Resultados de búsqueda - "machine"

Machine learning algorithms translate big data into predictive breeding accuracy por Crossa, José, Montesinos-Lopez, Osval A., Costa-Neto, Germano, Vitale, Paolo, Martini, Johannes W.R., Runcie, Daniel E., Fritsche-Neto, Roberto, Montesinos-Lopez, Abelardo, Perez-Rodriguez, Paulino, Gerard, Guillermo S., Dreisigacker, Susanna, Crespo-Herrera, Leonardo A., Saint Pierre, Carolina, Lillemo, Morten, Cuevas, Jaime, Bentley, Alison R., Ortiz, Rodomiro

Materias: “…machine learning…”
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Advancing food security: rice yield estimation framework using time-series satellite data & machine learning por Tiwari, Varun, Thorp, Kelly, Tulbure, Mirela G., Gray, Joshua, Kamruzzaman, Mohammad, Krupnik, Timothy J., Sankarasubramanian, A., Ardon, Marcelo

Materias: “…machine learning…”
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Transforming soil quality index predictions in the Nile River Basin using hybrid stacking machine learning techniques por Singha, Chiranjit, Sahoo, Satiprasad, Govind, Ajit

Materias: “…machine learning…”
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Global insights into nitrogen losses and efficiency in rice, wheat, and maize cultivation por Chakraborty, Debashis, Ladha, Jagdish Kumar, Das, Bappa, Dharamvir Singh Rana, Gathala, Mahesh Kumar, Jat, Mangi Lal, Krupnik, Timothy J.

Materias: “…machine learning…”
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Advancing Soil Moisture Prediction Using Satellite and UAV-based Imagery with Machine Learning Models por Hussain, S., Arshad, M., Cheema, Muhammad Jehanzeb Masud, Qamar, M. U., Wajid, S. A., Daccache, A.

Materias: “…machine learning…”
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Tracking Rangeland Degradation from Prosopis Invasion in Kenyan Rangeland: A Multi-Source Approach Combining Remote Sensing, Machine Learning and Citizen Science por Cherotich, Fredah, Galgallo, Diba, Dhulipala, Ram, Whitbread, Anthony M, Paliwal, Ambica

Materias: “…machine learning…”
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Insecticide use, farmers’ self-reported health status, and genetically modified cowpea in Nigeria: Findings from a clustered randomized controlled trial with causal por Amare, Mulubrhan, Andam, Kwaw S., Spielman, David J., Bamiwuye, Temilolu, Nwagboso, Chibuzo, Zambrano, Patricia, Chambers, Judith A.

Materias: “…machine learning…”
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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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Risks and farmers’ investment in productive assets in Nigeria por Takeshima, Hiroyuki, Yamauchi, Futoshi

Materias: “…milling machine…”
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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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