AI-Driven Insights into Soil Physio-Chemical Properties

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dc.contributor.authorRani, Sen_US
dc.contributor.authorBaloda, Sen_US
dc.contributor.authorDineshen_US
dc.contributor.authorMehta, A.en_US
dc.date.accessioned2025-05-09T09:27:36Z
dc.date.available2025-05-09T09:27:36Z
dc.date.issued2024-12
dc.description.abstractAI-driven insights into soil physio-chemical properties are transforming soil management and agricultural practices by leveraging advanced data analysis and predictive modeling. Utilizing remote sensing technologies, in-situ sensors, and historical data, AI systems can efficiently collect and integrate comprehensive soil information, including moisture, pH, and nutrient levels. Machine learning algorithms analyze this data to identify patterns, predict soil behavior, and detect anomalies, enabling precise recommendations for fertilization, irrigation, and soil health management. By integrating AI with technologies such as IoT and GIS, stakeholders can optimize resource use, enhance crop yields, and implement sustainable practices. AI's ability to provide real-time insights and forecast future conditions supports proactive management strategies, risk assessment, and environmental conservation. This synergy of AI and soil science not only advances agricultural productivity but also promotes sustainable land use and soil health.en_US
dc.identifier.affiliationsDepartment of Horticulture, CCS Haryana Agricultural University, Hisar, Indiaen_US
dc.identifier.affiliationsDepartment of Horticulture, CCS Haryana Agricultural University, Hisar, Indiaen_US
dc.identifier.affiliationsDepartment of Soil Science, CCS Haryana Agricultural University, Hisar, Indiaen_US
dc.identifier.affiliationsDepartment of Horticulture, CCS Haryana Agricultural University, Hisar, India.en_US
dc.identifier.citationRani S, Baloda S, Dinesh, Mehta A.. AI-Driven Insights into Soil Physio-Chemical Properties . International Journal of Environment and Climate Change. 2024 Dec; 14(12): 834-845en_US
dc.identifier.issn2581-8627
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/244664
dc.languageenen_US
dc.publisherMs. M. B. Mondalen_US
dc.relation.issuenumber12en_US
dc.relation.volume14en_US
dc.source.urihttps://doi.org/10.9734/ijecc/2024/v14i124666en_US
dc.subjectRemote sensingen_US
dc.subjectcomprehensiveen_US
dc.subjectIoTen_US
dc.subjectrisk assessmenten_US
dc.titleAI-Driven Insights into Soil Physio-Chemical Propertiesen_US
dc.typeJournal Articleen_US
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