Digital Transformation in Recreational Fisheries Monitoring: A Framework for Data Integration, Analysis, and Management Applications

ABSTRACT We examined opportunities and challenges associated with camera‐based monitoring systems in recreational fisheries management. These systems offer significant advantages in data collection efficiency and coverage compared to traditional monitoring methods, but present complex technical, analytical, and societal challenges that require innovative solutions. Current developments in camera monitoring technology highlight advances in hardware reliability, data management infrastructure, and artificial intelligence (AI)‐powered analytical capabilities that transform raw visual data into actionable management information. Key challenges include hardware constraints in marine environments, massive data storage and processing requirements, integration with existing management frameworks, and privacy considerations. Recent technological developments, particularly AI and edge computing, offer promising solutions for automated analysis, behavioral pattern recognition, and cost‐effective data management. AI‐powered computer vision systems enable automated angler counting, activity classification, and catch event detection, while edge computing capabilities provide real‐time processing and alert generation without requiring constant data transmission. Integration of drone‐based monitoring technologies with fixed camera systems adds opportunities for extensive spatial coverage while highlighting regulatory and operational challenges that must be systematically addressed. Successful implementation of effective camera‐based monitoring systems requires addressing institutional capacity constraints, particularly when resources limit technical expertise, computational infrastructure, and financial resources that may be inadequate for full‐scale deployment. Of critical importance are interdisciplinary collaborations among fisheries scientists, computer vision researchers, and statistical data scientists in realizing the full potential of these systems. Critical areas for future development include robust hardware solutions, scalable data infrastructure, standardized analytical frameworks, and inclusive governance structures. Continued advancement in this field will depend on supporting equilibrium between monitoring effectiveness and ethical implementation through participatory approaches, standardized protocols, and capacity‐appropriate deployment strategies.