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Open research questions in Food Supply Chain Traceability

141 gap statements mined from Food Supply Chain Traceability papers in our 4.5M-paper local library, which holds 578 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The gap is due to the limitations of manual inspection procedures and the need for robust segmentation methods. The paper aims to address this gap by evaluating three segmentation pipelines.

    Structural and Computational Analysis of Segmentation Pipelines for Broiler Carcass Inspection Under Industrial Acquisition Conditions · 2026 · DOI

  • Further research could investigate the impact of digitalization on supply chain performance in other contexts. Future studies could explore the role of other mediators, such as technology acceptance and organizational culture, in the relationship between digitalization and supply chain performance. Research could also examine the effects of digitalization on supply chain resilience and adaptability.

    Performance Impact of Digitalization in the Food Supply Chain: Evidence from the Food Processing Complex in Ethiopia · 2026 · DOI

  • The literature on digitalization and supply chain performance lacks a comprehensive analysis of the impact of digitalization on supply chain performance in developing countries. There is a need to examine the effect pathways of digitalization on the food supply chain. The study aims to fill this research gap by analyzing the impact of digitalization on food supply chain performance in Ethiopia.

    Performance Impact of Digitalization in the Food Supply Chain: Evidence from the Food Processing Complex in Ethiopia · 2026 · DOI

  • Lack of standardized error reporting for AI-assisted detection components. Unequal burden of implementation costs imposed on smallholder producers.

    A Scoping Review on Digital Technology-Enabled Food Supply Chain Traceability for Food Fraud Prevention · 2026 · DOI

  • The paper implies that there is a gap in the current literature regarding the application of structural equation modelling to define a traceability model for a shrimp food supply chain in Ecuador. The research highlights the need for greater transparency, safety, and traceability in food supply chains.

    Traceability Model in an Agri-Food Chain: Application of Structural Equations · 2026 · DOI

  • The existing YOLOv11 framework has limitations in recognizing small-scale cow behavior targets. Class imbalance in the dataset can negatively impact model robustness.

    FSCA-YOLO: An Enhanced YOLO-Based Model for Multi-Target Dairy Cow Behavior Recognition · 2025 · DOI

  • The complexity of pig activity and environmental factors can affect the accuracy of pig face identification. The study needs to address the issue of overfitting and local minima during training. The selection of the optimal kernel function and its coefficient for the SVM model is a challenge.

    Study on the Impact of LDA Preprocessing on Pig Face Identification with SVM · 2025 · DOI

  • The study identifies a gap in the current methods for individual pig identification, which can be addressed by using LDA preprocessing with an SVM. The existing methods may not be efficient or accurate enough for individual pig identification.

    Study on the Impact of LDA Preprocessing on Pig Face Identification with SVM · 2025 · DOI

  • The need for further research to improve PLF technologies and create affordable models. The problem of convincing pig farmers to adopt new technologies that are economically viable is still unresolved. The gap in the efficient conversion of feed into growth, which is referred to as the feed conversion ratio (FCR).

    Smart Pig Farms: Integration and Application of Digital Technologies in Pig Production · 2025 · DOI

  • The need to maintain network complexity and real-time performance while enhancing deep feature extraction and accuracy. The challenge of distinguishing between small-scale cow behaviors and those with similar characteristics.

    A Study on Multi-Scale Behavior Recognition of Dairy Cows in Complex Background Based on Improved YOLOv5 · 2025 · DOI

  • Substantial variations in multi-scale features and high inter-class similarity among different cow behaviors are also identified as challenges. The existing methods are not effective in addressing these challenges.

    RFR-YOLO-Based Recognition Method for Dairy Cow Behavior in Farming Environments · 2025 · DOI

  • Although individual artificial intelligence (AI) applications are emerging, the research synthesizing the entire “Farm-to-Table” continuum remains scarce.

    Transformative artificial intelligence integration in aquatic supply chains: synergizing precision aquaculture with intelligent logistics and data-driven consumption · 2026 · DOI

  • The increasing demand for transparency and food safety in agricultural supply chains has highlighted the need for reliable traceability systems. Traditional methods of tracking fruits and vegetables rely on centralized databases and manual record-keeping.

    Trusted Blockchain-Based Traceability System for Fruit and Vegetable Agricultural Products · 2026 · DOI

  • Future research may focus on integrating wireless monitoring or IoT-based data logging for remote supervision, incorporating adaptive control algorithms to optimize salinity and temperature dynamically, and conducting physicochemical and sensory evaluations to further validate product quality improvements.

    Automated Salted Egg Production: An Arduino-Based System for Optimized Salting and Temperature Control · 2026 · DOI

  • Frontline workers who understand and trust the technology will use it more effectively and drive better outcomes than those who feel the change was imposed on them. Prioritise Integration from Day One: When selecting technology vendors, prioritise those with proven integration capabilities and open APIs.

    Automation and Digital Transformation in Dairy Logistics · 2026 · DOI

  • Weak ground truth - Data drift - Fragmented identifiers - Misaligned incentives - AI/ML models are limited by the quality of ground-truth labels - Optimization models can appear precise yet remain…

    From Decision-Support Tools to Digital Twins: A Review of Digital Farming, Data Platforms, and AI for Sustainable Dairy Systems · 2026 · DOI

  • Designing and scaling decision-intelligence systems - Developing frameworks that explicitly address heterogeneity - Improving the representation of production dynamics and enabling more realistic scenario…

    From Decision-Support Tools to Digital Twins: A Review of Digital Farming, Data Platforms, and AI for Sustainable Dairy Systems · 2026 · DOI

  • The presence of varying sheep and goat face sizes results in leakage and false detections, - The distinction between the sheep and goat's facial area and the body was unclear due to uneven lighting distribution, - The similarity in the color of the sheep and goat's facial region and the torso led to inaccurate positioning of the sheep and goat face bounding box and key…

    Multi-Scale Sheep and Goat Face Detection Network Based on Multi-Task Optimization · 2026 · DOI

  • Further research could focus on improving the detection of sheep and goat faces in various poses, - exploring the application of the proposed method in other animal face detection…

    Multi-Scale Sheep and Goat Face Detection Network Based on Multi-Task Optimization · 2026 · DOI

  • The field is empirically immature, - there is a lack of alignment between on-chain immutability and GDPR right to erasure, - there is an unequal burden of implementation costs imposed on smallholder producers, - scalability claims are often…

    A Scoping Review on Digital Technology-Enabled Food Supply Chain Traceability for Food Fraud Prevention · 2026 · DOI

  • Data quality and standardization issues, - Technological barriers that hinder adoption, - Resistance from the industry to embrace innovative practices.

    Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making · 2025 · DOI

  • Future directions for YOLO algorithms in animal phenotyping - Integration of YOLO with animal individual…

    A Review of You Only Look Once Algorithms in Animal Phenotyping Applications · 2025 · DOI

  • The inherent complexity of farm environments, - The constraints imposed by limited hardware performance, - The need for real-time accuracy and non-contact…

    Method for Dairy Cow Target Detection and Tracking Based on Lightweight YOLO v11 · 2025 · DOI

  • Improving the technologies and creating cheap and affordable but accurate models for PLF systems - Investigating the use of precision feeding techniques that provide the animals with a diet adapted to their production goals - Developing algorithms to control parameters in the production…

    Smart Pig Farms: Integration and Application of Digital Technologies in Pig Production · 2025 · DOI

  • Further research on improving the efficiency of multi-scale feature fusion, - Investigating the application of RFR-YOLO in other animal behavior recognition tasks, - Exploring the use of other optimization…

    RFR-YOLO-Based Recognition Method for Dairy Cow Behavior in Farming Environments · 2025 · DOI

Most-cited papers in Food Supply Chain Traceability

Most recent work

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Open questions raised by papers listed on this page include: (1) Further research could investigate the impact of digitalization on supply chain performance in other contexts (“Performance Impact of Digitalization in the Food Supply Chain: Evidence from…”, 2026); (2) Lack of standardized error reporting for AI-assisted detection components (“A Scoping Review on Digital Technology-Enabled Food Supply Chain Traceability…”, 2026); (3) Although individual artificial intelligence (AI) applications are emerging, the research synthesizing the entire “Farm-to-Table” continuum remains scarce (“Transformative artificial intelligence integration in aquatic supply chains:…”, 2026). 141 gap statements have been mined from Food Supply Chain Traceability papers in our 4.5M-paper local library, which holds 578 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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