Food Industry Sustainability Through Digitalization: A Systematic Review
Bibliographic record
Abstract
Digitalization is transforming food security by enhancing efficiency, transparency, and sustainability in agriculture. This study systematically reviews the impact of digital technologies, including IoT, blockchain, AI, and automation, in addressing key food security challenges such as supply chain disruptions, resource inefficiencies, and climate risks. Using a structured methodology, peer-reviewed literature from 2023 to 2024 was analyzed from databases like Scopus and Web of Science. The study follows the PRISMA framework, identifying 29 relevant articles classified into five themes: Emerging Technologies, Digitalization & Sustainability, AI & Automation, Resilience & Optimization, and Knowledge & Innovation. The findings highlight how digitalization improves traceability, predictive analytics, and decision-making in agriculture, enhancing resource management and reducing food waste. However, challenges such as high implementation costs, interoperability issues, and digital literacy gaps hinder adoption. The study emphasizes the need for regulatory frameworks, stakeholder collaboration, and infrastructure investments to maximize the benefits of digital solutions. Integrating AI-driven predictive models and blockchain-enabled transparency mechanisms could further enhance food security by strengthening risk management and supply chain resilience. While digital technologies hold great potential, addressing socioeconomic and technical barriers is crucial for sustainable implementation. Future research should focus on developing inclusive policies and scalable digital solutions to ensure food security in an increasingly digital world.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".