Search Results - Intelligence tests.

The Great Use-It or Lose-It Intelligence Test by CGIAR Secretariat, Calvin, William H.

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CGIAR Initiative on Market Intelligence: Annual Technical Report 2022 by CGIAR Initiative on Market Intelligence

Subjects: “…equipment testing…”
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CGIAR Research Initiative on Market Intelligence: Annual Technical Report 2023 by CGIAR Initiative on Market Intelligence

Subjects: “…equipment testing…”
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CGIAR Research Initiative on Market Intelligence: Annual Technical Report 2024 by CGIAR Initiative on Market Intelligence

Subjects: “…equipment testing…”
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Real-time artificial intelligence driven diagnostic that can be used to assess diets by CGIAR Research Program on Agriculture for Nutrition and Health

Subjects: “…testing…”
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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 and Vietnam: Protocol of a randomized controlled pilot test by Braga, Bianca C., Arrieta, Alejandra, Bannerman, Boateng, Doyle, Frank, Koch, Bastien, Nguyen, Phuong Hong, Gelli, Aulo

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L'intelligence artificielle et protection des cultures by Athindehou, Faride, Euloge, Togbe, Ahohuendo, Bonaventure, Zandjanakou-Tachin, Martine, Omondi, Bonaventure Aman Oduor

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Behavioral market intelligence and its implications for seed systems development by Trachtman, Carly, Kramer, Berber, Demont, Matty

“…In this brief, we review part of the literature on behavioral constraints that result in slow varietal turnover and discuss the interventions that the CGIAR Initiative on Market Intelligence and Product Profiling for Impactful Breeding will be testing to overcome these behavioral barriers. …”
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Artificial intelligence for agricultural supply chain risk management: Constraints and potentials by Tzachor, Asaf

“…This research paper seeks to test these assumptions to inform the integration of AI in agricultural supply chains. …”
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Transforming food security and safety: the role of earth Intelligence in global food systems by Jampani, Mahesh, Lau, A., Aydin, O.

“…Yet in 2024, between 638 and 720 million people went hungry (according to the 2025 State of Food Security and Nutrition in the World Report), while climate change, polluted water, and intensive farming are making our food less safe. Earth Intelligence helps us see risks early, test scenarios, and act before problems escalate. …”
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Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation by Fu, Jiayi, Zheng, Shouzhi, Fan, Longjiang, Zheng, Xiaoming, Qian, Qian

“…This study introduces the Breeding 5.0 framework, driven by artificial intelligence (AI) and robotics, marking a shift from empirical selection to intelligent systems. …”
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Creating a Virtual Food Policy Expert Using Artificial Intelligence: Advantages and Disadvantages of Customized Chatbot by Koo, Jawoo

“…Artificial intelligence (AI) tools are rapidly transforming the way people work, providing opportunities for improving efficiency, accelerating innovations and supporting human decision-making. …”
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From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa by Smith Ruiz, Paulina, Grossi, Amanda, Chipunza, Unity

“…Artificial Intelligence (AI) offers new opportunities to improve the efficiency, accessibility, and inclusiveness of e-learning in agricultural and climate research. …”
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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

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Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana by Folson, Gloria, Bannerman, Boateng, Atadze, Vicentia, Ador, Gabriel, Kolt, Bastien, McCloske, Peter, Gangupantulu, Rohit, Arrieta, Alejandra, Braga, Bianca C., Arsenault, Joanne, Kehs, Annalyse, Doyle, Frank, Tran, Lan Mai, Hoang, Nga Thu, Hughes, David, Nguyen, Phuong Hong, Gelli, Aulo

“…Equivalence of nutrient intake was tested using mixed effect models adjusted for repeated measures, by comparing ratios (FRANI/WR and 24HR/WR) with equivalence margins at 10%, 15% and 20% error bounds. …”
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Market intelligence to understand groundnut preferences among Tanzanian farmers: A data-driven approach shows that gender is not the only factor behind farmer choice of crop variet... by 1000FARMS Platform, de Sousa, Kaue

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PROBES: Early-stage explorations of digital potential across CGIAR by Alliance of Bioversity International and CIAT, International Water Management Institute - IWMI

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Video based deep learning deciphers honeybee waggle dances in natural conditions by Grison, Sylvain, Siddaganga, Rajath, Hedge, Shrihari, Burridge, James, Blok, Pieter M., Krishnan, Smitha, Brockmann, Axel, Guo, Wei

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Automatic speech recognition model development report by Mganga, Nelson, Jones-Garcia, Eliot, Koo, Jawoo

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EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models by Yu, Tingxi, Zhang, Hao, Chen, Shoukun, Gao, Shang, Liu, Ze, Wang, Jiankang, Crossa, Jose, Montesinos-Lopez, Osval Antonio, Hearne, Sarah, Li, Huihui

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