An Abnormality Detection System for Piglets Based on Image Recognition

• Main Authors: Liao, Wei-Lun, 廖威綸
• Other Authors: Tsay, Yuh-Jiuan
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9qr6qd

碩士 === 國立屏東科技大學 === 資訊管理系所 === 106 === In Taiwan, the death of newborn piglets due to crushing, as well the frail and stunted piglets, is among the most neglected losses in the pig industry. The primary cause of the death of newborn piglets is due to crushing from sows. The reason for this is that the sows are so weak after they give birth to piglets that they were unable to keep away from the piglets. In addition, it is most likely for sows to crush piglets to death when they lie down. Piglets often cannot eat when sows are suckling. The reason for this may be that piglets are relatively thin and they are pushed aside. Pig farmers must constantly inspect the pig farm so that they can take measures when piglets are found to be abnormal. Therefore, this study used the image recognition technology to build “An Abnormality Detection System for Piglets Based on Image Recognition”, which uses the image preprocessing technology, and then imports the convolution neural network for learning. The experimental field of this study is located in a pig farm in the south. In this study, a video camera was installed in the pig house and a video of the pig farm for 5 months was recorded. It recorded video for 12 hours every day, with the frame width of 600 pixels and the frame height of 500 pixels, so as to create the image database. The experimental results showed that the identification rate of sow suckling was 94%, the identification rate of moving piglets was 91%, and the identification rate of newborn piglets was 85%, which could reduce the manpower burden of pig farmers and reduce the losses of practitioners in the pig industry.