Detection of sick laying hens by infrared thermal imaging and deep learning

Abstract

The laying hens are prone to get sick during the growing period, and the temperature will fluctuate within a relative range when the disease occurs. This temperature change range can be used as a sign of pathological phenomena in the laying hens. In order to find the floating range of the body surface temperature of the laying hens raised in the poultry house in both healthy and pathological states, and the areas where there is a significant difference in the body surface temperature of the two, a detection method combining infrared thermal imaging technology and neural network is proposed. First, use an infrared thermal imager to obtain an infrared image of the body surface of a layer, and then use a convolutional neural network to establish a recognition model for the characteristic area of the layer, and extract the highest temperature of the region of interest in a healthy and pathological layer. Finally, analyse the temperature difference of each area of interest in the chicken body under these two conditions. The test results show that the accuracy of the convolutional neural network recognition model is 97%; the temperature fluctuation range of the three characteristic areas of healthy and pathological layers are different, and the maximum temperature difference area is 7.8℃.