KR102642931B1 - Smart sericulture big data construction system - Google Patents

Abstract

The present invention relates to a smart sericulture agricultural big data construction system, and more specifically, a sensor module installed inside, outside and in the silkworm growth area of Jamsil to acquire environmental information including temperature, humidity, and carbon dioxide concentration, and A sensor unit including a thermal imaging camera module that acquires a thermal image; a data collection unit that collects the environmental information and thermal images in real time through the sensor unit; A big data construction unit that constructs big data by analyzing and processing environmental information and thermal images collected by the data collection unit; and an integrated management unit that controls the temperature, humidity, and ventilation control device of the jamsil according to the results analyzed and processed by the big data building unit, stores and manages the control history, and outputs it to an administrator terminal. The big data building unit includes , a data processing module that removes noise from the environmental information and thermal images collected by the data collection unit and performs correction processing; an environmental analysis module that compares the environmental information processed by the data processing module with a reference value for silkworm growth and transmits a control signal to the integrated management unit to maintain the silkworm growth environment at the reference value; The thermal image processed in the data processing module is analyzed to identify normal silkworms and abnormal silkworms, but abnormal silkworms have a lower temperature than normal silkworms, and the temperature difference between silkworms captured in the thermal image is used to identify abnormal silkworms. analysis module; An identification result providing module that transmits the location of the abnormal silkworm identified in the image analysis module to the integrated management unit; and a big data storage module that stores and manages environmental information and thermal images processed in the data processing module.
According to the smart sericulture agricultural big data construction system proposed in the present invention, by constructing a WSN where sensor modules and thermal imaging camera modules are wirelessly connected, silkworm growth is monitored from baby silkworms to red silkworms (silkworms just before silk extraction). Temperature, humidity, airflow, carbon dioxide, harmful gases, etc. can be collected and analyzed, and an environment for real-time monitoring can be created. Using WSN, environmental information collected from Anuae Jamsil and Big Silkworm Jamsil, silkworm growth, silkworm weight, By analyzing and processing thermal images to build big data, we establish optimal standard data and further improve the quality of domestic silkworm/red silkworm food through sharing, utilization, and management regardless of place and time through big data in sericulture agriculture. , quality equalization, purity improvement, etc. can be achieved, and by controlling the temperature, humidity, and ventilation control device of the dam using environmental information and thermal image analysis results, the competitiveness of sericulture agriculture can be improved by automating the operation of the dam.
In addition, according to the smart sericulture agricultural big data construction system proposed in the present invention, thermal images are analyzed to identify normal and abnormal silkworms, and abnormal silkworms are identified using the temperature difference between silkworms captured in the thermal images. The health status of growing silkworms can be automatically determined, and managers can easily identify the location of abnormal silkworms and take appropriate measures quickly.

Description

Smart Sericulture Agricultural Big Data Construction System{SMART SERICULTURE BIG DATA CONSTRUCTION SYSTEM}

The present invention relates to a smart sericulture agricultural big data construction system, and more specifically, to a smart sericulture agricultural big data construction system that automates sericulture agriculture and builds big data related to sericulture using wireless sensor networks and image processing technology. .

Sericulture generally refers to raising silkworms to produce cocoons. Previously, only spring and fall silkworms were raised for this type of sericulture, but if mulberry trees and labor are available, they can be reared several times a year.

In particular, in the case of traditional sericulture in existing farms, simple facilities such as cement, bricks, panels, and greenhouses are provided, and silkworm growth facilities are provided in the interior space of such simple facilities to raise silkworms. However, the aging of facilities in existing silkworm breeding facilities for traditional sericulture causes a decline in productivity, quality, and image as an eco-friendly, high-value agricultural product. In particular, the disinfection and temperature management required when raising silkworms is not possible, increasing the possibility of bedridden disease. In addition, there was a problem that it took a lot of labor and time to manage temperature and humidity and disinfect in a labor-dependent manner.

General farms as well as large-scale silkworm farms are making great efforts to obtain high income by equipping various facilities to improve the productivity of silkworms and growing silkworms regardless of the season, but this involves high facility costs and While labor is essential, there is a problem in that economic profit generation does not increase significantly, and the growth or harvest of silkworms is not properly achieved due to the inability to properly control the environment and diseases and pests that occur in cultivated silkworms.

Meanwhile, a smart agricultural production system based on sensors and networks can create an optimal environment based on information management such as temperature, humidity, and carbon dioxide (CO ₂ ) from the growth stage of crops and prevent damage such as pests and diseases. To this end, various agricultural product cultivation systems linked to IoT (Internet of Things) have been developed.

Recently, the crop cultivation system linked to IoT measures and displays the temperature and humidity within the cultivation facility and remotely controls the cultivation facility using a controller that can control cameras, pumps, various valves, electric heaters, lighting equipment, etc. It grows crops while monitoring, and is considered a technology that can improve agricultural competitiveness by managing a large number of cultivation facilities with minimal labor, and various control technologies using this are being studied.

As prior art related to the present invention, Publication Patent No. 10-2020-0031966 (title of the invention: Traditional sericulture factory system using containers, publication date: March 25, 2020) has been disclosed.

The present invention was proposed to solve the above-mentioned problems of previously proposed methods, by constructing a WSN in which a sensor module and a thermal imaging camera module are wirelessly connected, from baby silkworms to red silkworms (silkworms just before silk extraction). It is possible to collect and analyze temperature, humidity, airflow, carbon dioxide, harmful gases, etc. for silkworm growth, and create a real-time monitoring environment. By using WSN, environmental information and silkworm information collected from Anuae Jamsil and Big Silkworm Jamsil can be collected and analyzed. By constructing big data by analyzing and processing growth, silkworm weight, and thermal images, we establish optimal standard data and further share, utilize, and manage domestic silkworms/silkworms regardless of location and time through big data in sericulture agriculture. It is possible to improve the quality of red jam food, equalize quality, and improve purity. By controlling the temperature, humidity, and ventilation control device of the silkworm using the analysis results of environmental information and thermal images, it automates the operation of the silkworm and improves the competitiveness of sericulture agriculture. The purpose is to provide a smart sericulture agricultural big data construction system that can be improved.

In addition, the present invention analyzes thermal images to identify normal silkworms and abnormal silkworms, and uses the temperature difference between silkworms captured in thermal images to identify abnormal silkworms, thereby automatically determining the health status of silkworms growing in a room, Another purpose is to provide a smart sericulture agricultural big data construction system that allows managers to easily identify the location of abnormal silkworms and quickly take appropriate measures.

The smart sericulture agricultural big data construction system according to the characteristics of the present invention to achieve the above purpose is,

As a smart sericulture agricultural big data construction system,

A sensor unit installed inside, outside, and in the silkworm growth area of Jamsil and including a sensor module that acquires environmental information including temperature, humidity, and carbon dioxide concentration, and a thermal imaging camera module that acquires thermal images of the silkworms;

a data collection unit that collects the environmental information and thermal images in real time through the sensor unit;

A big data construction unit that constructs big data by analyzing and processing environmental information and thermal images collected by the data collection unit; and

An integrated management unit controls the temperature, humidity, and ventilation control device of the room according to the results analyzed and processed by the big data construction unit, and stores and manages control history and outputs it to an administrator terminal,

The big data construction department,

a data processing module that removes noise from the environmental information and thermal images collected by the data collection unit and performs correction processing;

an environmental analysis module that compares the environmental information processed by the data processing module with a reference value for silkworm growth and transmits a control signal to the integrated management unit to maintain the silkworm growth environment at the reference value;

The thermal image processed in the data processing module is analyzed to identify normal silkworms and abnormal silkworms, but abnormal silkworms have a lower temperature than normal silkworms, and the temperature difference between silkworms captured in the thermal image is used to identify abnormal silkworms. analysis module;

An identification result providing module that transmits the location of the abnormal silkworm identified in the image analysis module to the integrated management unit; and

Its structural feature includes a big data storage module that stores and manages environmental information and thermal images processed in the data processing module.

Preferably, the sensor unit,

The sensor module and the thermal imaging camera module can be wirelessly connected to form a wireless sensor network (Wireless Sensor Network, WSN).

More preferably, the sensor module,

An external sensor node installed outside Jamsil to measure temperature and humidity; and

It may be installed inside Jamsil and include an internal sensor node that measures temperature, humidity, carbon dioxide concentration, and harmful gases.

Even more preferably, the sensor module,

It may further include a growth sensor node installed in the silkworm growth area to measure silkworm growth status information, including the temperature of the silkworm, the growth state of each stage of the silkworm, the weight of the silkworm, the pH of the silkworm growth area, sound, and soil information. there is.

Preferably, the integrated management unit:

It supports monitoring of the results analyzed and processed by the big data construction unit, and the location of the abnormal silkworm received from the identification result providing module can be output to the administrator terminal.

Preferably, the data processing module:

Environmental information and thermal images collected from different sericulture farms and dams can be corrected and converted into a unified standard.

More preferably, the data processing module,

To enable learning using the global portal's deep learning service, it can be converted to deep learning service standards and stored in the big data storage module.

Preferably, the big data storage module,

The comparison results and control signals of the environmental analysis module, and the identification results of the image analysis module are stored in conjunction with environmental information and thermal images processed by the data processing module, and the control history by the integrated management unit is linked and stored, It can be stored and managed by sericulture farm and dam room.

According to the smart sericulture agricultural big data construction system proposed in the present invention, by constructing a WSN where sensor modules and thermal imaging camera modules are wirelessly connected, silkworm growth is monitored from baby silkworms to red silkworms (silkworms just before silk extraction). Temperature, humidity, airflow, carbon dioxide, harmful gases, etc. can be collected and analyzed, and an environment for real-time monitoring can be created. Using WSN, environmental information collected from Anuae Jamsil and Big Silkworm Jamsil, silkworm growth, silkworm weight, By analyzing and processing thermal images to build big data, we establish optimal standard data and further improve the quality of domestic silkworm/red silkworm food through sharing, utilization, and management regardless of place and time through big data in sericulture agriculture. , quality equalization, purity improvement, etc. can be achieved, and by controlling the temperature, humidity, and ventilation control device of the dam using environmental information and thermal image analysis results, the competitiveness of sericulture agriculture can be improved by automating the operation of the dam.

In addition, according to the smart sericulture agricultural big data construction system proposed in the present invention, thermal images are analyzed to identify normal and abnormal silkworms, and abnormal silkworms are identified using the temperature difference between silkworms captured in the thermal images. The health status of growing silkworms can be automatically determined, and managers can easily identify the location of abnormal silkworms and take appropriate measures quickly.

Figure 1 is a diagram showing the configuration of a smart sericulture agricultural big data construction system according to an embodiment of the present invention.
Figure 2 is a diagram illustrating, as an example, the overall configuration of a smart sericulture agricultural big data construction system according to an embodiment of the present invention.
Figure 3 is a diagram showing the detailed configuration of the sensor unit in the smart sericulture big data construction system according to an embodiment of the present invention.
Figure 4 is a diagram showing the detailed configuration of the big data construction unit in the smart sericulture big data construction system according to an embodiment of the present invention.
Figure 5 is a diagram showing changes in silkworm body temperature according to temperature changes in the silkworm rearing area.
Figure 6 is a diagram showing the amount of silkworm planting according to changes in humidity in the silkworm rearing area.
Figure 7 is a diagram showing silkworm mortality according to changes in carbon dioxide concentration in the silkworm rearing area.
Figure 8 is a photo of normal silkworms, cocoons, and abnormal silkworms arranged for the experiment.
Figure 9 is a diagram showing a thermal image captured by a thermal imaging camera module.
Figure 10 is a diagram showing a thermal image of a silkworm colony captured by a thermal imaging camera module in a smart sericulture agricultural big data construction system according to an embodiment of the present invention.

Hereinafter, with reference to the attached drawings, preferred embodiments will be described in detail so that those skilled in the art can easily practice the present invention. However, when describing preferred embodiments of the present invention in detail, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In addition, the same symbols are used throughout the drawings for parts that perform similar functions and actions.

Additionally, throughout the specification, when a part is said to be 'connected' to another part, this is not only the case when it is 'directly connected', but also when it is 'indirectly connected' with another element in between. Includes. Additionally, ‘including’ a certain component does not mean excluding other components, but rather including other components, unless specifically stated to the contrary.

Figure 1 is a diagram showing the configuration of a smart sericulture agricultural big data construction system according to an embodiment of the present invention. As shown in Figure 1, the smart sericulture agricultural big data construction system according to an embodiment of the present invention includes a sensor unit 100, a data collection unit 200, a big data construction unit 300, and an integrated management unit 400. ) may be configured to include.

In other words, the smart sericulture agricultural big data construction system according to an embodiment of the present invention configures a WSN in which the sensor module 110 and the thermal imaging camera module 120 of the sensor unit 100 are wirelessly connected and collects data. The unit 200 collects environmental information and thermal images from the WSN, and the big data construction unit 300 analyzes and processes them to build big data. In addition, the integrated management unit 400 can control the temperature, humidity, and ventilation control device of the room using environmental information and thermal image analysis results.

Figure 2 is a diagram illustrating, as an example, the overall configuration of the smart sericulture big data construction system according to an embodiment of the present invention. In Figure 2, a damsil using an idle house is shown, but a smart sericulture agricultural big data construction system according to an embodiment of the present invention can be built in various types of damsil, regardless of the specific form, such as a traditional damsil or a container damsil.

Hereinafter, with reference to FIGS. 1 and 2, each configuration of the smart sericulture big data construction system according to an embodiment of the present invention will be described in detail.

The sensor unit 100 includes a sensor module 110 that is installed inside, outside, and the silkworm growth area of Jamsil to acquire environmental information including temperature, humidity, and carbon dioxide concentration, and a thermal imaging camera that acquires thermal images of silkworms. It may include a module 120.

Figure 3 is a diagram showing the detailed configuration of the sensor unit 100 in the smart sericulture agricultural big data construction system according to an embodiment of the present invention. As shown in FIG. 3, the sensor unit 100 of the smart sericulture agricultural big data construction system according to an embodiment of the present invention includes an external sensor node 111, an internal sensor node 112, and a growth sensor node 113. ) may be configured to include a sensor module 110 including a thermal imaging camera module 120.

Here, the sensor unit 100 may form a wireless sensor network (Wireless Sensor Network, WSN) by wirelessly connecting the sensor module 110 and the thermal imaging camera module 120. WSN is a network consisting of a sensor node and a sink node that collects and exports it to the outside. It has a processor that can sense the information and processes the collected information, and is a small wireless transmitting and receiving device that transmits it. It can mean a network system with .

Depending on the embodiment, a WSN can be built using an Arduino board, and if a sensor network can be built using a programming language such as Python, a variety of boards other than the Arduino board can be used.

Below, each component constituting the sensor unit 100 will be described in detail.

The sensor module 110 is installed inside, outside, and in the silkworm growth area of Jamsil and can obtain environmental information including temperature, humidity, and carbon dioxide concentration. More specifically, the sensor module 110 includes an external sensor node 111 installed outside Jamsil to measure temperature and humidity, and an internal sensor installed inside Jamsil to measure temperature, humidity, carbon dioxide concentration, and harmful gases. It may be configured to include a node 112, and is installed in the silkworm growth area to provide silkworm growth status information including the temperature of the silkworm, the growth status of each stage of the silkworm, the weight of the silkworm, the pH of the silkworm growth area, sound, and soil information. It may be configured to further include a growth sensor node 113 that measures.

That is, the sensor module 110 measures temperature (external standard, inside the chamber, silkworm), humidity (external standard, inside the chamber), carbon dioxide concentration (inside the chamber), harmful gas (inside the chamber), growth status (by silkworm age), Environmental information such as weight (silkworm), symptoms, disease, PH (acidity), and sound (frequency) can be obtained through sensor nodes.

The thermal imaging camera module 120 can acquire thermal images of silkworms. A thermal imaging camera is a camera that takes pictures using heat. Unlike general cameras that capture images similar to what the human eye sees, it is a special equipment that takes pictures using only heat. A thermal imaging camera displays a screen depending on how much heat it generates. Thermal imaging camera module 120 can be installed in the silkworm growth area to obtain thermal images of silkworms.

The data collection unit 200 can collect environmental information and thermal images in real time through the sensor unit 100. In other words, it is possible to collect various data obtained from the sensor module 110 and the thermal imaging camera module 120 by connecting wired and wirelessly to the WSN.

The big data construction unit 300 can build big data by analyzing and processing environmental information and thermal images collected by the data collection unit 200. In other words, the big data construction unit 300 can digitally convert environmental information and thermal images and develop the data into big data by collecting, analyzing, processing, and visualizing the data in real time. The detailed configuration of the big data construction unit 300 will be described in detail later with reference to FIG. 4.

The integrated management unit 400 controls Jamsil's temperature, humidity, and ventilation control devices according to the results analyzed and processed by the big data construction unit 300, and can store and manage the control history and output it to the administrator terminal. That is, the integrated management unit 400 uses a temperature control device to control the temperature in the sleeping room, a humidity control device to control humidity using sprinklers or irrigation devices, and a ventilation control device to control ventilation devices such as fans to control temperature, humidity, and It can be controlled to keep airflow constant.

In particular, the integrated management unit 400 can support monitoring of results analyzed and processed by the big data construction unit 300. More specifically, by visualizing the data stored in the big data construction unit 300 and providing it as text, tables, graphs, images, videos, etc., the manager can remotely monitor the growth status and silkworm management status of silkworms through the integrated management unit 400. Monitoring can be made possible. In addition, by linking with the electronic device (administrator terminal) used by the manager, monitoring data can be output and matters that need to be checked by the manager can be notified through alarms or messages.

Here, the electronic devices include smartphones, tablet personal computers, mobile phones, video phones, e-book readers, desktop PCs, laptop PCs, netbook computers, and workstations. ), a server, a personal digital assistant (PDA), a media box, a game console, an electronic dictionary, or a wearable device. The wearable device may be an accessory type (e.g., a watch, a ring, Bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-devices (HMDs), fabric or clothing-integrated (e.g. electronic garments), body-worn (e.g. skin pads or tattoo), or an implantable circuit. In various embodiments, the electronic device is not limited to the above-described devices and may be a combination of two or more of the various devices described above. there is.

Figure 4 is a diagram showing the detailed configuration of the big data construction unit 300 in the smart sericulture big data construction system according to an embodiment of the present invention. As shown in Figure 4, the big data construction unit 300 of the smart sericulture agricultural big data construction system according to an embodiment of the present invention includes a data processing module 310, an environmental analysis module 320, and an image analysis module. It may be configured to include (330), an identification result providing module (340), and a big data storage module (350).

The data processing module 310 may remove noise from environmental information and thermal images collected by the data collection unit 200 and perform correction processing. More specifically, the data processing module 310 can correct, process and convert environmental information and thermal images collected from different sericulture farms and jamsils into a unified standard. In other words, data collected from different sericulture farms and dams may contain unique noise and have different specifications depending on the characteristics of the dam or sensor. The data processing module 310 can correct, process, transform, and unify such data and systematize it as big data.

Additionally, the data processing module 310 can convert the data into a deep learning service standard and store it in the big data storage module 350 to enable learning using the deep learning service of the global portal. More specifically, the data processing module 310 processes and converts environmental information and thermal images to enable use of deep learning services provided by portals such as Google, Facebook, Amazon, Daum, Naver, etc. through data operation software. It can be processed using commercial software provided by the global portal's big data service.

The environmental analysis module 320 may compare the environmental information processed by the data processing module 310 with a standard value for silkworm growth and transmit a control signal to the integrated management unit 400 to maintain the silkworm growth environment at the standard value.

In order to set standard values for silkworm growth, silkworm growth experiments depending on the environment were performed. Silkworm rearing was conducted in the spring of 2020. Silkworm eggs were grown at a temperature of 15-26℃, humidity of 75-80%, and photoperiod of 16 hours of light and 8 hours of darkness per day. In addition, in accordance with the Silkworm Research Project Guidelines (Rural Development Administration, 2010), the 1st to 3rd instars were to be raised in a covered room at a temperature of 25-26℃ and humidity of 75-80%, and the 4th to 5th instars were to be raised at a temperature of 23-23℃. Sleeping was performed at 24°C and humidity of 65-75%. Mulberries of each age were given 3 times a day, and the test group placement was repeated 5 times with 2,000 animals each in a 33㎡ breeding room.

First, to investigate changes according to temperature, 10 silkworms per test group were reared according to the standard silkworm rearing method in three repetitions, and the body temperature of the silkworms was measured at each temperature from the 3rd to the 5th instar to determine the temperature that could occur if a contact thermometer was used with a thermal imaging camera. Measurements were made by minimizing errors, and the air temperature in Jamsil and the silkworm body temperature were measured on the second day of each instar and the difference was calculated.

Figure 5 is a diagram showing changes in silkworm body temperature according to temperature changes in the silkworm rearing area. As shown in Figure 5, in the 3rd instar, there were deviations of -0.2℃, -0.4℃, -0.3℃, and -0.3℃, respectively, compared to the air temperature of 20℃, 23℃, 26℃, and 29℃, and in the 4th instar, Compared to the air temperature of 20℃, 23℃, 26℃, and 29℃, there were deviations of -0.3℃, -0.5℃, -0.4℃, and -0.3℃, respectively, but in the 5th instar, the temperatures were 20℃, 23℃, 26℃, and 29℃. The air temperature showed deviations of 0.5℃, 0.6℃, 0.9℃, and 1.1℃, respectively. The body temperature was 0.2 to 0.5°C lower than the external temperature in the 3rd and 4th instars, but the body temperature was 0.5 to 1.1°C higher in the 5th instar. This can be seen as due to the vigorous feeding and digestion activities at the 5th instar, and it can be seen that in order to raise them healthily, the management temperature must be lowered as the instar increases.

Based on these experimental results, the reference value of temperature can be set to decrease as the age of the silkworm increases.

Next, to investigate changes due to humidity, 1,000 silkworms were reared according to the standard silkworm rearing method and raised at 40-80% humidity from 3rd to 5th instar. 1kg for the 3rd instar, 3kg for the 4th instar, and 20kg for the 5th instar. The amount of mulberry leaves eaten by silkworms was investigated by rapidly raising the water, and then the ratio of the amount eaten compared to the amount fed was investigated to determine the optimal humidity by optimal age.

Figure 6 is a diagram showing the amount of silkworm planting according to changes in humidity in the silkworm rearing area. As shown in Figure 6, in the 3rd instar, the eating rate was excellent at 41.8% at 70% humidity, and in the 4th instar, the eating rate was similar at around 45% at 60-80% humidity, but in the 5th instar, the eating rate was 41.8% at 70% humidity. The eating rate was the highest at 56%. Considering the relationship between humidity and the moisture content of the mulberry leaves, there may be differences depending on whether the mulberry leaves are cut or given as eggplant mulberries. However, it can be seen from Figure 6 that in general, indoor humidity is a major factor involved in feeding activities of silkworms by affecting changes in the moisture content of mulberry leaves.

Based on these experimental results, the standard value of humidity can be set to 70%.

Next, to investigate changes according to carbon dioxide concentration, 1,000 silkworms for each CO ₂ concentration were divided into 5 sections of 500 ppm from 200 to 2,500 ppm and reared until the pupal stage according to the standard silkworm rearing method, and the death rate of silkworms was investigated. Considering that the average atmospheric _CO2 concentration is 340-450ppm, an experiment was conducted to determine the effect on silkworms when indoor ventilation is poor and the basic environmental control values.

Figure 7 is a diagram showing silkworm mortality according to changes in carbon dioxide concentration in the silkworm rearing area. As shown in Figure 7, as the concentration of CO ₂ increases, the mortality rate also increases. In particular, when it is above 1,500 ppm, the mortality rate increases rapidly. CO ₂ is closely related to rearing density. As the rearing density of silkworms increases, the CO ₂ concentration per certain area also increases. When silkworms are raised in a space with a lot of CO ₂ , pus disease occurs, and more disorders occur in the 4th to 5th instar stage than in the 3rd instar stage. Therefore, it can be seen that silkworms can be raised healthily while increasing the density if ventilation is managed to around 1,000 ppm in CO ₂ environmental control conditions in Jamsil, which allows breeding while minimizing economic loss. .

Based on these experimental results, the standard value of CO ₂ concentration can be set to around 1,000 ppm.

The image analysis module 330 analyzes the thermal image processed in the data processing module 310 to identify normal silkworms and abnormal silkworms, and uses the fact that abnormal silkworms have a lower temperature than normal silkworms to determine the temperature of the silkworms captured in the thermal image. The difference can be used to identify abnormal silkworms. Here, abnormal silkworm refers to a silkworm in the process of dying or dying, sleeping ill, etc., and can be distinguished from a normal silkworm, which refers to a healthy silkworm.

Figure 8 is a photograph of normal silkworms, cocoons, and abnormal silkworms arranged for an experiment, and Figure 9 is a diagram showing a thermal image captured by the thermal imaging camera module 120. That is, when normal silkworms, cocoons, and abnormal silkworms arranged as shown in Figure 8 are photographed with a thermal imaging camera, it can be confirmed that the cocoons have the highest temperature, followed by the normal silkworms and abnormal silkworms, as shown in Figure 9. You can. In other words, it can be seen that abnormal silkworms show a relatively lower temperature distribution than normal silkworms and that the temperature distribution is uneven. Using thermal imaging in this way, abnormal silkworms that cannot be clearly distinguished with the naked eye can be distinguished through temperature distribution, as shown in Figure 8.

Figure 10 is a diagram showing a thermal image of a silkworm colony captured by the thermal imaging camera module 120 in the smart sericulture big data construction system according to an embodiment of the present invention. As shown in Figure 10, in the smart sericulture big data construction system according to an embodiment of the present invention, abnormal silkworms can be identified within the silkworm colony using thermal imaging even when rearing silkworms in colonies in the silkworm growth area. there is.

Meanwhile, the image analysis module 330 can identify abnormal silkworms using image processing technology, and more specifically, can use deep learning-based image processing technology. At this time, abnormal silkworms can be identified by analyzing thermal images using deep learning services provided by global portals such as Google, Facebook, Daum, and Naver.

By identifying abnormal silkworms in this way, disease damage can be predicted early by identifying the location and number of the identified abnormal silkworms, and measures such as separating the abnormal silkworms, environmental control, and disinfection can be taken.

The identification result providing module 340 may transmit the location of the abnormal silkworm identified in the image analysis module 330 to the integrated management unit 400. The integrated management unit 400 may output the location of the abnormal silkworm received from the identification result providing module 340 to the manager terminal. Therefore, managers can identify the location and number of abnormal silkworms and quickly take appropriate measures.

The big data storage module 350 can store and manage environmental information and thermal images processed by the data processing module 310. More specifically, the big data storage module 350 records the comparison results and control signals of the environmental analysis module 320 and the identification results of the image analysis module 330 as environmental information and thermal images processed by the data processing module 310. It is stored in conjunction with, and the control history by the integrated management unit 400 is linked, stored and managed, and can be stored and managed for each sericulture farm and jamsil.

According to the smart sericulture agricultural big data construction system proposed in the present invention, the sensor module 110 and the thermal imaging camera module 120 form a WSN that is wirelessly connected, from baby silkworms to red silkworms (silk silks just before silk extraction). ), it is possible to collect and analyze temperature, humidity, carbon dioxide, harmful gases, etc. for silkworm growth and create a real-time monitoring environment, and by using WSN, environmental information collected from Anuae Jamsil and Big Silkworm Jamsil and silkworm growth , By analyzing and processing silkworm weight and thermal images to build big data, we establish optimal standard data, and furthermore, share, utilize, and manage domestic silkworms/red silkworms regardless of place and time through big data in sericulture agriculture. Food quality improvement, quality equalization, and purity improvement can be achieved, and by controlling the temperature, humidity, and ventilation control device of the dam using environmental information and thermal image analysis results, improving the competitiveness of sericulture agriculture by automating the operation of the dam. You can do it.

In addition, according to the smart sericulture agricultural big data construction system proposed in the present invention, thermal images are analyzed to identify normal and abnormal silkworms, and abnormal silkworms are identified using the temperature difference between silkworms captured in the thermal images. The health status of growing silkworms can be automatically determined, and managers can easily identify the location of abnormal silkworms and take appropriate measures quickly.

The present invention described above can be modified or applied in various ways by those skilled in the art, and the scope of the technical idea according to the present invention should be determined by the claims below.

100: sensor unit
110: sensor module
111: external sensor node
112: Internal sensor node
113: Growth sensor node
120: Thermal imaging camera module
200: data collection unit
300: Big data construction department
310: data processing module
320: Environmental analysis module
330: Video analysis module
340: Identification result providing module
350: Big data storage module
400: Integrated Management Department

Claims (8)

As a smart sericulture agricultural big data construction system,
A sensor module 110 installed inside, outside, and in the silkworm growth area of Jamsil to acquire environmental information including temperature, humidity, and carbon dioxide concentration, and a thermal imaging camera installed in the silkworm growth area to acquire thermal images of the silkworm colony. Sensor unit 100 including module 120;
A data collection unit 200 that collects the environmental information and thermal images in real time through the sensor unit 100;
A big data construction unit 300 that constructs big data by analyzing and processing environmental information and thermal images collected by the data collection unit 200; and
According to the results analyzed and processed by the big data construction unit 300, a temperature control device for controlling the temperature in the sleeping room, a humidity control device for controlling humidity using sprinklers or irrigation devices, and a ventilation control device for controlling the ventilation device. Control, store and manage the control history, output to the administrator terminal, support monitoring of results analyzed and processed in the big data construction unit 300, and visualize the data stored in the big data construction unit 300 to create text. , an integrated management unit 400 that provides tables, graphs, images and videos to the manager terminal, allowing the manager to remotely monitor the growth status and silkworm management status of the silkworms,
The sensor unit 100,
The sensor module 110 and the thermal imaging camera module 120 are wirelessly connected to form a wireless sensor network (Wireless Sensor Network, WSN),
The sensor module 110,
An external sensor node 111 installed outside Jamsil to measure temperature and humidity;
An internal sensor node 112 installed inside Jamsil to measure temperature, humidity, carbon dioxide concentration, and harmful gases; and
It includes a growth sensor node 113 that is installed in the silkworm growth area and measures silkworm growth status information, including the temperature of the silkworm, the growth status of each stage of the silkworm, the weight of the silkworm, the pH of the silkworm growth area, sound, and soil information. And
The big data construction unit 300,
a data processing module 310 that removes noise from the environmental information and thermal images collected by the data collection unit 200 and performs correction processing;
The environmental information processed in the data processing module 310 is compared with a reference value for silkworm growth, and a control signal is sent to maintain the silkworm growth environment at the reference value.
Environmental analysis module 320 transmitting to the integrated management unit 400;
When rearing silkworms in a colony in the silkworm growth area, the thermal image processed in the data processing module 310 is analyzed to identify abnormal silkworms in the silkworm colony, using the fact that abnormal silkworms have a lower temperature than normal silkworms. An image analysis module 330 that identifies abnormal silkworms using the temperature difference between silkworms captured in thermal images;
An identification result providing module 340 that transmits the location of the abnormal silkworm identified in the image analysis module 330 to the integrated management unit 400; and
It includes a big data storage module 350 that stores and manages environmental information and thermal images processed in the data processing module 310,
The reference value for silkworm growth used in the environmental analysis module 320 is,
The standard value of temperature is set to decrease as the age of the silkworm increases, the standard value of humidity is set to 70%, the standard value of carbon dioxide concentration is set to 1,000 ppm,
The data processing module 310,
The environmental information and thermal images collected from different sericulture farms and Jamsil are corrected and converted into unified standards, and the big data is converted into deep learning service standards to enable learning using the global portal's deep learning service. Stored in the storage module 350,
The image analysis module 330,
The image processing technology of the global portal's deep learning service is used to analyze the thermal image to identify abnormal silkworms,
The big data storage module 350,
The comparison results and control signals of the environmental analysis module 320 and the identification results of the image analysis module 330 are stored in conjunction with the environmental information and thermal images processed by the data processing module 310, and the integrated management unit ( 400), the control history is stored and managed in conjunction with each sericulture farm and dam room.
The integrated management unit 400,
A smart sericulture agricultural big data construction system, characterized in that the location of the abnormal silkworm received from the identification result providing module 340 is output to the manager terminal. delete delete delete delete delete delete delete

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