KR20220012108A - Apparatus and method for determining sowing time of sprout crops using image reading - Google Patents

Abstract

The present invention relates to an apparatus for determining a sowing time of sprout crops by using image reading. The apparatus includes: a communication part receiving a photographed image of the sprout crops; an image processing part generating an image photographing surface corresponding to a unit area of the sprout crops from the received image, thereby generating a first virtual line extended straight to the same height from a sowing determination threshold height point on the image photographing surface and a second virtual line extended straight to the same height from a final sowing determination height point on the image photographing surface, and calculating the ratio of the color of the sprout crops possessing the entire color of a virtual area made by the first and second virtual lines on the image photographing surface; and a sowing time determination part determining the sprout crops as proper to be sown when the ratio of the color of the sprout crops possessing the entire color of the virtual area is no less than a predetermined first threshold ratio while a ratio located above the second virtual line from the ratio of the color of the sprout crops possessing the entire color of the virtual area is no more than a predetermined second threshold ratio. The communication part transmits a message informing the sowing time in accordance with the determination of the sowing determination part to a terminal of a user or a network, and the sowing determination threshold height point can be the lowermost point in a height section of the sprout crops to be sown, set by the user, and the final sowing determination height point can be the uppermost point in the height section of the sprout crops to be sown by the user.

Description

Apparatus and method for determining the seeding time of sprout crops using image reading

The present invention relates to determining the seeding time of a sprout crop, and more particularly, to an apparatus and method for determining the seeding time of a sprout crop using image reading.

Smart farms are steadily growing by forming a global trend as a future agricultural method that uses Internet of Things (IoT) technology to improve labor-intensive production methods or increase crop productivity. Currently, smart farms are experiencing difficulties to the extent that A/S, management, and maintenance are impossible if the company goes out of business after several years have elapsed since its introduction. As the perception that the introduction of smart farms is expensive, data utilization in agriculture is not taking place well.

It is an era where productivity and convenience can be improved by applying the Internet of Things (IoT) technology to the agricultural environment easily and inexpensively due to the generalization of the technology. is not Therefore, in the case of smart farms, it is necessary to improve the problem that the technology that can be used inexpensively and usefully is not used. In the case of sowing of sprout crops, there are many difficulties in sowing sprout crops even in smart farms, such as a problem in that the commerciality of sprout crops sown using only the sowing method after a certain period of time has passed after germination occurs. there is a situation.

For sprout crops, the target nutrient content varies depending on the time of sowing. Therefore, the sowing time of sprout crops will affect the marketability. Conventionally, a sowing method in which sprouts are germinated and harvested after a certain period of time has elapsed was used. Although this conventional sowing method is an advantageous method in terms of managing the schedule of sowing targets, it is not a sowing method with optimized marketability, so it is not greatly helpful in improving the profits of farmers operating smart farms.

In order to solve these problems, it is a smart farm by recycling shipping containers of domestic companies to reduce costs as a smart farm and to determine the optimal sowing time for sprout crops so that farmers can sow sprout crops with optimal marketability. We would like to suggest methods.

The technical problem to be achieved in the present invention is to provide an apparatus for determining the sowing time of sprouts crops using image reading.

Another technical problem to be achieved in the present invention is to provide a method in which an apparatus determines the sowing time of sprouts crops using image reading.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention belongs from the description below. will be able

In order to achieve the above technical problem, an apparatus for determining a seeding time of a sprout crop using image reading includes a communication unit configured to receive an image photographed for the sprout crop; A first imaginary line and a first virtual line extending in a straight line from the seeding judgment critical height point to the image capturing surface by generating an image taking surface corresponding to the unit area of the sprouted crop from the received image and the image capturing surface A second imaginary line extending in a straight line to the same height from the final height of sowing judgment is generated, and the entire color of the imaginary area in the imaginary area created by the first imaginary line and the second imaginary line on the imaging plane. an image processing unit for calculating the ratio of the color of the sprouts in the ; The ratio of the color of the sprout crop in the total color of the virtual area is equal to or greater than a predetermined first threshold rate, and the ratio of the color of the sprout crop in the total color of the virtual area is located higher than the second virtual line and a sowing time determination unit that determines that it is time to sow the sprouts crop when the predetermined second threshold rate or less is lower than the second threshold rate, wherein the communication unit transmits a message indicating that it is the sowing time according to the determination of the sowing determination unit to the user's terminal or network to, the seeding judgment critical height point is the lowest point in the height section of the sprout crop desired for sowing set by the user, and the seeding judgment final height point is in the height section of the sprout crop desired by the user to sow could be the peak.

The first imaginary line and the second imaginary line may be parallel to each other, and the seeding judgment critical height point and the seeding judgment final height point may be set by the user according to the type of the sprouted crop. The apparatus may further include an input unit for receiving information about the seeding determination critical height point and the seeding determination final height point set by the user from the outside. The communication unit may receive information on the seeding determination critical height point and the seeding determination final height point set by the user. The image processing unit may detect the sprout crop from the received image.

In order to achieve the above other technical problem, a method for determining a seeding time of a sprout crop using an image reading apparatus includes: receiving an image photographed for the sprout crop; A first imaginary line and a first virtual line extending in a straight line from the seeding judgment critical height point to the image capturing surface by generating an image taking surface corresponding to the unit area of the sprouted crop from the received image and the image capturing surface generating a second virtual line extending in a straight line to the same height from the final height point of the seeding judgment; calculating a ratio of the color of the sprout crop to the total color of the virtual area in the virtual area created by the first and second virtual lines on the image taking surface; The ratio of the color of the sprout crop in the total color of the virtual area is equal to or greater than a predetermined first threshold rate, and the ratio of the color of the sprout crop in the total color of the virtual area is located higher than the second virtual line determining that it is time to sow the sprouts when it is less than or equal to the second predetermined threshold rate; And the communication unit comprises the step of transmitting a message indicating that it is a sowing time according to the determination of the sowing determination unit to a user terminal or to a network, wherein the seeding determination threshold height point is the seedling crop desired for sowing set by the user is the lowest point in the height section of and the final height point for sowing judgment may be the highest point in the height section of the sprouted crop desired by the user to sow.

The first imaginary line and the second imaginary line are parallel to each other,

The seeding judgment critical height point and the seeding judgment final height point may be those set by the user according to the type of the sprouted crop. The method may further include receiving information on the seeding determination critical height point and the seeding determination final height point set by the user. The method may further include receiving information about the seeding determination threshold height point and the seeding determination final height point set by the user.

According to the method of determining the sowing time of the apparatus 200 for determining the sowing time of sprouts according to an embodiment of the present invention, the time to be sown can be determined regardless of the growth environment of sprouts or the quality of seeds. be able to

In addition, according to an embodiment of the present invention, the apparatus 200 for determining the sowing time of sprouts crops determines the target sowing time through image reading, so that it is possible to obtain a constant number of crops at the target sowing time, and the quality is also can be kept constant

The effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned may be clearly understood by those of ordinary skill in the art to which the present invention belongs from the following description. will be.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as a part of the detailed description to help the understanding of the present invention, provide embodiments of the present invention, and together with the detailed description, explain the technical spirit of the present invention.
1 is an exemplary diagram for explaining a smart farm module (or system) 100 configured of IoT according to the present invention.
2 is a block diagram for explaining the apparatus 200 for determining the sowing time of sprouts crops through image reading according to an embodiment of the present invention.
3 is an exemplary view for explaining an image reading method used by the apparatus 200 for determining the sowing time of a sprout crop according to the present invention to determine the sowing time.
4 is a flowchart for explaining a method for the apparatus 200 for determining the sowing time of sprouts crops according to an embodiment of the present invention to determine the sowing times of sprouts crops through image reading.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION The detailed description set forth below in conjunction with the appended drawings is intended to describe exemplary embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. The following detailed description includes specific details in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced without these specific details.

In some cases, well-known structures and devices may be omitted or shown in block diagram form focusing on core functions of each structure and device in order to avoid obscuring the concept of the present invention. In addition, the same reference numerals are used to describe the same components throughout the present specification.

In the present invention, it is an object of the present invention to provide a smart farm method that enables even beginners to easily perform data-based agriculture using Internet of Thing (IoT) technology. There was a need to develop a smart farm module composed of Internet of Things (IoT) technology. For example, a 40FT shipping container that can be stacked vertically (eg, a domestic ship container) can be (re)used. Such a shipping container-based smart farm requires a system configuration that remotely controls the overall growth environment. This smart farm system can be equipped with an IoT sensor that converts growth environment measurement data, and an IP camera that can check the growth of agricultural products from a distance.

In addition, the IoT-based smart farm module according to the present invention provides an environment for growing sprout crops (or also called sprout crops). Here, sprout crops refer to crops having complete botanical characteristics of young leaves, stems, and roots by sprouting crop seeds. Since ancient times, various sprout crops have been used as medicinal herbs to prevent various diseases and improve health. Although the history of using sprout crops is short in the West, it has been developed as a variety of health foods, paying attention to health functional substances and efficacy. In Korea, the consumption of sprouted vegetables is also increasing due to the influence of well-being in recent years, and it is considered as a health functional food rather than a vegetable. Sprout vegetables are mainly consumed in the fresh convenience food market, and the size of the domestic market was KRW 95.6 billion in 2015 and has been steadily increasing since 2008.

1 is an exemplary diagram for explaining a smart farm module (or system) 100 configured of IoT according to the present invention.

Referring to Figure 1, the smart farm module is a shipping container 110, the IoT sensor units (120, 130) capable of transmitting information to the outside by sensing temperature, humidity, carbon dioxide concentration, etc. affecting the growth of sprout crops; It may include an IP camera 140 . Sprout crops have different growth rates due to the influence of temperature, humidity, moisture, light and carbon dioxide in the growing environment, and the condition of seeds also affects the growth time. As such, there are temperature/humidity and the like as growth environmental factors that affect the growth of sprout crops. The IoT temperature/humidity sensor 120 may measure the temperature and humidity inside the smart farm module and transmit the measured data to the system managing the smart farm module or to the user's terminal. Similarly, the IoT carbon dioxide concentration sensor 130 may measure the concentration of carbon dioxide inside the smart farm module and transmit it to the system managing the smart farm module or transmit it to the user's terminal.

In addition, the smart farm module may further include an IoT humidity controller 140 and an IoT thermostat 150 . The IoT humidity controller 140 performs a function of adjusting the humidity in the smart farm module by receiving a signal for humidity control that makes it suitable for the growth environment of sprouts crops from the outside. The IoT thermostat 150 performs a function of adjusting the temperature inside the smart farm module by receiving a signal for temperature control that makes it suitable for the growth environment of sprouts crops from the outside.

Hereinafter, the IP camera 140 mentioned in the present invention will be briefly described. An Internet Protocol camera (IP camera) is a type of digital video camera generally deployed for surveillance, and can transmit and receive data through a computer network and the Internet, unlike analog closed circuit television (CCTV) cameras. Most cameras that do this are webcams, but IP cameras or netcams are generally only for surveillance purposes that can be accessed directly through a network connection.Some IP cameras are centralized to handle recording, video and alarm management. Network video recorder (NVR) support is required. Other cameras can be operated in a decentralized manner without NVR, and the camera can record directly to a local or remote storage media. The existing CCTV system consists of an analog CCTV camera and a digital video storage device (DVR), and the analog video captured by the CCTV camera can be transmitted to the DVR system by connecting it with a coaxial cable.The transmitted analog video is converted into a digital signal and compressed. It can be saved, restored, reproduced, and displayed through a monitoring monitor.In this traditional video security system, separate independent equipment such as a DVR along with a CCTV camera is needed to monitor the video. However, network technology The development of video technology and image processing chip brought about a big change in the existing video implementation method as described above, that is, instead of the existing CCTV camera that transmits analog video signals using a coaxial cable according to the development of network technology, video signals Compressed into digital signals and equipped with a function to transmit long-distance through a general network (LAN), IP cameras were born, and monitoring became possible from a general PC or laptop without additional equipment such as a DVR. The development of IP cameras, which made it possible to transmit video data through the Internet, also made it possible to store data. By expanding the limit of the system to the Internet space connected to the world without any space restrictions, the function of the existing dedicated DVR device can be transferred to the PC, thus pioneering various fields of application.

These IP cameras have the following advantages compared to existing CCTV cameras. First, it is possible to implement a video surveillance system with only an IP camera. In the existing CCTV and DVR systems, even for the purpose of monitoring only one place, both CCTV cameras and system equipment had to be purchased. Accordingly, the installation cost was high, which was a factor that made popularization difficult. However, IP cameras have built-in video decoder and video codec that implement the functions of DVR system as well as camera ISP, process and compress the video itself, and then transmit the video to the user's PC using the network, can be stored and displayed on a PC, so a person who simply monitors a place or wants to monitor a remote location can achieve the purpose of monitoring only with an IP camera without the need to purchase a separate system. Second, the existing CCTV system or DVR system used a coaxial cable to connect the camera to the system device in order to deliver the image captured by the camera, but the IP camera uses the existing network. Therefore, in order to install the existing CCTV system or DVR system, the installation cost is high in addition to the basic video security equipment purchase cost. Third, images taken by IP cameras installed in remote locations can be viewed anywhere using the Internet. In other words, the existing CCTV system or DVR system had to connect the camera and the system with a coaxial cable, so you could only see an image from one building or a short distance. can be seen in The advantages of IP cameras as described above are judged to be a catalyst for expanding the forward market of the video security industry from industrial to home use, and are already establishing themselves as a key element in a new market called home networks.

The IP camera 140 located inside the smart farm module 100 according to the present invention observes and shoots the growth of sprouted crops and uses the captured image (or image) to determine the sowing time of sprouted crops through LAN, etc. can be sent to the device. In addition, the IP camera 140 is only an example, and if it is a camera that can capture the growth state of sprouts as an image and has a function that can transmit it to the outside or to the system through a LAN, the IP camera 140 is used. can be replaced

2 is a block diagram for explaining the apparatus 200 for determining the sowing time of sprouts crops through image reading according to an embodiment of the present invention.

Referring to FIG. 2 , the apparatus 200 for determining the seeding time of a sprout crop (or also called a sprout crop, etc.) through image reading includes a control unit 210 , a communication unit 240 , and an input unit 250 . can do. The control unit 210 may further include an image processing unit 220 and a seeding time determination unit 230 . The control unit 210 performs a function of overall controlling the smart farm module. In the smart farm module 100, the image capturing unit (eg, IP camera) shoots sprouts growing in the smart farm at a predetermined cycle or in an event-triggering method, and various captured images (or image captured images, etc.) may be called) can be transmitted to the device 200 for determining the sowing time of sprouts crops.

The communication unit 240 may receive an image of sprouting crops from the image capturing unit (eg, IP camera) of the smart farm module 100 through the IoT or the like, and transmit it to the image processing unit 220 . . The image processing unit 220 may acquire (or acquire) an image capturing surface corresponding to a unit area of the sprout crops from the images of the sprout crops received from the communication unit 250 . Specific details of the imaging plane corresponding to the unit area of the sprout crop will be described with reference to FIG. 3 .

The image processing unit 220 according to an embodiment of the present invention may recognize object recognition from the captured image. The image processing unit 220 receives a captured image from an external camera device (eg, IP camera 140), recognizes the type and arrangement of objects included in the captured image, and processes/reflects/creates content, etc. To do this, it is connected to the camera device, and is connected to the communication unit 240 for receiving a captured image and a memory (not shown) for storing the received captured image. The image processing unit 220 may be operated under the control of the control unit 210 .

3 is an exemplary view for explaining an image reading method used by the apparatus 200 for determining the sowing time of a sprout crop according to the present invention to determine the sowing time.

The image shown in FIG. 3 shows an environment in which sprout crops are stacked and grown. The smart farm module 100 is grown by stacking sprouts in a container (eg, a shipping container). The image received by the communication unit 240 from the image capturing unit 140 may be an image showing that sprout crops are stacked and grown. In the received image, an area corresponding to one layer of a sprout crop may be a unit area of a sprout crop. The image processing unit 240 may generate an image capturing surface corresponding to a unit area of a sprout crop from the image received from the communication unit 240, and the image capturing surface thus generated is, as an example, a red line shown in FIG. 3 . It is indicated by the box area 310 of .

To this end, first, the image processing unit 220 may perform a preprocessing process such as filtering to remove noise included in the captured image and increase the recognition rate before proceeding with the recognition process of the object included in the captured image. have. More specifically, if the contrast between the background and the block is small in the image captured by the lighting environment of the place where the shooting area is defined, or noise is included in the color or outline, it is difficult to accurately extract the original color and shape of the object. This may occur, and in order to increase the accuracy of recognition, the image processing unit 220 may be configured with a high-pass filter, etc., and may perform a process such as processing the difference between the background and the boundary line of the object to be conspicuous.

The image processing unit 220 may virtually generate a reference point (or reference height point) and/or reference lines related to a sowing time determination (or determination) on the image capturing surface 310 corresponding to a unit area of a sprout crop. Referring to FIG. 3 , virtual reference points of a point A 320 , a point B 330 , a point C 340 , and a point D 350 on the image taking surface are illustrated. Here, a point 320 may be defined as the bottom height point of the start of sowing on the image taking surface 310 , and the b point 330 may be defined as a sowing judgment critical height point on the image taking surface 310 . In addition, the d point 340 may be defined as the final height point of the sowing judgment on the image taking surface 310 , and the d point 350 may be defined as the final height of the sowing judgment on the image taking surface 310 .

Here, the bottom-height point 320 of the start of sowing can be defined as the bottom point (or bottom-height point) at which sprouts are located when sprouts grow on one layer in a stacked container. The sowing judgment critical height point 330 can be defined as the lowest height point of the sprout crops that can be determined as the sowing time, and the final high point of the sowing judgment is called the sowing time because the sprouts no longer grow taller and the marketability declines. It can be defined as the highest point of sprout crops that can be judged. In addition, the sowing judgment critical height point 330 may be defined as the lowest point in the height section of the sprout crop desired for sowing set by the user, and the seeding judgment final height point 340 is the sprout desired by the user to sow. It can be defined as the highest point in the height section of a type of crop.

The seeding determination critical height point 330 and the seeding determination final height point 340 may be set by a user, and the input unit 250 may receive an input from the user. The input unit 250 may be configured in various types such as a display unit. Alternatively, the communication unit 240 may receive information on the seeding determination critical height point 330 and the seeding determination final height point 340 set by the user and deliver it to the image processing unit 220 .

For example, in the case of sprouted barley, which is an example of a sprout crop, assuming that 15 cm to 20 cm is the optimal harvest standard, the height of the sprout crop desired by the user to sow will be 15 cm to 20 cm. At this time, the sowing judgment critical height point 330 is a point that is 15 cm from the bottom height point 320 at the start of sowing, and the final height point for sowing judgment is a point that becomes 20 cm from the bottom height point 320 of the sowing start. can When the height of the sprout crops is 15 cm from the bottom height point 320 at the start of sowing, the sprout crops are commercially viable. The productivity of crops decreases. The sowing judgment critical height point 330 and the sowing judgment final height point 340 may be changed by the user according to the type and characteristics of sprouted crops.

The image processing unit 220 generates a first virtual line 360 extending in a straight line at the same height from the seeding judgment critical height point 330 in the image capturing surface 310 shown in FIG. 3, and the image capturing surface ( In 310 , a second virtual line 370 extending in a straight line to the same height from the final height point 340 of the seeding decision may be generated. When the first virtual line 360 and the second virtual line 370 are drawn on the image capturing surface 310, an area is created, which may be referred to as a virtual area. That is, a virtual area is created by the first virtual line 360 and the second virtual line 370 on the image capturing surface 310 , and the image processing unit 220 may generate the virtual area. Here, if the virtual area is the first virtual area by the first virtual line 360 and the second virtual line 370 on the image taking surface 310, a straight line from the bottom height point 320 of the sowing start The virtual area created on the imaging surface 310 by the extended virtual line and the first virtual line 360 extending in a straight line from the seeding determination critical height point 330 may be defined as the second virtual area.

The image processing unit 220 may perform object recognition-based image processing to recognize an object based on a color on the image capturing surface 310 (eg, recognize green sprouts shown in FIG. 3 ). Therefore, the image processing unit 220 may recognize the sprout crops in the virtual area of the image capturing surface, and may calculate the ratio of the sprout crops in the virtual area using a color-based recognition method or the like. The color-based object recognition technology includes a gray model and an RGB model. The gray model expresses an image only with brightness information without using color information. An image pixel value is expressed as an intensity value of 256 levels from black 0 to white 255. The RGB model is the most basic color model and thinks of a color as a combination of three components: Red, Green, and Blue. In the RGB model, black is R=G=B=0, white is R=G=B=255, red is R=255, G=B=0, yellow is R=G=255, B=0 do. In case of R=G=B, it becomes an achromatic gray color. Since each of R, G, and B can have a value between 0 and 255, a total of 256*256*256 = 16,777,216 colors can be expressed using the RGB color model.

The image processing unit 200 may recognize an object through an artificial neural network learning module such as a deep learning algorithm, and may recognize an object based on color. The image processing unit 200 may calculate the ratio of the color of the sprout crop to the total color of the virtual area. In addition, the image processing unit 200 may calculate a ratio exceeding the second virtual line 340 among the ratios occupied by the color of sprouts in the total color of the virtual area. If a large portion of the ratio of the color of the sprout crop in the total color of the virtual area exceeds the second virtual line 340, this causes the sprout crop to grow too large, thereby weakening the marketability. Accordingly, a standard for the ratio that should not exceed the second imaginary line 340 among the ratios of the color of sprouts in the total color of the imaginary area is required, and this can be defined as a predetermined second critical ratio. Alternatively, a virtual line extending in a straight line from the bottom height point 320 at the start of sowing and a first virtual line 360 extending in a straight line from the seeding judgment critical height point 330 . A ratio in which a part of the ratio of sprout crops in the area should not exceed the second virtual line 340 may be defined as the predetermined second threshold ratio.

As described above, if the virtual area is the first virtual area by the first virtual line 360 and the second virtual line 370 on the image taking surface 310, the bottom height point 320 of the start of sowing. The virtual area created on the imaging surface 310 by the virtual line extending in a straight line from the first virtual line 360 extending in a straight line from the seeding judgment critical height point 330 can be defined as the second virtual area. . In this case, the predetermined second threshold rate may be defined based on the first virtual area or may be defined based on the second virtual area. If the predetermined second threshold rate is defined based on the second virtual area, the image processing unit 220 may additionally generate a virtual line extending in a straight line from the bottom height point 320 of the start of sowing, and additionally generate A second virtual area created on the image capturing surface 310 may be generated by the virtual line and the first virtual line 360 . Then, the image processing unit 220 may calculate a ratio, etc. of the sprouts in the second virtual area. Hereinafter, for convenience of explanation, calculation will be made based on the first virtual area.

The sowing time determination unit 230 determines that the ratio of the color of the sprouted crop in the total color of the first virtual area is equal to or greater than a predetermined first threshold rate (eg, 70%) and the image capturing surface 310 . In the case where the ratio located higher than the second imaginary line 370 among the ratios occupied by the color of the sprout crops is less than or equal to a predetermined second critical ratio (eg, 30%), it is determined that it is time to sow the sprout crops. will do In this way, the start time of the sowing time may be determined by the first predetermined threshold rate, and the final time of the sowing time may be determined by the second predetermined threshold rate. The user may set the predetermined first threshold rate, the predetermined second threshold rate, and the like according to the degree of marketability required for the sprout crops.

In this way, the seeding time determination unit 230 according to the present invention determines the seeding time through image reading, thereby maintaining the number of target crops at the sowing time for a unit area constant and obtaining uniformity of quality. have.

When the sowing time determination unit 230 determines that it is the sowing time, the control unit 210 may control the communication unit 240 to send a message informing the user of the sowing time to the user's terminal or network so that the user can know the sowing time. The communication unit 240 may transmit a message indicating that it is the sowing time according to the decision of the sowing determination unit 230 to the user's terminal or to the network.

Here, as an example, it has been described that the seeding time is determined through image reading from an image sent by one image capturing unit, but in the smart farm module 100, an image capturing unit capable of photographing crop growth is left, right, front, It can be located in various positions such as behind, so that the growth of each sprout crop can be photographed. The communication unit 240 receives images of sprouts from the image capturing units located at various locations, and the image processing unit 220 may process the images received from the various image capturing units 140 as described above, respectively. . The sowing time determining unit 230 determines the sowing time by averaging the information generated by the image processing unit 220 for the images received from the respective image capturing units 140 or by weighting the specific images. may be

4 is a flowchart for explaining a method for the apparatus 200 for determining the sowing time of sprouts crops according to an embodiment of the present invention to determine the sowing times of sprouts crops through image reading.

Referring to FIG. 4 , the image capturing unit (eg, IP camera) in the smart farm module 100 takes pictures of sprouts growing in the smart farm at a predetermined cycle or in an event-triggering method (S410). Thereafter, the image capturing unit transmits the captured image to the device 200 for determining the seeding time of sprouts (S420). The communication unit 240 may receive an image of sprouts crops from the image capturing unit in the smart farm module 100 through an IoT method or the like, and transmit it to the image processing unit 220 .

In FIG. 4 as well as in FIG. 3 , virtual reference points of the point A 320 , the point B 330 , the point C 340 , and the point D 350 in the image taking surface may be equally applied. Point a 320 may be defined as the bottom height point of the start of sowing on the imaging plane, and point 330 may be defined as the critical height point for sowing judgment on the imaging plane. In addition, the d point 340 may be defined as the final height of the sowing judgment on the image taking surface, and the d point 350 may be defined (in advance) as the final height of the sowing judgment on the image taking surface. Here, the bottom-height point 320 of the start of sowing can be defined as the bottom-height point at which sprouts are located when sprouts grow on one layer in a stacked container.

The sowing judgment critical height point 330 can be defined as the lowest height point of the sprout crops that can be determined as the sowing time, and the final high point of the sowing judgment is called the sowing time because the sprouts no longer grow taller and the marketability declines. It can be defined as the highest point of sprout crops that can be judged. In addition, the sowing judgment critical height point 330 may be defined as the lowest point in the height section of the sprout crop desired for sowing set by the user, and the seeding judgment final height point 340 is the sprout desired by the user to sow. It can be defined as the highest point in the height section of a type of crop.

The seeding determination critical height point 330 and the seeding determination final height point 340 may be set by a user, and the input unit 250 may receive an input from the user. The input unit 250 may include a display unit and the like in various types. Alternatively, the communication unit 240 may receive information on the seeding determination critical height point 330 and the seeding determination final height point 340 set by the user and deliver it to the image processing unit 220 .

The image processing unit 240 may generate an image capturing surface 310 corresponding to a unit area of a sprout crop from the image received from the communication unit 240 (S430). In addition, the image processing unit 220 may virtually generate a reference point (or reference height point) and/or reference lines related to a sowing time determination on the image capturing surface 310 corresponding to a unit area of a sprout crop (S440). . That is, the image processing unit 220 generates a first virtual line 360 extending in a straight line at the same height from the seeding determination critical height point 330 in the image capturing surface 310 shown in FIG. A second virtual line 370 extending in a straight line to the same height from the final height point 340 of the seeding determination may be generated on the surface 310 (S450).

When the first and second virtual lines 360 and 370 are drawn on the image capturing surface 310, an area is created, which may be referred to as a first virtual area. A first virtual area is created by the first virtual line 360 and the second virtual line 370 on the image capturing surface 310 . As such, the image processing unit 220 may generate a first virtual area using the first virtual line 360 and the second virtual line 370 on the image capturing surface (S460).

The image processing unit 220 is capable of object recognition-based image processing, so that the image taking surface 310 can recognize the green sprouts shown in FIG. 3 by a color-based recognition method. Accordingly, the image processing unit 220 may recognize the sprout crop in the first virtual area of the image capturing surface 310 and may calculate the ratio of the sprout crop in the first virtual area (S470). The image processing unit 220 may recognize an object through an artificial neural network learning module such as a deep learning algorithm, and may recognize an object based on color. The image processing unit 200 may calculate the ratio of the color of the sprout crop to the total color of the first virtual area (S470).

In addition, the image processing unit 200 may calculate a ratio exceeding the second virtual line 340 among the ratios occupied by the color of the sprout crop in the total color of the first virtual area. If a large portion of the ratio of the color of the sprout crop in the total color of the first virtual area exceeds the second virtual line 340, this causes the sprout crop to grow too large, thereby weakening the marketability. Therefore, a standard for the ratio that should not exceed the second imaginary line 340 among the ratio of the color of sprout crops in the total color of the first imaginary area is required, and this can be defined as a predetermined second threshold rate. have. As described above, if the predetermined second threshold rate is defined based on the second virtual area, the image processing unit 220 additionally generates a virtual line extending in a straight line from the floor height point 320 of the sowing start. Alternatively, a second virtual area may be separately created, and a ratio of sprouts may be calculated in the second virtual area (S470).

The sowing time determination unit 230 determines that the ratio of the color of the sprouted crop in the total color of the first virtual area is equal to or greater than a predetermined first threshold rate (eg, 70%) and the image capturing surface 310 . When the ratio located higher than the second imaginary line among the ratios occupied by the color of the sprout crops is less than or equal to a predetermined second critical ratio (eg, 30%), it may be determined that it is time to sow the sprout crops ( S480). The seeding time determining unit 230 according to the present invention determines the seeding time through image reading, thereby maintaining uniformity of quality by maintaining a constant number of target crops at the sowing time for a unit area.

When the sowing time determination unit 230 determines that it is the sowing time, the control unit 210 may control the communication unit 240 to send a message informing the user of the sowing time to the user's terminal or network so that the user can know the sowing time. The communication unit 240 may transmit a message indicating that it is the sowing time according to the decision of the sowing determination unit 230 to the user's terminal or to the network (S490).

In the above, it has been described that the apparatus 200 for determining the sowing time of sprouts crops determines the sowing time by receiving an image photographed for sprouts crops from a separate external device such as the image capturing unit 140, When the image capturing unit 140 (ie, a camera), etc. is built in the device 200 for determining the seeding time of sprouted crops, the device 200 for determining the sowing time of sprouted crops It is also possible to perform all a series of processes such as shooting and sowing time determination. In addition, the apparatus 200 for determining the seeding time of such sprouted crops may be implemented in a server of a network connected to the smart farm module.

As described above, according to the method for determining the seeding time according to the apparatus 200 for determining the seeding time of a sprout crop according to the present invention, the time to sow regardless of the growth environment of the sprout crop or the quality of the seed can be judged. In addition, there is an advantage in that the quality can be maintained consistently by obtaining a constant number of crops at the target sowing time.

Since the sowing time determination unit 230 determines the seeding time through image reading, the number of target crops at the sowing time for a unit area is constantly maintained and acquired, thereby ensuring uniformity of quality.

The embodiments described above are those in which elements and features of the present invention are combined in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. It is also possible to configure embodiments of the present invention by combining some elements and/or features. The order of operations described in the embodiments of the present invention may be changed. Some features or features of one embodiment may be included in another embodiment, or may be replaced with corresponding features or features of another embodiment. It is apparent that claims that are not explicitly cited in the claims can be combined to form an embodiment or included as a new claim by amendment after filing.

It is apparent to those skilled in the art that the present invention may be embodied in other specific forms without departing from the essential characteristics of the present invention. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present invention are included in the scope of the present invention.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors. Software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or apparatus, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with reference to the limited drawings, various modifications and variations are possible to those skilled in the art from the above description. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Claims (9)

In the device for determining the sowing time of sprouts crops using image reading,
a communication unit for receiving an image photographed for the sprouts;
A first imaginary line and a first virtual line extending in a straight line from the seeding judgment critical height point to the image capturing surface by generating an image taking surface corresponding to the unit area of the sprouted crop from the received image and the image capturing surface Create a second virtual line extending in a straight line to the same height from the final height of the sowing judgment,
an image processing unit for calculating a ratio of the color of the sprout crop to the total color of the virtual area in the virtual area created by the first virtual line and the second virtual line on the image taking surface;
The ratio of the color of the sprout crop in the total color of the virtual area is equal to or greater than a predetermined first threshold rate, and the ratio of the color of the sprout crop in the total color of the virtual area is located higher than the second virtual line Including a sowing time determination unit that determines that it is time to sow the sprouted crops when the predetermined second threshold rate is less than or equal to the predetermined second threshold rate,
The communication unit transmits a message to the user's terminal or network to inform that it is the sowing time according to the decision of the sowing determination unit,
The seeding judgment critical height point is the lowest point in the height section of the sprout crop desired to be planted set by the user, and the seeding judgment final height point is the highest point in the height section of the sprout crop desired by the user to sow. Image, A device for determining the sowing timing of sprouts crops using readout. The method of claim 1,
The first imaginary line and the second imaginary line are parallel to each other,
The seeding judgment critical height point and the seeding judgment final height point are set by the user according to the type of the sprouting crop, the apparatus for determining the seeding time of sprouts crops using image reading. 3. The method of claim 2,
The apparatus for determining seeding timing of sprouts crops using image reading, further comprising an input unit for receiving information on the seeding judgment critical height point and the seeding judgment final height set by the user from the outside. 3. The method of claim 2,
The communication unit receives information on the seeding judgment critical height point and the seeding judgment final height point set by the user, the device for determining the seeding time of sprouts crops using image reading. The method of claim 1,
The image processing unit for detecting the sprouted crop from the received image, a device for determining a seeding time of a sprouted crop using image reading. In the method for the device to determine the seeding time of sprouts using image reading,
Receiving an image photographed for the sprout crop;
A first imaginary line and a first virtual line extending in a straight line from the seeding judgment critical height point to the image capturing surface by generating an image taking surface corresponding to the unit area of the sprouted crop from the received image and the image capturing surface generating a second virtual line extending in a straight line to the same height from the final height point of the seeding judgment;
calculating a ratio of the color of the sprout crop to the total color of the virtual area in the virtual area created by the first and second virtual lines on the image taking surface;
The ratio of the color of the sprout crop in the total color of the virtual area is equal to or greater than a predetermined first threshold rate, and the ratio of the color of the sprout crop in the total color of the virtual area is located higher than the second virtual line determining that it is time to sow the sprouts when it is less than or equal to the second predetermined threshold rate; and
The communication unit comprising the step of transmitting a message informing that it is a sowing time according to the decision of the sowing determination unit to the user's terminal or to the network,
The seeding judgment critical height point is the lowest point in the height section of the sprout crop desired to be planted set by the user, and the seeding judgment final height point is the highest point in the height section of the sprout crop desired by the user to sow. Image, A method of determining the sowing timing of sprouted crops using readouts. 7. The method of claim 6,
The first imaginary line and the second imaginary line are parallel to each other,
The method of determining the seeding timing of sprouts crops using image reading, wherein the seeding judgment critical height point and the seeding judgment final height point are set by the user according to the type of sprout crops. 8. The method of claim 7,
Further comprising the step of receiving information on the seeding judgment critical height point and the seeding judgment final height point set by the user, the method of determining the seeding time of sprouts crops using image reading. 7. The method of claim 6,
The method of determining a seeding timing of sprouts crops using image reading further comprising the step of receiving information on the seeding judgment critical height point and the seeding judgment final height point set by the user.

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