RAPID: A rabbit pregnancy diagnosis device based on matrix optical sensing - Zhenhao Lai; Daoyi Song; Dongyu Liu; Yujie Zhang; Wei Jiang; Hongying Wang; Jinxia (Fiona) Yao; Xuanmin Niu; Liangju Wang (Smart Agricultural Technology, 2024)
Effective early pregnancy diagnosis is crucial for commercial rabbit breeding. Early pregnancy diagnosis enables the implementation of staged feeding for pregnant does, effectively preventing excessive weight gain and reducing the high mortality rates of kits during the birthing stage. This not only enhances production efficiency but also ensures the health and well-being of the breeding rabbits. The study introduces a method and device, the Rabbit Pregnancy Identification Device (RAPID), for detecting rabbit pregnancies using matrix optical sensing. RAPID comprises eight sensor modules and a central host unit. Each sensor module is equipped with three LEDs emitting light at wavelengths of 660 nm, 850 nm, and 940 nm, along with two photodiodes for data collection. A mobile application was developed to enable flexible control of the device. Signal-to-noise ratio tests were conducted to evaluate the stability of data collection by the device across varying light intensities. The experimental results reveal a direct correlation between light intensity levels and the signal-to-noise ratio of collected data. Notably, under a light intensity level of 4, RAPID achieves a signal-to-noise ratio ranging from 42 to 45 dB, satisfying the necessary criteria for data collection. Different classification models were trained using sample data from 216 does across various batches, and their generalization capabilities were evaluated. The experimental findings indicate that the optimal time for RAPID to diagnose the pregnancy status of does is on the 14th day after insemination, achieving an accuracy of 86.63 % and a recall of 80.49 %. Moreover, the model exhibits a degree of generalization, achieving an accuracy of 78.36 % when classifying another batch of sample data. RAPID achieves an accuracy of 97.25 % for pregnancy diagnosis of older does, which is 7.44 % higher than that of younger does; the accuracy rate for pregnancy diagnosis of does with sparse hair is 86.92 %, which is 4.78 % higher than that of does with dense hair. By comparing the effectiveness of using data from 8 sensor modules and data from a single sensor module for pregnancy diagnosis of different batches of does, it was found that the former exhibits more stable generalization capability in doe pregnancy detection.