Search Results - Intelligence levels.

Unlocking the Potential of AI in Agricultural Research: Insights from ICRISAT’s Sensitization Workshop and Survey Findings by Rupavatharam, Srikanth, Patil, Mukund, Gelaye, Kidia, Reddy, Nagarjuna, Mitnala, Sreevani, Dhungel, Rajeev, Ravula, Padmaja

“…ICRISAT organized an AI sensitization training session on September 2, 2025, to build awareness and strengthen institutional capacity in Artificial Intelligence, aligning with its broader mission of advancing digital agriculture. …”
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Evolution of Market Segmentation and Target Product Profile Development in the CIP Potato and Sweetpotato Breeding Programs by Friedmann, M., Chaudari, S., Mendes, T., Polar, Vivian, Ssali, R.T.

“…A survey questionnaire was developed to evaluate the development and use of market intelligence to inform the design of TPPs (see Annex 1). …”
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Maize farmers acquire early maturity seed across production environments by Rutsaert, Pieter, Donovan, Jason A., Willwerth, Hanna S., Nabwile, Colleta, Michelson, Hope C., Muthee, John

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The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers by Girmay, Mengisti Berihu

“…One example is precision agriculture - a practice that employs data collection and analysis to optimize the use of inputs such as water, fertilizers, and pesticides based on local environmental conditions at the sub-field level. Artificial Intelligence (AI) and the availability of low-cost sensors have renewed interest in precision agriculture and have broadened areas of application to the livestock sector. …”
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Building Climate Resilience in Agrifood Systems Through AI- Powered Agromet Advisory Services by Kumar, Shalander, Rao, K.P.C., Kumar, G. Kishore

“…South Asia’s agrifood systems are highly vulnerable to climate variability, with frequent droughts, floods, and heatwaves threatening rainfed farming and rural livelihoods. Traditional district-level advisories lack the precision and timeliness required for effective risk management at the farm scale. …”
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At the ground level : Accounting for farmers’ perspectives and inclusion issues in seed promotion by Cavicchioli, Martina, Kramer, Berber, Trachtman, Carly

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Exploring CGIAR Level Agricultural Results Interoperable System Architecture (CLARISA) by Tobon, Hector, Col, Valentina de, Ramírez, Margarita M., Bonaiuti, Enrico, Abreu, David

“…CLARISA represents an essential step of business intelligence towards enabling more effective strategic and operational decision-making. …”
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Economic AI on the move: the relationship between sugar-sweetened beverages consumption and weight gain in Kyrgyzstan by Suyunov, Alisher, Akramov, Kamiljon T.

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Measuring adherence, acceptability and likability of an artificial-intelligence-based, gamified phone application to improve the quality of dietary choices of adolescents in Ghana... by Braga, Bianca C., Arrieta, Alejandra, Bannerman, Boateng, Doyle, Frank, Koch, Bastien, Nguyen, Phuong Hong, Gelli, Aulo

“…We developed an artificial-intelligence-based phone application called Food Recognition Assistance and Nudging Insights (FRANI) to address these problems. …”
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Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI by Gomez, Daniela, Selvaraj, Michael Gomez, Casas, Jorge, Mathiyazhagan, Kavino, Rodriguez, Michael, Assefa, Teshale, Mlaki, Anna, Nyakunga, Goodluck, Kato, Fred, Mukankusi, Clare, Girma, Ellena, Mosquera, Gloria, Arredondo, Victoria, Espitia, Ernesto

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Assessing the relevance of 2022-2024 variety releases deriving from CGIAR Breeding Pipelines and Networks – an internal MELIA study by Bänziger, Marianne, Lenaerts, Bert, Gitonga, Agnes, Coaldrake, Peter, Demont, Matty, Quinn, Michael

“…The range or uncertainty is due to the level of data incompleteness. This is the first time that an estimate at such a scale (cross-CGIAR) and at the granularity of market segments has been made possible. …”
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Eco-efficiency and agricultural innovation systems in developing countries: evidence from macro-level analysis by Grovermann, C., Assfaw Wossen, Tesfamicheal, Müller, A, Nichterlein, K.

“…Innovations generally emerge from collective intelligence and action, but innovation systems are often poorly understood. …”
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Bridging Landscape-Level Insights and Plot-Scale Actions through a Natural Resource Management Tool by Reddy, Nagarjuna N., Garg, Kaushal K., Singh, Ramesh

“…To overcome this limitation, a digital decision dashboard is being developed to integrate landscape-level datasets with plot-scale decision support. …”
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WaterCopilot: an AI-driven virtual assistant for water management by Vickneswaran, Keerththanan, Garcia Andarcia, Mariangel, Retief, H., Dickens, Chris, Silva, Paulo

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Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? by Ferguson, Amy, Murray, Catherine, Tessema, Yared Mesfin, McKeown, Peter C., Reymondin, Louis, Loboguerrero Rodriguez, Ana María, Talsma, Tiffany, Allen, Brenden, Jarvis, Andy, Golden, Aaron, Spillane, Charles

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A flexible and practical approach for real-time weed emergence prediction based on Artificial Neural Networks by Chantre Balacca, Guillermo Ruben, Vigna, Mario Raul, Renzi Pugni, Juan Pablo, Blanco, Anibal Manuel

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What do we know about the future of crop pests and diseases in relation to food systems? by Petsakos, Athanasios, Montes, Carlo, Pequeno, Diego, Schiek, Benjamin, Sonder, Kai

“…Crop pests and diseases (P&D) can cause substantial yield losses and pose a threat to global food security. Losses at a regional level can even exceed 40 percent for crops like maize and rice. …”
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Impact of novel methods and research approaches in plant pathology: Are individual advances sufficient to meet the wider challenges of disease management? by Jeger, Michael, Beresford, Robert, Berlin, Anna, Bock, Clive, Fox, Adrian, Golding, Kaitlin, Newton, Adrian C., Vicent, Antonio, Xu, Xiangming

“…Advances have been made in applying artificial intelligence technologies across many areas of plant pathology but have yet to be integrated within any coordinated research agenda.…”
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Decomposing USDA ending stocks forecast errors by Goyal, Raghav, Adjemian, Michael K., Glauber, Joseph W., Meyer, Seth

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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 policy 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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