CN114586701A - Milk cow oestrus prediction device based on body temperature and exercise amount data - Google Patents
Milk cow oestrus prediction device based on body temperature and exercise amount data Download PDFInfo
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Abstract
The invention discloses a cow oestrus prediction device based on body temperature and exercise amount data, which is used for solving the problems that when the traditional cow oestrus prediction is carried out, the oestrus detection rate is not high due to the fact that the experience of an observer is relied on, and recessive oestrus cows are difficult to identify when the oestrus is predicted by adopting an accelerometer step counting method. The invention comprises the following steps: firstly, an accelerometer and a temperature measurement module are used for acquiring parameters to generate a data set, a worker carries out oestrus identification by adopting a rectal examination method to obtain a tag value, then data preprocessing is carried out on original data through a sliding window, and a characteristic vector is extracted. And inputting the characteristic vector into a supervised learning model to predict whether the oestrus is happened, wherein the prediction model comprises but is not limited to K nearest neighbor, a support vector machine, a decision tree, a random forest and a learning vector quantization LVQ model.
Description
Technical Field
The invention relates to a cow oestrus prediction device, in particular to a cow oestrus prediction device based on body temperature and exercise amount data.
Background
Nowadays, a large number of domestic pastures are still managed by means of experience, which is a rough way, so that the average milk yield of dairy cows in China is lower than that of developed countries in the dairy industry, and the quality of milk products has a large gap. With the development of the internet of things technology, enterprises put forward a lot of pasture plans to realize the development from traditional agriculture to modern agriculture, the real-time monitoring of environmental information and physical sign information by using sensors in the management of the modern pasture becomes more and more common, and the pasture efficiency can be greatly improved by using the refined and informationized management.
In recent years, large-scale and standardized breeding becomes the main body of milk industry production in China. In the process of breeding the dairy cows, the timely and accurate identification of the oestrus of the dairy cows can ensure that the dairy cows are timely pregnant, improve the pregnancy rate of the dairy cows, shorten the interval of calving and improve the economic benefit of the breeding of the dairy cows. Traditional milk cow oestrus detects and mainly relies on the manual work to patrol and examine, observes the record, judges according to managers' experience, wastes time and energy, causes easily to leak and examines. When the cows are in estrus, due to hormone regulation, excitation, agitation and remarkable increase of exercise amount can occur. Therefore, a pedometer is generally used to predict estrus. However, some cows show no obvious sign in estrus and are called recessive estrus. For example, the oestrus condition of the dairy cows cannot be accurately judged only by step counting due to the reasons of no exercise for a long time, poor nutrition or poor light during barn feeding in winter. In this case, if the cow is not observed, the cow in recessive estrus easily misses the mating period, and artificial economic loss is caused. In addition, a body temperature sensor is adopted in a scheme and combined with a neural network algorithm to realize oestrus prediction, but foreign body sensation of an implanted sensor in a body is obvious, so that the cow is not suitable, and the oestrus prediction accuracy is low.
In summary, how to realize accurate cow oestrus prediction has become a key issue for intelligent animal husbandry application.
Disclosure of Invention
In view of the above, the present invention provides a cow oestrus prediction device based on body temperature and exercise amount data, which is used to solve the problem that the recessive oestrous cow cannot be identified in time only by relying on a step counting function. After the device end collects body temperature and motion amount data, preprocessing is carried out through a sliding window, and characteristic vectors are extracted, wherein tag values need to be subjected to oestrus identification by pasture staff by adopting a rectum inspection method or other inspection methods. Estrus is predicted through a supervised learning model, and the estrus prediction model constructed by combining the physical sign information can greatly improve the prediction accuracy.
In order to achieve the purpose, the invention adopts the following technical scheme:
a cow estrus prediction apparatus based on body temperature and quantity of motion data, the prediction apparatus comprising:
a data set building module, comprising: continuously acquiring temperature and step data of a multi-head cow in a certain time period, and determining the oestrus condition of the multi-head cow in the time period, wherein the oestrus condition is oestrus or no oestrus;
a dataset preprocessing module, comprising: firstly, normalizing the data set of each cow, and then generating a characteristic vector by adopting a sliding window according to the normalized data set, wherein the characteristic vector comprises: the average activity factor of one estrus, the average temperature factor of the previous estrus, the average activity factor of the previous estrus, the average temperature factor of the previous estrus, the activity factor of the current time period, the temperature factor of the current time period and the label value are whether estrus occurs or not;
a predictive model training module, comprising: adopting a machine learning method, aiming at a classification model, taking a training sample set obtained by a data set preprocessing module as model input, taking the current oestrus condition as model output, carrying out iterative training, and obtaining an oestrus prediction model after model convergence;
an estrus prediction module comprising: and inputting the prediction sample set into an estrus prediction model obtained in the model training module, and predicting and evaluating the estrus condition of the prediction sample set.
Further, the data set constructing module further comprises: the system comprises a cattle ear tag device, a Bluetooth gateway and a relay gateway;
the cattle ear tag device is arranged on a cattle ear and used for continuously acquiring temperature and step data and sending the acquired data to the Bluetooth gateway at fixed time, wherein the cattle ear tag device is communicated with the Bluetooth gateway through BLE low-power Bluetooth;
the Bluetooth gateway scans broadcast signals of all devices in a coverage area, receives data, realizes long-distance transmission through LoRa and sends the data to the relay gateway;
the relay gateway collects the data received by all the Bluetooth gateways and uploads the data to the server through the WiFi module.
Further, the dataset preprocessing module, the predictive model training module, and the estrus prediction module are configured in the server.
Further, the certain period of time is 2 months, and the dairy cow is specifically: 4-5 adult-aged cows with weight of 500-600 kg and no pregnancy.
Further, determining the estrus condition of the multi-head cow in the time period by a rectal examination method.
Further, in the data set preprocessing module, the sliding window duration is 4 hours, and the data queue is preprocessed once every 4 hours corresponding to different devices to obtain a total step number and an average body temperature value; correspondingly, the oestrus of the cows is determined every four hours.
Further, in the predictive model training module, during model training, feature vectors labeled as oestrus and oestrus in a data set are extracted separately, then 66% of the data set is extracted from the oestrus and oestrus data set through a random function and is used as a training set, and the remaining 33% is used as a test set.
Further, in the prediction model training module, the classification model adopts: k nearest neighbors, support vector machines, decision trees, random forests, or learning vector quantization.
Further, the cattle ear tag device comprises an accelerometer and a temperature measurement module.
The invention has the beneficial effects that:
1. according to the invention, an accurate cow oestrus prediction model is constructed through the fusion body temperature and the motion amount, the cow oestrus can be predicted in time, and the economic benefit of a pasture can be effectively improved.
2. The physical sign monitoring equipment designed by the invention can also realize real-time monitoring on other animals, and has very high applicability and popularization in the field of Internet of things.
Drawings
Fig. 1 is a diagram showing the architecture of a transmission scheme of a cow oestrus prediction apparatus based on body temperature and motion amount data provided in embodiment 1;
FIG. 2 is a schematic flow chart of a method for obtaining a cow oestrus prediction model in example 1;
fig. 3 is a schematic diagram showing modules of the cow oestrus prediction apparatus based on body temperature and motion amount data according to embodiment 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1 to fig. 3, the present embodiment provides a cow oestrus prediction device based on body temperature and motion amount data, a specific architecture of data transmission of the device is shown in fig. 1, and the prediction device specifically includes:
1. a dataset construction module comprising:
the system comprises a cattle ear tag device, a Bluetooth gateway and a relay gateway; specifically, in this embodiment, a bovine ear tag device is arranged on a bovine ear, temperature and step data are continuously collected, and the collected data are sent to a bluetooth gateway at regular time, wherein the bovine ear tag device communicates with the bluetooth gateway through BLE bluetooth low energy;
the Bluetooth gateway scans broadcast signals of all devices in the coverage range, and after receiving the data, the Bluetooth gateway realizes long-distance transmission through LoRa and sends the data to the relay gateway;
the relay gateway collects data received by all the Bluetooth gateways and uploads the data to the server through the WiFi module, wherein other modules are arranged on the server and used for subsequent estrus prediction.
Specifically, in this embodiment, for a plurality of cows, the data of the temperature and the number of steps of the cows are continuously obtained by the data set construction module within a certain time period, and then the situation that the corresponding cow is in heat within the time period is determined, wherein heat or no heat is taken as a label.
More specifically, in this example, the collection duration is 2 months. For cows with normal physiological cycles, 2-3 estrus can be continuously measured within two months, and the summarized data set is sufficient for model prediction. The experimental animals are selected from adult cows 4-5 years old, with the weight of 500-600 kg and without pregnancy, and once the cows are pregnant, the cows are not estrated during pregnancy, so that the data set of estrus cannot be acquired, and therefore, in the embodiment, 30 cows without pregnancy are selected in total.
More specifically, in this embodiment, the data set construction module continuously collects data and implements the functions of step counting and temperature measurement. On line, the staff adopts a rectum test method or other test methods to carry out estrus identification; considering that the estrus is predicted only by the amount of exercise, the cow prediction with recessive estrus and unobvious amount of exercise is difficult to realize, and the body temperature of the cow changes along with the estrus, so that the prediction accuracy can be effectively improved by predicting the estrus by combining the temperature of the body and the amount of exercise, the rectal examination method is adopted to determine the estrus stage of the cow by judging the development condition of the ovarian follicle on the ovary, the identification method is the most accurate identification method at present, the correctness of the label value can be ensured, and the estrus identification includes but is not limited to the rectal examination method.
2. A dataset preprocessing module, comprising:
firstly, the data set of each cow is normalized to normalize the total steps and the body temperature value to an interval [0,1], the normalized total steps are called activity factors, and the normalized body temperature is called body temperature factors. And generating a feature vector based on sliding window preprocessing from the processed data set, wherein the feature vector comprises: average activity factor commonStep in one estrus, average temperature factor commonTemp in the previous estrus, average activity factor oestrusStep in the previous estrus, average temperature factor oestrusTemp in the previous estrus, activity factor curStep in the current time period, temperature factor curTemp in the current time period, and tag values flag whether estrus is occurring,
Specifically, in this embodiment, the length of the sliding window used is 4 hours, so that the data queue is preprocessed every 4 hours to obtain the total number of steps and the average body temperature value corresponding to different devices. Correspondingly, the pasture staff also carries out rectal examination or other detection methods every 4 hours to judge whether the cows estrus.
In particular, in this process, there is a high requirement on the time window for feature vector selection. If the time window selected by the characteristic vector is short, the interference of irregular movement of the dairy cow in a short period is easily caused, and the accuracy of model prediction is further influenced. If the selected time window is too long, the scheme can be degenerated into an estrus identification model instead of an estrus prediction model, the timeliness is not high, the estrus of the dairy cows is easily missed, and the economic benefit of a pasture is further influenced. Through investigation on the current research situation at home and abroad, the sliding window for predicting the estrus of the dairy cows is set to be 4 hours, which is reasonable.
More specifically, after obtaining the total steps and the average body temperature value, normalization is performed, in which the data set collected by each cow is normalized separately, rather than the data sets of all cows are normalized together. The normalized number of steps is referred to as the activity factor and the normalized temperature is referred to as the temperature factor. If all data sets are processed in a combined manner, the difficulty of prediction is greatly increased. Therefore, in order to offset the influence of the difference of different cow behaviors on the prediction result, the characteristics of the cow group are not selected, and the characteristics of each cow are individually extracted in a customized manner, so that the prediction accuracy can be greatly improved.
3. A model training module, comprising:
establishing a model which takes a training sample set obtained by a data set preprocessing module as input and takes the current oestrus condition as output by adopting a machine learning method, and training the model by adopting the training sample set to obtain an empirical model capable of predicting the oestrus condition;
specifically, in model training, feature vectors labeled as oestrus and oestrus in a data set are extracted separately, 66% of the data set is extracted from the oestrus and oestrus data sets through a random function and is used as a training set, and the rest 33% is used as a test set. Therefore, the estrus and episodic data in a certain proportion can be trained, and the accuracy of prediction is improved. Because oestrus prediction is a two-classification problem, only a supervised learning classification model is needed, and the classification model comprises but is not limited to K nearest neighbors, a support vector machine, a decision tree, a random forest and learning vector quantization.
4. An estrus prediction module comprising:
and inputting the prediction sample set into an estrus prediction model obtained in the model training module, and predicting and evaluating the estrus condition of the prediction sample set.
In conclusion, the cow oestrus prediction device based on the body temperature and the exercise amount data provided by the invention constructs an accurate cow oestrus prediction model by combining the body temperature and the exercise amount, can realize the timely prediction of the cow oestrus, and can effectively improve the economic benefit of a pasture; and it also provides a set of sign monitoring facilities, also can realize the real-time supervision to other animals, has very high suitability and popularization nature in the thing networking field.
The invention is not described in detail, but is well known to those skilled in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (9)
1. A cow estrus prediction apparatus based on body temperature and motion amount data, the prediction apparatus comprising:
a data set building module, comprising: continuously acquiring temperature and step data of a multi-head cow in a certain time period, and determining the estrus condition of the multi-head cow in the time period, wherein the estrus condition is estrus or no estrus;
a dataset preprocessing module, comprising: firstly, carrying out normalization operation on a data set of each cow, and then generating a characteristic vector by adopting a sliding window according to the normalized data set, wherein the characteristic vector comprises the following components: the average activity factor of one estrus, the average temperature factor of the previous estrus, the average activity factor of the previous estrus, the average temperature factor of the previous estrus, the activity factor of the current time period, the temperature factor of the current time period and the label value are whether estrus is generated or not;
a predictive model training module, comprising: adopting a machine learning method, aiming at a classification model, taking a training sample set obtained by a data set preprocessing module as model input, taking the current oestrus condition as model output, carrying out iterative training, and obtaining an oestrus prediction model after model convergence;
an estrus prediction module comprising: and inputting the prediction sample set into an estrus prediction model obtained in the model training module, and predicting and evaluating the estrus condition of the prediction sample set.
2. The device for predicting cow oestrus according to claim 1, wherein the data set constructing module further comprises: the system comprises a cattle ear tag device, a Bluetooth gateway and a relay gateway;
the cattle ear tag device is arranged on a cattle ear and used for continuously acquiring temperature and step data and sending the acquired data to the Bluetooth gateway at fixed time, wherein the cattle ear tag device is communicated with the Bluetooth gateway through BLE low-power Bluetooth;
the Bluetooth gateway scans broadcast signals of all devices in a coverage area, receives data, realizes long-distance transmission through LoRa and sends the data to the relay gateway;
the relay gateway collects the data received by all the Bluetooth gateways and uploads the data to the server through the WiFi module.
3. The device of claim 2, wherein the data set preprocessing module, the predictive model training module, and the oestrus prediction module are configured in the server.
4. The device for predicting the estrus of a cow based on the body temperature and the exercise amount data as claimed in claim 3, wherein the certain period of time is 2 months, and the cow is specifically: 4-5 adult-aged cows with weight of 500-600 kg and no pregnancy.
5. The device for predicting the oestrus of a cow according to claim 3, wherein the oestrus of the plurality of cows over the time period is determined by rectal examination.
6. The cow oestrus prediction device based on body temperature and exercise amount data as claimed in claim 3, wherein in the data set preprocessing module, the length of the sliding window is 4 hours, and corresponding to different devices, the data queue is preprocessed once every 4 hours to obtain the total step number and the average body temperature value; correspondingly, the oestrus condition of the cow is determined every four hours.
7. The device for predicting the oestrus of a cow based on the body temperature and the exercise amount data as claimed in claim 3, wherein in the model training module, during model training, the feature vectors labeled as oestrus and oestrus in the data sets are extracted separately, then 66% of the data sets are extracted from the oestrus and oestrus data sets through a random function and are used as training sets, and the rest 33% are used as test sets.
8. The device for predicting the oestrus of a cow according to claim 3, wherein the classification model in the prediction model training module adopts: k nearest neighbors, support vector machines, decision trees, random forests, or learning vector quantization.
9. The device for predicting the estrus of a cow according to claim 3, wherein the ear tag device comprises an accelerometer and a temperature measuring module.
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Application publication date: 20220607 |