AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management
Crop-livestock farming plays a crucial role in global agricultural communities by integrating crop production with livestock farming to create a sustainable and diversified farming system. However, this industry has become increasingly scrutinized due to environmental impact, climate change, and lan...
| Autores principales: | , , , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
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CRC Press
2025
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| Acceso en línea: | https://hdl.handle.net/10568/175472 |
| _version_ | 1855542758070026240 |
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| author | Sankararao, A.U.G. Rajalakshmi, P. Choudhary, Sunita Kholova, Jana Jones, Christopher S. |
| author_browse | Choudhary, Sunita Jones, Christopher S. Kholova, Jana Rajalakshmi, P. Sankararao, A.U.G. |
| author_facet | Sankararao, A.U.G. Rajalakshmi, P. Choudhary, Sunita Kholova, Jana Jones, Christopher S. |
| author_sort | Sankararao, A.U.G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Crop-livestock farming plays a crucial role in global agricultural communities by integrating crop production with livestock farming to create a sustainable and diversified farming system. However, this industry has become increasingly scrutinized due to environmental impact, climate change, and land degradation. As per present reports, crop residues, a significant livestock feed resource, are in shortage and have poor nutritional value. Moreover, various factors like heatwaves, drought, and diseases can negatively impact forage quality and reduce productivity. Conventional methods of assessing forage/crop residue quality face significant challenges, including labor-intensive, costly, time-consuming, and error-prone. UAV-based imaging can boost multi-dimensional crop improvement programs due to advantages like wider coverage, short revising times, high spatial resolutions, and ease of operation. Hyperspectral imaging (HSI) sensors provide enriched spectral information, enabling more precise investigations into feed quality evaluation, forage management, and livestock health. Artificial intelligence and machine learning (AI/ML) approaches can effectively analyze high-dimensional HSI data and extract meaningful insights. Integrating UAV-based HSI and AI/ML techniques is crucial to enhance crop-livestock farm management. This chapter explores the potential of UAV-based HSI and AI/ML for crop-livestock farm research and management, focusing on animal and forage health monitoring, and enhancing feed quality. We also emphasize AI/ML-based data analytics and algorithm development on UAV-borne HSI data to revolutionize crop-livestock farming. |
| format | Book Chapter |
| id | CGSpace175472 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | CRC Press |
| publisherStr | CRC Press |
| record_format | dspace |
| spelling | CGSpace1754722025-07-04T08:53:30Z AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management Sankararao, A.U.G. Rajalakshmi, P. Choudhary, Sunita Kholova, Jana Jones, Christopher S. animal health feeds integrated crop-livestock systems unmanned aerial vehicles Crop-livestock farming plays a crucial role in global agricultural communities by integrating crop production with livestock farming to create a sustainable and diversified farming system. However, this industry has become increasingly scrutinized due to environmental impact, climate change, and land degradation. As per present reports, crop residues, a significant livestock feed resource, are in shortage and have poor nutritional value. Moreover, various factors like heatwaves, drought, and diseases can negatively impact forage quality and reduce productivity. Conventional methods of assessing forage/crop residue quality face significant challenges, including labor-intensive, costly, time-consuming, and error-prone. UAV-based imaging can boost multi-dimensional crop improvement programs due to advantages like wider coverage, short revising times, high spatial resolutions, and ease of operation. Hyperspectral imaging (HSI) sensors provide enriched spectral information, enabling more precise investigations into feed quality evaluation, forage management, and livestock health. Artificial intelligence and machine learning (AI/ML) approaches can effectively analyze high-dimensional HSI data and extract meaningful insights. Integrating UAV-based HSI and AI/ML techniques is crucial to enhance crop-livestock farm management. This chapter explores the potential of UAV-based HSI and AI/ML for crop-livestock farm research and management, focusing on animal and forage health monitoring, and enhancing feed quality. We also emphasize AI/ML-based data analytics and algorithm development on UAV-borne HSI data to revolutionize crop-livestock farming. 2025-03-13 2025-07-03T10:52:13Z 2025-07-03T10:52:13Z Book Chapter https://hdl.handle.net/10568/175472 en Limited Access CRC Press Sankararao, A.U.G., Rajalakshmi, P., Choudhary, S., Kholova, J. and Jones, C.S. 2025. AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management. IN: Archangel, J.A., Krishnan, S. and Dejey, D. (eds), Smart technologies for sustainable livestock systems. Boca Raton, Florida: CRC Press. pp. 85–104. |
| spellingShingle | animal health feeds integrated crop-livestock systems unmanned aerial vehicles Sankararao, A.U.G. Rajalakshmi, P. Choudhary, Sunita Kholova, Jana Jones, Christopher S. AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management |
| title | AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management |
| title_full | AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management |
| title_fullStr | AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management |
| title_full_unstemmed | AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management |
| title_short | AI-enabled UAV borne hyperspectral imaging for crop-livestock farm management |
| title_sort | ai enabled uav borne hyperspectral imaging for crop livestock farm management |
| topic | animal health feeds integrated crop-livestock systems unmanned aerial vehicles |
| url | https://hdl.handle.net/10568/175472 |
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