面向圈养模式的智能精准投喂系统研究

Research of Intelligent Precision Feeding System for Captive Breeding

摘要:

随着水产养殖业的快速发展，过度投喂导致饵料浪费与水质污染及投喂不足所引发的鱼群生长营养不良等问题愈发凸显，针对圈养模式，提出了一种基于视觉与多种传感器的圈养模式智能精准投喂系统，对RGB图像、深度图像、压强传感器和加速度传感器等多源数据融合实时量化成年鱼摄食强度并实现精准投喂控制。以改进的YOLO v8n-seg模型为核心进行RGB图像分割，将水面波动状态分为强、弱和无3种状态；在水面状态分割区域内采用HSV颜色检测方法对水面鱼饵进行面积检测；通过帧差法分析深度图像连续两帧的深度差异，将水面波动量化为强、弱、无3个等级；利用压强传感器和加速度传感器采集的数据提取关键特征，通过随机森林模型对鱼群摄食状态进行分类弥补单一视觉特征的局限性。通过加权融合策略将5类数据决策模块的结果进行融合并建立实时投喂决策模型。多次实地试验结果表明，投饵系统摄食强度评估精度达到95.45%，投喂误差率仅为1.72%，能够准确识别鱼群摄食强度，有效减少饵料浪费和水体污染，在实际圈养模式环境中具有较好的实用性和实时性。

Abstract:

With the rapid development of aquaculture, problems such as excessive feeding leading to feed waste and water pollution, as well as insufficient feeding causing malnutrition in fish populations, have become increasingly prominent. An intelligent and precise feeding system for cage culture was proposed based on vision and multiple sensors. By integrating multi-source data such as RGB images, depth images, pressure sensors, and acceleration sensors, the system quantified the feeding intensity of adult fish in real time and achieved precise feeding. The system used an improved YOLO v8n-seg model for RGB image segmentation, dividing the water surface fluctuation state into three categories: strong, weak, and none. Within the segmented water surface state region, the HSV color detection method was employed to detect the area of fish feed on the water surface. The system analyzed the difference in depth values between two consecutive frames in the depth image by using the frame difference method, quantifying water surface fluctuations into three levels: strong, weak, and none. Key features were extracted from the data collected by pressure sensors and acceleration sensors, and a random forest model was used to classify the feeding state of the fish population to compensate for the limitations of single visual features. Ultimately, the results of the five data decision modules were fused through a weighted fusion strategy to establish a real-time feeding decision model. Through multiple field tests, the accuracy of feeding intensity assessment of this feeding system based on feeding behavior reached 95.45%, with a feeding error rate of 1.72%. It can accurately identify the feeding intensity of fish populations, effectively reduced feed waste and water pollution, and had good practicality and real-time performance in actual cage culture environments.