基于MFO-LSTM的母猪发情行为识别

及时准确识别母猪的发情行为可以有效增加受胎率和产仔量,对提高养殖企业的繁育水平和经济效益具有重要意义。该研究针对生猪养殖过程中母猪发情行为识别存在主观性强、智能化水平低、假警报和错误率高、识别不及时等问题,提出了一种基于飞蛾扑火算法(Moth-Flame Optimization,MFO)优化长短时记忆网络(Long Short Term Memory,LSTM)的母猪发情行为识别方法。利用安装在母猪颈部的姿态传感器获得母猪姿态数据,然后使用姿态数据训练MFO-LSTM姿态分类模型,将母猪姿态分为立姿、卧姿和爬跨3类。通过对姿态分类结果进行分析,确定以爬跨行为和活动量2个特征作为发情行为识别依据,使用MFO-LSTM分类算法判断母猪是否发情。以山西省太原市杏花岭区五丰养殖场的试验数据对该方法进行验证,结果表明,该方法在以30 min为发情行为识别时间时的识别效果最好,发情行为识别的错误率为13.43%,召回率为90.63%,特效性为81.63%,与已有的母猪发情行为识别方法相比错误率降低了80%以上。该方法在保证识别准确率的情况下有效降低了错误率,可满足母猪养殖生产过程中发情行为自动识别要求。

Identification of sow oestrus behavior based on MFO-LSTM

Abstract: Timely and accurate identification of oestrus behavior of sows can effectively increase the conception rate and litter size, which is of great significance to improve the breeding level and economic benefits of breeding enterprises.The traditional identification methods of oestrus behavior of sows have the problems of high working intensity, strong subjectivity and low automation level. Some identification methods of oestrus behavior of sows based on internet of things also have some problems, such as high false alarm times, high error rate and long recognition time. In order to solve the problems of low identification accuracy and efficiency, a oestrus behavior recognition method of sows based on Moth-Flame Optimization optimized Long Short Term Memory(MFO-LSTM) was proposed in this paper, and the verification test was carried out at WuFeng breeding farm in Xinghualing district, Taiyuan city, Shanxi Province. The posture data of sows were obtained by posture sensors installed on the neck of the sows. The posture data describing the sow''s motion state contained three-axis acceleration, three-axis angular velocity, three-axis angle, three-axis magnetic field and quaternion. The collected posture data was manually marked according to the videos, and preprocessed to obtain a posture classification data set. Then, the MFO algorithm was used to optimize the numbers of first and second hidden layer neurons, maximum training period, block size and learning rate, thus, the LSTM network model was built. Postures of sows were divided into three categories, i.e. standing, lying and mounting. Through the statistics of the duration of each complete mounting behavior of the sows, the range of the recognition time of the oestrus behavior was obtained. The sows posture classification results were counted with different oestrus behavior recognition duration, and two characteristics of mounting behavior and activity were then extracted, so as to obtain the recognition feature matrix of oestrus behavior of sows. Finally, the feature matrix was input into the MFO-LSTM classification model to judge whether the sow was oestrus. The experimental results showed that the classification method of sows posture proposed could effectively distinguish the three postures of standing, lying and mounting. The classification effects of proposed method were better than that of the Support Vector Machine (SVM), Probabilistic Neural Network(PNN), Learning Vector Quantization (LVQ) and Extreme Learning Machine(ELM). The average accuracy, recall rate and F1 in the attitude data set were 98.02%, 96.26% and 96.18, respectively. On the basis of accurately identifying the sows'' posture, the effect of oestrus behavior recognition based on MFO-LSTM algorithm was verified. The test results showed that the recognition effect was best when the oestrus recognition duration was 30 min at this time, the error rate, recall rate and specificity of oestrus recognition were 13.43%, 90.63% and 81.63%, respectively. The oestrus behavior of sows recognition method proposed effectively reduced the error rate while maintaining a high recall rate and specificity. Compared with other methods, the error rate was reduced by more than 80%, the oestrus behavior of sow could be recognized after 30 min of oestrus.