Development of the early pregnancy diagnosis device for female rabbits based on spatial diffuse light

Abstract: Pregnancy diagnosis is one of the most important links in the reproductive management of meat rabbits. Especially, the early identification of non-pregnant rabbits can advance the re-insemination time, further improving the service rate of breeding rabbits for a shorter reproductive cycle in commercial productions. However, the manual touch diagnosis can present some stress responses of rabbits at present. A non-invasive approach can be an alternative for the early pregnancy diagnosis in the future large-scale production of rabbits. In this study, a non-invasive portable diagnosis device was developed to rapidly and accurately identify the early pregnancy of rabbits using spatial diffuse light. The optical characteristics also presented differently from the gestational sac tissue in the abdomen of pregnant and non-pregnant rabbits. The portable diagnostic device consisted of a sensing probe and a signal processing host with two infrared LEDs (the light-emitting wavelengths of 850 and 930 nm), three silicon-based photodiodes, and peripheral circuits. 130 rabbits were selected to test after 14 days of artificial insemination, including 63 pregnant rabbits and 67 non-pregnant rabbits. The collected data was then divided into the training set and test set, according to the ratio of 7:3. Specifically, the training set was used to establish the partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) classification model. The test set was used to evaluate the classification performance of the established model. At the same time, the PLS-DA was conducted for the supervised principal component analysis (PCA) and variable importance analysis (VIA) on the sampling data. The results showed that there were great differences between the sampling datasets of pregnant and non-pregnant rabbits, indicating a better classification. Furthermore, it was found that the SVM presented a better classification performance than that of the PLS-DA for the pregnant and non-pregnant rabbits, where the sensitivity, specificity, and accuracy of the test set data were 80.95%, 83.33%, and 82.05%, respectively. Consequently, the developed diagnosis device can be widely expected to rapidly identify the pregnancy of rabbits within 14 days after insemination, indicating a feasible optical pregnancy diagnosis. The finding can provide a strong reference to the early pregnancy diagnosis of rabbits, further enhancing the intelligent level of industrial equipment in rabbit production.