Search Results - "machine"

Understanding users formal meetings by Alliance of Bioversity International and CIAT

Subjects: “…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 by Porciello, J., Lipper, L., Bourne, T., Ivanina, M., Lin, S., Langleben, S.

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Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India by 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

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Developing timely and actionable drought forecasts for the Limpopo River Basin by Montes, Carlo

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Agriculture, Food and Nutrition Security: Concept, Datasets and Opportunities for Computational Social Science Applications by Amjath-Babu, T.S., López Ridaura, Santiago, Krupnik, Timothy J.

Subjects: “…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 by 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

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

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

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

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

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

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

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

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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

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

Subjects: “…machine learning…”
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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

Subjects: “…machine learning…”
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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.

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

Subjects: “…machine learning…”
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