Resultados de búsqueda - "MACHINE LEARNING"

Global habitat suitability of Spodoptera frugiperda (JE Smith) (Lepidoptera, Noctuidae): key parasitoids considered for its biological control por Tepa-Yotto, Ghislain T., Tonnang, Henri E.Z., Goergen, Georg E., Subramanian, S., Kimathi, E.K., Abdel-Rahman, E.M., Flo, D., Thunes, K.H., Fiaboe, K., Niassy, S., Bruce, A., Mohamed, S.A., Tamo, Manuele, Ekesi, S., Saethre, M.G.

Materias: “…machine learning…”
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Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 por Ali, Ashenafi, Erkossa, Teklu, Gudeta, Kiflu, Abera, Wuletawu, Mesfin, Ephrem, Mekete, Terefe, Haile, Mitiku, Haile, Wondwosen, Abegaz, Assefa, Tafesse, Demeke, Belay, Gebeyhu, Getahun, Mekonen, Beyene, Sheleme, Assen, Mohamed, Regassa, Alemayehu, Selassie, Yihenew G., Tadesse, Solomon, Abebe, Dawit, Walde, Yitbarek, Hussien, Nesru, Yirdaw, Abebe, Mera, Addisu, Admas, Tesema, Wakoya, Feyera, Legesse, Awgachew, Tessema, Nigat, Abebe, Ayele, Gebremariam, Simret, Aregaw, Yismaw, Abebaw, Bizuayehu, Bekele, Damtew, Zewdie, Eylachew, Schulz, Steffen, Tamene, Lulseged D., Elias, Eyasu

Materias: “…machine learning…”
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Modeling the spatial distribution of soil organic carbon and carbon stocks in the Casanare flooded savannas of the Colombian Llanos por Martin López, Javier Mauricio, Verchot, Louis V., Martius, Christopher, Silva, Mayesse Aparecida da

Materias: “…machine learning…”
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Predictive modeling of nutritional quality in Urochloa pastures from multispectral sensors and images using machine learning approaches por Camelo-Munevar, Rodrigo Andrés, Hernández, Luis Miguel, Jauregui, Rosa, Cardoso Arango, Juan Andrés

Materias: “…machine learning…”
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Unveiling Drought-Resilient Pathways: Integrating High Throughput Phenotyping and Multivariate Modeling to Enhance Barley Adaptation to Climate Change por Ouahid, Safae, Backhaus, Anna Elisabeth, Jimenez, José-Antonio, Visioni, Andrea, Sanchez-Garcia, Miguel

Materias: “…machine learning…”
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Working women and caste in India: A study of social disadvantage using feature attribution por Joshi, Kuhu, Joshi, Chaitanya K.

Materias: “…machine learning…”
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Nowcasting food insecurity interest with google trends data por Caravaggio, Nicola, Carneiro, Bia, Resce, Giuliano

Materias: “…machine learning…”
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Estimating elements susceptible to urban flooding using multisource data and machine learning por Asfaw, Wegayehu, Rientjes, T., Bekele, Tilaye Worku, Haile, Alemseged Tamiru

Materias: “…machine learning…”
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Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids por He, Kunhui, Yu, Tingxi, Gao, Shang, Chen, Shoukun, Li, Liang, Zhang, Xuecai, Huang, Changling, Xu, Yunbi, Wang, Jiankang, Boddupalli, Prasanna, Hearne, Sarah, Li, Xinhai, Li, Huihui

Materias: “…machine learning…”
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Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning por Rejith, Rajan G., Sahoo, Rabi N., Ranjan, Rajeev, Kondraju, Tarun, Bhandari, Amrita, Gakhar, Shalini

Materias: “…machine learning…”
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ICRISAT and Partners Launch AI-Powered Climate Advisory Initiative to Boost Farmer Resilience por Kumar, Shalander, Subbarao, AVM, Dhulipala, Ram, Kumar; Kishore G., Santanu Bal, Chandran, Sarath

Materias: “…machine learning…”
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Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning por Hengl, T., Leenaars, Johan G.B., Shepherd, Keith D., Walsh, Markus G., Heuvelink, Gerard B.M., Mamo, T., Tilahun, H., Berkhout, Ezra D., Cooper, M., Fegraus, E., Wheeler, I., Kwabena, N. A.

Materias: “…machine learning…”
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Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? por Ferguson, Amy, Murray, Catherine, Tessema, Yared Mesfin, McKeown, Peter C., Reymondin, Louis, Loboguerrero Rodriguez, Ana María, Talsma, Tiffany, Allen, Brenden, Jarvis, Andy, Golden, Aaron, Spillane, Charles

Materias: “…machine learning…”
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A protocol for measuring disease resistance to Cassava Bacterial Blight, using machine-learning image analysis por Pardo, Juan Manuel, Jiménez Polo, Jenyfer, Rodríguez, Rafael, Leiva Sandoval, Ana Maria, Cuéllar, Wilmer Jose

Materias: “…machine learning…”
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Digitally improving the identification of aquatic macroinvertebrates for indices used in biomonitoring por Koen, R. C. J., Koen, F. J., Pattinson, N. B., Dickens, Chris W. S., Graham, P. M.

Materias: “…machine learning…”
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Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure por Gokool, S., Mahomed, M., Brewer, K., Naiken, V., Clulow, A., Sibanda, M., Mabhaudhi, Tafadzwanashe

Materias: “…machine learning…”
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Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning por Mcbride, Linden, Barrett, Christopher B., Browne, Christopher, Hu, Leiqiu, Liu, Yanyan, Matteson, David S., Sun, Ying, Wen, Jiaming

Materias: “…machine learning…”
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Remote sensing and machine learning for food crop production data in Africa post-COVID-19 por Ly, Racine, Dia, Khadim, Diallo, Mariam A.

Materias: “…machine learning…”
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Multivariate random forest prediction of poverty and malnutrition prevalence por Browne, Chris, Matteson, David S., McBride, Linden, Hu, Leiqiu, Liu, Yanyan, Sun, Ying, Wen, Jiaming, Barrett, Christopher B.

Materias: “…machine learning…”
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Research-policy linkages: Empirical evidence from agroeconomic research in India por Balaji, S. J., Babu, Suresh Chandra, Pal, Suresh

Materias: “…machine learning…”
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