Resultados de búsqueda - "Machining."

Understanding users formal meetings por Alliance of Bioversity International and CIAT

Materias: “…machine learning…”
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Mining the gaps:using machine learning to map a million data points from agricultural research from the global south por Porciello, J., Lipper, L., Bourne, T., Ivanina, M., Lin, S., Langleben, S.

Materias: “…machine learning…”
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Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India por Nayak, Harisankar, Silva, ‪João Vasco, Parihar, Chiter Mal, Krupnik, Timothy J., Sena, Dipaka Ranjan, Kakraliya, S., Jat, Hanuman Sahay, Sidhu, Harminder Singh, Sharma, Parbodh Chander, Jat, Mangi Lal, Sapkota, Tek Bahadur

Materias: “…machine learning…”
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Developing timely and actionable drought forecasts for the Limpopo River Basin por Montes, Carlo

Materias: “…machine learning…”
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Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications por Amjath-Babu, T.S., López Ridaura, Santiago, Krupnik, Timothy J.

Materias: “…machine learning…”
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Untangling the effect of soil quality on rice productivity under a 16-years long-term fertilizer experiment using conditional random forest por Garnaik, Saheed, Samant, Prasanna Kumar, Mandal, Mitali, Mohanty, Tushar Ranjan, Dwibedi, Sanat Kumar, Patra, Ranjan Kumar, Mohapatra, Kiran Kumar, Wanjari, Ravi H., Sethi, Debadatta, Sena, Dipaka Ranjan, Sapkota, Tek Bahadur, Nayak, Jagmohan, Patra, Sridhar, Parihar, Chiter Mal, Nayak, Harisankar

Materias: “…machine learning…”
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Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier por Schulthess, Urs, Rodrigues, Francelino, Taymans, Matthieu, Bellemans, Nicolas, Bontemps, Sophie, Ortíz Monasterio, Jose Iván, Gerard, Bruno G., Defourny, Pierre

Materias: “…machine learning…”
Enlace del recurso

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy por Silva, ‪João Vasco, Heerwaarden, Joost van, Reidsma, Pytrik, Laborte, Alice G., Tesfaye, Kindie, Ittersum, Martin K. van

Materias: “…machine learning…”
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Mapping major land use types in Nandi County using remote sensing and machine learning por Guo, Zhe, Walter, Kibet

Materias: “…machine learning…”
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Drought vulnerability of central Sahel agrosystems: a modelling-approach based on magnitudes of changes and machine learning techniques por Hanade Houmma, I., El Mansouri, L., Gadal, S., Faouzi, E., Touré, A.A., Garba, M., Imani, Y., El-Ayachi, M., Hadria, R.

Materias: “…machine learning…”
Enlace del recurso

Small reservoirs in the northern regions of Ghana and their vulnerability to drying por Siabi, Ebenezer K., Akpoti, Komlavi, Zwart, Sander J.

Materias: “…machine learning…”
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Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India por International Food Policy Research Institute, Ghent University, University of Manchester

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

Materias: “…machine learning…”
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Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning por Adesokan, M., Otegbayo, B., Alamu, E.O., Olutoyin, M.A., Maziya-Dixon, B.

Materias: “…machine learning…”
Enlace del recurso

Predicting aflatoxin risk in maize using machine learning and satellite data in East and Southern Africa por Gachoki, Stella, Muthoni, Francis, Mahuku, George S., Atehnkeng, J., Tripathi, Leena

Materias: “…machine learning…”
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Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of Sorghum Crop’s Nitrogen Content por 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

Materias: “…machine learning…”
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Advancing an internet research agenda for CGIAR Focus Climate Security por Carneiro, Bia, Tucci, Giulia, Taiwo, Ibukun, Olomu, Rukeme

Materias: “…machine learning…”
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In-season crop-type mapping in Kenya using Sentinel-2 imagery por Li, Hui, Guo, Zhe, Di, Liping, Guo, Liying, Zhang, Chen, Lin, Li

Materias: “…machine learning…”
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Underlying drivers of deforestation in the Peruvian amazon and the highland region of the Andes por Juarez, H., Pradel, W., Navarrete, C., Gutiérrez, D., Hualla, V., Vanegas, M., Sylvester, J., Castro, A.

Materias: “…machine learning…”
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Mapping mechanization suitability using machine learning, presence data and spatial co-variates: Methodological approach por Adhikari, Subash, Koirala, Pankaj, Kafle, Sagar, Mponela, Powell

Materias: “…machine learning…”
Enlace del recurso