Search Results - Machine translating.

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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Speech recognition, machine translation, and corpus analysis for identifying farmer demands and targeting digital extension by Jones-Garcia, Eliot

“…This report reviews several approaches to overcoming technical constraints and then presents a cutting-edge approach that utilizes innovations in unsupervised learning to deliver highly accurate speech recognition and machine translation in a diverse set of languages.…”
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Translating virome analyses to support biosecurity, on-farm management, and crop breeding by Briseño, R.I.A., Batuman, O., Brawner, J., Cuéllar, Wilmer Jose, Delaquis, Erik, Etherton, B.A., French-Monar, R.D., Kreuze, Jan F., Navarrete, I., Ogero, K., Sulá, A.P., Yilmaz, S., Garrett, K.A.

“…There are many opportunities for translating virome analysis to support plant health. …”
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Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation by Garrett, K.A., Bebber, D.P., Etherton, B.A., Gold, K.M., Plex Sulá, A.I., Selvaraj, Michael G.

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Explainable machine learning driven nutrient recommendation for maize production in Malawi by Liben, Feyera, Kihara, Job, Abera, Wuletawu, AbebeMesfin, Tewodrofins, Homann-kee Tui, Sabine, Munthali, Moses, Munthali, Chandiona, Nalivata, Patson, Mzumara, Edward, Tuimene, Lulseged

“…This study aimed to (1) evaluate and compare the predictive performance of multiple machine learning algorithms for maize yield estimation in Malawi; (2) identify the most important yield-determining features through recursive feature elimination (RFECV) and SHAP-based interpretation; (3) examine interaction effects between key nutrient inputs and environmental variables using two-dimensional partial dependence plots; and (4) translate model outputs into site-specific nutrient management insights for precision agronomy in smallholder maize systems. …”
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Video based deep learning deciphers honeybee waggle dances in natural conditions by Grison, Sylvain, Siddaganga, Rajath, Hedge, Shrihari, Burridge, James, Blok, Pieter M., Krishnan, Smitha, Brockmann, Axel, Guo, Wei

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Assessing climate resilience in rice production: Measuring the impact of the Millennium Challenge Corporation’s IWRM scheme in the Senegal River Valley using remote sensing and mac... by Fionnagáin, D.Ó., Geever, M., O’Farrell, J., Codyre, P., Trearty, R., Tessema, Y.M., Reymondin, Louis, Loboguerrero, Ana Maria, Spillane, Charlie, Golden, A

“…Abstract Satellite remote sensing and machine learning can be combined to develop methods for measuring the impacts of climate change on biomass and agricultural systems. …”
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Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction by Yunbi Xu, Zhang Xingping, Huihui Li, Hongjian Zheng, Jianan Zhang, Olsen, Michael, Varshney, Rajeev K., Boddupalli, P.M., Qian Qian

“…Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. …”
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The progression in developing genomic resources for crop improvement by Ruperao, P., Rangan, P., Shah, T., Thakur, V., Kalia, S., Mayes, S., Rathore, A.

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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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Effects of land use land cover change on streamflow of Akaki Catchment, Addis Ababa, Ethiopia by Negash, E. D., Asfaw, Wegayehu, Walsh, C. L., Mengistie, G. K., Haile, Alemseged Tamiru

“…Since the comparative performance of classification algorithms is poorly understood, we compared the performance of one parametric and five non-parametric machine learning methods for LULC mapping using Landsat imageries. …”
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Free online trainings on soil health monitoring with satellite based remote sensors by Huq, Rafiq, Lesueur, Didier

“…Each session featured three one-hour lectures as outlined below: 1. « Earth Observation and Soil Health: Where Innovation Meets Practice » - Focus: The origins and development of Earth Observation (EO) technology, its applications innatural sciences and soil health monitoring, as well as its opportunities and limitations. 2. « Satellite-Based Remote Sensing: How Earth Observation Enhances Soil Health Monitoring » - Focus: Various remote sensing (RS) technologies, their application in soil data collection, and the role of RS data in monitoring soil health. 3. « From Data to Decisions: Translating Remote Sensing Insights into Practical Soil Health Solutions » - Focus: AI-based agricultural informatics, the use of machine learning in data analysis and automation, and the importance of baseline data for machine learning algorithms.…”
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Conceptualising ecosystem services and implications for human nature relations by Chorell, Joel

“…Although still debated, the language of economics makes possible a translation of nature’s values to a wider audience than traditional conservation. …”

Building Climate Resilience in Agrifood Systems Through AI- Powered Agromet Advisory Services by Kumar, Shalander, Rao, K.P.C., Kumar, G. Kishore

“…These systems employ machine learning and decision-tree logic to translate complex weather data into actionable insights, enabling timely recommendations on sowing, irrigation, and crop protection. …”
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Digital solutions to transform agriculture: lessons and experiences in Ethiopia by Tamene, Lulseged D., Abera, Wuletawu, Erkossa, Teklu

“…Expected results in the coming few years will involve taking data and data use to the next level, whereby data are “translated” into information and farmer-relevant, gender-specific extension content and disseminated digitally and via analog agricultural advisory services. …”
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Characterizing degradation of palm swamp peatlands from space and on the ground: An exploratory study in the Peruvian Amazon by Hergoualc'h, Kristell, Gutiérrez Vélez, Victor Hugo, Menton, Mary, Verchot, Louis V.

“…For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. …”
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Bedömning av spermiemotilitet i färsk, kyld samt selekterad hingstsperma med Qualisperm by Strutz, Hanna

“…Therefore, there have been a number of objective methods designed, for example CASA (Computer Assisted Semen Analysis) where QualispermTM (Biophos AG, Switzerland) is a new system based on different principles compared to earlier CASA-systems. While other CASA-machines takes a series of photographs and the trajectories are used to separate the sperm tracks into categories of different motility patterns, QualispermTM determines the number of particles (spermatozoa) crossing fields of view, yielding a regression fluctuation algorithm of sperm numbers and translation classes. …”
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A study of Village Milking Centre in China by Främling, Maja-Lena

“…VMC is a centre provided with milking machines and bulk milk coolers. Local farmers bring their cows to the VMC two to three times per day. …”
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A qualitative study exploring women’s empowerment in coffee cooperatives in Chiapas, Mexico by Eissler, Sarah, Rubin, Deborah, de Anda, Victoria

“…The data were transcribed into Spanish and then translated into English. These transcripts were analyzed using thematic analysis in NVivo software. …”
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Desarrollo y escalamiento de bioplaguicidas by Díaz García, Andrés, Gómez Álvarez, Martha Isabel, Grijalba Bernal, Erika Paola, Santos Diaz, Adriana Marcela, Cruz Barrera, Fredy Mauricio, León Moreno, Diana Marcela, Alarcón Torres, Érika Andrea, Cotes Prado, Alba Marina

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