Resultados de búsqueda - "Machine Learning"

Design and Development of a Digital Platform for Soil Health Monitoring and Management por Patil, Mukund, Burry, Sukanya, Katakamsetti, Nagendra, Choudhari, Pushpajeet L., Perumal, Cuba, Gogumalla, Pranuthi, Rozario, Gilbert, Shaik, Jameeruddin, Rupavatharam, Srikanth

Materias: “…machine learning…”
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Irrigation scheduling using complex machine scheduling por Vries, T.T. de, Anwar, Arif A.

Materias: “…machine learning…”
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Mango varietal discrimination using hyperspectral imaging and machine learning por Castro, Wilson, Tene, Baldemar, Castro, Jorge, Guivin, Alex, Ruesta Campoverde, Nelson Asdrubal, Avila George, Himer

Materias: “…Machine learning…”
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A scalable scheme to implement data-driven agriculture for small-scale farmers por Jiménez, Daniel, Delerce, Sylvain Jean, Dorado, Hugo Andres, Cock, James H., Muñoz, Luis Armando, Agamez, Alejandro, Jarvis, Andy

Materias: “…machine learning…”
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Using phenomics to identify and integrate traits of interest for better-performing common beans: A validation study on an interspecific hybrid and its Acutifolii parents por Conejo Rodriguez, Diego Felipe, Urban, Milan Oldřich, Santaella, Marcela, Gereda, Javier Mauricio, Contreras, Aquiles Darghan, Wenzl, Peter

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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Predicting Wheat Yield Gap and its Determinants in Rainfed Mediterranean Climate of Morocco: Using Ground Information, Satellite Images and Machine Learning por Devkota, Krishna, Devkota Wasti, Mina Kumari, Bouasria, Abdelkrim

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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Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and... por Conejo Rodriguez, F., Gonzalez Guzman, J., Ramirez, Gil J., Urban, Milan Oldřich, Wenzl, Peter

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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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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RGB Image Dataset of Urochloa Hybrids for High-Throughput Phenotyping and Artificial Intelligence Applications por Arrechea-Castillo, Darwin Alexis, Espitia Buitrago, Paula Andrea, Florian Vargas, David Alberto, Estupinan Arboleda, Ronald David, Velazquez Hernandez, Riquelmer, Camelo Munevar, Rodrigo Andres, Cardoso Arango, Juan Andres

Materias: “…machine learning…”
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Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR por Herath, Achini, Tiozon, Rhowell Jr., Kretzschmar, Tobias, Sreenivasulu, Nese, Mahon, Peter, Butardo, Vito

Materias: “…machine learning…”
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Machine learning algorithms translate big data into predictive breeding accuracy por Crossa, José, Montesinos-Lopez, Osval A., Costa-Neto, Germano, Vitale, Paolo, Martini, Johannes W.R., Runcie, Daniel E., Fritsche-Neto, Roberto, Montesinos-Lopez, Abelardo, Perez-Rodriguez, Paulino, Gerard, Guillermo S., Dreisigacker, Susanna, Crespo-Herrera, Leonardo A., Saint Pierre, Carolina, Lillemo, Morten, Cuevas, Jaime, Bentley, Alison R., Ortiz, Rodomiro

Materias: “…machine learning…”
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Advancing food security: rice yield estimation framework using time-series satellite data & machine learning por Tiwari, Varun, Thorp, Kelly, Tulbure, Mirela G., Gray, Joshua, Kamruzzaman, Mohammad, Krupnik, Timothy J., Sankarasubramanian, A., Ardon, Marcelo

Materias: “…machine learning…”
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Transforming soil quality index predictions in the Nile River Basin using hybrid stacking machine learning techniques por Singha, Chiranjit, Sahoo, Satiprasad, Govind, Ajit

Materias: “…machine learning…”
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Global insights into nitrogen losses and efficiency in rice, wheat, and maize cultivation por Chakraborty, Debashis, Ladha, Jagdish Kumar, Das, Bappa, Dharamvir Singh Rana, Gathala, Mahesh Kumar, Jat, Mangi Lal, Krupnik, Timothy J.

Materias: “…machine learning…”
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Advancing Soil Moisture Prediction Using Satellite and UAV-based Imagery with Machine Learning Models por Hussain, S., Arshad, M., Cheema, Muhammad Jehanzeb Masud, Qamar, M. U., Wajid, S. A., Daccache, A.

Materias: “…machine learning…”
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Tracking Rangeland Degradation from Prosopis Invasion in Kenyan Rangeland: A Multi-Source Approach Combining Remote Sensing, Machine Learning and Citizen Science por Cherotich, Fredah, Galgallo, Diba, Dhulipala, Ram, Whitbread, Anthony M, Paliwal, Ambica

Materias: “…machine learning…”
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Insecticide use, farmers’ self-reported health status, and genetically modified cowpea in Nigeria: Findings from a clustered randomized controlled trial with causal por Amare, Mulubrhan, Andam, Kwaw S., Spielman, David J., Bamiwuye, Temilolu, Nwagboso, Chibuzo, Zambrano, Patricia, Chambers, Judith A.

Materias: “…machine learning…”
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