Search Results - "machine"

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

Subjects: “…machine learning…”
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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

Subjects: “…machine learning…”
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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.

Subjects: “…machine learning…”
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Global habitat suitability of Spodoptera frugiperda (JE Smith) (Lepidoptera, Noctuidae): key parasitoids considered for its biological control by Tepa-Yotto, Ghislain T., Tonnang, Henri E.Z., Goergen, Georg E., Subramanian, S., Kimathi, E.K., Abdel-Rahman, E.M., Flo, D., Thunes, K.H., Fiaboe, K., Niassy, S., Bruce, A., Mohamed, S.A., Tamo, Manuele, Ekesi, S., Saethre, M.G.

Subjects: “…machine learning…”
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Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 by Ali, Ashenafi, Erkossa, Teklu, Gudeta, Kiflu, Abera, Wuletawu, Mesfin, Ephrem, Mekete, Terefe, Haile, Mitiku, Haile, Wondwosen, Abegaz, Assefa, Tafesse, Demeke, Belay, Gebeyhu, Getahun, Mekonen, Beyene, Sheleme, Assen, Mohamed, Regassa, Alemayehu, Selassie, Yihenew G., Tadesse, Solomon, Abebe, Dawit, Walde, Yitbarek, Hussien, Nesru, Yirdaw, Abebe, Mera, Addisu, Admas, Tesema, Wakoya, Feyera, Legesse, Awgachew, Tessema, Nigat, Abebe, Ayele, Gebremariam, Simret, Aregaw, Yismaw, Abebaw, Bizuayehu, Bekele, Damtew, Zewdie, Eylachew, Schulz, Steffen, Tamene, Lulseged D., Elias, Eyasu

Subjects: “…machine learning…”
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Modeling the spatial distribution of soil organic carbon and carbon stocks in the Casanare flooded savannas of the Colombian Llanos by Martin López, Javier Mauricio, Verchot, Louis V., Martius, Christopher, Silva, Mayesse Aparecida da

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Predictive modeling of nutritional quality in Urochloa pastures from multispectral sensors and images using machine learning approaches by Camelo-Munevar, Rodrigo Andrés, Hernández, Luis Miguel, Jauregui, Rosa, Cardoso Arango, Juan Andrés

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Unveiling Drought-Resilient Pathways: Integrating High Throughput Phenotyping and Multivariate Modeling to Enhance Barley Adaptation to Climate Change by Ouahid, Safae, Backhaus, Anna Elisabeth, Jimenez, José-Antonio, Visioni, Andrea, Sanchez-Garcia, Miguel

Subjects: “…machine learning…”
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Working women and caste in India: A study of social disadvantage using feature attribution by Joshi, Kuhu, Joshi, Chaitanya K.

Subjects: “…machine learning…”
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Nowcasting food insecurity interest with google trends data by Caravaggio, Nicola, Carneiro, Bia, Resce, Giuliano

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Risks and farmers’ investment in productive assets in Nigeria by Takeshima, Hiroyuki, Yamauchi, Futoshi

Subjects: “…milling machine…”
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Estimating elements susceptible to urban flooding using multisource data and machine learning by Asfaw, Wegayehu, Rientjes, T., Bekele, Tilaye Worku, Haile, Alemseged Tamiru

Subjects: “…machine learning…”
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Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids by He, Kunhui, Yu, Tingxi, Gao, Shang, Chen, Shoukun, Li, Liang, Zhang, Xuecai, Huang, Changling, Xu, Yunbi, Wang, Jiankang, Boddupalli, Prasanna, Hearne, Sarah, Li, Xinhai, Li, Huihui

Subjects: “…machine learning…”
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Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning by Rejith, Rajan G., Sahoo, Rabi N., Ranjan, Rajeev, Kondraju, Tarun, Bhandari, Amrita, Gakhar, Shalini

Subjects: “…machine learning…”
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ICRISAT and Partners Launch AI-Powered Climate Advisory Initiative to Boost Farmer Resilience by Kumar, Shalander, Subbarao, AVM, Dhulipala, Ram, Kumar; Kishore G., Santanu Bal, Chandran, Sarath

Subjects: “…machine learning…”
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Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning by Hengl, T., Leenaars, Johan G.B., Shepherd, Keith D., Walsh, Markus G., Heuvelink, Gerard B.M., Mamo, T., Tilahun, H., Berkhout, Ezra D., Cooper, M., Fegraus, E., Wheeler, I., Kwabena, N. A.

Subjects: “…machine learning…”
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