Resultados de búsqueda - "machine learning"

Factors affecting deep learning model performance in citizen science–based image data collection for agriculture: A case study on coffee crops por Rivera-Palacio, Juan Camilo, Bunn, Christian, Ryo, Masahiro

Materias: “…machine learning…”
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Spatial-temporal coupling of malaria vector habitat suitability and biting probability por Aduvukha, G.R., Abdel-Rahman, E.M., Mutanga, O., Odindi, J., Tonnang, H.E.Z.

Materias: “…machine learning…”
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Predicting climate smart agriculture (CSA) practices using machine learning: A prime exploratory survey por Noma, Freddy, Babu, Suresh Chandra

Materias: “…machine learning…”
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Terra-i+ Using machine learning to manage impacts of coffee production in Ocotepeque, Honduras por Alliance of Bioversity International and CIAT

Materias: “…machine learning…”
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A map of global peatland extent created using machine learning (Peat-ML) por Melton, Joe R., Chan, Ed, Millard, Koreen, Fortier, Matthew, Winton, R. Scott, Martin López, Javier Mauricio, Cadillo-Quiroz, Hinsby, Kidd, Darren, Verchot, Louis V.

Materias: “…machine learning…”
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Predicting yields using biophysical crop simulations, machine learning and remote sensing in India por CGIAR Platform for Big Data in Agriculture

Materias: “…machine learning…”
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Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia por Sleimi, Rim, Ghosh, Surajit, Amarnath, Giriraj

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Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review por Pokhariyal, Shweta, Patel, N. R., Govind, Ajit

Materias: “…machine learning…”
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Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia por Leggesse, E. S., Derseh, W. A., Zimale, F. A., Tilahun, Seifu A., Meshesha, M. A.

Materias: “…machine learning…”
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Mapping aflatoxin risk using machine learning and remote sensing in East and Southern Africa por Gachoki, S., Muthoni, F., Mahuku, G., Atehnkeng, J., Njeru, N., Kamau, J., Tripathi, L.

Materias: “…machine learning…”
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Machine Learning Approach for Prediction of Area Under Cultivation and Production for Vegetatively Propagated Crops por Ahishakiye, E., Ogero, K., Namada, S., Rajendran, S.

Materias: “…machine learning…”
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Machine learning reveals drivers of yield sustainability in five decades of continuous rice cropping por Yamaguchi, Tomoaki, Angeles, Olivyn, Iizumi, Toshichika, Dobermann, Achim, Katsura, Keisuke, Saito, Kazuki

“…The long-term sustainability of intensive rice systems under climate change is a critical challenge for global food security. Here, we use machine learning techniques to assess the impact of climate change, genotype, and nutrient management on rice yield in the world's longest-running continuous cropping experiment (LTCCE) at the International Rice Research Institute (IRRI) in the Philippines. …”
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Enhancing decision-making processes of small farmers in tropical crops by means of machine learn por Satizábal, H.F., Barreto Sáenz, MA, Jiménez, D, Pérez Uribe, A., Cock, James H.

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Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning por Vélez-Rivera, Nayeli, Gómez-Sanchís, Juan, Chanona-Perez, Jorge, Carrasco, Juan José, Millán-Giraldo, Mónica, Lorente, Delia, Cubero, Sergio, Blasco, José

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Enhancing anthracnose detection in mango at early stages using hyperspectral imaging and machine learning por Velásquez, Carlos, Aleixos, Nuria, Gómez-Sanchís, Juan, Prieto, Flavio, Blasco, José

“…Hyperspectral images of control and infected fruit were captured in the 450–970 nm spectral range. Five machine-learning models were used to obtain the method that best fits the spectral data. …”
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Early detection of infection by Penicillium digitatum in oranges using hyperspectral imaging and machine learning por Castillo-Gironés, Salvador, Gómez-Sanchis, Juan, López-Chulia, Marina, Guirao, Alberto, Palou, Lluís, Souza, Ricardo-Lima-de, Blasco, José

Materias: “…machine learning…”
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Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia por Lamos Díaz, Henry, Puentes Garzón, David Esteban, Zarate Caicedo, Diego Alejandro

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Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties por Delgadillo Durana, Diego A., Vargas García, Cesar A., Varón Ramíreza, Viviana M., Calderón, Francisco C., Montenegroa, Andrea C., Reyes Herreraa, Paula H.

“…Current approaches use mathematical and statistical techniques, avoiding machine learning frameworks. This proposal uses vis-NIRS in sugarcane soils and machine learning techniques such as three regression and six classification methods. …”
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Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms por Duarte Carvajalino, Julio Martin, Silva Arero, Elías Alexander, Góez Vinasco, Gerardo Antonio, Torres Delgado, Laura Marcela, Ocampo Paez, Oscar Dubán, Castaño Marín, Angela María

“…We use hyperspectral imagery and state of the art machine learning algorithms: random decision forest, multilayer perceptron, convolutional neural networks, support vector machines, extreme gradient boost, and AdaBoost. …”
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Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in west Africa por Alabi, T.R., Abebe, A.T., Chigeza, G., Fowobaje, K.R.

Materias: “…machine learning…”
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