Search Results - "Machine Learning"

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

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Irrigation scheduling using complex machine scheduling by Vries, T.T. de, Anwar, Arif A.

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Mango varietal discrimination using hyperspectral imaging and machine learning by Castro, Wilson, Tene, Baldemar, Castro, Jorge, Guivin, Alex, Ruesta Campoverde, Nelson Asdrubal, Avila George, Himer

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A scalable scheme to implement data-driven agriculture for small-scale farmers by Jiménez, Daniel, Delerce, Sylvain Jean, Dorado, Hugo Andres, Cock, James H., Muñoz, Luis Armando, Agamez, Alejandro, Jarvis, Andy

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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 by Conejo Rodriguez, Diego Felipe, Urban, Milan Oldřich, Santaella, Marcela, Gereda, Javier Mauricio, Contreras, Aquiles Darghan, Wenzl, Peter

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Mekong River Delta crop mapping using a machine learning approach by Ghosh, Surajit, Wellington, Michael, Holmatov, Bunyod

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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 by Devkota, Krishna, Devkota Wasti, Mina Kumari, Bouasria, Abdelkrim

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

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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... by Conejo Rodriguez, F., Gonzalez Guzman, J., Ramirez, Gil J., Urban, Milan Oldřich, Wenzl, Peter

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Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes by Nantongo, J.S., Serunkuma, E., Burgos, C., Nakitto, M., Davrieux, F., Ssali, R.T.

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

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RGB Image Dataset of Urochloa Hybrids for High-Throughput Phenotyping and Artificial Intelligence Applications by 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

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

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Machine learning algorithms translate big data into predictive breeding accuracy by 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

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

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Transforming soil quality index predictions in the Nile River Basin using hybrid stacking machine learning techniques by Singha, Chiranjit, Sahoo, Satiprasad, Govind, Ajit

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Global insights into nitrogen losses and efficiency in rice, wheat, and maize cultivation by Chakraborty, Debashis, Ladha, Jagdish Kumar, Das, Bappa, Dharamvir Singh Rana, Gathala, Mahesh Kumar, Jat, Mangi Lal, Krupnik, Timothy J.

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Advancing Soil Moisture Prediction Using Satellite and UAV-based Imagery with Machine Learning Models by Hussain, S., Arshad, M., Cheema, Muhammad Jehanzeb Masud, Qamar, M. U., Wajid, S. A., Daccache, A.

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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 by Cherotich, Fredah, Galgallo, Diba, Dhulipala, Ram, Whitbread, Anthony M, Paliwal, Ambica

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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 by Amare, Mulubrhan, Andam, Kwaw S., Spielman, David J., Bamiwuye, Temilolu, Nwagboso, Chibuzo, Zambrano, Patricia, Chambers, Judith A.

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