Food production Looking to the future, several potential advancements in AI technologies could further transform the food industry

The gap

Evidence profile

Sourced from the inline gaps and future work of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 72 times in total.

Supporting evidence — 3 representative gaps

• A design concept for data-driven brewing: sensor-based system architecture and ML applications for sustainability in micro-breweries (2026) · Discover Artificial Intelligence · doi

  • Model Evaluation and data strategyFuture work should investigate the practical performance of the proposed ML approaches, including their adaptability to dynamic brewing conditions, sensitivity to data quality, and the feasibility of implementation given available infrastructure. • Scalability and transferabilityFurther research is needed to adapt and scale the system architecture for larger breweries or other food production environments, including the evaluation of industry-specific constraints and requirements. The use cases, which include predictive maintenance for valves and motors, predictive quality during lautering and fermentation, and real-time optimization of CIP processes, illustrate that the application of intelligent systems in brewing is not limited to a single stage, but can be extended across the entire process chain. Secondly, the ML models proposed, including one-class SVMs, NN, and deep rein- forcement learning, are well-established in other domains but have not yet been tested under the realistic conditions of a micro-brewery. Moreover, the economic feasibility of the proposed system has not been assessed.

  generalinline gaps
  Keywords: proposed including evaluation brewing conditions quality feasibility system predictive model strategyfuture investigate practical performance approaches
• The role of artificial intelligence in transforming food innovation: Advances and future directions (2026) · African Journal of Food Science · doi

  food production Looking to the future, several potential advancements in AI technologies could further transform the food industry. A key area of focus will be sustainability initiatives, where future AI applications may focus on creating circular food systems where waste is minimized, and resources are reused effectively. For instance, AI could optimize facilities recycling processes within (Jagtap et al., 2019). Additionally, enhanced consumer engagement through personalization will become increasingly important. As consumers seek tailored dietary solutions, AI-driven platforms will become more prevalent in providing personalized nutrition plans based on individual health data and preferences (Javaid et al., 2021; Shah, 2024). Finally, the integration with emerging technologies, such as blockchain for improved traceability and transparency in supply chains is expected to enhance compliance with food safety protocols while fostering consumer trust (Bader and Rahimifard, 2020; Raj, 2024). The convergence of these technologies promises a more resilient and efficient food ecosystem capable of meeting future challenges.

  generalfuture work
  Keywords: food future technologies focus consumer become production looking several potential advancements further transform industry area
• Machine Learning for Quality Control in the Food Industry: A Review (2025) · Foods · cited 72× · doi

  In this review, we systematically analyzed the role of ML and AI-driven QC systems in enhancing QC processes across the food industry. Thee selected publications were categorized based on application domain, AI/ML technique, learning paradigm, and task objective. As shown in Figure 11, 6 major application areas were identified across the 25 reviewed studies. The most represented categories were Food Quality Applications, Defect Detection and Visual Inspection Systems, and Food Industry Efficiency and Industry 4.0 Models, each comprising 20% of the total (five publications each). These were followed by Ingredient Optimization and Nutritional Assessment and Packaging—Sensors and Predictive QC, both accounting for 16% (four publications each). The least represented category was Supply Chain—Traceability and Transparency, with 8% (two publications). This distribution reflects a strong focus on product-centric AI applications, while supply chain-related innovations remain relatively underexplored. Figure 11. Pie chart presenting the approaches according to each category. Foods 2025, 14, 3424 28 of 35 A clear methodological trend emerges with NNs dominating the landscape, ap- pearing in 17 of the reviewed studies. Their widespread use underscores their strong suitability for modeling complex nonlinear relationships and high-dimensional data patterns across food-related applications. Ensemble learning methods are the second most common approach, applied in six studies, suggesting moderate interest in leverag- ing combined model strategies for improved robustness. Regression-based techniques are used in two studies, while Bayesian methods, SVMs, and Instance-Based Learning each appear in only one study. This distribution, illustrated in Figure 12, highlights the dominant reliance on neural architectures for AI/ML tasks in the food sector, reflecting their predictive power and flexibility. However, the underrepresentation of alternative methods also suggests a lack of comprehensive benchmarking and limited exploration of techniques that may offer advantages in terms of interpretability, computational efficiency, or uncertainty modeling. Figure 12. Distribution of AI/ML techniques. According to the application domain, NNs are the most dominant technique, account- ing for 16 out of 27 model applications (59.3%) (Figure 13). They are applied across all six sub-domains, with the highest concentration in Defect Detection and Visual Inspec- tion Systems and Ingredient Optimization and Nutritional Assessment (four studies each, 14.8%), followed by Packaging—Sensors and Predictive QC and Industry 4.0 Models (three studies each, 11.1%) and Food Quality Applications (two studies, 7.4%). Ensemble learning represents the second most frequently used approach, appearing in six studies (22.2%). These are distributed across Food Quality Applications (three studies, 11.1%) and one study each (3.7%) in Defect Detection and Visual Inspection Systems, Packaging and Pr

  generalfuture work
  Keywords: food applications across systems industry publications learning based application quality defect detection visual packaging predictive

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