AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification
Vision2Biomass is an AI-driven approach for quantifying crop residue retention in paddy fields to support digital Monitoring, Reporting, and Verification (MRV) systems for agricultural carbon accounting. The probe addresses limitations of manual residue assessment, which is labor-intensive, subjecti...
| Autores principales: | , , , , , |
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| Formato: | Brochure |
| Lenguaje: | Inglés |
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International Crops Research Institute for the Semi-Arid Tropics
2025
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| Acceso en línea: | https://hdl.handle.net/10568/179446 |
| _version_ | 1855522443476598784 |
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| author | Sowjanya, Palle Sai Patil, Mukund Rupavatharam, Srikanth Gogumalla, Pranuthi Choudhari, Pushpajeet L. Dihudi, Munmun |
| author_browse | Choudhari, Pushpajeet L. Dihudi, Munmun Gogumalla, Pranuthi Patil, Mukund Rupavatharam, Srikanth Sowjanya, Palle Sai |
| author_facet | Sowjanya, Palle Sai Patil, Mukund Rupavatharam, Srikanth Gogumalla, Pranuthi Choudhari, Pushpajeet L. Dihudi, Munmun |
| author_sort | Sowjanya, Palle Sai |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Vision2Biomass is an AI-driven approach for quantifying crop residue retention in paddy fields to support digital Monitoring, Reporting, and Verification (MRV) systems for agricultural carbon accounting. The probe addresses limitations of manual residue assessment, which is labor-intensive, subjective, and difficult to scale. Using geo-tagged field photographs, computer vision models segment residue, soil, and vegetation at the pixel level and estimate residue retention categories. By combining image segmentation with deep feature extraction and classification, the system provides consistent, interpretable residue estimates that can be directly linked to carbon models. Vision2Biomass demonstrates the potential of scalable, image-based analytics for climate-smart agriculture and verifiable carbon mitigation. |
| format | Brochure |
| id | CGSpace179446 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Crops Research Institute for the Semi-Arid Tropics |
| publisherStr | International Crops Research Institute for the Semi-Arid Tropics |
| record_format | dspace |
| spelling | CGSpace1794462026-01-07T02:05:41Z AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification Sowjanya, Palle Sai Patil, Mukund Rupavatharam, Srikanth Gogumalla, Pranuthi Choudhari, Pushpajeet L. Dihudi, Munmun crop residues carbon sequestration carbon mitigation crop residue digital solutions Vision2Biomass is an AI-driven approach for quantifying crop residue retention in paddy fields to support digital Monitoring, Reporting, and Verification (MRV) systems for agricultural carbon accounting. The probe addresses limitations of manual residue assessment, which is labor-intensive, subjective, and difficult to scale. Using geo-tagged field photographs, computer vision models segment residue, soil, and vegetation at the pixel level and estimate residue retention categories. By combining image segmentation with deep feature extraction and classification, the system provides consistent, interpretable residue estimates that can be directly linked to carbon models. Vision2Biomass demonstrates the potential of scalable, image-based analytics for climate-smart agriculture and verifiable carbon mitigation. 2025-12-31 2026-01-06T22:10:12Z 2026-01-06T22:10:12Z Brochure https://hdl.handle.net/10568/179446 en Open Access application/pdf International Crops Research Institute for the Semi-Arid Tropics Sowjanya, Palle Sai; Patil, Mukund; Rupavatharam, Srikanth; Gogumalla, Pranuthi; Choudhari, Pushpajeet L.; & Dihudi, Munmun. 2025. AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification. Patancheru, India: ICRISAT. |
| spellingShingle | crop residues carbon sequestration carbon mitigation crop residue digital solutions Sowjanya, Palle Sai Patil, Mukund Rupavatharam, Srikanth Gogumalla, Pranuthi Choudhari, Pushpajeet L. Dihudi, Munmun AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification |
| title | AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification |
| title_full | AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification |
| title_fullStr | AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification |
| title_full_unstemmed | AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification |
| title_short | AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification |
| title_sort | ai4carbon smart image analytics platform for monitoring residue retention levels in rice production systems automated visual quantification |
| topic | crop residues carbon sequestration carbon mitigation crop residue digital solutions |
| url | https://hdl.handle.net/10568/179446 |
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