Fish Feeding Behavior Activity Analysis | 鱼类摄食行为活跃度分析

Through built-in cameras of smart feeders or fixed cameras on aquariums, the system captures fish feeding videos after feeding. Using AI object detection and motion analysis, it identifies the number of fish gathering for food, feeding intensity (fish swimming speed, feeding action frequency), and remaining feed amount, and computes a comprehensive feeding activity score (0-100). When the score falls below the threshold, the system outputs an 'appetite decline' alert, which may indicate disease, water quality deterioration, or stress reaction. Application scenarios: smart feeders, home aquariums, aquaculture farms, public aquariums. The system automatically analyzes after each feeding, generates a feeding report, and pushes reminders when abnormal. Skill features: appetite decline is an early signal of fish diseases (e.g. enteritis, parasites). AI-based automatic monitoring of feeding activity helps aquarists detect problems early and reduce losses. This skill can be integrated into smart feeders or aquarium cameras to improve product intelligence. | 通过智能喂食器内置摄像头或鱼缸固定摄像头，在投喂后拍摄鱼群摄食视频，利用 AI 目标检测和运动分析技术，识别鱼群聚集抢食的数量、摄食强度（鱼只游动速度、摄食动作频率）以及剩余饲料量，综合计算摄食活跃度评分（0-100 分）。当活跃度评分低于阈值时，输出'食欲下降'提示，可能预示疾病、水质恶化或应激反应。应用场景：智能喂食器、家庭鱼缸、水产养殖场、水族馆。系统在每次投喂后自动分析，生成摄食报告，异常时推送提醒。技能特点：食欲减退是鱼类疾病（如肠炎、寄生虫）的早期信号。通过 AI 自动监测摄食活跃度，可帮助养鱼者及早发现问题，减少损失。该技能可集成到智能喂食器或鱼缸摄像头中，提升产品智能化水平。

by @18072937735 (smyx-skills)·MIT-0