Resultados de búsqueda - "machine learning"

Demand intelligence portal - annotated wireframe documentation por Sartas, M., Konlambigue, M.

Materias: “…machine learning…”
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Rainfed wheat extent (2020/21) across Ethiopia's complex and highly fragmented agricultural smallholder landscape por Blasch, Gerald, Lesne, Louise, Wolter, Jolan, Alemayehu, Yoseph, Bontemps, Sophie, Hodson, David, Defourny, Pierre

Materias: “…machine learning…”
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Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning por Towett, Erick K., Drake, L. B., Acquah, G. E., Haefele, S. M., McGrath, S. P., Shepherd, Keith D.

“…Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. …”
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Machine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africa por Muthoni, Francis K., Thierfelder, Christian L., Mudereri, B.T., Manda, J., Bekunda, Mateete A., Hoeschle-Zeledon, Irmgard

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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Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India por Nayak, Harisankar, Silva, ‪João Vasco, Parihar, Chiter Mal, Krupnik, Timothy J., Sena, Dipaka Ranjan, Kakraliya, S., Jat, Hanuman Sahay, Sidhu, Harminder Singh, Sharma, Parbodh Chander, Jat, Mangi Lal, Sapkota, Tek Bahadur

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

Materias: “…machine learning…”
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Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience por Cock, James H., Jiménez, Daniel, Dorado, Hugo, Oberthür, Thomas

Materias: “…machine learning…”
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A protocol for measuring cassava bacterial blight disease progression, using machine-learning image analysis por Pardo, JM, Jimenez, J, Rodríguez, R., Leiva, A.M, Cuéllar, Wilmer Jose

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Use of machine learning approaches for quantification of red spider mite (Acari: Tetranychidae) damage in Urochloa sp. por Espitia-Buitrago, Paula, Cotes-Torres, José M., Mating'i Kimani, Adrian, Chidawanyika, Frank, Hernández, Luis Miguel, Cardoso Arango, Juan Andrés, Jauregui, Rosa

Materias: “…machine learning…”
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Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report por Ileperuma, Kaveesha, Jampani, Mahesh, Sellahewa, Uvindu, Panjwani, Shweta, Amarnath, Giriraj

Materias: “…machine learning…”
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Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability por El Bilali, A., Brouziyne, Youssef, Attar, O., Lamane, H., Hadri, A., Taleb, A.

Materias: “…machine learning…”
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Coupling Process-Based Models and Machine Learning Algorithms for Predicting Yield and Evapotranspiration of Maize in Arid Environments por Attia, Ahmed, Govind, Ajit, Qureshi, Asad Sarwar, Feike, Til, Rizk, Mosa Sayed, Shabana, Mahmoud Mohamed Abd ElHay, Kheir, Ahmed M.S.

“…Crop models (CMs) are powerful tools for predicting yield and water use, but they still have some limitations and uncertainties; therefore, combining them with machine learning algorithms (MLs) could improve predictions and reduce uncertainty. …”
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A simple algorithm outperforms a machine learning approach for quantifying spittlebug damage in tropical grasses por Ruiz-Hurtado, Andres Felipe, Espitia, Paula, Cardoso, Juan Andres, Jauregui, Rosa Noemi

“…The objective of this study was to compare a simple algorithm (Heckbert’s median-cut colour quantisation) with the k-means unsupervised machine learning approach for quantification of plant damage (green/chlorotic leaf tissue) by spittlebugs using colour images. …”
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Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach por Negussie, Kaleb Gizaw, Gebrekidan, Bisrat Haile, Wyss, Daniel, Kappas, M.

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Mapping mechanization suitability using machine learning, presence data and spatial co-variates: Methodological approach por Adhikari, Subash, Koirala, Pankaj, Kafle, Sagar, Mponela, Powell

Materias: “…machine learning…”
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Machine learning based gridded/ digital soil mapping for Kapiti Research Station and Wildlife Conservancy, Kenya por Cherotich, Fredah, Leitner, Sonja, Pearce, F., Rufino, Mariana C., Quinton, J., Dhulipala, Ram, Salavati, M., Gluecks, Ilona V., Whitbread, Anthony M., Paliwal, Ambica

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Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms por Cem Akcapınar, M., Cakmak, B.

“…In obtaining the input parameters for the models, the growth periods of the varieties in the region were also considered. Using various machine learning algorithms, 21 yield prediction models for Bayraktar-2000, 12 for Kızıltan-91 and 8 for Bezostaya-1 were presented as alternatives, with model performances (coefficient of determination, R2) ranging between 0.74 and 0.97, 0.73 and 0.96, and 0.69 and 0.87, respectively.…”
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How consistent are Earth Observation-based machine learning models for predicting poverty in sub-Saharan Africa? por Mlambo, Reason

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High-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warning por Constenla-Villoslada, Susana, Liu, Yanyan, McBride, Linden, Ouma, Clinton, Mutanda, Nelson, Barrett, Christopher B.

Materias: “…machine learning…”
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