Research On Fish Feeding Behavior Detection Method Based On Computer Vision

In recent years,with the vigorous development of China's aquaculture industry,refined and intelligent farming methods have gradually become the main trend.The problem of precise control of the feeding amount of fish in aquaculture has received more and more attention.However,at present,the feeding of fish is still mainly based on artificial observation feeding or machine feeding,and it is impossible to precisely control according to the actual feeding behavior changes and growth status of the shoal,resulting in rising breeding costs and deterioration of water quality environment,affecting the healthy growth of fish.Therefore,it is of great significance to improve the aquaculture benefit by studying and analyzing the feeding behavior of shoal to guide the feeding equipment for accurate feeding.With the rapid development of computer technology,the study of fish behavior through computer vision technology has become an important means.Aiming at the problem of water turbidity in pond culture and low detection accuracy of shoal,this paper take Cyprinus carpio var.color as an experimental object and the process of feeding change of shoal is observed by computer vision technology.Then the SVM model was used to detect the feeding behavior of shoal.The main research work is as follows:(1)Research on fish feeding image preprocessing: In the pond culture environment,the image data of the swimming process before and after the fish feeding is obtained through the video acquisition system.The obtained shoal image is subjected to median filtering and histogram equalization preprocessing to reduce image noise interference and obtain high quality images,thereby improving the contrast of shoal image.The water surface background is removed by adaptive Gaussian background modeling,and the gray image of the foreground shoal is extracted.(2)Research on features extraction method of fish feeding behavior: In view of the water surface ripple and reflection phenomenon during the fish swimming process,this paper uses image texture to characterize the variation of the feeding activity intensity and the number of feeding fish in the whole shoal,and extracts energy,homogeneity,contrast and entropy have a total of four texture features of shoal image.Because the fish feeding process environment is complex and varied,and the movement process before and after feeding is different,the motion direction vector of shoal is obtained by Lucas-Kanade optical flow method,and the direction vector distribution of shoal is calculated by establishing the direction histogram,and the direction entropy feature quantifies the overall degree of chaos in shoal.By combining the fish image static image texture feature and the dynamic optical flow direction entropy feature to quantify the feeding state change of shoal,it can prevent misjudgment of the low density fish image and help to improve the detection accuracy.(3)Research on detection method of fish feeding status: The texture features of the fish feeding image and the characteristics of the optical flow direction entropy were analyzed by SVM classifier,and the results of the SVM model were evaluated and analyzed.In order to reflect the effect of this method,the fish feeding behavior test results of this article were compared with the fish feeding behavior detection results based on shape and texture.The experimental results show that the detection accuracy of this method is 97%,and the detection accuracy is improved by 4.5%,which can better solve the problem of shoal feeding behavior detection in pond culture environment.The method can be used to guide the solution of the precision feeding problem in aquaculture.In the subsequent research,the feeding control strategy of the shoal can be established to better apply to the research of intelligent bait feeding equipment.