Individual and Synergistic Contributions of GIS, Remote Sensing, and AI in Advancing Climate-Resilient Agriculture
Agriculture faces a dual challenge in the context of climate change, serving as both a significant contributor to greenhouse gas (GHG) emissions and a sector highly vulnerable to its impacts. Addressing this requires a transition toward climate-resilient agriculture (CRA). Emerging technologies, inc...
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MDPI AG
2025-07-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/7/230 |
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| author | Cristian-Dumitru Mălinaș Florica Matei Ioana Delia Pop Tudor Sălăgean Anamaria Mălinaș |
| author_facet | Cristian-Dumitru Mălinaș Florica Matei Ioana Delia Pop Tudor Sălăgean Anamaria Mălinaș |
| author_sort | Cristian-Dumitru Mălinaș |
| collection | DOAJ |
| description | Agriculture faces a dual challenge in the context of climate change, serving as both a significant contributor to greenhouse gas (GHG) emissions and a sector highly vulnerable to its impacts. Addressing this requires a transition toward climate-resilient agriculture (CRA). Emerging technologies, including geospatial tools (e.g., Geographic Information Systems (GISs) and remote sensing (RS)), as well as artificial intelligence (AI), offer promising methods to support this transition. However, their individual capabilities, limitations, and appropriate applications are not always well understood or clearly delineated in the literature. A common issue is the frequent overlap between GISs and RS, with many studies assessing GIS contributions while concurrently employing RS techniques, without explicitly distinguishing between the two (or vice versa). In this sense, the objective of this review is to conduct a critical analysis of the existing state of the art in terms of the distinct roles, limitations, and complementarities of GISs, RS, and AI in advancing CRA, guided by an original definition we propose for CRA (structured around three key dimensions and their corresponding targets). Furthermore, this review introduces a synthesis matrix that integrates both the individual contributions and the synergistic potential of these technologies. This synergy-focused matrix offers not just a summary, but a practical decision support matrix that could be used by researchers, practitioners, and policymakers in selecting the most appropriate technological configuration for their objectives in CRA-related work. Such support is increasingly needed, especially considering that RS and AI have experienced exponential growth in the past five years, while GISs, despite being the more established “big brother” among these technologies, remain underutilized and is often insufficiently understood in agricultural applications. |
| format | Article |
| id | doaj-art-a615a57429fa4ed88c0de59938d3f06a |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-a615a57429fa4ed88c0de59938d3f06a2025-08-20T03:36:15ZengMDPI AGAgriEngineering2624-74022025-07-017723010.3390/agriengineering7070230Individual and Synergistic Contributions of GIS, Remote Sensing, and AI in Advancing Climate-Resilient AgricultureCristian-Dumitru Mălinaș0Florica Matei1Ioana Delia Pop2Tudor Sălăgean3Anamaria Mălinaș4Department of Land Measurements and Exact Sciences, Faculty of Forestry and Cadastre, University of Agricultural Sciences and Veterinary Medicine, Calea Mănăştur 3–5, 400372 Cluj-Napoca, RomaniaDepartment of Land Measurements and Exact Sciences, Faculty of Forestry and Cadastre, University of Agricultural Sciences and Veterinary Medicine, Calea Mănăştur 3–5, 400372 Cluj-Napoca, RomaniaDepartment of Land Measurements and Exact Sciences, Faculty of Forestry and Cadastre, University of Agricultural Sciences and Veterinary Medicine, Calea Mănăştur 3–5, 400372 Cluj-Napoca, RomaniaDepartment of Land Measurements and Exact Sciences, Faculty of Forestry and Cadastre, University of Agricultural Sciences and Veterinary Medicine, Calea Mănăştur 3–5, 400372 Cluj-Napoca, RomaniaDepartment of Environmental Protection and Engineering, Faculty of Agriculture, University of Agricultural Sciences and Veterinary Medicine, Calea Mănăştur 3–5, 400372 Cluj-Napoca, RomaniaAgriculture faces a dual challenge in the context of climate change, serving as both a significant contributor to greenhouse gas (GHG) emissions and a sector highly vulnerable to its impacts. Addressing this requires a transition toward climate-resilient agriculture (CRA). Emerging technologies, including geospatial tools (e.g., Geographic Information Systems (GISs) and remote sensing (RS)), as well as artificial intelligence (AI), offer promising methods to support this transition. However, their individual capabilities, limitations, and appropriate applications are not always well understood or clearly delineated in the literature. A common issue is the frequent overlap between GISs and RS, with many studies assessing GIS contributions while concurrently employing RS techniques, without explicitly distinguishing between the two (or vice versa). In this sense, the objective of this review is to conduct a critical analysis of the existing state of the art in terms of the distinct roles, limitations, and complementarities of GISs, RS, and AI in advancing CRA, guided by an original definition we propose for CRA (structured around three key dimensions and their corresponding targets). Furthermore, this review introduces a synthesis matrix that integrates both the individual contributions and the synergistic potential of these technologies. This synergy-focused matrix offers not just a summary, but a practical decision support matrix that could be used by researchers, practitioners, and policymakers in selecting the most appropriate technological configuration for their objectives in CRA-related work. Such support is increasingly needed, especially considering that RS and AI have experienced exponential growth in the past five years, while GISs, despite being the more established “big brother” among these technologies, remain underutilized and is often insufficiently understood in agricultural applications.https://www.mdpi.com/2624-7402/7/7/230climate changesustainable agricultureGeographic Information Systemsremote sensing |
| spellingShingle | Cristian-Dumitru Mălinaș Florica Matei Ioana Delia Pop Tudor Sălăgean Anamaria Mălinaș Individual and Synergistic Contributions of GIS, Remote Sensing, and AI in Advancing Climate-Resilient Agriculture AgriEngineering climate change sustainable agriculture Geographic Information Systems remote sensing |
| title | Individual and Synergistic Contributions of GIS, Remote Sensing, and AI in Advancing Climate-Resilient Agriculture |
| title_full | Individual and Synergistic Contributions of GIS, Remote Sensing, and AI in Advancing Climate-Resilient Agriculture |
| title_fullStr | Individual and Synergistic Contributions of GIS, Remote Sensing, and AI in Advancing Climate-Resilient Agriculture |
| title_full_unstemmed | Individual and Synergistic Contributions of GIS, Remote Sensing, and AI in Advancing Climate-Resilient Agriculture |
| title_short | Individual and Synergistic Contributions of GIS, Remote Sensing, and AI in Advancing Climate-Resilient Agriculture |
| title_sort | individual and synergistic contributions of gis remote sensing and ai in advancing climate resilient agriculture |
| topic | climate change sustainable agriculture Geographic Information Systems remote sensing |
| url | https://www.mdpi.com/2624-7402/7/7/230 |
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