Search Results - "Artificial Intelligence"

From the basics to emerging diagnostic technologies: What is on the horizon for tilapia disease diagnostics? by Ha Thanh Dong, Chaijarasphong, T., Barnes, A.C., Delamare-Deboutteville, Jerome, Lee, P.A., Senapin, S., Mohan, Chadag V., Tang, K.F.J., McGladdery, S.E., Bondad-Reantaso, M.G.

“…The review also covers current and emerging molecular diagnostic technologies for tilapia pathogens including polymerase chain reaction methods (conventional, quantitative, digital), isothermal amplification methods Loop-mediated Isothermal Amplification (LAMP), recombinase polymerase amplification (RPA), clustered regularly interspaced short palindromic repeats (CRISPR)-based detection, lateral flow immunoassays, as well as discussing what is on the horizon for tilapia disease diagnostics (next generation sequencing, artificial intelligence, environmental Deoxyribonucleic Acid (DNA) and Ribonucleic Acid (RNA) and point-of-care testing) providing a future vision for transferring these technologies to farmers and stakeholders for a sustainable aquatic food system transformation.…”
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Food system innovations and digital technologies to foster productivity growth and rural transformation by Benfica, Rui, Chambers, Judith A., Koo, Jawoo, Nin-Pratt, Alejandro, Falck-Zepeda, José B., Stads, Gert-Jan, Arndt, Channing

“…Given their transformative potential and the urgency of developing the enabling R&D and trajectories required for impact, we highlight genome editing bio-innovations, specifically CRISPR-Cas9, to address sustainable agricultural growth; and digital technologies, including remote sensing, connected sensors, artificial intelligence, digital advisory services, digital financial services, and e-commerce, to help guide the operations and decision-making of farmers, traders, and policymakers in agricultural value chains. …”
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The evolution of Kenya's animal health surveillance system and its potential for efficient detection of zoonoses by Kahariri, Samuel, Thumbi, S.M., Bett, Bernard K., Mureithi, M.W., Nyaga, N., Ogendo, A., Muturi, Mathew, Thomas, Lian F.

“…Furthermore, we recommend enhancing technological advancements and adopting artificial intelligence in surveillance practices, as well as implementing risk-based surveillance to optimize the allocation of surveillance resources.…”
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Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains by Ahmed, Zia Uddin, Krupnik, Timothy J., Timsina, Jagadish, Saiful Islam, Hossain, M. Khaled, Alanuzzaman Kurishi, A.S.M., Shah-Al Emran, Harun-Ar-Rashid, McDonald, Andrew, Gathala, Mahesh K.

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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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Harnessing Regional Frameworks for Weather, Water, and Climate Services in Africa Key to Securing its Climate Future by Amha, Yosef, Demissie, Teferi, Solomon, Dawit

“…If it is executed correctly, the RFWWCS will increase coordination of the regional institutions in co-producing and communicating WWCS; governance and institutional arrangements through clarity in roles and responsibilities among the stakeholders; alignment with global, regional, and national initiatives on the long-term sustainability of climate services within various sectors; building on existing initiatives and infrastructure; integrating recent advances in technology for improved service delivery in areas such as Earth observation and artificial intelligence. Success in the RFWWCS would be realized through fundamental challenges posed by adequate weather and climate observations, enormous, underdeveloped access, and effective sharing of data together with forecasting skills. …”
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Remotely sensed high-resolution soil moisture and evapotranspiration: bridging the gap between science and society by Huang, J., Sehgal, V., Alvarez, L. V., Brocca, L., Cai, S., Cheng, R., Cheng, X., Du, J., El Masri, B., Endsley, K. A., Fang, Y., Hu, J., Jampani, Mahesh, Kibria, Md. G., Koren, G., Li, L., Liu, L., Mao, J., Moreno, H. A., Rigden, A., Shi, M., Shi, X., Wang, Y., Zhang, X., Fisher, J. B.

“…SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. …”
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High value crop products and rural prosperity: The role of operations research and analytics by Cock, James H, Jimenez, Daniel R

“…Thus, the integration of operations research and advanced analytics, including data acquisition, machine learning, and artificial intelligence, in optimizing the cultivation and marketing of HVCPs. …”
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AI-powered detection and quantification of Post-harvest Physiological Deterioration (PPD) in cassava using YOLO foundation models and K-means clustering by Gomez Ayalde, Daniela, Giraldo Londono, Juan Camilo, Quiroga Mosquera, Audberto, Luna-Melendez, Jorge Luis, Gimode, Winnie, Tran, Thierry, Zhang, Xiaofei, Selvaraj, Michael Gomez

“…Despite challenges with similar color intensities in the image data, the combination of SAM, image processing techniques such as RGB color filtering, and machine learning (ML) algorithms was effective in removing yellow and gray color sections, significantly reducing the Mean Absolute Error (MAE) in PPD estimation from 20.01 to 15.50. Moreover, Artificial Intelligence (AI)-based algorithms allow for efficient analysis of large datasets, enabling rapid screening of cassava roots for PPD symptoms. …”
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Women at the Helm: Navigating the Digital Landscape of Agriculture by Dominic, D.M., Bhuvana, N., Mittal, N., Sulaiman, R.V., Puskur, Ranjitha

“…Technologies like big data, Artificial Intelligence (AI), Internet of Things (IoT), and various digital platforms play a crucial role, aiding aspiring entrepreneurs from idea conceptualization to identifying opportunities in production, marketing, and distribution. …”
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Genomic resources in plant breeding for sustainable agriculture by Thudi, Mahendar, Palakurthi, Ramesh, Schnable, James C., Chitikineni, Annapurna, Dreisigacker, Susanne, Mace, Emma, Srivastava, Rakesh K., Satyavathi, C. Tara, Odeny, Damaris, Tiwari, Vijay K., Lam, Hon-Ming, Hong, Yan Bin, Singh, Vikas K., Li, Guowei, Xu, Yunbi, Chen, Xiaoping, Kaila, Sanjay, Nguyen, Henry, Sivasankar, Sobhana, Jackson, Scott A., Close, Timothy J., Shubo, Wan, Varshney, Rajeev K.

“…We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.…”
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Entrenamiento sobre Trips, BMS, Observatorio del Frijol, procesamiento industrial, cadena de valor, perfil del producto, sistemas alimentarios, y modelos de negocio socios del pr... by Gomez Jimenez, Maria Isabel, Wilker, Jennifer Leanne, Buendia Castellanos, Hector Fabio, Guerrero Rodriguez, Alberto Fabio, Rivera, Tatiana, Hurtado, Jhon Jairo, Gallego Castillo, Sonia, Andrade, Robert, Pedreros, Ricardo, Vergara, Jose Daniel, Blanco Garcia, Maria Alejandra, Garcia, Carolina, Barrios Perez, Camilo

“…The training on IoT technologies and agroclimatic modeling introduced an integrated ecosystem of real-time monitoring, artificial intelligence, and crop modeling, highlighting its usefulness in addressing climate variability, improving common bean monitoring, and supporting breeding and crop management decisions, with active participation from farmers. …”
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Support the implementation of 2025 International Forum of Low-Emission Agrifood Systems such as organizing a low carbon food system workshop at WAFI in China and conducting consult... by CGIAR Science Program on Climate Action

“…Technologies from climate-smart agriculture and biological breeding to digital technology and artificial intelligence, technological innovation have provided new solutions to global challenges. …”
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Convolutional neural networks to assess bergamot essential oil content in the field from smartphone images by Anello, Matteo, Mateo, Fernando, Bernardi, Bruno, Giuffrè, Angelo Maria, Blasco, José, Gómez-Sanchis, Juan

“…The application of advanced modelling techniques based on artificial intelligence and digital device technology can contribute to this goal. …”
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From pledges to places: action agendas need spatial data to integrate climate and biodiversity action by Hagenström, Paul, Pettorelli, Nathalie, Boran, Idil, Bridgewater, Peter, Delgado Pugley, Deborah, Folkard-Tapp, Hollie, Hsu, Angel, Imbach Bartol, Pablo Andrés, J. Kok, Marcel T, Van Deveer, Stacy D, Widerberg, Oscar, Chan, Sander

“…They highlight how advances in satellite technology and artificial intelligence create new opportunities for assessing integrated climate–biodiversity initiatives. …”
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Improving sawmill scheduling through Industry 4.0 by Dahl, Petter

“…This new era has developed from combining new technologies into the manufacturing system, such as artificial intelligence, additive printing, and cyber-physical systems. …”
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Adoption of Climate-Smart Agricultural Practices (CSAPs) in Ethiopia by Abegaz, Assefa, Abera, Wuletawu, Jaquet, Stephanie, Tamene, Lulseged D.

“…In future, deploying remote-sensing technology, artificial intelligence and modelling approaches, and implementing Agricultural Weather Index-Based Insurance may also support CSAPs adoption.…”
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Assessment of Current Drying Practices for South American Pepper Varieties ( Capsicum spp ) with Respect to Final Product Quality by Halle, Christian, Nagle, Marcus, Argyropoulos, Dimitrios, Serrano, E., Peña Pineda, Karla Mónica, Martínez, C. B., Jäger, Matthias, Mueller, Joachin

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Restoration of grazing lands by One CGIAR researchers and partners by Sircely, Jason, Belgacem, Azaiez Ouled, Cardoso Arango, Juan Andres, Arango, Jacobo

“…Degraded landscapes in the Orinoquia need effective solutions to minimize the impacts of overgrazing and restore cover of native forest trees, leading to the development of monitoring methods using remote sensing and artificial intelligence (AI) that can track tree cover, aboveground biomass, and soil carbon over private ranches such as Hacienda San José at 9,000 ha.…”
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Transitioning from consensual to objective seasonal climate and hydrological forecasting in West Africa and the Sahel by Tanimoune, Laouali I, Assoumana, Boubacar Toukal, Mohamed, Hamatan, Halidou, Tinni Seydou, Ali, Abdou, Kiema, Arsene, Nimon, Pouwereou, Soumana, Djibo, Houngnibo, Mandela C M, Segnon, Alcade Christel, Zougmore, Robert Bellarmin

“…A new objective seasonal climate and hydrological forecasting system has been developed, with the support of AICCRA project, combining advanced statistical methods, multi-model ensembles, bias correction, and artificial intelligence. Performance evaluation shows significant improvements in accuracy and usability. …”
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