Search Results - "machine learning"

Free online trainings on soil health monitoring with satellite based remote sensors by Huq, Rafiq, Lesueur, Didier

“…Each session featured three one-hour lectures as outlined below: 1. « Earth Observation and Soil Health: Where Innovation Meets Practice » - Focus: The origins and development of Earth Observation (EO) technology, its applications innatural sciences and soil health monitoring, as well as its opportunities and limitations. 2. « Satellite-Based Remote Sensing: How Earth Observation Enhances Soil Health Monitoring » - Focus: Various remote sensing (RS) technologies, their application in soil data collection, and the role of RS data in monitoring soil health. 3. « From Data to Decisions: Translating Remote Sensing Insights into Practical Soil Health Solutions » - Focus: AI-based agricultural informatics, the use of machine learning in data analysis and automation, and the importance of baseline data for machine learning algorithms.…”
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Real-Time Monitoring of Cropping Pattern Using Geospatial Analysis by Gumma, Murali K.

“…This contribution will significantly impact agricultural monitoring and decision-making processes through the integration of remote sensing and machine learning. These advancements will lead to more accurate and timely data on crop patterns, enabling better resource management and policy-making.…”
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Compositional nutrient diagnosis (CND) and associated yield predictions in maize: a case study in the northern Guinea savanna of Nigeria by Shehu, B.M., Garba, I.I., Jibrin, J.M., Kamara, A., Adam, A.M., Craufurd, Peter Q., Aliyu, K.T., Rurinda, J., Merckx, Roel

“…We also compared and contrasted the use of linear regression models and bootstrap forest machine learning to predict maize yield based on nutrient concentration in ear leaves. …”
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Inspire Challenge Award: Seeing is believing – Using smartphone camera data by CGIAR Platform for Big Data in Agriculture

“…This project integrates machine-learning analysis of cellphone camera images of crops into the functioning of an insurance and farm advisory service. …”
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Estimation of height and aerial biomass in Eucalyptus globulus plantations using UAV-LiDAR by Enriquez Pinedo, Lucía, Ortega Quispe, Kevin, Ccopi Trucios, Dennis, Urquizo Barrera, Julio, Rios Chavarría, Claudia, Pizarro Carcausto, Samuel, Matos Calderon, Diana, Patricio Rosales, Solanch, Rodríguez Cerrón, Mauro, Ore Aquino, Zoila, Paz Monge, Michel, Castañeda Tinco, Italo

“…Therefore, the use of LiDAR in conjunction with machine learning represents an effective alternative for biomasss estimation, with great potential in such plantations and contribute to more sustainable exploitation of timber resources.…”
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NextGen agroadvisory expanding in scope and extent: The effort to cover more crops and bundle with lime advisory by Tilaye, Asmalu, Abera, Wuletawu, Liben, Feyera, Ali, Ashenafi, Assefa, Feben, Tibebe, Degefie, Ebrahim, Mohammed, Mesfin, Tewodros, Erkossa, Teklu, Chernet, Meklit, Tamene, Lulseged D.

“…The system uses machine learning models to predict site-specific fertilizer rates using filed trial and covariates (soil, topography, and climate) data. …”
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Regional Training on Climate-Smart Agriculture, Soil Fertility Management, and Soil Health Monitoring in Eastern and Southern Africa by Ambaw, Gebermedihin, Recha, John W.M., Gitau, Angela, Solomon, Dawit

“…It emphasized integrating geospatial tools, artificial intelligence, and machine learning for soil mapping and addressing policy and gender considerations to promote sustainable agricultural practices across varied agroecological zones. …”
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Development of a 3D accelerometer to predict dairy goats behaviour by Cano, C., Fajardo, B., Marchese, M., Iervolino, V., Gómez-Maqueda, I., Calvet, Salvador, Estellés, Fernando, Villagrá, Arantxa

“…In conclusion, this study highlights the potential of using these specific accelerometers and machine learning algorithms to monitor and assess livestock behavior, providing valuable and promising insights into animal health and well being…”
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From space to soil: Advancing crop mapping and ecosystem insights for smallholder agriculture by Guo, Zhe

“…This project centers on in-season crop type mapping in Nandi County, Kenya, utilizing time-series Sentinel-2 imagery and supervised machine learning techniques. The objective is to produce accurate crop-type maps to support agricultural management activities such as yield estimation, acreage statistics, disaster damage assessment, and ecosystem evaluation. …”
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Impressed with ChatGPT's agricultural knowledge? CGIAR's open access effort (probably)* enabled it by Koo, Jawoo, Devare, Medha, King, Brian

“…It is possible that in the near future we could see these large-scale machine learning models having an impact in agri-food systems, for example by providing individually tailored advice to smallholder farmers.…”
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Optimization of the ILCYM platform by Carhuapoma, Pablo, Gamarra, H., Kreuze, Jan F.

“…With a roadmap that includes machine learning, integration with global databases, and visualization enhancements, the document aligns strongly with Resilient Agrifood Systems, Climate-smart Agriculture, and Regenerative Agriculture, solidifying ILCYM as a strategic CGIAR tool for plant health under climate change.…”
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Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 by Dzurume, Tatenda, Darvishzadeh, Roshanak, Dube, Timothy, Amjath Babu, T.S., Billah, Mutasim, Syed Nurul Alam, Kamal, Mustafa, Md. Harun-Or-Rashid, Biswas, Badal Chandra, Md. Ashraf Uddin, Md. Abdul Muyeed, Md Mostafizur Rahman Shah, Krupnik, Timothy J., Nelson, Andrew

“…Moreover, the results demonstrated the feasibility of detecting the severity of FAW infestation using temporal Sentinel-2 data and machine learning techniques. These findings underscore the potential of remote sensing and machine learning techniques for effectively monitoring and managing crop pests. …”
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AI-powered tool for spidermite damage assessment in tropical grasses by Ruiz-Hurtado, Andres Felipe, Espitia Buitrago, Paula Andrea, Kimani, Adrian Matingl, Chidawanyika, Frank, Cardoso Arango, Juan Andres, Jauregui, Rosa Noemi

“…This software uses advanced image processing and machine learning models to perform damage classification for field images and plant segmentation using a salient object detection approach. …”
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ECOSat (Estimation of carbon offsets with satellites) - Final report by Schulthess, Urs, Fonteyne, Simon, Gardeazabal Monsalve, Andrea

“…These data can then be used to train machine learning based algorithms.…”
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Informe final Datathon CIP 2025 by Fuentes, A., Juarez, H., Ochoa, J., Haan, Stef de

“…Using open datasets on climate, hydrology, vegetation, soil carbon, and wildlife, participants developed machine learning models, geospatial analyses, and decision-support tools to assess pastureland restoration, ecosystem services, and climate resilience. …”
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Replication Data for: The impact of climate change on cacao production in Central America and the Caribbean by Bunn, Christian, Lundy, Mark M., Castro-Llanos, Fabio Alexander

“…This analysis was done with a machine learning approach: Random Forest model, which took into account climatic variables, such as precipitation, temperature and evapotranspiration. …”
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Tailored, climate-informed and location-specific agro-advisory services in the highlands of Ethiopia increased smallholder farmers’ wheat grain yields and profitability by Tamene, Lulseged D., Wuletawu, Abera, Liben, Feyera Merga

“…The data-driven DST was developed by integrating over 25,000 crop responses to fertilizer application datasets and corresponding biophysical co-variants, using machine learning algorithms. Currently, the DST is being piloted across the highlands of Ethiopia. …”
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Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions by Harris, E., Barthel, M., Leitner, Sonja, Ouma, T., Agredazywczuk, P., Otinga, A., Njoroge, R., Oduor, Collins, Oluoch, K. C.

“…Novel measurements, models and machine learning can be used in combination with existing techniques to understand drivers, increase spatial coverage, and extrapolate to new locations. …”
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A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest by Singh, B. K., Chakraborty, Dulal, Kalra, Naveen, Singh, Jaya

“…On the other hand, the machine learning (CNN) model has better ability to capture lower yields. …”
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Predicting rice phenotypes with meta and multi-target learning by Orhobor, Oghenejokpeme I., Alexandrov, Nickolai N., King, Ross D.

“…The features in some machine learning datasets can naturally be divided into groups. …”
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