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In the dairy industry, maintaining the welfare of every cow and curbing financial losses requires individual-level surveillance, which is achieved through cow Re-Identification (Re-ID). Computer vision-based Re-ID hinges on visually distinguishing characteristics, such as the specific coat patterns seen in breeds like Holstein. However, manually labelling every cow across all farms is financially unfeasible. We aim to formulate Re-ID strategies that work on both annotated and non-annotated farms, accommodating new individuals and varied settings. Unsupervised Domain Adaptation (UDA) approaches aim to close this divide by transferring knowledge from labelled source domains to unlabelled target domains. However, they have largely been tailored to person and vehicle Re-ID scenarios. Our research proposes Cumulative Unsupervised Multi-Domain Adaptation (CUMDA) to address the challenges of constrained identity variation and dissimilar farm appearances. CUMDA gathers knowledge across all domains, reinforcing specialization in familiar domains while strengthening generalization to unknown ones. Our offerings include a CUMDA technique that adapts to multiple unlabelled target domains while safeguarding source-domain efficacy, along with thorough cross-dataset testing across three cattle Re-ID datasets. These tests reveal substantial gains in source conservation, target-domain specialization, and adaptation to unseen domains.

Intelligent agriculture is often presented as a technological pathway for increasing productivity, reducing input waste, and improving farm-level decision-making. In this view, the farm is commonly treated as an optimisation target in which sensors, analytics, and automated recommendations are used to improve decisions at a particular moment. Although this perspective has advanced digital agriculture, it does not fully explain how farms accumulate experience, revise practices, and become more capable over time. This article argues that the intelligent farm should be conceptualised not only as a data-driven production system but also as a learning system. A learning system is one that senses changing conditions, interprets feedback, adapts action, and modifies its decision logic across repeated cycles of experience. This framing shifts attention from isolated optimisation events to the longer-term evolution of farm knowledge, adaptive capacity, and agronomic intelligence. The objective of the article is to develop a theory of the intelligent farm as a learning system. The theory integrates three mechanisms: continuous feedback, local adaptation, and agronomic intelligence evolution. Together, these mechanisms explain how intelligent farms can move from collecting data to building situated knowledge that improves future decision quality. The proposed theory shows that intelligent farms become smarter when feedback streams are interpreted through local context and converted into evolving decision rules. Two tables are included: one contrasts optimisation and learning system views of the intelligent farm, and the other presents the full theoretical model. The article concludes that treating farms as learning systems offers a more realistic and resilient paradigm for intelligent agriculture.

Agri-food systems increasingly operate under compound crises in which climate shocks, supply disruptions, market instability, ecological stress, and social vulnerability interact. These crises do not affect production, distribution, livelihoods, and food access in isolation. They move through connected risk pathways that can destabilise farms, supply chains, public institutions, and households at the same time. Artificial intelligence can support prediction, monitoring, early warning, and decision support in these contexts. However, technical forecasting alone does not constitute resilience when risks cascade across multiple domains. Agri-food resilience requires that predictive information be translated into adaptive decisions, coordinated response, and food security protection. This article develops a conceptual model of AI-supported agri-food resilience under compound crises. The model links climate shocks, supply disruptions, farmer adaptation, and food security governance as interacting domains. It treats AI as a decision-support function embedded within social, institutional, ecological, and market systems rather than as a stand-alone technological solution. The model identifies resilience mechanisms that connect risk detection, signal integration, uncertainty communication, adaptive decision support, institutional coordination, feedback learning, and accountability. It also clarifies boundary conditions that determine whether AI strengthens or weakens resilience. These include data quality, interpretability, local relevance, farmer participation, institutional response capacity, equity, and governance accountability. The article contributes a structured conceptual model for understanding how AI can support agri-food resilience when analytics are aligned with adaptation needs and food security objectives. It argues that resilient agri-food AI systems must connect prediction with human judgement, local knowledge, public action, and accountable coordination. The proposed model offers a basis for future empirical testing, policy design, and responsible innovation in crisis-sensitive food systems.