Scientometric Analysis of the Application of Artificial Intelligence in Agriculture (original) (raw)
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Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis
PLOS ONE, 2022
There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000-2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000-2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI's role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multidisciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities.
International Journal of Electrical and Computer Engineering (IJECE), 2024
Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field's transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.
Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review
IEEE Access
Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.
The Digital Agricultural Revolution: a Bibliometric Analysis Literature Review
IEEE Access
The application of digital technologies in agriculture can improve traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG) emissions, and promote a sustainable intensification for food security. Some authors argued that we are experiencing a Digital Agricultural Revolution (DAR) that will boost sustainable farming. This study aims to find evidence of the ongoing DAR process and clarify its roots, what it means, and where it is heading. We investigated the scientific literature with bibliometric analysis tools to produce an objective and reproducible literature review. We retrieved 4995 articles by querying the Web of Science database in the timespan 2012-2019, and we analyzed the obtained dataset to answer three specific research questions: i) what is the spectrum of the DAR-related terminology?; ii) what are the key articles and the most influential journals, institutions, and countries?; iii) what are the main research streams and the emerging topics? By grouping the authors' keywords reported on publications, we identified five main research streams: Climate-Smart Agriculture (CSA), Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT), and Artificial Intelligence (AI). To provide a broad overview of each of these topics, we analyzed relevant review articles, and we present here the main achievements and the ongoing challenges. Finally, we showed the trending topics of the last three years (2017, 2018, 2019). INDEX TERMS Agriculture 4.0, bibliometrics, climate-smart agriculture, digital agriculture, literature review, precision agriculture.
Influence of Artificial Intelligence on Agriculture
National Seminar on National Development through Science and Technology, 2017
Agriculture is facing new major challenges nowadays. The world population is expected to reach 9.7 billion by 2050. China and India, the two largest countries in the world, have populations totaling around 2.7 billion. In four years, by 2022, India is predicted to have the largest population in the world, surpassing China. This means we need new ways to grow food that are smarter and helps regulate our use of land, water and energy in order to feed the planet and avoid a global food crisis. The rise of digital agriculture and its related technologies has released a treasure of incredible opportunities. Due to the recent advances in remote sensors, robotics, satellites, and Unmanned Ariel Vehicles (UAV), it is possible to gather information over an entire field regularly. The application of Artificial Intelligence (AI), Expert Systems and Data mining to this information can help in monitoring plant health, soil condition, temperature, humidity, etc. AI has already influenced the agricultural industry in different ways and research is going on to improve agricultural crop yield prediction, understanding of relationship of climate and other factors on crop production using AI. So, there is gigantic potential for AI and machine learning to revolutionize agriculture by incorporating these technologies to meet the upcoming challenges. This study outlines the potential benefits of artificial intelligence (AI) that has given impetus to the establishment of AI research with regard to solving problems facing agriculture.
Special Issue on Artificial Intelligence in Agriculture
KI - Künstliche Intelligenz, 2013
Agriculture and AI-intuitively, these domains seem to denote two separate worlds. Agriculture processes the soil and cares about food production and elementary supply-this is really down-to-earth! AI, in contrast, is deeply interwoven with computerized systems, complex interactions, modeling and reasoning approaches, and in public perception still suffers from flair of science fiction. So why do we dedicate the present special issue to this combination? Modern Agriculture faces tremendous challenges. Today, the agricultural sector has grown into a highly competitive and globalized industry, where farmers and other actors have to consider local climatic and geographic aspects as well as global ecological and political factors in order to guarantee economical survival and sustainable production. Feeding a growing world population asks for continuous increases in food production, but arable land remains a limited resource. New requests for bio energy or changing diet preferences put additional strains on agricultural production, while settlement and transport consume increasing shares of land. Expected and observable changes in global climate, shifting rainfall patterns, global warming, droughts, or the increasing frequency and duration of extreme weather events endanger traditional production areas and bring new risks and uncertainties for global harvest yields. To cope with these challenges, Agriculture requires a continuous and sustainable increase in productivity and efficiency on all levels of agricultural production, while resources like water, energy, fertilizers etc. need to be used carefully and efficiently in order to protect and sustain the environment and the soil quality of the arable land. The complexity of the challenge is A. Dengel (B) DFKI GmbH,
Understanding the potential applications of Artificial Intelligence in Agriculture Sector
Artificial Intelligence (AI) has been extensively applied in farming recently. To cultivate healthier crops, manage pests, monitor soil and growing conditions, analyse data for farmers, and enhance other management activities of the food supply chain, the agriculture sector is turning to AI technology. It makes it challenging for farmers to choose the ideal time to plant seeds. AI helps farmers choose the optimum seed for a particular weather scenario. It also offers data on weather forecasts. AI-powered solutions will help farmers produce more with fewer resources, increase crop quality, and hasten product time to reach the market. AI aids in understanding soil qualities. AI helps farmers by suggesting the nutrients they should apply to increase the quality of the soil. AI can help farmers choose the optimal time to plant their seeds. Intelligent equipment can calculate the spacing between seeds and the maximum planting depth. An AI-powered system known as a health monitoring system provides farmers with information on the health of their crops and the nutrients that need to be given to enhance yield quality and quantity. This study identifies and analyses relevant articles on AI for Agriculture. Using AI, farmers can now access advanced data and analytics tools that will foster better farming, improve efficiencies, and reduce waste in biofuel and food production while minimising the negative environmental impacts. AI and Machine Learning (ML) have transformed various industries, and the AI wave has now reached the agriculture sector. Companies are developing several technologies to make monitoring farmers' crop and soil health easier. Hyperspectral imaging and 3D laser scanning are the leading AI-based technologies that can help ensure crop health. These AI-powered technologies collect precise data on the health of the crops in greater volume for analysis. This paper studied AI and its need in Agriculture. The process of AI in Agriculture and some Agriculture parameters monitored by AI are briefed. Finally, we identified and discussed the significant applications of AI in agriculture.
Precision Agriculture under a bibliometric view
International Journal for Innovation Education and Research, 2021
Precision Agriculture comprises techniques to monitor and control the differentiated application of agricultural inputs, considering the variability of cultivation areas over time to increase productivity and maintain environmental sustainability. Its current form considers the use of high-tech equipment to ensure food safety in the future and, therefore, constantly seeks research that produces innovations for the sector. However, there is a tremendous challenge in evaluating scientific development, given the large volume of information. This study aimed to carry out a scientific mapping of Precision Agriculture from a set of bibliometric techniques supported using the R bibliometrix tool. Based on this objective, the research questions were formulated and answered throughout qualitative quantitative and descriptive exploratory study. The data processing resulted 5,807 articles (13,705 authors) obtained from 1993 to 2020. Among the main results, there is constant growth in the numbe...
Will artificial intelligence and machine learning change agriculture: A special issue
Agronomy Journal, 2024
In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting-edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning.