Resultados de búsqueda - "machine learning"

Machine learning innovations for rapid assessments in wheat and rice systems in South Asia. por CGIAR Research Program on Wheat

Materias: “…machine learning…”
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New machine learning models that predict the performance of seed varieties in particular conditions por CGIAR Platform for Big Data in Agriculture

Materias: “…machine learning…”
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Machine learning based groundwater prediction in a data-scarce basin of Ghana por Siabi, E. K., Dile, Y. T., Kabo-Bah, A. T., Amo-Boateng, M., Anornu, G. K., Akpoti, Komlavi, Vuu, C., Donkor, P., Mensah, S. K., Incoom, A. B. M., Opoku, E. K., Atta-Darkwa, T.

Materias: “…machine learning…”
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Mekong River Delta crop mapping using a machine learning approach por Ghosh, Surajit, Wellington, Michael, Holmatov, Bunyod

Materias: “…machine learning…”
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Smallholder maize yield estimation using satellite data and machine learning in Ethiopia por Guo, Zhe, Chamberlin, Jordan, You, Liangzhi

Materias: “…machine learning…”
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Using machine learning for image-based analysis of sweetpotato root sensory attributes por Nakatumba-Nabende, J., Babirye, C., Tusubira, J., Mutegeki, H., Nabiryo, A., Murindanyi, S., Katumba, A., Nantongo, J.S., Sserunkuma, E., Nakitto, M., Ssali, R.T., Makunde, G.S., Moyo, M., Campos, Hugo

“…In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. …”
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Integrating APSIM model with machine learning to predict wheat yield spatial distribution por Kheir, A.M.S., Mkuhlani, S., Mugo, J.W., Elnashar, A., Nangia, V., Devare, Medha, Govind, A.

Materias: “…machine learning…”
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Insights from above: Satellite and machine learning for agriculture and food security in Myanmar por Guo, Zhe, Zhang, Xiaobo, Kanee, Sarah, Masias, Ian, You, Liangzhi, van Asselt, Joanna, Steinhuebel-Rasheed, Linda, Yang, Ruixin, Minten, Bart, Belton, Ben, Fang, Peixun, Liu, Shuo, Zhang, Kaifeng

Materias: “…machine learning…”
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Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes por Nantongo, J.S., Serunkuma, E., Burgos, C., Nakitto, M., Davrieux, F., Ssali, R.T.

Materias: “…machine learning…”
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Early detection of plant virus infection using multispectral imaging and machine learning por Grieve, B., Duffy, S., Dallas, M. M., Ascencio‑Ibanez, J. T., Alonso-Chavez, V., Legg, J., Hanley-Bowdoin, L., Yin, H.

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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Enhancing groundwater predictions by incorporating response lag effects in machine learning models por Yang, F., Zhang, X., Yang, J., Zhang, J., Dai, Q., Zhu, S.

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Machine learning approach to predicting Rift Valley fever disease outbreaks in Kenya por Mulwa, D., Kazuzuru, B., Misinzo, G., Bett, Bernard K.

Materias: “…machine learning…”
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Machine learning algorithms identified relevant SNPs for milk fat content in cattle por Ríos, Pablo J., Raschia, Maria Agustina, Maizon, Daniel Omar, Demitrio, Daniel Arturo, Poli, Mario Andres

Materias: “…Machine Learning Methods…”
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Relevant loci for milk production in dairy cattle, obtained by machine learning algorithms por Raschia, Maria Agustina, Ríos, Pablo J., Maizon, Daniel Omar, Demitrio, Daniel Arturo, Poli, Mario Andres

Materias: “…Machine Learning…”
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Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods por García-Rodríguez, David, Catret, Pablo, Iglesias, Domingo J., Martínez, Juan J., López, Ernesto, García, Antonio

Materias: “…machine learning…”
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Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery por Pizarro, S., Pricope, N.G., Figueroa, D., Carbajal, C., Quispe, M., Vera, J., Alejandro, L., Achallma, L., Gonzalez, I., Salazar, W., Loayza, H., Cruz, J., Arbizu, C.I.

Materias: “…machine learning…”
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Satellite-based Tracking of Agricultural Adaptation Progress in Sub-Saharan Africa por Reymondin, Louis

Materias: “…machine learning…”
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Predicting poverty with vegetation index por Tang, Binh, Liu, Yanyan, Matteson, David S.

Materias: “…machine learning…”
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MusaDeepMosaic: Development of a machine learning genomic mosaic classifier tool. por Vicens, Romain, Cenci, Alberto, Guillaume, Martin, Sardos, Julie, Rouard, Mathieu, Breton, Catherine

Materias: “…machine learning…”
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