Resultados de búsqueda - "machine"

A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches por Roy, Ripon Kumar, Hosseiniyan Khatibi, Seyed Mahdi, Kim, Sung-Ryul, Trijatmiko, Kurniawan Rudi, Diaz, Maria Genaleen Q., Ocampo, Eureka Teresa M., Hernandez, Jose E., Pirmoradi, Saeed, Henry, Amelia, Kohli, Ajay

Materias: “…machine learning…”
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Detection of Alternaria padwickii (Ganguly) M.B. Ellis in Rice Seeds Using Hyperspectral Imaging to Enhance Seed Health Assessment in Germplasm Exchange por Cortez, Camille Rae, Castellion, Martina, Catausan, Sheryl, Gentallan, Renerio Jr., Calayugan, Mark Ian, Mercado, Ma. Fatima O., Hay, Fiona R.

Materias: “…machine learning…”
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Online fitted policy iteration based on extreme learning machines por Escandell-Montero, Pablo, Lorente, Delia, Martínez-Martínez, José M., Soria-Olivas, Emilio, Vila-Francés, Joan, Martín-Guerrero, José D.

Materias: “…Extreme learning machine…”
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Enlace del recurso

Applying Swedish programmes projecting forest development to Polish forestry conditions : a study made on data from Gostynin forest district in Poland por Skreta, Magdalena

Materias: “…Forest Time Machine…”

About identification of features that affect the estimation of citrus harvest por Brenes Pérez, Cristian F., y 5 autores más

Materias: “…machine learning…”
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Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley por Pizarro Carcausto, Samuel, Vera Vilchez, Jesús Emilio, Huamani, Joseph, Cruz, Juancarlos, Lastra Paucar, Sphyros Roomel, Solórzano Acosta, Richard Andi, Verástegui Martínez, Patricia

Materias: “…Machine learning…”
Enlace del recurso
Enlace del recurso

Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging por Urquizo Barrera, Julio Cesar, Ccopi Trucios, Dennis, Ortega Quispe, Kevin, Castañeda Tinco, Italo, Patricio Rosales, Solanch, Passuni Huayta, Jorge, Figueroa Venegas, Deyanira, Enriquez Pinedo, Lucia, Ore Aquino, Zoila, Pizarro Carcausto, Samuel

Materias: “…Machine learning…”
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Enlace del recurso

Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method por CGIAR Research Program on Climate Change, Agriculture and Food Security

Materias: “…machine learning…”
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Pollination supply models from a local to global scale por Giménez-García, Angel, Allen-Perkins, Alfonso, Bartomeus, Ignasi, Balbi, Stefano, Knapp, Jessica L., Hevia, Violeta, Woodcock, Ben Alex, Smagghe, Guy, Miñarro, Marcos, Eeraerts, Maxime, Colville, Jonathan F., Hipólito, Juliana, Cavigliasso, Pablo, Nates-Parra, Guiomar, Herrera, José M., Cusser, Sarah, Simmons, Benno I., Wolters, Volkmar, Jha, Shalene, Freitas, Breno M., Horgan, Finbarr G., Artz, Derek R., Sidhu, Sheena, Otieno, Mark, Boreux, Virginie, Biddinger, David J., Klein, Alexandra-Maria, Joshi, Neelendra K., Stewart, Rebecca I.A., Albrecht, Matthias, Nicholson, Charlie C., O’Reilly, Alison D., Crowder, David William, Burns, Katherine L.W., Nabaes Jodar, Diego Nicolás, Garibaldi, Lucas Alejandro, Sutter, Louis, Dupont, Yoko L., Dalsgaard, Bo, da Encarnação Coutinho, Jeferson Gabriel, Lázaro, Amparo, Andersson, Georg K.S., Raine, Nigel E., Krishnan, Smitha, Dainese, Matteo, van der Wer, Wopke, Smith, Henrik G., Magrach, Ainhoa

Materias: “…pollination supply models, ecological intensification, global, pollinators, machine learning…”
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Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning por Steinbach, S., Bartels, A., Rienow, A., Kuria, B. T., Zwart, Sander J., Nelson, A.

Materias: “…machine learning…”
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Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations por Nakajima, Kota, Saito, Kazuki, Tsujimoto, Yasuhiro, Takai, Toshiyuki, Mochizuki, Atsushi, Yamaguchi, Tomoaki, Ibrahim, Ali, Mairoua, Salifou Goube, Andrianary, Bruce Haja, Katsura, Keisuke, Tanaka, Yu

Materias: “…machine learning…”
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Technical report: Next-Generation decision support tool for data-driven fertilizer recommendations to enhance maize performance in Ethiopia por Mesfin, Tewodros, Abera, Wuletawu, Ebrahim, Mohammed, Tilaye, Amsalu, Tibebe, Degefie, Tamene, Lulseged, Adimassu, Zenebe, Liben, Feyera, Bekere, Jemal, Nazib, Nibras

Materias: “…machine learning…”
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Detección de outliers en muestras de entrenamiento generadas mediante interpretación visual por Banchero, Santiago, Veron, Santiago Ramón, Petek, Mariana, Sarrailhe, Sofia, De Abelleyra, Diego

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…”
Enlace del recurso
Enlace del recurso

Naturlig föryngring efter markberedning med harv eller Bracke Planter i Småland por Sjögren, Vidar

Materias: “…planting machine…”

Predicting infectious diseases: a bibliometric review on Africa por Phoobane, P., Masinde, M., Mabhaudhi, Tafadzwanashe

Materias: “…machine learning…”
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Banana mapping in heterogenous smallholder farming systems using high-resolution remote sensing imagery and machine learning models with implications for banana bunchy top disease... por Alabi, T.R., Adewopo, Julius, Duke, O.P., Kumar, P. Lava

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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Multiyear Maize management dataset collected in Chiapas, Mexico por Trevisan, Rodrigo G., Martin, Nicolas F., Fonteyne, Simon, Verhulst, Nele, Dorado Betancourt, Hugo Andres, Jiménez, Daniel, Gardeazábal Monsalve, Andrea

Materias: “…machine learning…”
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Near real-time monitoring of cassava cultivation area por Phan, Trong Van, Reymondin, Louis, Vantalon, Thibaud, Delaquis, Erik, Nguyen, Thuy Thanh, Mienmany, Bandit

Materias: “…machine learning…”
Enlace del recurso