this chapter. This chapter also motivates and gives guidelines to the readers to apply bio-inspired optimization algorithms in machine learning to solve various optimization problems in smart agriculture.

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

The Internet of Things (IoT) is an innovative technology with billions of sensors in various IoT applications. Important elements used in the IoT are sensors that collect data for desired analyses. The IoT and sensors are very important in smart cities, smart agriculture, smart education, healthcare systems, and other applications. The healthcare system uses the IoT to meet global health challenges, and the newest example is COVID-19. Demand has increased during COVID-19 for healthcare to reach patients remotely and digitally at their homes. The IoT properly monitors patients using an interconnected network to overcome the issues of healthcare services. The aim of this paper is to discuss different applications, technologies, and challenges related to the healthcare system. Different databases were searched using keywords in Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, Springer, etc. This paper discusses, highlights, and identifies the applications of IoT healthcare systems to provide research directions to healthcare, academia, and researchers to overcome healthcare system challenges. Hence, the IoT can be beneficial by providing better treatments using the healthcare system efficiently. In this paper, the integration of the IoT with smart technologies not only improves computation, but will also allow the IoT to be pervasive, profitable, and available anytime and anywhere. Finally, some future directions and challenges are discussed, along with useful suggestions that can assist the IoT healthcare system during COVID-19 and in a severe pandemic.