The Role of Generative Artificial Intelligence in Digital Agri-Food

Journal of Agriculture and Food Research, Год журнала: 2025, Номер unknown, С. 101787 - 101787

Опубликована: Март 1, 2025

Язык: Английский

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Exploring Attributions in Convolutional Neural Networks for Cow Identification

Applied Sciences, Год журнала: 2025, Номер 15(7), С. 3622 - 3622

Опубликована: Март 26, 2025

Face recognition and identification is a method that well established in traffic monitoring, security, human biodata analysis, etc. Regarding the current development implementation of digitalization all spheres public life, new approaches are being sought to use opportunities high technology advancements animal husbandry enhance sector’s sustainability. Using machine learning present study aims investigate possibilities for creation model visual face farm animals (cows) could be used future applications manage health, welfare, productivity at herd individual levels real-time. This provides preliminary results from an ongoing research project, which employs attribution methods identify parts facial image contribute most cow using triplet loss network. A dataset identifying cows environments was therefore created by taking digital images holdings with intensive breeding systems. After normalizing images, they were subsequently segmented into background regions. Several then explored analyzing attributions examine whether or regions have greater influence on network’s performance animal.

Язык: Английский

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The Emergence of Generative AI in Higher Education

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 73 - 96

Опубликована: Апрель 25, 2025

The emergence of Generative AI (GAI) in education brings both benefits and challenges. As GAI tools become more common schools, concerns about ethics, academic honesty, how well teachers students adapt to are a major concern. This chapter explored the challenges using as experienced by rural areas, where access technology digital skills may affect use. Following Husserlian phenomenological research design, participants were interviewed, transcripts examined thematic analysis. findings show that struggle balance with traditional teaching, while face literacy integrity. Despite these issues, see potential improving learning. emphasizes need for clear ethical guidelines, training, school support ensure used responsibly. These importance further on AI's role education.

Язык: Английский

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GANs for Data Augmentation with Stacked CNN Models and XAI for Interpretable Maize Yield Prediction

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100992 - 100992

Опубликована: Май 1, 2025

Язык: Английский

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