Technology assisted farming: Implications of IoT and AI

Aggarwal, Neil Krishan; Singh, Dinesh

doi:10.1088/1757-899x/1022/1/012080

Cited by 29 publications

(15 citation statements)

References 6 publications

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“…On the other hand, Zhang et al (2021b) emphasized on informatics and material science to find sustainable solutions in sustainable agriculture by using nanotechnology and AI [ 29 ]. Aggarwal & Singh (2021) asserted on technology assistance in precision farming and discussed the implications of AI and internet of things (IoT) in agriculture to assess water requirements, humidity, need for fertilizers etc [ 30 ]. Spanaki et al (2021) addressed the issues concerning food security and proposed an AI technique as a solution by adopting a design science methodology [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

2022

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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 multi-disciplinary 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.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

2022

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“…This is because of the diversity of articles and the range of disciplines analysed. For example, some of the papers were in social science journals, but there were also some in engineering and computer science journals (Aggarwal and Singh 2021;Albiero 2019;Krishnan and Swarna 2020;Marinoudi et al 2019;Shamshiri et al 2018). Not surprisingly, the articles by social scientists and ethicists had the greatest emphasis on social and ethical themes, which can be illustrated in Table 2 (Gardezi and Stock 2021;Rose et al 2021;Ryan 2020Ryan , 2019Sparrow and Howard 2020;Stock and Gardezi 2021).…”

Section: Methodsmentioning

confidence: 98%

“…Carolan (2020) noted that because of the historically low numbers of women in STEM and farming, the increased integration of technologizing the farm, may further exacerbate the number of women in agriculture. The challenge is to ensure that everyone will benefit from AI in a fair and inclusive way (Aggarwal and Singh 2021).…”

Section: Justice and Fairnessmentioning

confidence: 99%

The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature

Ryan

2022

AI & Soc

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This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, through a thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed.

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“…In cases where technology and services providers have access to data from a large number of farms and can control the AI systems, this power can be misused to control the market, share information with third parties, and override farmers rights to their own assets (Ryan, 2020). With proper adoption of fariness principle, all members in the community will benefit from AI tools fairly and inclusively (Aggarwal and Singh, 2021).…”

Section: Ethical Framework For Assessment Of Artificial Intelligence-...mentioning

confidence: 99%

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Dara

Fard

Kaur

2022

Front. Artif. Intell.

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Artificial intelligence (AI) applications are an integral and emerging component of digital agriculture. AI can help ensure sustainable production in agriculture by enhancing agricultural operations and decision-making. Recommendations about soil condition and pesticides or automatic devices for milking and apple picking are examples of AI applications in digital agriculture. Although AI offers many benefits in farming, AI systems may raise ethical issues and risks that should be assessed and proactively managed. Poor design and configuration of intelligent systems may impose harm and unintended consequences on digital agriculture. Invasion of farmers' privacy, damaging animal welfare due to robotic technologies, and lack of accountability for issues resulting from the use of AI tools are only some examples of ethical challenges in digital agriculture. This paper examines the ethical challenges of the use of AI in agriculture in six categories including fairness, transparency, accountability, sustainability, privacy, and robustness. This study further provides recommendations for agriculture technology providers (ATPs) and policymakers on how to proactively mitigate ethical issues that may arise from the use of AI in farming. These recommendations cover a wide range of ethical considerations, such as addressing farmers' privacy concerns, ensuring reliable AI performance, enhancing sustainability in AI systems, and reducing AI bias.

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Technology assisted farming: Implications of IoT and AI

Cited by 29 publications

References 6 publications

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

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