AI4Biochar: applying AI-driven field boundary recognition to the biochar sector

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dc.coverageCambodia
dc.coverageViet Nam
dc.creatorConchedda, G.; Flammini, A.; Colangeli, M.; Testa, L.; Casse, L.; Piccoli, M.; Obli-Laryea, G.; Celik, F.; Paris, C.; Persello, C.; Nelson, A.; Morese, M.M.; Tubiello, F.N.;
dc.date2025-09-15T13:32:08Z
dc.date2025-09-15T13:32:08Z
dc.date2025
dc.date2025-09-15T13:25:56Z
dc.date.accessioned2026-06-28T00:29:18Z
dc.descriptionAgricultural residues in Viet Nam, a major agricultural producer, are abundant yet underutilized. Converting these residues into biochar offers a sustainable alternative to the widespread burning practices, which release greenhouse gases and air pollutants. The use of biochar as soil amendment could enhance soil structure while being a powerful carbon sequestrator. However, the sector’s development is hindered by a lack of detailed data, including geospatial, for cost–benefit analyses and for optimal placement of biochar production unit networks. To address these gaps, this paper introduces AI4Biochar, an AI-driven tool that automatically delineates crop field boundaries, integrates production data and sustainability indicators, and streamlines biochar production, resource management, environmental impact assessment, and market development within a geospatial framework. This paper presents the methodological approach used in the development of AI4Biochar and the results from a use-case application of the tool in a rice-producing district of Viet Nam.
dc.format46 p.
dc.formatapplication/pdf
dc.identifier978-92-5-139991-0
dc.identifierhttps://openknowledge.fao.org/handle/20.500.14283/cd6383en
dc.identifier.urihttp://hdl.handle.net/123456789/310556
dc.languageEnglish
dc.publisherFAO ;
dc.relationFAO Statistics Working Paper Series
dc.relationNo. 25-49
dc.rightsFAO
dc.rightsCC BY 4.0
dc.titleAI4Biochar: applying AI-driven field boundary recognition to the biochar sector
dc.typeBook (series)

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