基于神经网络和机器学习的白羽肉鸡体重估测算法

针对规模化肉鸡养殖生产中,传统肉鸡称重方法易造成应激问题,设计一种基于神经网络和机器视觉技术的非接触式肉鸡体重估测方法.应用深度相机采集白羽肉鸡的红外图像和深度信息,以目标识别算法YOLOv3和卷积网络分割算法FCN(Fully convolutional networks)为基础构建肉鸡区域提取模型,YOLOv3和F...

Weight estimation model of breeding chickens based on neural network and machine learning

Aiming at the problems of broiler weighing difficulty and easy to cause stress during the large-scale broiler breeding process, a non-contact broiler weight estimation method based on neural network and machine vision technology is adopted to study the change and monitoring of broiler weight. The results show that the infrared image and depth information of white feather broilers are collected by depth camera. Based on the target recognition algorithm YOLOv3 and the convolutional network segmentation algorithm FCN(Fully convolutional networks), a broiler region extraction model is constructed. The precisions of YOLOv3 and FCN models reach 98. 1% and 97. 8%, respectively, and the recall rate is 100%. Combining the depth images of broilers, extracting the relevant characteristics of broiler weight, constructing multiple regression model estimation algorithms, training and adjusting and optimizing the broiler weight estimation model, the Adaboost regressor model achieves the best estimation effect on the test set. The coefficient of determination is 0. 95, and the absolute error varies within the range of 0. 01-0. 32 kg. In conclusion, the estimation algorithm model proposed in this paper can better predict the weight of broilers, and provides technical support for the automatic weighing of broilers in the actual production environment.