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Smart farming has entered a phase in which agricultural fields, machines, livestock systems, greenhouses, and advisory platforms increasingly generate continuous streams of digital information. Sensors promise greater precision, faster monitoring, and more responsive management, yet their proliferation has not automatically improved farmer decision-making. Many farms now face a paradox in which more data are available than can be interpreted, trusted, or acted upon in routine agronomic practice. The central problem addressed in this article is the widening gap between sensor abundance and actionable agronomy. Smart farming systems often excel at data collection but remain less effective at filtering, prioritising, and translating data into decisions that farmers can use under time, labour, weather, and resource constraints. This creates a data-to-decision gap that reduces the practical value of agricultural digitalisation. The objective of the article is to propose a conceptual pathway for reducing data overload in smart farming systems. The pathway moves from sensor data acquisition to information filtering, decision relevance, decision translation, and actionable agronomic delivery. It is designed to help researchers, developers, advisors, and farm managers evaluate not only whether data are collected, but whether they are converted into usable decisions. The proposed pathway identifies four sequential stages: sensor data acquisition, information filtering and relevance assessment, decision translation, and actionable agronomy delivery. Three tables support the framework by cataloguing sensor sources, classifying filtering strategies, and presenting the full pathway. The article concludes that the value of smart farming should be judged less by the amount of data generated and more by the quality, usability, and timeliness of the agronomic decisions enabled.

Agri-food supply chains are increasingly exposed to overlapping disruptions arising from climate variability, geopolitical instability, pandemic effects, price volatility, labour shortages, and changing consumer demand. These pressures have intensified interest in resilience-oriented technologies that can help supply chains anticipate disruption, absorb shocks, adapt operations, and recover service continuity. Artificial intelligence has emerged as a prominent candidate because it can transform fragmented supply chain data into forecasts, alerts, recommendations, and operational decisions. This systematic review examines how artificial intelligence is being applied to strengthen agri-food supply chain resilience. The review focuses on four functional domains: forecasting, traceability, risk detection, and decision support. It asks whether current AI applications merely improve isolated technical tasks or whether they provide demonstrable evidence of resilience improvement across agri-food supply systems. The review follows a systematic design informed by PRISMA reporting principles and structured literature review procedures. A corpus of 32 peer-reviewed journal articles published between 2017 and 2025 was synthesised. The selected studies cover AI forecasting models, blockchain-enabled traceability, quality and safety monitoring, logistics optimisation, supply chain decision support, and review-methodology guidance. The synthesis shows that forecasting research is technically advanced, especially in price prediction, demand forecasting, and quality-sensitive perishable supply chain management. Traceability and risk detection studies show strong growth around blockchain, IoT, big data, computer vision, and food safety monitoring. Decision support research connects AI to resilience more directly, but most evidence remains conceptual, model-based, or function-specific rather than validated through real disruption events. The review concludes that AI has substantial potential to support agri-food supply chain resilience, but the evidence base remains fragmented. Existing studies tend to focus on single functions such as forecasting accuracy, provenance visibility, or routing efficiency, while system-level resilience outcomes are rarely measured. Future research should therefore move from isolated AI applications toward integrated, empirically validated, resilience-focused agri-food intelligence systems.

Circular food systems require a shift from linear models of production, consumption, disposal, and remediation toward systems that continuously recover value from biological materials. This conceptual article argues that such a transition cannot rely on policy, behavioural change, or technological substitution alone. It also requires an intelligence layer capable of sensing waste generation, interpreting material quality, identifying recovery options, and coordinating decisions across fragmented food-system actors. Artificial intelligence offers this intelligence layer by enabling predictive, diagnostic, and decision-support capacities across the food chain. Rather than treating AI as a narrow automation tool, this article positions AI as cognitive infrastructure for circular food economies. Its role is to connect data on food loss, by-product composition, logistics, environmental impacts, recovery technologies, and stakeholder constraints. The article develops a conceptual framework for AI-enabled circular food systems. The framework integrates five functions: waste detection, valorisation pathway selection, resource recovery, sustainability-oriented decision support, and cross-actor coordination. These functions are presented as mutually reinforcing layers that allow food materials to be tracked, evaluated, redirected, and valorised before they become unmanaged waste. The article contributes to sustainability and circular economy scholarship by linking food-waste valorisation with material flow intelligence and multi-criteria decision support. It highlights how AI can support real-time evaluation of waste streams, match heterogeneous residues with suitable recovery pathways, and improve the coordination of producers, processors, retailers, consumers, waste managers, and policymakers. It also clarifies the risks of fragmented digitalisation when data systems are not aligned with circular outcomes. The proposed framework is intended for researchers, practitioners, and policymakers seeking to design circular food systems that are adaptive, evidence-based, and sustainability-oriented. It does not present new empirical data. Instead, it provides a conceptual basis for future empirical testing, digital platform development, policy design, and interdisciplinary research on AI-enabled food circularity.