Resultados de búsqueda - "machine learning"

Soybean rust early warning system in East and Southern Africa por Gachoki, S., Muthoni, F., Mureithi, H., Tripathi, L.

Materias: “…machine learning…”
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EasyIDP: A python package for intermediate data processing in UAV-based plant phenotyping por Wang, Haozhou, Duan, Yulin, Shi, Yun, Kato, Yoichiro, Ninomiya, Seishi, Guo, Wei

Materias: “…machine learning…”
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Mature products & assets: AGROTUTOR por Excellence in Agronomy

Materias: “…machine learning…”
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Dataset: Forage grasses in crop fields from ultra-high spatial resolution UAV-based imagery por Cardoso Arango, Juan Andres, Jauregui, Rosa Noemi, Camelo-Munevar, Rodrigo Andres, Ruiz-Hurtado, Andres Felipe, Arrechea-Castillo, Darwin Alexis

Materias: “…machine learning…”
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Fine-tuned AI for tracking policy demands and studies por Yego, Francis, Song, Chun, Laporte, Marie-Angelique

Materias: “…machine learning…”
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Farmers agronomic management responses to extreme drought and rice yields in Bihar, India por Mkondiwa, Maxwell, Kishore, Avinash, Veettil, Prakashan Chellattan, Sherpa, Sonam, Saxena, Satyam, Pinjarla, Bhavani, Urfels, Anton, Poonia, Shishpal, Ajay, Anurag, Craufurd, Peter, Malik, Ram K., McDonald, Andrew J.

Materias: “…machine learning…”
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Forages ROIs: Automated forage grass detection in aerial imagery por Ruiz-Hurtado, Andres Felipe, Camelo-Munevar, Rodrigo Andres, Jauregui, Rosa Noemi, Cardoso Arango, Juan Andres

Materias: “…machine learning…”
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Video based deep learning deciphers honeybee waggle dances in natural conditions por Grison, Sylvain, Siddaganga, Rajath, Hedge, Shrihari, Burridge, James, Blok, Pieter M., Krishnan, Smitha, Brockmann, Axel, Guo, Wei

Materias: “…machine learning…”
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Recent development and innovative tools for climate and hydrological data collection and analysis in the Abbay Basin por Getaneh, Yonas, Abera, Wuletawu, Ayehu, Getachew Tesfaye, Degefie, Tibebe, Tamene, Lulseged

Materias: “…machine learning…”
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T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy por Li, Lei, Tian, Wenfei, Zhao, Zihui, Xiao, Yonggui, Zhang, Yong, Liu, Jindong, Velu, Govindan, Zhonghu, He, Ibba, Maria Itria

Materias: “…machine learning…”
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Inclusive agricultural insurance for sustainable wheat intensification as a pathway to smallholder resilience in Ethiopia por Ebrahim, Mohammed

Materias: “…machine learning…”
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Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings por Rupavatharam, Srikanth, Patil, Mukund, Gelaye, Kidia, Reddy, Nagarjuna, Mitnala, Sreevani, Dhungel, Rajeev, Ravula, Padmaja

Materias: “…machine learning…”
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Pathogen and pest communities in agroecosystems across climate gradients: anticipating future challenges in the highland tropics por Mouafo-Tchinda, R.A., Sula, A.I.P., Etherton, B.A., Okonya, J.S., Nakato, G.V., Xing, Y., Robledo, J., Adhikari, A., Blomme, G., Kantungeko, D., Nduwayezu, A., Kreuze. J.F., Kroschel, J., Legg, J.P., Garrett, K.A.

Materias: “…machine learning…”
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Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon por Silatsa, F.B., Yemefack, Martin, Tabi, F.O., Heuvelink, G.B., Leenaars, J.G.

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Mining the gaps: Using machine learning to map 1.2 million agri-food publications from the Global South por Commission on Sustainable Agriculture Intensification

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Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches por Álvarez Mendoza, Cesar I., Guzman, Diego, Casas, Jorge, Bastidas, Mike, Polanco, Jan, Valencia Ortiz, Milton Orlando, Montenegro, Frank, Arango, Jacobo, Ishitani, Manabu, Gómez Selvaraj, Michael

Materias: “…machine learning…”
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Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia por Leggesse, E.S., Zimale, Fasikaw A., Sultan, D., Enku, T., Srinivasan, R., Tilahun, Seifu A.

Materias: “…machine learning…”
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A comprehensive analysis of machine learning and remote sensing techniques in studying climate hazards-induced crop yield variations por Obahoundje, Salomon, Tilahun, Seifu A., Zemadim, Birhanu, Schmitter, Petra S.

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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Using machine learning tools for salinity forecasting to support irrigation management and decision-making in a polder of coastal Bangladesh por Behera, Abhijit, Sena, Dipaka Ranjan, Matheswaran, Karthikeyan, Jampani, Mahesh, Hasib, Md. R., Mondal, M. K.

Materias: “…machine learning…”
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