Field scale wheat yield prediction using ensemble machine learning techniques

Smart Agricultural Technology, Год журнала: 2024, Номер 9, С. 100543 - 100543

Опубликована: Сен. 7, 2024

Язык: Английский

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Harnessing Large Language Models and Stochastic Programming for Optimized Plant Breeding Strategies

Опубликована: Янв. 14, 2025

The convergence of Generative AI (GenAI) and stochastic programming introduces unprecedented opportunities for optimizing plant breeding strategies under uncertainty. This paper presents a hybrid framework that integrates Large Language Models (LLMs) with to enhance decision-making in crop improvement. LLMs are employed analyze vast datasets, generate insights on genotype-environment interactions, simulate scenarios, while optimizes the selection genotypes maximum yield resilience. Case studies demonstrate effectiveness this approach addressing challenges such as climate variability evolving market demands, offering transformative solution sustainable agriculture.

Язык: Английский

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Agroforlight: 3D light distribution modelling in agroforestry systems using high-resolution tree LiDAR scans

Agroforestry Systems, Год журнала: 2025, Номер 99(5)

Опубликована: Май 23, 2025

Язык: Английский

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From Prediction to Foresight: The Role of AI in Designing Responsible Futures

Journal of artificial intelligence for sustainable development., Год журнала: 2024, Номер 1(1)

Опубликована: Дек. 1, 2024

In an era marked by rapid technological advancements and complex global challenges, responsible foresight has emerged as essential framework for policymakers aiming to navigate future uncertainties shape the future. Responsible entails ethical anticipation of emerging opportunities risks, with a focus on fostering proactive, sustainable, accountable design. This paper coins term "responsible computational foresight", examining role human-centric artificial intelligence computationalmodeling in advancing foresight, establishing set foundational principles this new field presenting suite AI-driven tools currently shaping it. AI, particularly conjunction simulations scenario analysis, enhances policymakers' ability address uncertainty, evaluate devise strategies geared toward resilient futures. However, extends beyondmere technical forecasting; it demands nuanced understanding interdependencies within social, environmental, economic political systems, alongside commitment ethical, long-term decision-making that supports human intelligence. We argue AI will play supportive tool responsible, human-centered complementing rather than substituting policymaker judgment enable proactive ethically sound advocates thoughtful integration into practices empower communities they confront grand challenges 21st century.

Язык: Английский

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Ontological Model of Wheat Production Process for Digital Twin of Plant

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 117 - 126

Опубликована: Янв. 1, 2024

Язык: Английский

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