Search Results - "machine learning"

Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India by International Food Policy Research Institute, Ghent University, University of Manchester

Subjects: “…machine learning…”
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Site-specific fertilizer recommendation using data driven machine learning enhanced wheat productivity and resource use efficiency by Liben, Feyera, Abera, Wuletawu, Chernet, Meklit Tariku, Ebrahim, Mohammed, Tilaye, Amsalu, Erkossa, Teklu, Degefie, Tibebe Degefie, Mponela, Powell, Kihara, Job, Tamene, Lulseged D.

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Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of Sorghum Crop’s Nitrogen Content by Hammouch, Hajar, Patil, Suchitra, Choudhary, Sunita, El-Yacoubi, A. Mounim, Masner, Jan, Kholová, Jana, Anbazhagan, Krithika, Vanek, Jirí, Qin, Huafeng, Stoces, Michal, Berbia, Hassan, Jagarlapudi, Adinarayana, Chandramouli, Magesh, Mamidi, Srinivas, Prasad, K.V.S.V., Baddam, Rekha

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Advancing an internet research agenda for CGIAR Focus Climate Security by Carneiro, Bia, Tucci, Giulia, Taiwo, Ibukun, Olomu, Rukeme

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In-season crop-type mapping in Kenya using Sentinel-2 imagery by Li, Hui, Guo, Zhe, Di, Liping, Guo, Liying, Zhang, Chen, Lin, Li

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Underlying drivers of deforestation in the Peruvian amazon and the highland region of the Andes by Juarez, H., Pradel, W., Navarrete, C., Gutiérrez, D., Hualla, V., Vanegas, M., Sylvester, J., Castro, A.

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Excellence in Agronomy (EiA) product - CG Labs by Excellence in Agronomy

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Foodgravity: understand food flows using classic gravity model and explainable artificial intelligence techniques by Dusabe, B.

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NIRCoreVision: A novel deep learning-based framework with GUI integration for core set selection from NIRS data using 1D CNN and k-means clustering by Singh, Naseeb, Kaur, Simardeep, Ranjan Padhi, Siddhant, John, Racheal, Shruti, Kumar, Amit, Rana, Jai Chand, Bhardwaj, Rakesh, Riar, Amritbir

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Mapping expected returns to fertilizer investments across Sub-Saharan Africa by Gebrekidan, Bisrat Gebrekidan, Chamberlin, Jordan

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500m gridded surfaces for changes in climate suitability for coffee production in Risaralda, Colombia by Vallejo Arango, Eliana, Navarro Racines, Carlos Eduardo, Ramírez Villegas, Julián Armando, Aguilar-Ariza, Andrés, Delerce, Sylvain Jean

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Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches by Prasetyo, L.B., Setiawan, Y., Condro, A.A., Kustiyo, K., Putra, E.I., Hayati, N., Wijayanto, A.K., Ramadhi, A., Murdiyarso, D.

“…We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). …”
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Integrating spatial analysis and machine learning approaches into the protocol for assessing spittlebug (Hemiptera: Cercopidae) resistance in Urochloa sp. by Hernández, Luis Miguel, Espitia-Buitrago, Paula, Cardoso Arango, Juan Andrés, Jauregui, Rosa

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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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Predicting High-Magnitiude, Low-Frequency Crop Losses Using Machine Learning: An Application to Cereal Crops in Ethiopia by Warner, James

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A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI) by Saha, Shree, Narayanan, Sudha

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Predicting climate-driven changes in reservoir inflows and hydropower in Côte d'Ivoire using machine learning modeling by Obahoundje, Salomon, Diedhiou, A., Akpoti, Komlavi, Kouassi, K. L., Ofosu, E. A., Kouame, D. G. M.

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Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia by Mann, Michael L., Malik, Arun S., Warner, James

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Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia by Mann, Michael L., Warner, James, Malik, Arun S.

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Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands by Zwick, Mike, Cardoso, Juan Andres, Gutiérrez-Zapata, Diana María, Cerón-Muñoz, Mario, Gutiérrez, Jhon Freddy, Raab, Christoph, Jonsson, Nicholas, Escobar, Miller, Roberts, Kenny, Barrett, Brian

Subjects: “…machine learning…”
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