The role of generative artificial intelligence in digital agri-food
Bibliographic record
Abstract
The agriculture and food (agri-food) sector faces rising global concerns about its sustainability and resilience to climate events. Thus, new solutions are needed to ensure environmental and food security. Artificial Intelligence (AI) offers inventive solutions to improve agricultural and food production practices. Generative AI methods, such as generative adversarial networks (GANs), variational autoencoders, and large language models (LLMs), add to the transformative process initiated by AI and expert systems in agricultural and food-related practices to enhance productivity, sustainability, and resilience. This study categorizes generative AI approaches and their capabilities in agri-food systems and provides a comprehensive review of the current landscape of generative AI applications in the sector. It discusses the impact of these technologies on enhancing agricultural productivity, food quality, and safety, as well as sustainability, presenting potential use cases like combatting climate change and foodborne disease modeling that highlight the practical applications and benefits of generative AI in agri-food. Furthermore, it addresses the ethical implications of deploying generative AI, including privacy, security, reliability, and unbiased decision-making. • Generative AI enhances agri-food systems through predictive analytics, food design, and sustainability. • Applications include disease modeling, supply chain optimization, and combating climate change. • Emerging technologies like transformers and large language models drive innovation in agriculture and food production. • Ethical considerations address transparency, bias, and privacy in AI-driven agri-food solutions. • Practical insights bridge AI advancements with agri-food challenges for researchers and policymakers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".