TW202315518A - Poultry health monitoring system and method thereof - Google Patents

Abstract

A poultry health monitoring system includes: a cloud module, a learning calibration module, and a monitoring module. The learning calibration module is used to sense a weight of at least one poultry carried and a first poultry image to analyze the number of poultry in the first poultry image. And the learning calibration module generates a poultry image characteristic and a weight formula. The monitoring module is used to generate a second poultry image including at least one poultry. The cloud module obtains a unit weight, and an activity value of each poultry based on the second poultry image. The present disclosure further includes a method for monitoring a health states of poultry.

Description

Poultry health monitoring system and method thereof

The invention relates to a health monitoring system and method thereof, in particular to a poultry health monitoring system and method that can be automatically corrected and monitored to save manpower.

Chickens in poultry and pigs in livestock have always been the main sources of protein intake in our daily diet. They not only have high nutritional value, but are also the main raw materials for many processed foods. In recent years, the economic output value of Taiwan's chicken commodity has reached NT$39.1 billion, accounting for 23.92% of the total output value of Taiwan's animal husbandry industry. It is an important domestic agricultural product. The health status of chickens is often closely related to the eating behavior of chickens. At present, the detection of eating behaviors in poultry houses is mostly manual. However, the number of chickens in poultry houses is huge, and traditional management methods are quite time-consuming and labor Intensive and dependent on the experience of the owner, it is easy to have problems in cost control and quality control.

However, during the period of poultry breeding, it is often limited by factors such as the number, range, or response speed of monitoring devices, resulting in irreversible losses. Especially in the tropics and subtropics, heat stress is one of the most challenging issues for poultry farmers due to the hot summer climate. Heat stress reduces the growth rate of poultry and adversely affects egg quality, and has even been associated with sudden mass mortality of poultry. Early breeding experience found that heat stress is the key to stabilizing poultry growth and egg quality. Usually, heat stress is estimated by using the temperature humidity index (THI), which is to measure temperature and humidity at the same time. However, the temperature and humidity index is an indirect indicator, and the standard of heat stress may also vary depending on the breed of chicken and the food and drinking water supply of the chicken, which may easily lead to errors in the assessment of heat stress or growth conditions, resulting in losses in poultry farming .

And further, for general poultry farmers, they are well aware that the health status of poultry is closely related to their weight and activity. Or when the activity time is insufficient, it will seriously affect the health of the poultry. Traditionally, to solve this problem, it usually requires a lot of manpower to measure the weight of each poultry individual, observe and evaluate the health status of each poultry on the spot, and manually record the activity and activity time and other behaviors. In this way, not only often a lot of manpower and time costs are spent, but also the processing speed cannot be improved, resulting in the technical problems that it is difficult for poultry farmers to reduce the cost of feeding and maintenance, and the monitoring efficiency is not good.

For this reason, how to design a poultry health monitoring system and method thereof to solve the aforementioned technical problems is an important topic studied by the inventor of this case.

The purpose of the present invention is to provide a poultry health monitoring system, which can solve the technical problems of difficulty in reducing feeding and maintenance costs and poor monitoring efficiency in the prior art, and achieve the goals of low maintenance costs, quick response and full-time monitoring.

In order to achieve the aforementioned purpose, the poultry health monitoring system proposed by the present invention includes: a cloud module, a computing core, a learning correction module and a monitoring module. Wherein, the cloud module is used to store at least one poultry image feature and an image weight relational expression corresponding to each poultry image feature. The computing core is coupled to the cloud module, the computing core receives the weight value and the first poultry image, analyzes the quantity of the at least one poultry in the first poultry image, and generates the at least one poultry image feature and the image weight Relational expression, and the image weight relational expression includes the relative relationship between image feature value and weight. The learning correction module is coupled to the computing core and the cloud module, and includes a weighing structure and a first camera; wherein, the weighing structure is used to sense the weight value of at least poultry carried; the first The camera is arranged in the weighing structure and is used to generate a first poultry image of the at least one poultry carried by the weighing structure. The monitoring module is coupled to the cloud module and includes a second camera; wherein, the second camera is used to generate a second poultry image including at least one poultry. Wherein, the cloud module obtains a unit weight of each poultry according to the second poultry image, the at least one poultry image feature, and the image weight relational expression.

Furthermore, the poultry health monitoring system of the present invention further includes an early warning analysis module, the early warning analysis module is coupled to the cloud module, and the early warning analysis module is based on the unit weight, the activity At least one of coefficient value and uniformity to output at least one of statistical report and warning message.

Furthermore, the poultry health monitoring system of the present invention further includes a mobile communication platform, the mobile communication platform is wirelessly coupled to the early warning analysis module, and receives at least one of the statistical report and the warning message .

Furthermore, in the poultry health monitoring system of the present invention, the mobile communication platform includes one of a workstation, a server, a desktop computer, a notebook computer, a tablet computer, a personal digital assistant or a smart phone.

Furthermore, in the poultry health monitoring system described in the present invention, the calculation core is a deep learning framework that uses an object detection calculation tool as the calculation core to identify the target object, and the object detection calculation tool is deep learning or image In the processing method, the cloud module uses the at least one convolutional layer and the at least one pooling layer to compare whether the second poultry image conforms to the characteristics of each poultry image, and then obtains unit weight, activity, and uniformity values.

Further, in the poultry health monitoring system of the present invention, the cloud module includes a server and a cloud database; wherein the server is used to obtain at least one of the unit weight and the activity value; the The cloud database is coupled to the server, and is used to store at least one of the at least one poultry image feature, the image weight relationship, the unit weight, and the activity coefficient value

Further, in the poultry health monitoring system of the present invention, the server is coupled by one of narrowband internet of things (NB-Iot), LoRa WAN, LTE and Wi-Fi The cloud database.

Furthermore, in the poultry health monitoring system of the present invention, the weighing structure includes a weighing platform and an intermediate platform. Wherein the weighing platform is used to carry at least one poultry, the middle platform is arranged on the weighing platform, and the first camera is arranged under the middle platform.

Furthermore, in the poultry health monitoring system of the present invention, the weighing platform is coupled to the intermediate platform by at least two columns.

When using the poultry health monitoring system and method thereof in the present invention, the cloud module of the poultry health monitoring system may pre-store a weight judgment database. And, firstly, the learning correction module performs the machine learning (machine learning, ML) program of the artificial intelligence (AI) model, and the learning correction module senses the weight carried by the weighing structure first. The weight value of at least one poultry, and using the first camera to generate the first poultry image of the at least one poultry carried by the weighing structure. Finally, the learning correction module uses the calculation core to receive the weight value and the first poultry image to analyze the quantity of the at least one poultry in the first poultry image, and generate the at least one poultry image The features and the image weight relationship are stored in the cloud module, so as to complete the machine learning program. The cloud module is made to store at least a poultry image feature and an image weight relational expression. Continuing or simultaneously with respect to the above steps, the monitoring module generates the second poultry image corresponding to at least one poultry through the second camera. Finally, the cloud module can obtain the unit weight of each poultry according to the second poultry image, the feature of the at least one poultry image, and the image weight relational expression. Alternatively, the cloud module can obtain the activity value of each of the poultry according to the second poultry image and the features of the at least one poultry image. Further, the learning correction module can continuously repeat machine learning actions over time, so as to continuously perform the at least poultry image features and the image weight relationship stored in the cloud module. Correction, so that the poultry health monitoring system described in the present invention is more sensitive and accurate. Since the above-mentioned learning, monitoring and correction actions do not require redundant manpower intervention, and can be operated full-time without duty, it not only saves labor costs, but also is not limited by time, making poultry breeding and maintenance more efficient.

For this reason, the poultry health monitoring system described in the present invention can solve the technical problems of difficulty in reducing feeding and maintenance costs and poor monitoring efficiency in the prior art, and achieve the goals of low maintenance costs, quick response and full-time monitoring.

In order to further understand the technology, means and effects adopted by the present invention to achieve the predetermined purpose, please refer to the following detailed description and accompanying drawings of the present invention. It is believed that the characteristics and characteristics of the present invention should be able to gain a deep and specific understanding. , however, the accompanying drawings are provided for reference and illustration only, and are not intended to limit the present invention.

The implementation of the present invention is described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific examples, and various modifications and changes can be made to the details in the description of the present invention based on different viewpoints and applications without departing from the spirit of the present invention.

It should be noted that the structure, proportion, size, number of components, etc. shown in the drawings attached to this specification are only used to match the content disclosed in the specification, for those who are familiar with this technology to understand and read, and are not used to limit the scope of the present invention. Implementation restrictions, so there is no technical substantive meaning, any modification of the structure, the change of the proportional relationship or the adjustment of the size, without affecting the effect and the purpose of the present invention, should fall within the scope of the present invention. The technical content disclosed by the invention must be within the scope covered.

Hereby, the technical content and detailed description of the present invention are described as follows in conjunction with the drawings.

Please refer to FIG. 1 to FIG. 2 , wherein FIG. 1 is a schematic structural diagram of the first embodiment of the poultry health monitoring system of the present invention; FIG. 2 is a schematic configuration diagram of the first embodiment of the poultry health monitoring system of the present invention. In the first embodiment of the present invention, the poultry health monitoring system proposed by the present invention includes: a cloud module 10 , a computing core 23 , a learning correction module 20 and a monitoring module 30 . Wherein, the cloud module 10 is used to store at least one poultry image feature and an image weight relational expression corresponding to each poultry image feature. In the first embodiment of the present invention, the cloud module 10 includes a server 11 and a cloud database 12 . Wherein the server 11 is used to obtain at least one of the unit weight of each poultry (ie the individual weight of any chicken 100 ) and the activity coefficient value. Moreover, the cloud database 12 is coupled to the server 11 and is used to store at least one of poultry image features, image weight relational formula, unit weight, and activity coefficient value. Further, the server 11 is coupled to the cloud database 12 through one of narrowband internet of things (NB-Iot), LoRa WAN, LTE and Wi-Fi. The computing core 23 is coupled to the cloud module 10 .

The learning correction module 20 is coupled to the computing core 23 and the cloud module 10 , and the learning correction module 20 includes a weighing structure 21 and a first camera 22 . Wherein, the weighing structure 21 is used for sensing the weight value of at least one poultry (at least one poultry 100 shown in FIG. 2 ) carried. The first camera 22 is configured in the weighing structure 21 and is used to generate a first poultry image of the at least one poultry carried by the weighing structure 21 . The computing core 23 is configured in the weighing structure 21, the computing core 23 receives the weight value and the first poultry image to analyze the quantity of the at least one poultry in the first poultry image, And generate the at least one poultry image feature and the image weight relational expression. In the first embodiment of the present invention, the weighing structure 21 includes a weight sensor 210 , a weighing platform 211 and an intermediate platform 212 . Wherein, the weighing platform 211 is used to carry at least one poultry, the intermediate platform 212 is arranged on the weighing platform 211, and the first camera 22 is arranged under the intermediate platform 212 for Overhead shot at least one chicken 100. Wherein, the weighing platform 211 is coupled to the intermediate platform 212 through at least two columns 213 , so that the weighing platform 211 and the intermediate platform 212 move together. In the first embodiment of the present invention, the computing core 23 is a deep learning framework using an object detection computing tool as the computing core 23 to identify objects, and the object detecting computing tool is deep learning or The image processing method is, for example, a mask region-based convolutional neural network (mask R-CNN) including at least one convolution layer and at least one pooling layer. The cloud module 10 uses at least one convolutional layer and at least one pooling layer to compare whether the first poultry image conforms to the characteristics of each poultry image, and then obtains the unit weight according to the image weight relationship (that is, each chicken 100 individual weights), or to obtain a mobility or uniformity value. As shown in FIG. 2 , the first camera 22 can capture an overhead image of at least one chicken 100 (for example, two chickens as shown in the figure), and send the image data to the computing core 23, so that the computing core 23 can calculate The number of chickens on the weighing platform 211. The weighing platform 211 transmits the total weight of the chickens on it to the computing core 23, so that the computing core 23 can calculate the average weight of the chickens on the weighing platform 211. According to one embodiment of the present invention, if the computing core 23 judges that there is only one chicken on the weighing platform 211, the corresponding relationship between the bird's bird's-eye view image features (such as body length, bird-view area, etc.) and weight can be established, that is, the image weight Relational formula (weight formula), but the above-mentioned implementation mode does not limit the scope of this case. For example, the calculation core 23 can also calculate the average value (such as the average body length) and the average weight of the bird's-eye view image feature on the weighing platform 211, and can also establish an image weight relational expression.

According to an embodiment of the present invention, the computing core 23 can also determine whether the chicken is moving or not by using a single chicken image corresponding to the time relationship. For example, if the daytime operation core 23 judges that a chicken image is still or does not have a moving range within a predetermined value (for example, the moving distance does not exceed 1 meter) within a predetermined time (for example, 10 minutes), then it can be determined The bird is not active enough. The computing core 23 can integrate the image data of the first camera 22 and the second camera 31, and judge the proportion of chickens with insufficient activity. If the lack of activity ratio of the chickens exceeds a threshold value, it means that the chickens in the chicken farm may have infectious diseases, and the calculation core 23 can issue a warning notice.

Further, the learning correction module 20 of the present invention is aimed at the evaluation results of the confusion matrix method (Confusion Matrix) of the machine learning of the red-feathered native chicken as follows: only count actually true actually not predicted true TP = 197 FP = 18 predicts no FN = 13 TN Precision = TP/(TP+FP) = 91.6% Recall = TP/(TP+FN) = 93.8%

Among them: true positive (TP) means the number of "yes" counted manually and deep learning counts "yes"; false positive (FP) means the number of "yes" counted manually and deep learning counts; false negative (FN) refers to the number of "yes" counted manually and "no" counted by deep learning; and true negative (TN) means the number of "no" counted manually and "no" counted by deep learning. The aforementioned results were obtained by using machine learning counting methods and manual counting methods respectively during the three-month feeding cycle. It can be proved that the machine learning counting of the present invention can replace manual counting to effectively estimate the average weight of chickens. The multiple average weight data collected every day were further converted into daily standard deviations. Whether it is in the experimental results of red-feathered cocks or red-feathered hens, it can be observed that the daily standard deviation of the average body weight of the chickens in the later rearing period is getting larger and larger. Fighting is prone to occur, so that when eating, thin birds cannot compete with stronger birds for food, resulting in significant size differences between birds. It can be seen that the standard deviation of the average weight helps to monitor the overall health status of the chickens. If the standard deviation of the average weight is getting larger and larger, you can consider breeding in different areas to stabilize the average weight of the chickens.

The monitoring module 30 is coupled to the cloud module 10 , and the monitoring module 30 includes a second camera 31 . Furthermore, the monitoring module 30 can quickly determine whether there is any abnormality in the weight of the chicken by using the previously established image weight relational expression. Wherein, the second camera 31 is used to generate a second poultry image including at least one poultry (it may be any other chicken 100 carried outside the weighing structure 21 ). Alternatively, the cloud module 10 obtains the activity value of each poultry according to the second poultry image and the features of the at least one poultry image. Further, the activity value may be determined by the cloud module 10 as a cloud model according to the judgment conditions of the individual chickens 100 in the second poultry image, such as the activity distance, activity frequency, and resting cycle time. The basis for group 10 to judge the activity value, for example, if the activity distance is short and the activity frequency is low, it is judged as poor activity, and a threshold value can be set as a group classification.

Please refer to FIG. 3 , which is a schematic structural diagram of the second embodiment of the poultry health monitoring system of the present invention. In the second embodiment of the present invention, it is substantially the same as the aforementioned first embodiment, but further includes an early warning analysis module 40 and a mobile communication platform 50 . Wherein, the early warning analysis module 40 is coupled to the cloud module 10, and the early warning analysis module 40 outputs at least one of a statistical report and a warning message according to at least one of unit weight and activity coefficient value By. The mobile communication platform 50 is wirelessly coupled to the early warning analysis module 40, and receives at least one of the statistical report and the warning message, so that the poultry breeder can predict the health status of the chicken 100 in advance or The growth trend of chickens 100 enables poultry farmers to respond early or take preventive measures in advance to reduce the risk and cost of raising poultry. In the second embodiment of the present invention, the mobile communication platform 50 includes one of a workstation, a server, a desktop computer, a notebook computer, a tablet computer, a personal digital assistant or a smart phone. However, the present invention is not limited thereto.

Please refer to FIG. 4 , which is a flow chart of the poultry health monitoring method of the present invention. And, firstly, the machine learning (machine learning, ML) program of the artificial intelligence (artificial intelligence, AI) model is first performed by the learning correction module 20, and the learning correction module 20 first uses the weighing structure 21 to sense The weight value of the at least one poultry carried, and the first poultry image of the at least one poultry carried by the weighing structure 21 is generated by the first camera 22 . Finally, the learning correction module uses the calculation core 23 to receive the weight value and the first poultry image to analyze the quantity of the at least one poultry in the first poultry image, and generate the at least one poultry Image features and the image weight relationship are stored in the cloud module (step S1), thereby completing the machine learning program. The cloud module 10 is made to store at least one poultry image feature and image weight relational expression (step S2). The monitoring module 30 generates the second poultry image corresponding to at least one poultry through the second camera 31 (step S3 ), which may be carried out successively or simultaneously with respect to the above steps. Finally, the cloud module 10 can obtain the unit weight of each poultry according to the second poultry image, the feature of the at least one poultry image, and the image weight relational expression (step S4). Alternatively, the cloud module 10 can obtain the activity value of each poultry according to the second poultry image and the features of the at least one poultry image (step S5). Further, the learning correction module 20 can continuously repeat the machine learning action over time, so that the at least poultry image features and the image weight relationship stored in the cloud module 10 can be continuously Calibration is carried out accurately, so that the poultry health monitoring system described in the present invention is more sensitive and accurate. Since the above-mentioned learning, monitoring and correction actions do not require redundant manpower intervention, and can be operated full-time without duty, it not only saves labor costs, but also is not limited by time, making poultry breeding and maintenance more efficient.

For this reason, the poultry health monitoring system described in the present invention can solve the technical problems of difficulty in reducing feeding and maintenance costs and poor monitoring efficiency in the prior art, and achieve the goals of low maintenance costs, quick response and full-time monitoring.

The above is only a detailed description and drawings of preferred embodiments of the present invention, but the features of the present invention are not limited thereto, and are not intended to limit the present invention. As the standard, all embodiments that conform to the spirit of the patent scope of the present invention and its similar changes should be included in the scope of the present invention. Any person familiar with the art can easily think of changes or Modifications can all be covered by the patent scope of the following case.

10:Cloud module 11:Server 12:Cloud database 20: Learning Correction Module 21: weighing structure 22: First camera 23: Computing core 30:Monitoring module 31:Second camera 40: Early warning analysis module 50:Mobile communication platform 100: Chicken 210: weight sensor 211: weighing platform 212: Middle platform S1~S5: steps

Fig. 1 is the structure diagram of the first embodiment of the poultry health monitoring system of the present invention;

Fig. 2 is a configuration diagram of the first embodiment of the poultry health monitoring system of the present invention;

FIG. 3 is a schematic structural diagram of the second embodiment of the poultry health monitoring system of the present invention; and

Fig. 4 is a flow chart of the poultry health monitoring method of the present invention.

10:Cloud module

11:Server

12:Cloud database

20: Learning Correction Module

21: weighing structure

22: First camera

23: Computing core

30:Monitoring module

31:Second camera

Claims (9)

A poultry health monitoring system comprising: A cloud module for storing at least one poultry image feature and an image weight relationship corresponding to each poultry image feature; A computing core, coupled to the cloud module, the computing core receives the weight value and the first poultry image, analyzes the quantity of the at least one poultry in the first poultry image, and generates the at least one poultry image feature and The image weight relationship, and the image weight relationship includes the relative relationship between image feature value and weight; A learning correction module, coupled to the computing core and the cloud module, and includes a weighing structure and a first camera; wherein, the weighing structure is used to sense the weight value of at least poultry carried; the first camera is disposed in the weighing structure and is used to generate a first poultry image of the at least one poultry carried by the weighing structure; and A monitoring module, coupled to the cloud module, and includes a second camera; wherein, the second camera is used to generate a second poultry image including at least one poultry; Wherein, the cloud module obtains a unit weight of each poultry according to the second poultry image, the at least one poultry image feature, and the image weight relational expression. The poultry health monitoring system as described in claim item 1 further includes an early warning analysis module, the early warning analysis module is coupled to the cloud module, and the early warning analysis module is based on the unit weight, the activity coefficient value and uniformity at least one of them to output at least one of a statistical report and a warning message. The poultry health monitoring system as described in Claim 2 further includes a mobile communication platform that is wirelessly coupled to the early warning analysis module and receives at least one of the statistical report and the warning message. The poultry health monitoring system as described in claim 3, wherein the mobile communication platform includes one of a workstation, a server, a desktop computer, a notebook computer, a tablet computer, a personal digital assistant, or a smart phone. The poultry health monitoring system as described in Claim 1, wherein, the calculation core is a deep learning framework that uses an object detection calculation tool as the calculation core to identify objects, and the object detection calculation tool is deep learning or image processing In this way, the cloud module uses the at least one convolutional layer and the at least one pooling layer to compare whether the second poultry image conforms to the characteristics of each poultry image, and then obtain the unit weight, activity, and uniformity values. The poultry health monitoring system as described in claim 1, wherein the cloud module includes a server and a cloud database; wherein the server is used to obtain at least one of the unit weight and the activity coefficient value; the cloud The database is coupled to the server and is used to store at least one of the at least one poultry image feature, the image weight relationship, the unit weight, and the activity coefficient value. The poultry health monitoring system as described in claim 6, wherein the server is coupled to the cloud through one of narrowband internet of things (NB-Iot), LoRa WAN, LTE and Wi-Fi database. The poultry health monitoring system as described in claim 1, wherein the weighing structure includes a weighing platform and an intermediate platform; wherein the weighing platform is used to carry at least one poultry, and the intermediate platform is configured on the weighing platform above the platform, and the first camera is disposed under the intermediate platform. The poultry health monitoring system according to claim 8, wherein the weighing platform is coupled to the intermediate platform by at least two columns.

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