An intelligent VegeCareAI tool for next generation plant growth management

Ikeda, Makoto; Ruedeeniraman, Natwadee; Barolli, Leonard

doi:10.1016/j.iot.2021.100381

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…This phase deals with how the growth of plants can be made more sustainable through AI. The use of fertilizer (Heiß et al, 2020), pesticides (Albanese et al, 2021;Esmail Karar et al, 2021;Ikeda et al, 2021;Lin et al, 2021;Pallathadka et al, 2021;Partel et al, 2021;Shin et al, 2020;Umamageswari et al, 2021), and the detection of weeds (Espejo-Garcia et al, 2020;Hussain et al, 2021;Khan et al, 2020;Sabzi et al, 2020) are aspects that takes into account the growth phase of plants. Reducing the use of fertilizers and pesticides has a significant impact on sustainable agriculture (Kim et al, 2019).…”

Section: Stages In Plant Growthmentioning

confidence: 99%

“…The individual plants in images can thus be identified using AI-based systems and then subsequently classified into "weed" and "wanted plant" groups (Hussain et al, 2021;Khan et al, 2020;Sabzi et al, 2020). For plant protection, the captured images are examined for pest infestation (Albanese et al, 2021;Esmail Karar et al, 2021;Ikeda et al, 2021), plant diseases (Ikeda et al, 2021;Lin et al, 2021;Pallathadka et al, 2021;Shin et al, 2020;Umamageswari et al, 2021), and to distinguish plant from non-plant (e.g., human, field constructions) (Partel et al, 2021). Based on the results of AI algorithms, the dosage of pesticides can be optimized, resulting in a lower environmental impact.…”

Section: Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

What is Community?: Informing the Design of a Community Building Platform for Low-Income Black and Latino Residents

Mcleod,

Gonzales,

King

et al. 2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Global warming and the increasing food demand are problems of the current generation and require a change towards sustainable agriculture. In recent years, research in the field of artificial intelligence has made considerable progress. Thus, the use of artificial intelligence in agriculture can be a promising solution to ensure sufficient food supply on a global scale. To investigate the state-of-the-art in the use of artificial intelligence-based systems in agriculture, we provide a structured literature review. We show that research has been done in the field of irrigation and plant growth. In this regard, camera systems often provide images as training/input data for artificial intelligence-based systems. Finally, we provide a research agenda to pave the way for further research on the use of artificial intelligence in sustainable agriculture.

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Section: Stages In Plant Growthmentioning

confidence: 99%

Section: Technologiesmentioning

confidence: 99%

What is Community?: Informing the Design of a Community Building Platform for Low-Income Black and Latino Residents

Mcleod,

Gonzales,

King

et al. 2024

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

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Unleashing the potential of IoT, Artificial Intelligence, and UAVs in contemporary agriculture: A comprehensive review

Alaoui,

Amraoui,

Masmoudi

et al. 2024

Journal of Terramechanics

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Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

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PurposeThis study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.Design/methodology/approachThis study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.FindingsSix clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.Originality/valueThis study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

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An intelligent VegeCareAI tool for next generation plant growth management

Cited by 3 publications

References 13 publications

What is Community?: Informing the Design of a Community Building Platform for Low-Income Black and Latino Residents

What is Community?: Informing the Design of a Community Building Platform for Low-Income Black and Latino Residents

Unleashing the potential of IoT, Artificial Intelligence, and UAVs in contemporary agriculture: A comprehensive review

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

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