关键词: 牦牛, 动态称重, BP神经网络, 生成对抗网络, 预测模型

Abstract:  To address the issue of difficulties in weighing yaks, a dynamic weight measurement algorithm based on GAN and BPNN is developed by integrating IoT and AI technologies. The STM32 microcontroller is used to acquire raw pressure sensor data from 80 yaks while the yaks are walking smoothly. Subsequently, 3 000 pieces of fake data were generated using a GAN network, and regression prediction is performed using a BPNN neural network to achieve dynamic weight measurement of yaks. Under normal walking conditions, the position judgment is carried out by using the pair‑projecting infrared devices, and using these for data collection. Then, collected raw pressure data were submitted to the prediction model for regression prediction. The experimental results show that resulting in an average weighing time of approximately 4 seconds per yak and the average absolute error between the predicted and actual weights of the yaks is 0.92%. Surpassing the best accuracy (±5%) achieved by experienced technicians in weight estimation. The yak dynamic weighing algorithm based on BPNN and GAN can quickly obtain accurate and automated yak body weight data. It meets the actual application requirements, provides technical support for yak automated weighing, and has significance for achieving yak precision farming.

Key words: yak, dynamic weighing, BP neural network, generative adversarial network, predictive model