Image analysis system and image analysis method

Abstract

To easily acquire information regarding a growth level of a crop to be measured.SOLUTION: A reference gauge for measurement used as a reference of measurement of a growth level of crops comprises: a body part of a columnar structure which extends in a vertical direction; a scale part which is etched on the body part at a predetermined gap as a reference for measuring a height of a crop to be measured; and a leaf color plate part which is attached to a predetermined position of the body part as a reference colored in a predetermined hue for measuring a leaf color of the crop to be measured by comparing with a leaf color of the crop. An image analyzer 200 has: a correction part 232 which corrects an image captured so as to include a scale and leaf color plate provided in the reference gauge for measurement and the crop to be measured on the basis of a positional relation between the reference gauge for measurement and the crop to be measured; and an analysis part 233 which analyzes an image of the corrected crop, and extracts information regarding the growth level of the crop to be measured.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an image analysis system and an image analysis method. [Background technology]

[0002] In conventional agriculture, where farmers manually cultivate crops, the farmers visit the cultivated land and visually check the crops' growth, such as their height and leaf color. Then, the farmers apply fertilizer to the crops according to their growth.

[0003] However, with the aging of agricultural workers and the ongoing labor shortage, it can be difficult to actually visit cultivated land to check the growth of crops as needed and to fertilize the crops as needed.In response to this, in recent years, the "Agricultural DX (Digital Transformation) Concept" has been proposed, which uses digital technology to carry out highly efficient farming, while capturing consumer needs through data and providing agricultural products and food in a way that consumers can experience the value.

[0004] For example, a known prior art related to the above-mentioned agricultural DX is a technology that calculates reflectance by spectrally separating and receiving sunlight reflected by plants, and measures the growth rate of the plants from the calculated reflectance (see, for example, Patent Document 1). Another known prior art is a technology that accurately measures the water level in paddy fields by eliminating errors due to external factors (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-317195 [Patent Document 2] Patent Publication No. 2021-156617 Summary of the Invention [Problem to be solved by the invention]

[0006] However, with the above-mentioned conventional technologies, it is sometimes difficult to easily measure the growth level of crops, and it is sometimes difficult to easily obtain information about the growth level of the crop being measured. For example, while the above-mentioned conventional technologies can measure the growth level of plants, they may be difficult to use for farmers who are accustomed to traditional manual farming. Furthermore, while the conventional technologies can automatically obtain water level information in paddy fields, they cannot measure the height or leaf color of crops. As a result, it is sometimes difficult to easily obtain information about the growth level of the crop being measured. [Means for solving the problem]

[0007] Therefore, in order to solve the above-mentioned problems and achieve the object, the image analysis system of the present invention is an image analysis system having a measurement standard used to measure the growth level of crops, and an image analysis device that corrects an image of the crop captured including the measurement standard, wherein the measurement standard has a body portion which is a columnar structure facing vertically upward relative to the ground, a scale portion engraved on the body portion at predetermined intervals as a standard for measuring the height of the crop to be measured, and a leaf color plate portion attached to a predetermined position on the body portion as a standard colored in a predetermined color tone for measuring the leaf color of the crop to be measured by comparing it with the leaf color of the crop, and the image analysis device has a correction unit that corrects the image captured to include the scale and leaf color plate provided on the measurement standard and the crop to be measured based on the positional relationship between the measurement standard and the crop to be measured, and an analysis unit that analyzes the image of the crop corrected by the correction unit and extracts information regarding the growth level of the crop to be measured.

[0008] In addition, the image analysis method of the present invention is an image analysis method executed by an image analysis system having a measurement standard used to measure the growth level of crops and an image analysis device that corrects an image of the crop captured including the measurement standard, wherein the measurement standard has a body portion which is a columnar structure facing vertically upward relative to the ground, a scale portion engraved on the body portion at predetermined intervals as a standard for measuring the height of the crop to be measured, and a leaf color plate portion attached to a predetermined position on the body portion as a standard colored in a predetermined color tone for measuring the leaf color of the crop to be measured by comparing it with the leaf color of the crop, and is characterized in that the image analysis device includes a step of correcting an image captured to include the scale and leaf color plate provided on the measurement standard and the crop to be measured based on the positional relationship between the measurement standard and the crop to be measured, and a step of the image analysis device analyzing the image of the crop corrected by the correction step and extracting information regarding the growth level of the crop to be measured. [Effects of the Invention]

[0009] According to the present invention, information regarding the growth level of a crop to be measured can be easily obtained. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating a measurement standard device according to this embodiment. [Figure 2] FIG. 2 is a diagram illustrating the structure of the measurement standard device according to the first embodiment. [Figure 3] FIG. 3 is a diagram illustrating the image analysis system according to the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of the image analysis system according to the first embodiment. [Figure 5] FIG. 5 is a table illustrating an example of analysis information according to the first embodiment. [Figure 6] FIG. 6 is a sequence diagram showing a processing procedure of the image analysis method according to the first embodiment. [Figure 7] FIG. 7 is a diagram illustrating a conventional measurement method. [Figure 8] FIG. 8 is a diagram showing an example of the flow of processing by the image analysis system according to the second embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of the configuration of an image analysis system according to the second embodiment. [Figure 10] FIG. 10 is a table illustrating an example of image correction information according to the second embodiment. [Figure 11] FIG. 11 is a sequence diagram showing a processing procedure of the image analysis method according to the second embodiment. [Figure 12] FIG. 12 is a diagram showing an example of the flow of processing by the image analysis system according to the second embodiment. [Figure 13] FIG. 13 is a diagram illustrating an example of the configuration of an image analysis system according to the third embodiment. [Figure 14] FIG. 14 is a sequence diagram showing a processing procedure of the image analysis method according to the third embodiment. [Figure 15] FIG. 15 is a diagram illustrating an example of a computer that implements an image analyzing apparatus by executing a program. DETAILED DESCRIPTION OF THE INVENTION

[0011] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention (hereinafter referred to as "embodiments") will be described with reference to the drawings. Note that the embodiments are not limited to the following description.

[0012] [Embodiment 1] [Overview] Fig. 1 is a diagram illustrating a measurement standard device 100 in this embodiment. The measurement standard device 100 shown in Fig. 1 is an example of a structure used to measure the height and leaf color of a crop to be measured (hereinafter, may be simply referred to as "crop"). In this embodiment, the measurement standard device 100 is installed, for example, adjacent to the crop to be measured.

[0013] As shown in FIG. 1(1), measurement standard 100 has body 101, which is a columnar structure extending vertically. Furthermore, measurement standard 100 has scale 102 engraved on body 101 at predetermined intervals as a reference for measuring the height of the crop to be measured. Measurement standard 100 also has leaf color plate 103 attached to a predetermined position on body 101 as a reference colored in a predetermined color tone for measuring the leaf color of the crop to be measured by comparing it with the leaf color of the crop.

[0014] Hereinafter, the flow of measuring the growth rate of a crop using the measurement standard device 100 according to this embodiment will be described, taking the measurement of rice planted in a paddy field as an example.

[0015] First, the measurement standard 100 is installed at a predetermined position near the rice plants. For example, as shown in Fig. 1 (1), the measurement standard 100 is installed near the rice stalks planted in the paddy field.

[0016] For example, as shown in Fig. 1 (2-1), it is possible to measure the height of rice by comparing the height of the rice with the scale (scale portion 102) provided on the measurement standard 100. Also, for example, as shown in Fig. 1 (2-2), it is possible to measure the leaf color of the rice by comparing the leaf color of the rice with the leaf color plate (leaf color plate portion 103) provided on the measurement standard 100. Note that although the drawings according to this embodiment are shown as black and white diagrams, if the rice is green, it may be expressed as a color tone based on green or the like.

[0017] [Measurement standard 100] First, the structure of the measurement standard device 100 will be described with reference to the drawings. Figure 2 is a diagram illustrating the structure of the measurement standard device 100 according to the first embodiment.

[0018] The measurement standard 100 is used as a standard for measuring the growth level of crops. The measurement standard 100 has a body 101, a scale 102, and a leaf color plate 103.

[0019] [Body 101] Body 101 is a structure that serves as the base of measurement standard 100, and is a columnar structure that extends vertically. Specifically, as shown in Figure 2, body 101 has an underground region that is buried underground, and an aboveground region that is exposed above ground and is provided with scale 102 and leaf color plate 103, which will be described later. Details of scale 102 and leaf color plate 103 will be described later.

[0020] For example, the body 101 may be a cylindrical structure having an outer diameter of 40 to 50 mmφ or the like and a length of 1600 to 1700 mm or the like, or a rectangular cylindrical structure having a polygonal cross section (e.g., a polygonal pillar or a cylindrical structure). Note that the above dimensions are merely examples, and the dimensions of the body 101 are not limited thereto.

[0021] The body 101 may be made of wood, stone, metal, plastic, or the like. The surface shape and color of the body 101 are not particularly limited. For example, the surface of the body 101 may be smooth, or may be provided with a non-slip finish to improve handling efficiency by construction workers when installing the body. The color of the body 101 may be a color that stands out in the cultivated field, such as white, red, a specified fluorescent color, a complementary color to the crop, or a combination of the above colors.

[0022] [Scale 102] The scale portion 102 is provided on the body portion 101 as a reference line marked at a predetermined interval in the vertical upward direction, with the boundary between the area of the body portion 101 buried in the ground and the area exposed from the ground as the zero point.

[0023] For example, the scale portion 102 may be a reference line (e.g., (2-1) in Figure 2) engraved horizontally to the ground at intervals of "xx mm" or "xx cm" from the boundary ((1) in Figure 2) between the underground area of the body portion 101 buried in the ground and the exposed aboveground area, with the boundary set as "zero (0)."

[0024] Furthermore, the length of any part of the reference line may be changed in advance. For example, if the reference lines are marked at intervals of 1 cm, they may be marked at intervals of 5 cm as lines longer than the reference lines indicating other positions, as shown in (2-2) of Figure 2.

[0025] [Leaf colored plate part 103] Leaf color plate 103 has a structure in which multiple green regions colored with green are arranged in descending order of color density. Specifically, leaf color plate 103 has a structure in which a first green region having the highest color density among the multiple green regions is located closest to the ground. Furthermore, leaf color plate 103 has a structure in which green regions having lower color density than the first green region are arranged in descending order of color density in a vertically upward direction from the ground.

[0026] Here, the leaf color plate section 103 will be further explained using the diagram shown in Fig. 2(3). Note that Fig. 2(3) is expressed in black and white color tones, but in the following explanation it will be explained as having a "green" color tone.

[0027] For example, as shown in (3) of Figure 2, the leaf color plate unit 103 is composed of seven regions of multiple color densities, namely, the first region ((3-1) of Figure 2) to the seventh region ((3-2) of Figure 2). For example, in the case of a leaf color plate used to measure the leaf color of paddy rice, the first region closest to the ground may be the green with the highest color density, and the seventh region farthest from the ground may be the green with the lowest color density. Note that the number of regions described above is merely an example, and the number of regions for each color tone on the leaf color plate unit 103 is not limited.

[0028] Each region of the leaf color plate 103 may be expressed in a color system such as the L*a*b color space, the L*C*h color space, or RGB (Red Green Blue) color data. A known color scale for paddy rice may also be used.

[0029] Next, a description will be given of the image analysis system 1 according to the embodiment 1. Fig. 3 is a diagram illustrating the image analysis system 1 according to the embodiment 1.

[0030] A terminal device 300, such as a smartphone or digital camera operated by the farmer 10, captures an image that includes the crop and the measurement standard 100 ((1) in FIG. 3). The terminal device 300 may assist the farmer in capturing an image by displaying a guide that includes the crop and the measurement standard 100 in the captured image range. Alternatively, a permanently installed imaging device may be used instead of the terminal device 300, as long as it can capture an image that includes the crop and the measurement standard 100 and transmit the image to the image analysis device 200.

[0031] The image analyzing device 200 corrects the image captured by the terminal device 300 using the environmental information acquired by the measurement standard device 100. The image analyzing device 200 then analyzes the corrected image to extract information about the growth level of the crop, such as height, leaf color, and number of stems ((2) in FIG. 3). The image analyzing device 200 transmits the information about the growth level of the crop to the farmer's terminal device 300.

[0032] [Image analysis system 1] Next, we will explain the configuration of the image analysis system 1. Fig. 4 is a diagram showing an example of the configuration of the image analysis system 1 according to embodiment 1. As shown in Fig. 4, the image analysis system 1 has a terminal device 300 and an image analysis device 200 that corrects an image of a crop captured including the measurement standard device 100.

[0033] The image analysis system 1 is connected to a terminal device 300 such as a digital camera or smartphone operated by an agricultural worker or the like, and receives captured images and outputs information related to the growth level of crops. Note that the image analysis system 1 is bidirectionally connected between the measurement standard 100 and the image analysis device 200, and between the image analysis device 200 and the terminal device 300, via communication units provided in each device.

[0034] [Image analysis device 200] Next, the configuration of the image analyzing device 200 will be described. As shown in Fig. 4, the image analyzing device 200 has a communication unit 210, a storage unit 220, and a control unit 230. Although not shown in Fig. 4, the image analyzing device 200 can also have an input unit such as a keyboard or a mouse for receiving input such as operations from an administrator or the like. The image analyzing device 200 can also have a display unit such as a display for displaying to an administrator or the like information related to image correction and information related to the degree of crop growth extracted by image analysis.

[0035] [Communications Department 210] The communication unit 210 performs data communication related to the input of environmental information and information about the crop to be measured transmitted from the measurement standard device 100, captured images transmitted from the terminal device 300, and the output of extracted information about the growth level of the crop. The communication unit 210 is realized by a NIC or the like, and controls communication via electrical communication lines such as a LAN or the Internet. The communication unit 210 is connected to a network via wired or wireless connection as necessary, and can transmit and receive information bidirectionally.

[0036] [Storage section 220] The storage unit 220 stores data and programs used for various processes by the control unit 230, and various data acquired through the operation of the control unit 230. The storage unit 220 is realized by a semiconductor memory element such as RAM or flash memory, or a storage device such as a hard disk or optical disk. As shown in Fig. 4, the storage unit 220 has an image correction information DB 221 and an analysis information DB 222, and accumulates and centralizes images of the crop to be measured and analysis results.

[0037] [Image correction information DB221] The image correction information DB 221 is a database that stores information about the crop to be measured as image correction information used when the correction unit 232 (described later) performs image correction. The image correction information is, for example, a group of images taken from fixed points of the measurement standard 100 and the crop in various stages of growth, or a correction function. The image correction information DB 221 may also store corrected images.

[0038] [Analysis information DB222] The analysis information DB 222 is a database that stores the results of an analysis performed using a corrected image by the later-described analysis unit 233. Specifically, the analysis information DB 222 stores information on the growth level of the crop, such as the height, leaf color, and number of stems of the crop, as the analysis results.

[0039] Here, the analysis information stored in the analysis information DB 222 will be explained using a table diagram. Fig. 5 is a table diagram showing an example of analysis information according to embodiment 1. As shown in Fig. 5, the analysis information DB 222 stores information on items such as crop, height, leaf color, number of stems, etc. as analysis information, in association with "No.", which is information identifying individual analysis information.

[0040] The above-mentioned crop is information that identifies the crop to be measured. For example, the name of the crop is the type, a unique number, etc. The height is information that indicates the height of the above-ground part of the crop. The leaf color is information that indicates the result of comparing the leaf color of the crop with a leaf color plate, etc. The number of stems is information that indicates the number of stems of the crop.

[0041] For example, the analysis information DB 222 stores, as analysis information, information such as crop "paddy rice", height "50 cm", leaf color "green", and number of stems "30", in association with No. "1".

[0042] [Control unit 230] The control unit 230 has an internal memory for temporarily storing programs that define various processing procedures and the like of the image analyzing device 200 and processing data, and is realized by electronic circuits such as a CPU or MPU, or integrated circuits such as an ASIC or FPGA. The control unit 230 has a receiving unit 231, a correcting unit 232, an analyzing unit 233, and an output unit 234, as shown in FIG.

[0043] [Reception Department 231] The receiving unit 231 receives the captured image and information for correcting the image transmitted from the terminal device 300. Then, the receiving unit 231 stores the captured image and information about the crop to be measured, which is data for correcting the received image, in the image correction information DB 221.

[0044] [Correction unit 232] The correction unit 232 corrects the image of the crop captured by the farmer. Specifically, the correction unit 232 corrects the image captured to include the scale 102 and leaf color plate 103 provided on the measurement standard 100, based on the positional relationship between the measurement standard 100 and the crop.

[0045] For example, when extracting information about the height of a crop from an image, an error in the height of the crop may occur depending on the imaging position. Therefore, the correction unit 232 corrects the error caused by the imaging position for the image of the crop based on the positional relationship between the measurement standard 100 and the crop.

[0046] For example, the correction unit 232 compares the length of the measurement standard 100 that is set in advance with the length of the measurement standard 100 in the image obtained by analyzing the captured image. Then, based on the comparison result, the correction unit 232 corrects distortion of the measurement standard 100 in the captured image caused by the imaging angle and imaging distance so that the dimensions are the same as the preset dimensions of the measurement standard 100. As described above, the correction unit 232 can correct the captured image so that the scale of the measurement standard 100 and the height of the crop can be accurately compared in the image.

[0047] This allows the correction unit 232 to correct the captured image so that the scale of the measurement standard 100 and the height of the crop can be accurately compared within the image, even if the shooting angle or imaging distance from the crop is different.

[0048] For example, the correction unit 232 compares information (e.g., an RGB color model) that quantitatively represents the color tone of a preset leaf color plate with information that quantitatively represents the color tone of the leaf color plate in an image obtained from a captured image. Then, based on the comparison result, the correction unit 232 corrects the color tone of the leaf color plate in the captured image that has changed due to the imaging environment, such as weather, sunlight, and shadows, so that it becomes the same as the color tone of the preset leaf color plate. As described above, the correction unit 232 can correct the captured image so that the leaf color plate of the measurement standard device 100 can be accurately compared with the leaf color of the crop in the image.

[0049] Furthermore, when extracting information about the number of crop stems from an image, errors may occur depending on the degree of overlap with other crops, and differences in the amount of solar radiation may affect the efficiency of recognizing the contours of the crop stems, which may result in a change in the number of extracted stems. Therefore, the correction unit 232 corrects errors caused by the imaging position for images of crops based on environmental information about the area around the crop collected by the measurement standard device 100 and image information about other crops included in the image.

[0050] For example, the correction unit 232 corrects changes in the color tone of the leaf color plate in the captured image due to changes in the imaging environment, such as weather, sunlight, and shadows, using the method described above. Furthermore, for example, the correction unit 232 corrects the image based on a machine learning model that has been trained to convert the color tone of a crop into a predetermined color tone when a captured image of the crop is input, using pre-prepared training data that associates image data captured in environments with different weather, sunlight, and shadows with the number of crop stalks. As described above, the correction unit 232 can correct the captured image so that the number of stems can be accurately extracted even when the crop stalks are overlapping or when the light hits the crops differently.

[0051] The correction unit 232 may perform the above-described image correction using a known technique. In this section, a description of the principles of image correction based on the above-described known technique will be omitted.

[0052] [Analysis Department 233] The analysis unit 233 analyzes the image of the crop corrected by the correction unit 232 and extracts information related to the growth level of the crop to be measured. Specifically, the analysis unit 233 extracts information indicating the growth level of the crop to be measured as information related to the growth level of the crop to be measured. For example, the analysis unit 233 recognizes the crop included in the image, extracts information related to the height of the crop, extracts information related to the leaf color of the crop, extracts information related to the number of stalks of the crop, etc., based on known technology. Furthermore, the analysis unit 233 may extract information indicating measures to be taken on the crop to be measured corresponding to the growth level of the crop to be measured (e.g., harvesting time, timing of top dressing, timing of pesticide spraying, abnormality detection) as information related to the growth level of the crop to be measured. The analysis unit 233 extracts information indicating the growth level of the crop to be measured and / or measures to be taken on the crop to be measured corresponding to the growth level of the crop to be measured.

[0053] For example, the analysis unit 233 can recognize the crop to be measured, calculate the height of the crop, calculate the leaf color, and calculate the number of stems based on a machine learning model trained using pre-prepared training data on the crop to be measured.

[0054] Furthermore, the analysis unit 233 may analyze the crop image corrected by the correction unit 232 based on external information including at least weather information for the region where the crop to be measured is grown. The analysis unit 233 acquires weather information (including a history of weather information) and, based on information about the growth level of the crop and the weather information, recommends to the terminal device 300 the harvesting time, timing for top dressing, timing for pesticide spraying, and abnormality detection. For example, for each region, the harvesting timing is determined based on the calculation of the height of the crop, calculation of leaf color, and number of stalks. Based on this, the analysis unit 233 determines whether it is time to harvest, and makes a recommendation when it is time to harvest.

[0055] The analysis unit 233 also acquires weather information, calculates the cumulative temperature since the crop was planted in the paddy field based on the temperature history of the area where the paddy field is located, and recommends the timing of harvesting. Alternatively, the analysis unit 233 may acquire weather information and recommend harvesting before a typhoon.

[0056] [Terminal device 300] Next, the terminal device 300 will be described. The terminal device 300 is an information processing device operated by a user, and performs operations such as capturing images of crops, inputting information for image analysis, and displaying information on the growth level of crops output from the image analysis device 200. The terminal device 300 may be an information processing terminal device with a camera, such as a smartphone, tablet terminal, PC (Personal Computer), notebook PC, or PDA (Personal Digital Assistant). The terminal device 300 may also be an imaging device such as a digital camera that transmits images to the image analysis device 200 via a network or a parent device.

[0057] The terminal device 300 receives and displays information about the growth level of the crop analyzed by the image analysis device 200. The terminal device 300 also receives and displays information about the harvesting time, timing for top dressing, timing for pesticide spraying, and abnormality detection recommended by the image analysis device 200.

[0058] [Image analysis processing] FIG. 6 is a sequence diagram showing a processing procedure of the image analysis method according to the first embodiment.

[0059] First, the terminal device 300 is operated by a farmer to capture an image including the crop to be measured and the measurement standard device 100 (step S1), and transmits the captured image to the image analyzing device 200 (step S2).

[0060] The image analyzing device 200 corrects the captured image (step S3). Next, the image analyzing device 200 extracts information about the growth level of the crop to be measured from the corrected captured image (step S4). Then, the image analyzing device 200 transmits the extracted information about the growth level of the crop to the terminal device 300, which outputs the information (steps S5 and S6).

[0061] At this time, the image analysis system 1 ends the process. The image analysis device 200 may transmit information regarding the growth level of the crop not only to farmers but also to parties involved in the growth of the crop being measured (for example, actual users (purchasing intermediaries)). The image analysis device 200 recommends bringing forward the harvesting time in accordance with the growth level of the crop, based on whether the growth level of the crop is good or bad, the estimated harvesting time, the estimated time for top dressing, and weather information (including the history of weather information), and in the event of an impending typhoon. The harvesting time and the time for top dressing are recommended to the terminal device 300.

[0062] [Effects of the First Embodiment] Here, the effects achieved by the image analysis system 1 according to the first embodiment will be described with reference to the drawings. Fig. 7 is a diagram for explaining a conventional measurement method. Fig. 7 shows the conventional measurement method (left diagram of Fig. 7) and the measurement standard device 100 of the present application (right diagram of Fig. 7).

[0063] In conventional agriculture, farmers manually measure the height and leaf color of crops, as shown in the left diagram of Figure 7. For example, as shown in (1-1) of Figure 7, farmers measure the height by using a ruler or other tool to measure length as a reference and placing the ruler or tool directly on the crop. Also, as shown in (1-2) of Figure 7, farmers hold a leaf color plate for measuring the leaf color of crops in their hands and measure the leaf color by placing the leaf color plate directly on the crop.

[0064] However, due to the aging of agricultural workers and the decline in the agricultural workforce, it is predicted that manual measurement of crop growth will become more difficult in the future.In addition, due to the aging of agricultural workers and the decline in the agricultural workforce, it is becoming more difficult to pass on the know-how of determining the growth level by measuring crop height and leaf color.

[0065] To address this issue, sensing / monitoring-based methods for measuring crop growth have been proposed, but these methods can be difficult for farmers who have traditionally measured crop growth manually, and it is predicted that the adoption of new technology will not progress.

[0066] Therefore, in the first embodiment, a measurement standard 100 having a scale used to measure the height of a crop and a leaf color plate used to measure the leaf color of the crop is installed near (for example, next to) the crop to be measured. Then, in the first embodiment, for example, an image is taken by a farmer so that the scale and leaf color plate of the measurement standard 100 and the crop to be measured are included, and the image analyzing device 200 collects the image.

[0067] The image analysis device 200 corrects the collected images based on the positional relationship between the measurement standard and the crop to be measured, analyzes the corrected crop images, and extracts information regarding the growth level of the crop to be measured. The image analysis device 200 transmits the information regarding the growth level of the crop to the terminal device 300 used by the farmer.

[0068] Therefore, farmers can obtain information regarding the growth rate of the crop being measured simply by capturing an image on the terminal device 300 that includes the scale and leaf color plate of the measurement standard 100 and the crop being measured.

[0069] This eliminates the need for agricultural businesses to visually inspect crop growth using a scale and leaf color plate, as was previously done manually, and allows them to automatically obtain information on the growth of the target crop by correcting and analyzing the captured images. This makes it possible to easily obtain information on the growth of the target crop without making farmers feel resistant to the introduction of new technology.

[0070] Furthermore, based on the analysis results, image analysis device 200 makes recommendations to farmers and others regarding crop production, such as harvesting times, which eliminates the need for farmers to check the crops themselves or to research harvesting times, allowing for stable crop growth. Furthermore, by obtaining information on the growth level of the crops being measured from image analysis device 200, actual users can accurately grasp the quantity, quality, and timing of crops when purchasing, thereby stabilizing purchases.

[0071] [Embodiment 2] Next, a description will be given of Embodiment 2. Fig. 8 is a diagram showing an example of the flow of processing by the image analysis system 2 according to Embodiment 2.

[0072] Figure 8 shows a measurement standard 100 installed near the crop, an image analysis device 2200 that corrects images captured by a terminal device 300 using environmental information acquired by the measurement standard 100 and analyzes the corrected images, the terminal device 300 that captures images including the measurement standard 100 and the crop, and a group of sensors 50.

[0073] The sensor group 50 is installed near the measurement standard device 100 and acquires environmental information around the crops, such as air temperature, humidity, water level, water temperature, and soil temperature ((1-1) in FIG. 8). The sensor group 50 then transmits the acquired environmental information to the image analysis device 2200 via a wireless device ((1-2) in FIG. 8).

[0074] The terminal device 300 is operated by a farmer and captures an image including the crop and the measurement standard 100 installed near the crop ((2-1) in FIG. 8). The terminal device 300 then transmits the captured image to the image analyzing device 2200 ((2-2) in FIG. 8).

[0075] The image analysis device 2200 corrects the captured image transmitted from the terminal device 300 using the environmental information transmitted from the sensor group 50 ((3-1) in FIG. 8). Next, the image analysis device 2200 analyzes the corrected image and extracts information related to the growth level of the crop, such as the height, leaf color, and number of stems of the crop ((3-2) in FIG. 8). Then, the image analysis device 2200 outputs the information related to the growth level of the crop extracted by the image analysis to the terminal device 300 or the like ((3-3) in FIG. 8).

[0076] In this way, the image analysis system 2 corrects the images taken by the farmer using the environmental information acquired by the sensor group 50 and then analyzes the images.

[0077] [Image analysis system] Fig. 9 is a diagram showing an example of the configuration of an image analysis system 2 according to embodiment 2. As shown in Fig. 9, the image analysis system 2 includes a measurement standard 100, an image analysis device 2200 that corrects an image of a crop captured including the measurement standard 100, and a sensor group 50.

[0078] The sensor group 50 acquires at least one of information about the environment surrounding the measurement standard device 100 and information about the crop being measured. The sensor group 50 is installed near the measurement standard device 100 and acquires environmental information about the surroundings of the crop, such as air temperature, humidity, water level, water temperature, and soil temperature. The sensor group 50 then transmits the acquired environmental information to the image analysis device 2200 via a wireless device or the like.

[0079] The sensor group 50 includes, for example, a humidity sensor 51, an air temperature sensor 52, a water level sensor 53 that measures the water level in the paddy field, a water temperature sensor 54 that measures the water temperature in the paddy field, and a soil temperature sensor 55 (FIG. 8) that measures the soil temperature in the paddy field. The humidity sensor 51 and the air temperature sensor 52 are installed in positions where they can measure the air temperature and humidity of the cultivated land such as the paddy field. The water level sensor 53 and the water temperature sensor 54 are installed in positions where they come into contact with the water in the cultivated land such as the paddy field. The soil temperature sensor 55 is installed in a position where it is buried underground.

[0080] The storage unit 220 of the image analysis device 2200 has an image correction information DB 2221. The image correction information DB 2221 stores environmental information collected by the sensor group 50 and information on the crop to be measured as image correction information to be used when a correction unit 2232 (described later) performs image correction.

[0081] Here, a description will be given with reference to a table of the image correction information stored in the image correction information DB 2221. Fig. 10 is a table showing an example of the image correction information in the second embodiment.

[0082] As shown in Figure 10, the image correction information DB2221 stores, as image correction information, information on items such as crop, air temperature, humidity, water level, water temperature, wind speed, wind direction, rainfall, illuminance, soil moisture, soil temperature, electrical conductivity, solar radiation, soil pH, carbon dioxide concentration, and vapor pressure deficit contained in the environmental information, and information on items such as crop leaf surface wetness, crop leaf surface temperature, photosynthetically active radiation, growing point temperature, and image contained in the information on the crop to be measured, in association with "No.", which is information that identifies individual image correction information.

[0083] The crop mentioned above is information that identifies the crop being measured, such as the name, type, or unique number of the crop. Air temperature is an index indicating the ambient temperature around the measurement standard 100. Humidity is an index indicating the proportion of moisture contained in the ambient air around the measurement standard 100. Water level is an index indicating the height of the water surface around the measurement standard 100 set up in, for example, a rice paddy. Water temperature is an index indicating the temperature of the water around the measurement standard 100 set up in, for example, a rice paddy. Wind speed is an index indicating the speed of air movement around the measurement standard 100. Wind direction is an index indicating the direction of air movement around the measurement standard 100. Rainfall is an index indicating the amount of precipitation around the measurement standard 100. Illuminance is an index indicating the brightness around the measurement standard 100. Soil moisture is an index indicating the amount of moisture in the soil around the measurement standard 100. Soil temperature is an index that indicates the temperature in the soil around the measurement standard device 100. Electrical conductivity is an index that indicates the total amount of water-soluble salts in the soil around the measurement standard device 100. Solar radiation is an index that indicates the amount of sunlight irradiating the soil around the measurement standard device 100. Soil pH is an index that indicates the hydrogen ion concentration in the soil around the measurement standard device 100. Carbon dioxide concentration is an index that indicates the amount of carbon dioxide present around the measurement standard device 100. Vapor saturation deficit is the value obtained by dividing the total amount of water in the soil by the amount of water in the soil around the measurement standard device 100 by the amount of water in the soil. 3 This is an indicator of how many grams of water vapor can remain in the air.

[0084] Crop leaf surface wetness is an index that indicates how wet the crop leaves are. Crop leaf surface temperature is an index that indicates the temperature of the crop leaves. Photosynthetically active radiation is an index that indicates the components of the wavelengths (photosynthetically active wavelength range) used for photosynthesis in green leafy plants. Merge point temperature is an index that indicates the temperature at the meristem of the crop. Images are information related to images, such as images captured by the terminal device 300, or images captured by an imaging device such as a camera if the measurement standard 100 is equipped with such an imaging device, or information that identifies such images.

[0085] 4, the image analyzing device 2200 has a control unit 2230 equipped with a correction unit 2232 and an analysis unit 2233. The reception unit 231 receives environmental information and information on the crop to be measured transmitted from the sensor group 50. The reception unit 231 then stores the environmental information and information on the crop to be measured, which are data for correcting the received image, and the captured image in the image correction information DB 2221.

[0086] The correction unit 2232 corrects the image captured to include the scale portion 102 and leaf color plate portion 103 provided on the measurement standard device 100 using information acquired by the sensor group 50 (environmental information and information about the crop being measured).

[0087] Furthermore, when extracting information about the leaf color of a crop from an image, errors may occur in the extracted leaf color due to weather, the amount of sunlight, the presence or absence of shadows from other crops, etc. Therefore, the correction unit 2232 corrects errors caused by the imaging position for an image captured of the crop, based on environmental information about the vicinity of the crop collected by the sensor group 50.

[0088] Furthermore, when extracting information regarding the number of crop stems from an image, errors may occur depending on the degree of overlap with other crops, and the efficiency of recognizing the contours of crop stems may change due to differences in the amount of solar radiation, which may result in a change in the number of extracted stems. Therefore, the correction unit 2232 corrects errors that occur due to the imaging position of the image captured of the crop, based on environmental information around the crop collected by the sensor group 50 and image information of other crops included in the image.

[0089] For example, the correction unit 2232 corrects changes in the color tone of the leaf color plate in the captured image due to changes in the imaging environment, such as weather, sunlight, and shadows, using the method described above. Furthermore, for example, the correction unit 2232 corrects the image based on a machine learning model that has been trained to convert the color tone of a crop into a predetermined color tone when a captured image of the crop is input, using pre-prepared training data that associates image data captured in environments with different weather, sunlight, and shadows with the number of crop stalks. As described above, the correction unit 2232 can correct the captured image so that the number of stems can be accurately extracted even when the crop stalks are overlapping or when the crops are illuminated differently.

[0090] The analysis unit 2233 analyzes the image of the crop corrected by the correction unit 2232 and extracts information related to the growth level of the crop. Specifically, the analysis unit 2233 can recognize the crop included in the image, extract information related to the height of the crop, extract information related to the leaf color of the crop, extract information related to the number of stalks of the crop, etc., based on known technology.

[0091] For example, the analysis unit 2233 can recognize the crop to be measured, calculate the height of the crop, calculate the leaf color, and calculate the number of stems based on a machine learning model trained using pre-prepared training data on the crop to be measured. The analysis unit 2233 also acquires weather information (including the history of weather information) and recommends the harvesting time, timing for top dressing, timing for pesticide spraying, and anomaly detection based on information on the growth level of the crop, the weather information, and environmental information around the crop collected by the sensor group 50. For example, the analysis unit 2233 calculates the cumulative temperature since the crop was planted in the paddy field based on the temperature history of the area where the paddy field is located and the measurement history of the temperature sensor 52, and recommends the harvesting time.

[0092] [Image analysis processing] FIG. 11 is a sequence diagram showing a processing procedure of the image analysis method according to the second embodiment.

[0093] The sensor group 50 acquires environmental information at a predetermined timing (for example, periodically or when instructed to measure by the image analyzing device 2200) (step S11), and transmits the acquired environmental information to the image analyzing device 2200 (step S12). Steps S13 and S14 are the same processes as steps S1 and S2 in FIG.

[0094] The image analysis device 2200 corrects the captured image using the information (environmental information and information about the crop being measured) acquired by the sensor group 50 (step S15). The image analysis device 2200 analyzes the crop image corrected in step S15 and extracts information about the growth level of the crop (step S16). Steps S17 and S18 are the same processes as steps S5 and S6 shown in FIG. 6.

[0095] [Effects of the second embodiment] Thus, in the second embodiment, in addition to the measurement standard device 100, a sensor group 50 is further provided that acquires at least one of information about the environment around where the measurement standard device 100 is installed (environmental information) and information about the crop being measured, and the image is corrected using the information acquired by the sensor group 50. That is, in the second embodiment, an image taken by a farmer is corrected using the environmental information acquired by the sensor group 50 and then analyzed, thereby making it possible to automatically generate information about the growth level of the crop with greater accuracy than before.

[0096] [Embodiment 3] In the third embodiment, a case will be described in which a sensor unit for acquiring environmental information is provided in a measurement standard device 3100. Fig. 12 is a diagram showing an example of the flow of processing by an image analysis system 3 according to the second embodiment.

[0097] The measurement standard device 3100 is placed near the crops and acquires environmental information around the crops, such as air temperature, humidity, water level, water temperature, and soil temperature ((1-1) in FIG. 12). The measurement standard device 3100 then transmits the acquired environmental information to the image analysis device 2200 via the communication unit 110, such as a wireless device ((1-2) in FIG. 12).

[0098] The terminal device 300 is operated by a farmer and captures an image including the crop and a measurement standard 3100 installed near the crop ((2-1) in FIG. 12). The terminal device 300 then transmits the captured image to the image analyzing device 2200 ((2-2) in FIG. 12).

[0099] The image analyzing device 2200 corrects the captured image transmitted from the terminal device 300 using the environmental information transmitted from the measurement standard device 3100 ((3-1) in FIG. 12). Next, the image analyzing device 2200 analyzes the corrected image and extracts information related to the growth level of the crop, such as the height of the crop, leaf color, and number of stems ((3-2) in FIG. 12). Then, the image analyzing device 2200 outputs the information related to the growth level of the crop extracted by the image analysis to the terminal device 300 or the like ((3-3) in FIG. 12).

[0100] [Image analysis system] Fig. 13 is a diagram showing an example of the configuration of an image analysis system 3 according to embodiment 3. As shown in Fig. 13, the image analysis system 3 has a measurement standard device 3100 instead of the sensor group 50 shown in Fig. 9. The image analysis device 2200 corrects the captured image transmitted from the terminal device 300 using the environmental information transmitted from the measurement standard device 3100.

[0101] The measurement standard device 3100 has a body portion 101, a scale portion 102, a leaf color plate portion 103, a sensor portion 104, a label portion 105, a communication portion 110, a memory portion 120, and a control portion 130. Note that the body portion 101, the scale portion 102, and the leaf color plate portion 103 are the same as those in the first embodiment, and therefore their explanation will be omitted in this section.

[0102] The installation location of the information processing device that realizes the communication unit 110, the storage unit 120, and the control unit 130 is not particularly limited. For example, an information processing device that integrates the communication unit 110, the storage unit 120, and the control unit 130 may be attached to the body 101 or the like of the measurement standard device 3100. Alternatively, only the information processing device having the functions of the communication unit 110 and the control unit 130 may be attached to the body 101 or the like of the measurement standard device 3100, and the information processing device that realizes the functions of the storage unit 120 and the control unit 130 may be installed at a different location.

[0103] The sensor unit 104 is attached to the body 101 and acquires environmental information about the surrounding area where the measurement standard device 3100 is installed. Specifically, the sensor unit 104 acquires at least one of environmental information about the area where the measurement standard device 3100 is installed and information about the crop to be measured.

[0104] For example, the sensor unit 104 is installed at multiple locations on the body 101. As a specific example, as shown in Fig. 12, the sensors of the sensor unit 104 that measure the air temperature and humidity are installed on the upper part of the body 101. Furthermore, the sensors of the sensor unit 104 that measure the water level and water temperature are installed in positions that come into contact with water in cultivated land such as rice paddies. Furthermore, the sensors of the sensor unit 104 that measure the soil temperature are installed in positions that are buried underground in the body 101.

[0105] The sensor unit 104 can collect the above-mentioned air temperature, humidity, water level, water temperature, and soil temperature (soil temperature) as information about the surrounding environment where the measurement standard device 3100 is installed, as well as wind speed, wind direction, rainfall, illuminance, soil moisture, electrical conductivity, solar radiation, soil pH, carbon dioxide concentration, and vapor pressure deficit.The sensor unit 104 also collects at least one of the following information about the crop being measured: leaf wetness of the crop, leaf temperature of the crop, photosynthetically active radiation, growing point temperature, and image.

[0106] It should be noted that known techniques may be used to acquire the environmental information using the various sensors described above, and therefore, in this section, explanations of the principles of acquiring each piece of environmental information will be omitted.

[0107] The marker unit 105 is a marker for identifying the imaging position when capturing an image including the scale unit 102 and the leaf color plate unit 103. Specifically, the marker unit 105 is predetermined identification information recognized by an imaging device or the like, and has the effect of allowing a farmer to fix the position of the crop by capturing an image of the crop using the marker unit 105 as a target. Note that the marker unit 105 may be identification information recognizable by an imaging device, such as a graphic marker combining various figures, a two-dimensional barcode, or a predetermined character string.

[0108] The communication unit 110 inputs control commands that control the operation of the measurement standard device 3100, and performs data communication related to the output of environmental information acquired by the sensor unit 104. The communication unit 110 is realized by a NIC (Network Interface Card) or the like, and controls communication via electrical communication lines such as a LAN (Local Area Network) or the Internet. The communication unit 110 is connected to a network via wired or wireless connection as necessary, and can transmit and receive information bidirectionally.

[0109] The storage unit 120 stores data and programs used for various processes by the control unit 130, and various data acquired by the operation of the control unit 130. The storage unit 120 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 can also temporarily or permanently store environmental information acquired by a sensor unit controlled by the control unit 130.

[0110] The control unit 130 has an internal memory for temporarily storing programs defining various processing procedures and the like of the measurement standard device 3100, and processing data, and is realized by electronic circuits such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit), and integrated circuits such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array). The control unit 130 has a storage unit 131 and a transmission unit 132, as shown in FIG.

[0111] The storage unit 131 acquires the environmental information collected by the sensor unit 104. Then, the storage unit 131 stores the acquired environmental information in the memory unit 120.

[0112] The transmission unit 132 transmits the environmental information stored in the storage unit 120 to the image analysis device 2200 via the communication unit 110 described above.

[0113] [Image analysis processing] FIG. 14 is a sequence diagram showing a processing procedure of the image analysis method according to the third embodiment.

[0114] The measurement standard device 3100 acquires environmental information at a predetermined timing (for example, periodically or when instructed to measure by the image analyzing device 2200) (step S21), and transmits the acquired environmental information to the image analyzing device 2200 (step S22). Steps S23 and S24 are the same processes as steps S1 and S2 in FIG.

[0115] The image analyzing device 2200 corrects the captured image using the information (environmental information and information about the crop being measured) transmitted from the measurement standard device 3100 (step S25). Step S26 is the same process as step S16 shown in Fig. 11. Steps S27 and S28 are the same processes as steps S5 and S6 shown in Fig. 6.

[0116] [Effects of the Third Embodiment] In this way, the image analysis system 3 according to the third embodiment is provided with a sensor unit that acquires environmental information in the measurement standard device 3100. This allows a farmer to automatically acquire information about the growth level of the crop with higher accuracy than before, simply by placing the measurement standard device 3100 near the crop to be measured.

[0117] [Variations] Modifications realized by the image analysis systems 1 to 3 according to the first to third embodiments will be described below.

[0118] [Data, etc.] The environmental information, information about the crop to be measured, scale, leaf color plate, sensor, crop, height (plant height), leaf color, number of stems, names of functional parts of measurement standard device 100, 3100, image analysis device 200, 2200 and terminal device 300, steps, processes, names of steps or processes, etc. used in the description of the above embodiments are merely examples and can be changed as desired.

[0119] For example, the image correction information DB221, 2221 stores information on items such as air temperature, humidity, water level, water temperature, wind speed, wind direction, rainfall, illuminance, soil moisture, soil temperature, electrical conductivity, solar radiation, soil pH, carbon dioxide concentration, crop leaf surface wetness, crop leaf surface temperature, crop surface temperature, vapor pressure deficit, photosynthetically active radiation, growing point temperature, and images, in association with "No.", which is information that identifies individual image correction information, but is not limited to this.

[0120] For example, the analysis information DB222 stores information about items such as crop, height, leaf color, number of stems, etc. as analysis information, in association with "No.", which is information that identifies individual analysis information, but is not limited to this.

[0121] [Flowchart etc.] The steps in the flowcharts may be interchanged as long as there is no contradiction, and some steps may not be performed. In addition, conjunctions such as "next," "continue," "further," "at this time," and "on this occasion" used in the explanation of the flowcharts do not limit the order or timing of the execution of the processes in the flowcharts.

[0122] [System configuration of the embodiment] The image analyzing devices 200 and 2200 are conceptual functional units and do not necessarily have to be physically configured as shown in the drawings. In other words, the specific form of distribution and integration of the functions of the image analyzing devices 200 and 2200 is not limited to that shown in the drawings, and all or part of them can be functionally or physically distributed or integrated in any unit depending on various loads, usage conditions, etc.

[0123] Furthermore, all or any part of the processes performed in image analysis devices 200 and 2200 may be realized by a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a program analyzed and executed by the CPU and the GPU (Graphics Processing Unit). Furthermore, each process performed in image analysis devices 200 and 2200 may be realized as hardware using wired logic.

[0124] Furthermore, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually. Alternatively, all or part of the processes described as being performed manually can be performed automatically using a known method. In addition, the processing procedures, control procedures, specific names, and information including various data and parameters described above and illustrated can be changed as appropriate unless otherwise specified.

[0125] [program] 15 is a diagram showing an example of a computer in which image analyzing devices 200, 2200 are realized by executing a program. Computer 1000 includes, for example, memory 1010 and CPU 1020. Computer 1000 also includes hard disk drive interface 1030, disk drive interface 1040, serial port interface 1050, video adapter 1060, and network interface 1070. These components are connected by bus 1080.

[0126] The memory 1010 includes a ROM 1011 and a RAM 1012. The ROM 1011 stores a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to a hard disk drive 1090. The disk drive interface 1040 is connected to a disk drive 1100. A removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1110 and a keyboard 1120, for example. The video adapter 1060 is connected to a display 1130, for example.

[0127] The hard disk drive 1090 stores, for example, an OS (Operating System) 1091, an application program 1092, a program module 1093, and program data 1094. That is, the programs that define the processes of the image analyzing devices 200, 2200 are implemented as program modules 1093 in which code executable by the computer 1000 is written. The program modules 1093 are stored, for example, in the hard disk drive 1090. For example, the program modules 1093 for executing processes similar to those of the functional configurations of the image analyzing devices 200, 2200 are stored in the hard disk drive 1090. The hard disk drive 1090 may be replaced by an SSD (Solid State Drive).

[0128] Furthermore, setting data used in the processing of the above-described embodiment is stored as program data 1094, for example, in memory 1010 or hard disk drive 1090. Then, CPU 1020 reads program module 1093 and program data 1094 stored in memory 1010 or hard disk drive 1090 into RAM 1012 as necessary and executes them.

[0129] The program module 1093 and program data 1094 are not limited to being stored in the hard disk drive 1090, but may also be stored in, for example, a removable storage medium and read by the CPU 1020 via the disk drive 1100 or the like. Alternatively, the program module 1093 and program data 1094 may be stored in another computer connected via a network (such as a local area network (LAN) or a wide area network (WAN)). The program module 1093 and program data 1094 may then be read by the CPU 1020 from the other computer via the network interface 1070.

[0130] Although the present invention has been described above as an embodiment, the present invention is not limited to the descriptions and drawings that form part of the disclosure of the present invention. In other words, other embodiments, examples, and operational techniques that can be made by those skilled in the art based on the present invention are all included in the scope of the present invention. [Explanation of symbols]

[0131] 1,2,3 Image analysis system 50 sensors 100,3100 Measuring standards 104 Sensor unit 105 Sign section 110,210 Communications Department 120,220 storage section 130,230,2230 Control unit 131 Storage area 132 Transmitter 200,2200 Image analyzer 221,2221 Image correction information DB 222 Analysis information DB 231 Reception Department 232,2232 Correction section 233,2233 Analysis Department 234 Output section 300 Terminal Device

Claims

1. An image analysis system having a measurement standard used to measure the growth rate of a crop, and an image analysis device that corrects an image of the crop captured including the measurement standard, The measurement standard is a body portion which is a columnar structure extending vertically upward relative to the ground; A scale portion is engraved on the body portion at a predetermined interval as a reference for measuring the height of the crop to be measured; A leaf color plate attached to a predetermined position of the body portion as a reference colored in a predetermined color tone for measuring the leaf color of the target crop by comparing it with the leaf color of the crop; and The image analysis device a correction unit that corrects an image captured to include the scale and leaf color plate provided on the measurement standard and the crop to be measured based on the positional relationship between the measurement standard and the crop to be measured; an analysis unit that analyzes the image of the crop corrected by the correction unit and extracts information regarding the growth level of the crop to be measured; An image analysis system comprising:

2. The image analysis system includes: a sensor that acquires at least one of information about the environment surrounding the measurement standard device and information about the crop being measured; and The image analysis system described in claim 1, characterized in that the correction unit corrects an image captured to include the scale portion and leaf color plate portion provided on the measurement standard and the crop to be measured using information acquired by the sensor.

3. 3. The image analysis system according to claim 2, wherein the measurement standard comprises the sensor.

4. 2. The image analysis system according to claim 1, wherein the analysis unit transmits information relating to the growth of the crop to be measured to a terminal device of a person involved in the growth of the crop to be measured.

5. The image analysis system described in claim 4, characterized in that the information regarding the growth level of the crop being measured is information indicating the growth level of the crop being measured, and / or an action to be taken on the crop being measured in accordance with the growth level of the crop being measured.

6. The image analysis system described in claim 1, characterized in that the analysis unit analyzes the image of the crop corrected by the correction unit based on external information including at least meteorological information for the area where the crop to be measured is grown, and extracts information regarding the growth degree of the crop to be measured.

7. The image analysis system of claim 2, characterized in that the sensor collects at least one of air temperature, humidity, water level, water temperature, wind speed, wind direction, rainfall, illuminance, soil moisture, soil temperature, electrical conductivity, solar radiation, soil pH, carbon dioxide concentration, vapor pressure deficit, leaf wetness of the crop, leaf surface temperature of the crop, photosynthetically active radiation, growing point temperature, and images.

8. The measurement standard is The image analysis system of claim 1, further comprising a marker for identifying the position of an image captured to include the scale and the leaf color plate.

9. The scale portion is The boundary between the area buried in the ground and the area exposed from the ground of the body part is set as the zero point, and the reference lines are engraved at predetermined intervals in a vertical upward direction.

2. The image analysis system according to claim 1.

10. The image analysis system described in claim 1, characterized in that the leaf color plate section arranges multiple green areas colored using green as the specified color tone in order of increasing color density of the green areas.

11. The leaf color plate portion is Among the plurality of green regions, a first green region having the highest color density is located closest to the ground, the green regions having a lower color density than the first green region are arranged in an upward direction vertically from the ground in descending order of color density from the ground; The image analysis system according to claim 10 .

12. An image analysis method executed by an image analysis system having a measurement standard used to measure a growth level of a crop and an image analysis device that corrects an image of the crop captured including the measurement standard, The measurement standard is a body portion which is a columnar structure extending vertically upward relative to the ground; A scale portion is engraved on the body portion at a predetermined interval as a reference for measuring the height of the crop to be measured; A leaf color plate attached to a predetermined position of the body portion as a reference colored in a predetermined color tone for measuring the leaf color of the target crop by comparing it with the leaf color of the crop; and a step in which the image analysis device corrects an image captured so as to include the scale and leaf color plate provided on the measurement standard and the crop to be measured, based on the positional relationship between the measurement standard and the crop to be measured; a step of the image analysis device analyzing the image of the crop corrected by the correcting step and extracting information regarding the growth level of the crop to be measured; An image analysis method comprising:

Images