Design And Implementation Of Monitoring System For Body Weight And Breeding Environment Of White Feather Broiler

With the development of China’s economy,the significant improvement of people’s living standard and the rapid growth of the demand for meat and other necessities have made the scale and intensification of broiler breeding a necessary and mainstream breeding mode at this stage.In the intensive feeding mode,broiler weight is one of the important indicators of concern in the broiler breeding process.Uneven weight distribution and inconsistent growth conditions of broilers in the chicken house are important factors affecting the healthy breeding of broilers.At present,the weight of broilers is mainly estimated by instruments such as weight boxes,electronic scales or weighbridges,which are time-consuming,highly subjective and poorly feasible,and are likely to cause stress reaction in broilers.They are also extremely detrimental to the physical and mental health of administrators who are in a long-term environment in chicken coops.Therefore,accurate and real-time estimation of broiler weight can detect abnormal weight broilers early and continuously improve the productivity and production efficiency of broiler farms.This paper takes the broilers of New Hope White Feather Broiler Farm in Shandong Province as the research object,applies the depth camera(Kinect2.0)to collect the infrared images and depth information of white feather broilers in real time.It adopts machine learning techniques such as Mask R-CNN,ABR,Springboot,Springcloud and HDFS and big data technology to research and design monitoring system of white feather broiler weight and breeding environment.The administrator can keep abreast of the changes of broiler weight in the chicken coops through the monitoring system.And at the same time,the system can monitor and control the environmental parameters in the chicken coops in real time.It makes the broilers in a more suitable growth environment,improves the welfare breeding of broilers and ensures the safe production of broilers.The main research contents are as follows:(1)Build broiler infrared image and depth information acquisition system:To effectively obtain the infrared image,depth information and real weight of experimental broilers,acquisition system of broiler infrared image and depth information is built in combination with the experimental site,and the real weight of broilers is obtained by carrying broiler image acquisition equipment on a retractable frame and using electronic scales.A depth camera image acquisition algorithm is designed to realize infrared image and depth information acquisition of broilers in complex environments,and the collected data is transmitted to the cloud server through wireless bridge and network switch.(2)Design the environmental information collection and control system of broiler coops: To keep broilers in a suitable growth environment and improve the welfare breeding of broilers,this paper designs and builds system of environmental information collection and control of broiler coops.The system consists of NB-Io T environment sensing node,PLC controller and loop control equipment.The NB-Io T environment sensor node transmits the collected environment data to the remote server through the base station,and the middleware system on the remote server parses and stores the received environment data.And at the same time,when the environment parameters in the chicken coops do not meet the needs of broiler growth,the middleware system remotely sends control commands to the PLC to realize the environmental regulation of broiler coops.(3)Research on broiler image recognition and segmentation model: Based on Mask R-CNN deep learning algorithm to analyze the broiler region(ROI region)in the image,the research results show that the recall ratio(P)and average segmentation accuracy(MAP)of Mask R-CNN model on the experimental data set are above 0.97.And the model can recognize and segment the broiler images more accurately.(4)Study of broiler weight estimation model: The broiler feature extraction algorithm is constructed by Open CV to extract five features related to broiler weight,namely,broiler projected area,projected perimeter,average depth,maximum depth and fitted ellipse short axis to long axis ratio.To make the broiler weight estimation model more dynamic,fault-tolerant and robust,this study used the decision tree regressor,linear regressor,support vector regressor,kneighbors regressor,random forest regressor,gradient boosting regressor,Adaboost regressor and Back propagation network.The seven regression algorithms were grouped and trained on the extracted feature dataset,and the Adaboost regressor model with better estimation results was selected as the weight estimation model for white feather broilers.On the experimental dataset,the coefficient of determination of the model was 0.95,and the absolute error was 0.01～0.32 kg,which provided technical support for automatic weighing of broilers in the actual production environment.(5)Development of broiler weight and breeding environment monitoring system: Based on D3.js,Springboot,Springcloud and Dubbo,monitoring system of white feather broiler weight and breeding environment(Web side)was developed.This system uses Dubbo for load balancing,forwards data and accesses evenly to each middleware server for processing,and stores the processed image information and environmental information in My SQL relational database.HDFS is used to store broiler infrared images to make the storage system more reliable and efficient,and can display broiler weight estimation information,environmental data information and information of administrator of broiler coops to users through the web side.It ensures the stable operation of the system and makes white feather broiler weight estimation,environmental monitoring and control more convenient and efficient.