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A novel hybrid AI-driven optimization architecture is proposed to advance climbing perch cultivation in intelligent aquaculture environments, aiming for simultaneous gains in yield and cost performance. By merging Taguchi experimental design with reinforcement learning and metaheuristic strategies, the system pinpoints the key environmental and operational drivers of fish growth and economic return. A multi-objective regression technique is deployed to fine-tune crucial variables, namely water temperature, dissolved oxygen, pH, feeding regimes, and stocking density. The RL-AMIS approach, leveraging Pareto front evaluation and TOPSIS, effectively reconciles the tension between expenditure and output. Empirical outcomes document a 15.3% drop in operating expenses and a 17.8% boost in growth efficiency relative to traditional practice. These insights confirm that AI-based optimization can strengthen scalability and flexibility within aquaculture operations, thereby advancing sustainable production agendas.

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.