Search Results - "MACHINE LEARNING"

Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia by Mann, Michael L., Malik, Arun S., Warner, James

Subjects: “…machine learning…”
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Zambia: Systematic analysis of domestic production and world market shocks by Mukashov, Askar, Diao, Xinshen, Jones, Eleanor, Thurlow, James

Subjects: “…machine learning…”
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Bangladesh: Systematic analysis of domestic production and world market shocks by Mukashov, Askar, Jones, Eleanor, Thurlow, James

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Papua New Guinea: Systematic analysis of domestic production and world market shocks by Mukashov, Askar, Dorosh, Paul A., Schmidt, Emily, Thurlow, James

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Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software by Crossa, José, Martini, Johannes W.R., Vitale, Paolo, Perez-Rodriguez, Paulino, Costa-Neto, Germano, Fritsche-Neto, Roberto, Runcie, Daniel E., Cuevas, Jaime, Toledo, Fernando H., Huihui Li, De Vita, Pasquale, Gerard, Guillermo S., Dreisigacker, Susanne, Crespo-Herrera, Leonardo A., Saint Pierre, Carolina, Bentley, Alison R., Lillemo, Morten, Ortiz, Rodomiro, Montesinos-Lopez, Osval A., Montesinos-López, Abelardo

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Parametric and machine learning approaches to examine yield differences between control and treatment considering outliers and statistical biases: The case of insect resistant/herb... by Falck-Zepeda, José B., Zambrano, Patricia, Sanders, Arie, Trabanino, Carlos Rogelio

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Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments by Alimagham, Seyyedmajid, van Loon, Marloes P, Ramirez Villegas, Julian, Berghuijs, Herman N.C., Rosenstock, Todd Stuart, van Ittersum, Martin K.

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Early detection of infection by Penicillium digitatum in oranges using hyperspectral imaging and machine learning by Castillo-Gironés, Salvador, Gómez-Sanchis, Juan, López-Chulia, Marina, Guirao, Alberto, Palou, Lluís, Souza, Ricardo-Lima-de, Blasco, José

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Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery by Pizarro Carcausto, Samuel Edwin, Pricope, Narcisa G., Figueroa Venegas, Deyanira Antonella, Carbajal Llosa, Carlos Miguel, Quispe Huincho, Miriam Rocío, Vera Vilchez, Jesús Emilio, Alejandro Méndez, Lidiana Rene, Achallma Mendoza, Lino, González Tovar, Izamar Estrella, Salazar Coronel, Wilian, Loayza, Hildo, Cruz Luis, Juancarlos Alejandro, Arbizu Berrocal, Carlos Irvin

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Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems by Brewer, K., Clulow, A.D., Sibanda, M., Gokool, S., Naiken, V., Mabhaudhi, Tafadzwanashe

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Climate variability and extremes impact on seasonal occurrence patterns of malaria cases in Senegal [Abstract only] by Jampani, Mahesh, Panjwani, Shweta, Ghosh, Surajit, Sambou, Mame Henriette Astou, Amarnath, Giriraj

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PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia by Mabilangan, Abigail, Mkuhlani, Siyabusa, Saito, Kazuki

Subjects: “…machine learning…”
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A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches by 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

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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 by Cortez, Camille Rae, Castellion, Martina, Catausan, Sheryl, Gentallan, Renerio Jr., Calayugan, Mark Ian, Mercado, Ma. Fatima O., Hay, Fiona R.

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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 by Pizarro Carcausto, Samuel, Vera Vilchez, Jesús Emilio, Huamani, Joseph, Cruz, Juancarlos, Lastra Paucar, Sphyros Roomel, Solórzano Acosta, Richard Andi, Verástegui Martínez, Patricia

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Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging by 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

Subjects: “…Machine learning…”
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Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method by CGIAR Research Program on Climate Change, Agriculture and Food Security

Subjects: “…machine learning…”
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Pollination supply models from a local to global scale by 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

Subjects: “…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 by Steinbach, S., Bartels, A., Rienow, A., Kuria, B. T., Zwart, Sander J., Nelson, A.

Subjects: “…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 by Nakajima, Kota, Saito, Kazuki, Tsujimoto, Yasuhiro, Takai, Toshiyuki, Mochizuki, Atsushi, Yamaguchi, Tomoaki, Ibrahim, Ali, Mairoua, Salifou Goube, Andrianary, Bruce Haja, Katsura, Keisuke, Tanaka, Yu

Subjects: “…machine learning…”
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