| In recent years,large-scale and standardized farming has become the main body of dairy production in China.With the continuous advancement of large-scale farming,new requirements have been put forward for the management of dairy farming.It is necessary to rely on information technology to improve the scientific management level of dairy farming.In dairy cattle breeding,timely and accurate identification of estrus can make cows conceive in time,improve the fertility rate of cows,shorten the calving interval,and improve the economic benefits of dairy cattle breeding.Traditional dairy estrus detection mainly depends on manual observation,which is time-consuming and laborious,and the detection efficiency is low.There are still some problems to be solved urgently,such as the detection method based on pedometer is single and low accuracy,the latent oestrus of cows is difficult to detect,and the traditional machine vision method has poor robustness and adaptability to the recognition of oestrus of cows.In order to improve the automation,informationization and intelligence level of cow estrus monitoring,this paper studies the key technologies of cow estrus sign monitoring based on Internet of Things and cloud,cow vaginal implantable resistance sensor and the wireless remote monitoring of resistance value,the enhancement of cow video image in complex environment,and the recognition of cow estrus behavior based on machine vision.The main work and conclusions are as follows.(1)Aiming at the problems of single pedometer in monitoring cows estrus,such as poor mobility,real-time performance,low accuracy,even missed detection and wrong detection,a scheme of monitoring estrus signs of dairy cows based on Internet of Things and cloud was proposed.Using non-contact temperature sensor and three-axis accelerometer,the temperature and movement of dairy cows were collected,and the long-distance transmission of estrus signs of dairy cows was realized by ZigBee network,RS485 bus and Modbus communication protocol.The real-time monitoring system of estrus signs of dairy cows was developed.The real-time monitoring,storage and historical data query of temperature and movement of dairy cows were completed.Based on HTTP protocol,the data of cow oestrus signs was uploaded to cloud server platform.The communication between cloud server platform and WeChat public platform was designed,and the real-time remote monitoring of cow oestrus signs was realized through mobile phone WeChat client.The test results showed that the temperature measurement error of the terminal of physical signs acquisition was within±0.2?,the data loss rate of ZigBee network was less than 2.33%in the 100-meter range of dairy farm,the data transmission from the upper computer monitoring terminal to the cloud terminal and the mobile platform terminal was stable,and there was no packet loss phenomenon.(2)In view of the low detection rate of estrus prediction in dairy cows with single exercise volume,a prediction model of estrus in dairy cows with fusion of body temperature and activity volume was proposed.By collecting the estrus parameters of cow's body temperature and activity,the typical characteristics of cow's estrus and interphase were analyzed.The BP neural network model of estrus prediction for dairy cows was established based on the three time-slice unit's movement and body temperature in the significant window of estrus period as the characteristic vectors of estrus judgment.The test results showed that the accuracy of estrus prediction model for dairy cows was 89.47%and the error rate was 3.70%.The model could detect the estrus of dairy cows with high accuracy.(3)Aiming at the problem that activity and non-contact video analysis methods were difficult to monitor recessive oestrus in dairy cows,according to the changes of physiological characteristics of vaginal mucus during oestrus,an oestrus monitoring scheme for dairy cows based on the changes of vaginal mucus resistance was proposed.An implantable resistance sensor for dairy cow vagina was innovatively designed,which consisted of brass resistance probe and 8-claw anti-skid device.The accurate acquisition of dairy cow vaginal resistance was realized.The vaginal resistance ZigBee network transmission system and the upper computer real-time monitoring system were developed.The accurate acquisition and remote real-time monitoring of vaginal resistance of dairy cows were realized.The experimental results showed that the implantable resistance sensor node could measure the resistance in the range of 1?~1k?with a precision of±2%.The maximum fluctuation of the resistance in 24 hours was 2?.The vaginal resistance of dairy cows during oestrus and intersexuals changed above 100?,and the sensor had a high measurement accuracy.It could work continuously for 38 days under the energy supply of7.4V/6500mAh lithium battery.In 450m~2 dairy farming area,the success rate of ZigBee network data transmission was over 98.5%,which could accurately and real-time monitor the changes of vaginal resistance in dairy cows.It could realize the timely identification of estrus of dairy cows.(4)In cow estrus monitoring based on video analysis,due to weather and light,it would cause the degradation of cow video image and lead to low recognition rate.Aiming at these problems,a cow image enhancement algorithm under complex illumination based on dual-domain decomposition was proposed.Firstly,the algorithm decomposed the input image into low-frequency image and high-frequency image by dual-domain filtering.Secondly,the wavelet thresholds of different high-frequency image was obtained according to bayesian estimation.The improved Garrote threshold function was used to denoise the image,and the high-frequency image of denoising was corrected by gamma transform.The filtering and contrast adjustment of high-frequency image were realized.Then,the low-frequency image was defogged by dark channel prior algorithm,and the low-frequency image was enhanced by contrast limited adaptive histogram equalization algorithm to further improve the contrast and overall brightness.Finally,the processed high and low frequency images were reconstructed and the final enhanced image was obtained.The experimental results showed that the double domain decomposition algorithm could effectively denoise cow images under complex illumination,enhance overall and detailed information,improve image visual effect.It could provide a good and effective image support for automatic recognition of cow estrus behavior based on machine vision,thus realizing 24-hour non-contact real-time monitoring of cow estrus.(5)Aiming at the problems of time-consuming and laborious manual estrus detection of dairy cows and stress behavior of dairy cows caused by pedometer contact detection,according to the characteristics of external span behavior of dairy cows during estrus,a recognition method of dairy cows estrus behavior based on convolution neural network was proposed.The convolution neural network of 32*32-20c-2s-50c-2s-200c-2 was constructed.The experimental results showed that the recognition accuracy of the proposed method was98.25%,the missed detection rate was 5.80%,the false recognition rate was 1.75%,and the average recognition time of single frame image was 0.257 s.This method could realize the non-contact,real-time and high-precision recognition of cow's span behavior. |
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