Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts
In the context of greenhouse agriculture, the integration of Artificial Intelligence (AI) is evaluated for its potential to enhance sustainability and crop production efficiency. This study reanalyzes publicly available datasets, using advanced time series analysis and noise reduction techniques thr...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-10-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259017452400179X |
| _version_ | 1826929166970257408 |
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| author | Siamak Hoseinzadeh Davide Astiaso Garcia |
| author_facet | Siamak Hoseinzadeh Davide Astiaso Garcia |
| author_sort | Siamak Hoseinzadeh |
| collection | DOAJ |
| description | In the context of greenhouse agriculture, the integration of Artificial Intelligence (AI) is evaluated for its potential to enhance sustainability and crop production efficiency. This study reanalyzes publicly available datasets, using advanced time series analysis and noise reduction techniques through seasonality detection and removal. This novel approach reveals trends more clearly, providing a detailed comparison between AI-driven methods and traditional agricultural practices. An extensive review of literature on AI applications in agriculture is conducted to establish a broad understanding of its current state and future prospects. The core focus is the Autonomous Greenhouses Challenge, an initiative where research teams apply AI technologies in real-world greenhouse settings. This challenge offers crucial data for a thorough assessment of AI’s practical impact. The analysis reveals that AI significantly reduces heating energy consumption, indicating a notable improvement in energy efficiency. However, reductions in CO2 emissions, along with improvements in electricity and water usage, are only marginal when compared to traditional farming methods. Similarly, enhancements in crop quality and profitability achieved through AI are found to be on par with conventional techniques. These findings highlight the dual nature of AI’s impact in greenhouse agriculture: it shows significant promise in some areas, while its effectiveness in other key sustainability aspects remains limited. The study emphasizes the need for further research and investment in technological advancements, as well as the importance of a robust data infrastructure. It also highlights the necessity of education and training in AI technologies for effective implementation in the agricultural sector. The results of this research aim to inform policymakers, researchers, and industry stakeholders about the mixed impacts of AI on sustainable greenhouse farming. By offering a comprehensive evaluation of the benefits and challenges of AI integration, this study contributes to the ongoing discussion on sustainable agricultural practices and provides insights into the future direction of AI in this field. |
| first_indexed | 2025-02-17T16:01:46Z |
| format | Article |
| id | doaj.art-fa6b2aa1d4364ae2ab65ea5c11aafc35 |
| institution | Directory Open Access Journal |
| issn | 2590-1745 |
| language | English |
| last_indexed | 2025-02-17T16:01:46Z |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj.art-fa6b2aa1d4364ae2ab65ea5c11aafc352024-12-18T08:51:19ZengElsevierEnergy Conversion and Management: X2590-17452024-10-0124100701Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impactsSiamak Hoseinzadeh0Davide Astiaso Garcia1Corresponding authors.; Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Via Flaminia 72, 00196 Rome, ItalyCorresponding authors.; Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Via Flaminia 72, 00196 Rome, ItalyIn the context of greenhouse agriculture, the integration of Artificial Intelligence (AI) is evaluated for its potential to enhance sustainability and crop production efficiency. This study reanalyzes publicly available datasets, using advanced time series analysis and noise reduction techniques through seasonality detection and removal. This novel approach reveals trends more clearly, providing a detailed comparison between AI-driven methods and traditional agricultural practices. An extensive review of literature on AI applications in agriculture is conducted to establish a broad understanding of its current state and future prospects. The core focus is the Autonomous Greenhouses Challenge, an initiative where research teams apply AI technologies in real-world greenhouse settings. This challenge offers crucial data for a thorough assessment of AI’s practical impact. The analysis reveals that AI significantly reduces heating energy consumption, indicating a notable improvement in energy efficiency. However, reductions in CO2 emissions, along with improvements in electricity and water usage, are only marginal when compared to traditional farming methods. Similarly, enhancements in crop quality and profitability achieved through AI are found to be on par with conventional techniques. These findings highlight the dual nature of AI’s impact in greenhouse agriculture: it shows significant promise in some areas, while its effectiveness in other key sustainability aspects remains limited. The study emphasizes the need for further research and investment in technological advancements, as well as the importance of a robust data infrastructure. It also highlights the necessity of education and training in AI technologies for effective implementation in the agricultural sector. The results of this research aim to inform policymakers, researchers, and industry stakeholders about the mixed impacts of AI on sustainable greenhouse farming. By offering a comprehensive evaluation of the benefits and challenges of AI integration, this study contributes to the ongoing discussion on sustainable agricultural practices and provides insights into the future direction of AI in this field.http://www.sciencedirect.com/science/article/pii/S259017452400179XArtificial intelligenceCO2 emissionsEnergy efficiencyHeating energy consumptionGreenhouseAgriculture |
| spellingShingle | Siamak Hoseinzadeh Davide Astiaso Garcia Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts Energy Conversion and Management: X Artificial intelligence CO2 emissions Energy efficiency Heating energy consumption Greenhouse Agriculture |
| title | Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts |
| title_full | Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts |
| title_fullStr | Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts |
| title_full_unstemmed | Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts |
| title_short | Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts |
| title_sort | ai driven innovations in greenhouse agriculture reanalysis of sustainability and energy efficiency impacts |
| topic | Artificial intelligence CO2 emissions Energy efficiency Heating energy consumption Greenhouse Agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S259017452400179X |
| work_keys_str_mv | AT siamakhoseinzadeh aidriveninnovationsingreenhouseagriculturereanalysisofsustainabilityandenergyefficiencyimpacts AT davideastiasogarcia aidriveninnovationsingreenhouseagriculturereanalysisofsustainabilityandenergyefficiencyimpacts |