Understanding the potential applications of Artificial Intelligence in Agriculture Sector

Advanced Agrochem, Journal Year: 2022, Volume and Issue: 2(1), P. 15 - 30

Published: Oct. 28, 2022

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, enhance other management activities of the food supply chain, agriculture sector is turning to AI technology. It makes it challenging farmers choose ideal time plant seeds. helps optimum seed a particular weather scenario. also offers on forecasts. AI-powered solutions will help produce more with fewer resources, increase crop quality, hasten product reach market. aids understanding qualities. by suggesting nutrients they should apply quality soil. can optimal their Intelligent equipment calculate spacing between seeds maximum planting depth. An system known as health monitoring provides information crops that need be given yield quantity. This study identifies analyses relevant articles Agriculture. Using AI, now access advanced analytics tools foster better farming, improve efficiencies, reduce waste biofuel production while minimising negative environmental impacts. Machine Learning (ML) have transformed various industries, wave reached sector. Companies are developing several technologies make farmers' easier. Hyperspectral imaging 3D laser scanning leading AI-based ensure health. These collect precise greater volume analysis. paper studied its The process Agriculture some parameters monitored briefed. Finally, we identified discussed significant applications agriculture.

Language: Английский

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Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 96, P. 104653 - 104653

Published: May 15, 2023

Climate change and rapid urbanisation exacerbated multiple urban issues threatening sustainability. Numerous studies integrated machine learning remote sensing to monitor develop mitigation strategies for However, few comparatively analysed joint applications of This paper presents a systematic review formulates framework integrating in studies. The literature analysis reveals: Most occurred Asia, Europe, North America, driven by technical ethical factors, highlighting responsible approaches data-scarce regions; Reviewed prioritised physical spatial aspects over socioeconomic requiring multi-source data comprehensive analysis; Conventional satellite, aerial images, Lidar are prevalent due affordability, quality, accessibility; Although supervised dominates, unsupervised methods algorithm selection paradigms require exploration; Integration offers accurate results thorough image processing analytics, while acquisition decision-making necessitate human supervision. provides an integrative sensing, enriching insights into their potential analytics. study informs planning policymaking promoting efficient management via enhanced integration, bolstering data-driven decision-making.

Language: Английский

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Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction

Published: Aug. 8, 2023

Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences they build. It also supports prospect incorporating controls help minimize that emerge interplay technologies and social cultural dynamics. A growing body scholarship has identified a wide range across different technologies. However, computing research lack high level synthesized overview systems. Based on scoping review (n=172), we present an applied taxonomy sociotechnical support more systematic surfacing in The final builds refers existing taxonomies, classifications, terminologies. Five major themes related — representational, allocative, quality-of-service, interpersonal harms, system/societal sub-themes are presented along with description these categories. We conclude discussion challenges opportunities for future research.

Language: Английский

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Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 10482 - 10482

Published: July 3, 2023

Food waste is a global issue with significant economic, social, and environmental impacts. Addressing this problem requires multifaceted approach; one promising avenue using artificial intelligence (AI) technologies. This article explores the potential for AI to tackle food enhance circular economy discusses current state of economy, highlighting specific ways that can be used monitor optimise production supply chains, redistribute excess those in need, support initiatives. As result, we maximise resource efficiency minimise impact these applications, ultimately creating more sustainable equitable system.

Language: Английский

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Perceived risks and vulnerabilities of employing digitalization and digital data in agriculture – Socially robust orientations from a transdisciplinary process

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 358, P. 132034 - 132034

Published: April 29, 2022

The digitalization of agricultural production and the use digital data are fundamentally transforming processes, products, services agro-food systems. Digitalization improves efficiency facilitates sophisticated farm management, thus increasing productivity, efficacy, profitability. At same time, it promises many opportunities for a more sustainable and, especially, ecological cleaner production. However, with comes potential number unintended side effects risks that may increase vulnerability have, far, received scant scientific societal attention. This article presents results two-year transdisciplinary process aimed to identify side-effects (short "unseens") perceived in German agriculture. Results base on triangulation knowledge integration from group involving twelve representatives science practice an ethnographic qualitative meta-analysis. findings have shown that, despite digitalization's numerous resource efficient production, broad range was by some key stakeholders involved. These were anticipated be caused negative uncertain agro-ecological social Data rights, restructuring value chain new market concentrations, power structures dependencies, changing requirements farmers (lacking "digital literacy"), information asymmetries cause potentially food security identified as causal factors. Based these results, we co-developed socially robust orientations (SoROs) coping resulting vulnerabilities. We argue SoROs provide perspectives how can turned into responsible action within RRI (responsible research innovation) framework. Finally, regard preventive anticipatory paradigm "cleaner production", our methodology shows way adaptively govern highly complex socio-technological transitions agriculture sense sustainability-oriented transformation.

Language: Английский

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Digital innovations for sustainable and resilient agricultural systems

European Review of Agricultural Economics, Journal Year: 2023, Volume and Issue: 50(4), P. 1277 - 1309

Published: June 27, 2023

Abstract Digitalisation is rapidly transforming the agri-food sector. This paper investigates emerging opportunities, challenges and policy options. We show that digital innovations can contribute to more sustainable resilient agricultural systems. For example, enable increased productivity, reduced environmental footprints higher resilience of farms. However, these optimistic outcomes increasing digitalisation sector will not emerge on their own, but this development comes with several challenges, costs risks, e.g. in economic, social ethical dimensions. provide recommendations explore opportunities avoid risks. Moreover, we discuss implications for future research economics.

Language: Английский

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Sustainable AI: An integrated model to guide public sector decision-making

Technology in Society, Journal Year: 2022, Volume and Issue: 68, P. 101926 - 101926

Published: Feb. 1, 2022

Ethics, explainability, responsibility, and accountability are important concepts for questioning the societal impacts of artificial intelligence machine learning (AI), but insufficient to guide public sector in regulating implementing AI. Recent frameworks AI governance help operationalize these by identifying processes layers which they must be considered, do not provide workers with guidance on how should pursued or understood. This analysis explores concept sustainable can fill this gap. It does so reviewing has been used research community aligning development Doing identifies utility boundary conditions that have asserted social sustainability according Framework Strategic Sustainable Development, here integrated prominent from discourse society. results a conceptual model integrates five assist decision-making about govern AI: Diversity, Capacity learning, self-organization Common meaning, Trust. These presented together practical approaches their presentation, guiding questions aid making decisions required other operational ethical

Language: Английский

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Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research

Technology in Society, Journal Year: 2023, Volume and Issue: 74, P. 102321 - 102321

Published: July 5, 2023

Language: Английский

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Recent applications of AI to environmental disciplines: A review

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 906, P. 167705 - 167705

Published: Oct. 11, 2023

Language: Английский

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A critical review towards artificial general intelligence: Challenges, ethical considerations, and the path forward

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(3), P. 1262 - 1268

Published: March 17, 2024

The pursuit of Artificial General Intelligence (AGI) has captivated researchers and industry leaders alike, promising a future where machines possess human-like cognitive abilities. However, this ambitious endeavor is fraught with multifaceted challenges ethical dilemmas that necessitate careful examination. This critical review surveys the landscape AGI research, identifying key hurdles considerations while outlining potential pathways forward. Firstly, technical loom large on path to AGI. These encompass fundamental problems such as developing robust learning algorithms capable generalizing across diverse domains, well engineering systems can exhibit adaptive autonomous behavior akin human intelligence. Additionally, ensuring safety reliability presents formidable obstacle, concerns ranging from algorithmic bias for catastrophic outcomes in unanticipated scenarios. Ethical permeate every facet development deployment. Questions accountability, transparency, control surface central concerns, implications relinquishing decision-making authority raise profound dilemmas. Moreover, socio-economic ramifications widespread adoption, including job displacement inequality, demand scrutiny proactive mitigation strategies. Navigating these requires concerted effort interdisciplinary stakeholders. Collaboration between computer scientists, ethicists, policymakers, public essential establish frameworks responsible deployment fostering an inclusive dialogue prioritizes principles societal values paramount shaping augments capabilities safeguarding against risks. While holds immense promise, its realization demands holistic approach addresses alongside considerations. By charting forward safety, governance, we harness transformative alignment interests.

Language: Английский

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