Processing apparatus, processing system, processing method, and program

Abstract

To provide a processing device capable of acquiring the growth status of plants with high precision. [Solution] The processing device includes a first acquisition unit that acquires a first image obtained by photographing a location including plants at a first timing, a second image obtained by photographing the location at a second timing prior to the first timing, first measurement information of plants at a first position obtained by a sensor installed at the location at the first timing, and second measurement information of plants at the first position obtained by a sensor at the second timing; a second acquisition unit that acquires first image information relating to plants at a first position based on the first image, second image information relating to plants at a first position based on the second image, and third image information relating to plants at a predetermined position based on the first image; and a third acquisition unit that acquires information about plants at a predetermined position at the first timing using the first and second measurement information and the first to third image information.

Description

Technical Field

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[0001] The present invention relates to a processing device, a processing system, a processing method, and a program.

Background Art

[0002] Conventionally, techniques for quantitatively obtaining the growth state of plants have been proposed in order to appropriately perform cultivation management such as water management, fertilization, and chemical spraying. In Patent Document 1, a configuration capable of obtaining the growth state of plants in all fields with high accuracy is disclosed by using measurement results indicating the growth state of plants in some fields where a sensing device is installed and measurement results obtained by a device capable of photographing all fields.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in the agricultural field, sensing capable of obtaining the growth state of plants with higher accuracy has been demanded.

[0005] An object of the present invention is to provide a processing device capable of obtaining the growth state of plants with high accuracy.

Means for Solving the Problems

[0006] An apparatus as one aspect of the present invention is characterized by comprising: a first acquisition unit that acquires a first image obtained by photographing a place including a plant at a first timing; a second image obtained by photographing the place at a second timing prior to the first timing; first measurement information of the plant at a first position obtained by a sensor installed at the place at the first timing; and second measurement information of the plant at the first position obtained by the sensor at the second timing; a second acquisition unit that acquires first image information relating to the plant at the first position based on the first image; second image information relating to the plant at the first position based on the second image; and third image information relating to the plant at a predetermined position based on the first image; and a third acquisition unit that acquires information of the plant at the predetermined position at the first timing using the first and second measurement information and the first to third image information. [Effects of the Invention]

[0007] According to the present invention, it is possible to provide a processing device that can acquire the growth state of plants with high precision. [Brief explanation of the drawing]

[0008] [Figure 1] A diagram illustrating the configuration of the sensing system. [Figure 2] This is a diagram showing the configuration of the growth monitoring sensor. [Figure 3] This diagram illustrates the method for obtaining information on the crops in Example 1. [Figure 4] This diagram illustrates a method for estimating the growth stage of rice plants in locations where the growth monitoring sensor from Example 1 is not installed. [Figure 5] This diagram illustrates a method for estimating the growth stage of rice plants in locations where the growth monitoring sensor from Example 1 is not installed. [Figure 6] This figure shows an example of an estimated diagram of the SPAD value distribution, plant height value distribution, and stem value distribution for the entire field in Example 1. [Figure 7]This diagram illustrates the method for obtaining information on the crops in Example 2. [Figure 8] This diagram illustrates a method for estimating the growth stage of rice plants in locations where the growth monitoring sensor from Example 2 is not installed. [Figure 9] This figure shows an example of an estimated diagram of the SPAD value distribution, plant height value distribution, and stem value distribution for the entire field in Example 2. [Figure 10] This diagram schematically shows each step for obtaining information on the crops in Example 3. [Modes for carrying out the invention]

[0009] The embodiments of the present invention will be described in detail below with reference to the drawings. In each figure, the same reference numeral is used for identical components, and redundant explanations are omitted. [Examples]

[0010] Figure 1 is a diagram of the sensing system (processing system) SS. The sensing system SS is a collective term for the growth monitoring sensor (hereafter referred to as sensor) 10, the server (processing device) 20, and the user terminal 30.

[0011] The entire location F is a collection of locations, each containing plants. In this embodiment, we will describe a case where crops are cultivated as an example of plants. That is, the entire location F is a collection of multiple fields where crops are cultivated. Sensor 10 is a sensing device capable of quantitatively acquiring the growth status of crops. In this embodiment, the sensor 10 installed in field FA is referred to as sensor 10A. The sensor 10 installed in field FB is referred to as sensor 10B. No sensor 10 is installed in field FC.

[0012] The measurement results (measurement information) from sensors 10A and 10B are transmitted to the server 20 via wireless communication. This communication may also be wired.

[0013] The user terminal 30 is, for example, a smartphone, a tablet, or a PC, and can be connected to the server 20 via a wireless connection. The measurement results stored in the server 20 and the analysis results (calculation results) based on the measurement results are transmitted from the server 20 to the user terminal 30 at a predetermined time every day, for example. The user (farmer, producer) of the sensing system SS can quantitatively confirm the growth state of crops in each field in the form of graphs, tables, etc. via the user terminal 30.

[0014] The device 100 is an artificial satellite capable of photographing the entire location F, and obtains a photographing result (image) of the entire location F by photographing the entire location F from above. The device 100 may be a drone capable of flying in the air. The photographing result of the entire location F by the device 100 is transmitted to the server 20 via wireless communication. The photographing result of the entire location F by the device 100 is displayed on the user terminal 30 via the server 20, or used for analysis in the server 20, and the analysis result is transmitted from the server 20 to the user terminal 30.

[0015] The server 20 functions as a first acquisition unit that acquires the measurement results measured by the sensor 10 and the photographing result of the entire location F obtained by the device 100. Further, as will be described later, the server 20 functions as a second acquisition unit that acquires image information regarding crops based on an image, and a third acquisition unit that acquires crop information at a predetermined position in the entire location F using the measurement results and the image information.

[0016] FIG. 2 is a configuration diagram of the sensor 10. The camera 101 is a camera capable of photographing an RGB color image, and the camera 102 is a camera capable of photographing a monochrome image. The various sensors 103 are a general term for, for example, a distance measuring sensor that measures the distance from the cameras 101, 102 to the crop to be measured, an environmental sensor that measures the ambient temperature and humidity, and a water level sensor that measures the water level of a paddy field if the crop is rice.

[0017] The wireless communication unit 104 transmits each data acquired by the cameras 101 and 102 and various sensors 103 to the server 20 via wireless communication. The power supply unit 105 is a power supply for supplying power to each functional unit of the sensor 10, and in this embodiment, it is a solar power generation panel. The control unit 107 controls each functional unit of the sensor 10. For example, the control unit 107 activates the sensor 10 at a predetermined time every day to start data acquisition.

[0018] The support column 106 holds each functional unit. When the installation position of the sensor 10 is a paddy field, it is desirable for the support column 106 to have a pile shape for deeply inserting into the paddy field.

[0019] FIG. 3 is a diagram for explaining the method of acquiring crop information in this embodiment. FIG. 3(a) schematically shows an RGB image obtained by photographing the entire field (six fields) with the camera mounted on the device 100 from above on the first day (the first timing). FIG. 3(b) schematically shows an RGB image obtained by photographing the entire field with the camera mounted on the device 100 from above on the second day (the second timing) which is past the first day. Here, it is assumed that the crop is rice.

[0020] In FIG. 3, the leaf color of the rice is different for each field, and the color tone of the RGB image is different. Also, there is a distribution of leaf color within the same field, and there is a distribution in the RGB image. FIG. 3(c) shows a graph of the results of having the sensor 10A acquire the three index values of the leaf color value (SPAD value), plant height value, and stem number value of the rice every day. In FIGS. 3(a) and 3(b), the position (the first position, hereinafter referred to as the sensor installation position) measured by the sensor 10A in the field is indicated by (A). Also, in FIG. 3(a), the position where the sensor 10 is not installed is indicated by (C).

[0021] First, the server 20 acquires RGB images of the first and second days from the device 100. Then, the server 20 calculates (acquires) the RGB image values ​​(image information related to the crop) at the sensor installation location (A) based on the RGB images. For example, if the RGB value of each pixel in the RGB image is 256 gradations, then the RGB image values ​​at the sensor installation location (A) in Figure 3(a) are calculated to be R1=80, G1=120, B1=30. Also, the RGB image values ​​at the sensor installation location (A) in Figure 3(b) are calculated to be R2=60, G2=90, B2=40.

[0022] Furthermore, it is assumed that on the first day, the RGB image values ​​at a location (C) where the sensor 10 is not installed were calculated to be R3=70, G3=110, and B3=35 from the RGB image acquired by the device 100.

[0023] One method for converting RGB image values ​​to SPAD values ​​is to use the following conversion formula (1).

[0024] SPAD = -0.17 * G + 0.19 * B + 36.5 (1) The conversion formula (1) is described in Kasumi Ohara et al., "Study on Estimating SPAD Values ​​of Rice Based on UAV Remote Sensing," Journal of the Crop Science Society of Japan (Jpn.J.Crop Sci.) 89(1):p.50-51 (2020). Using conversion formula (1), the SPAD equivalent value SPAD1 of the RGB image value at sensor installation position (A) on the first day is calculated to be 21.8. Furthermore, the SPAD equivalent value SPAD2 of the RGB image value at sensor installation position (A) on the second day is calculated to be 28.8. In addition, the SPAD equivalent value SPAD3 of the RGB image value at position (C) where sensor 10 was not installed on the first day is calculated to be 24.5.

[0025] On the other hand, the SPAD value SPAD4 measured by sensor 10A at sensor installation location (A) on the first day is shown as (2) in the SPAD value graph in Figure 3(c) and is assumed to be 30. Also, the SPAD value SPAD5 measured by sensor 10A at sensor installation location (A) on the second day is shown as (4) in the SPAD value graph in Figure 3(c) and is assumed to be 40. The measured SPAD values ​​are transmitted to server 20.

[0026] Although the SPAD values ​​in the RGB images acquired by device 100 and the RGB images acquired by sensor 10 do not match due to the influence of the atmosphere, sunlight, and the ratio of crops to the ground surface (vegetation cover), they are in a nearly direct proportional relationship. Server 20 uses the following equation (2) to calculate (acquire) the estimated SPAD value (crop information) SPAD6 at location (C) where sensor 10 is not installed on the first day.

[0027] SPAD6=[(SPAD5-SPAD4)*SPAD3+SPAD2*SPAD4 -SPAD5*SPAD1] / (SPAD2-SPAD1) (2) From equation (2), the estimated SPAD value SPAD6 is calculated to be 33.9, as shown in (D) of Figure 3(c).

[0028] At this time, the relationship between the SPAD values ​​for the first and second days in the graph of SPAD values ​​in Figure 3(c) and the estimated SPAD value at location (C) where sensor 10 is not installed allows us to estimate the growth stage (growth state) of the rice plants at location (C) where sensor 10 is not installed on the first day. The growth stages of rice plants are: transplanting → establishment stage → effective tillering stage → ineffective tillering stage → panicle formation stage → panicle emergence stage → heading stage → ripening stage.

[0029] Figures 4 and 5 illustrate a method for estimating the growth stage of rice plants at location (C) where sensor 10 is not installed. In the graph of SPAD values ​​at sensor installation location (A) shown by the thick solid line in Figure 4(a), the SPAD value SPAD4 shown by (2), the SPAD value SPAD5 shown by (4), and the estimated SPAD value SPAD6 shown by (D) are in the positional relationship shown on the graph. At this time, it is estimated that the growth stage of rice plants at location (C) where sensor 10 is not installed is slower than the growth stage of rice plants at sensor installation location (A).

[0030] Furthermore, in the graph of SPAD values ​​at the sensor installation location (A) shown by the thick solid line in Figure 4(b), the SPAD value SPAD4 shown in (2), the SPAD value SPAD5 shown in (4), and the estimated SPAD value SPAD6 shown in (D) are in the positional relationship shown on the graph. It is presumed that the growth stage of the rice at the location where sensor 10 is not installed (C) is earlier than the growth stage of the rice at the sensor installation location (A).

[0031] Furthermore, the estimated SPAD value may be calculated for the location (C) where sensor 10 is not installed on the second day. This makes it possible to estimate the growth stage whether the growth of the rice at location (C) where sensor 10 is not installed is significantly slower or more vigorous compared to the rice at location (A) where sensor 10 is installed.

[0032] For example, suppose that server 20 calculates the RGB image values ​​for location (C) where sensor 10 is not installed based on the RGB image from the second day as (i) R4=70, G4=110, B4=35 and (ii) R4=40, G4=80, B4=40. In this case, using the conversion formula (1) mentioned above, the SPAD conversion value SPAD7 for location (C) where sensor 10 is not installed based on the RGB image from the second day is calculated to be 24.5 and 30.5 for cases (i) and (ii), respectively.

[0033] Server 20 can calculate the estimated SPAD8 at location (C) where sensor 10 is not installed on the second day using the following equation (3).

[0034] SPAD8=[(SPAD5-SPAD4)*SPAD7+SPAD2*SPAD4 -SPAD5*SPAD1] / (SPAD2-SPAD1) (3) From equation (3), the estimated SPAD8 values ​​are calculated to be 33.9 and 42.4 in cases (i) and (ii), respectively, as shown in Figures 5(E) and (F).

[0035] In the case where the rice plants in a location (C) where sensor 10 is not installed are growing significantly slower or growing vigorously, the estimated SPAD6 shown in Figure 5 (D) is the same, but the estimated SPAD8 is different.

[0036] In Figure 5, the thick solid line shows the SPAD value at the sensor installation location (A), while the thick dotted line and thick dashed line show the SPAD value at the location where sensor 10 is not installed (C). If the estimated SPAD value SPAD8 is calculated to be 33.9, it is presumed that the growth of rice at the location where sensor 10 is not installed (C) is significantly slower than that of rice at the sensor installation location (A). Conversely, if the estimated SPAD value SPAD8 is calculated to be 42.4, it is presumed that the growth of rice at the location where sensor 10 is not installed (C) is more vigorous than that of rice at the sensor installation location (A).

[0037] By applying the method for obtaining estimated SPAD values ​​at predetermined locations within the entire location F on the first day to all locations within the entire location F, it is possible to estimate the SPAD value distribution and growth stage of the entire location F on the first day, assuming that sensors 10 were installed and measured throughout the entire location F. Figure 6(a) shows an example of an estimated SPAD value distribution for the entire location F obtained in this way.

[0038] Furthermore, by correlating the graph of SPAD values ​​measured by sensor 10 with the graphs of grass height and stem values, it is possible to estimate the grass height and stem values ​​for a given day with a predetermined SPAD value. This makes it possible to estimate the distribution of grass height and stem values ​​on the first day of the entire area F, assuming that sensor 10 is installed and measurements are taken throughout the entire area F. Figures 6(b) and 6(c) show examples of estimated graphs of the distribution of grass height and stem values ​​for the entire area F obtained in this way.

[0039] By obtaining distribution information of F across the entire area, including not only SPAD values ​​but also plant height values ​​and stem values, it becomes possible to grasp the growth stage of rice more accurately and in detail, enabling more appropriate water management, fertilization, and pesticide application at the appropriate time.

[0040] In this embodiment, an example is shown in which the SPAD value converted from the RGB image value is compared with the SPAD value measured by the sensor 10. However, instead of the SPAD value, the color number value of the leaf color scale may be used. One method for converting the RGB image value to the color number value of the leaf color scale is to use the following conversion formula (4) or (5).

[0041] G = -11.163 * color number + 224.76 (4) R = -12.595 * color number + 203.75 (5) Conversion formulas (4) and (5) are described in Wang Xinyue et al., "Evaluation of Rice Paddy Color Scales Using a Digital Camera," Abstracts of the 2011 Annual Meeting of the Japanese Society of Agricultural Engineering [7-46(P)], pp. 794-795.

[0042] Furthermore, although this embodiment uses rice as an example of a crop in the field, the present invention can also be applied to other crops such as wheat, corn, cabbage, lettuce, and spinach. [Examples]

[0043] This embodiment describes a method for acquiring crop information that differs from that of Embodiment 1. The basic configuration of the sensing system in this embodiment is the same as that of Embodiment 1. In this embodiment, only the configurations that differ from those of Embodiment 1 will be described, and the common configurations will not be explained.

[0044] Figure 7 illustrates the method for acquiring crop information in this embodiment. Figure 7(a) schematically shows RGB images acquired on the first day by photographing the entire field (6 fields) from above with a camera mounted on equipment 100. Here, the crop is assumed to be paddy rice.

[0045] Figure 7 shows that the leaf color of rice plants differs from field to field, resulting in different color tones in the RGB images. Furthermore, there is a distribution of leaf color even within the same field, and this distribution is reflected in the RGB images.

[0046] In this embodiment, sensors 10A and 10B capable of quantitatively acquiring the growth status of crops are installed at two locations in multiple fields. Three indicator values ​​of the crops—leaf color value, plant height / plant height value, and stem value—are acquired daily and graphed as shown in Figure 7(b). In Figure 7(a), the location measured by sensor 10A (sensor installation location) in the field is shown as (A), and the location measured by sensor 10B (second location, hereinafter referred to as sensor installation location) is shown as (B). Also in Figure 7(a), the location where no sensor 10 is installed is shown as (C).

[0047] First, server 20 acquires the RGB image for the first day from device 100. Then, server 20 calculates the RGB image values ​​for sensor installation locations (A) and (B) based on the RGB image. For example, if the RGB value of each pixel in the RGB image has 256 gradations, suppose the RGB image value for sensor installation location (A) is calculated to be R1=80, G1=120, B1=30. Also, suppose the RGB image value for sensor installation location (B) is calculated to be R2=60, G2=90, B2=40.

[0048] Furthermore, it is assumed that the RGB image values ​​for position (C) where sensor 10 is not installed were calculated to be R3=70, G3=110, and B3=35 from the RGB image acquired by device 100 on the first day.

[0049] When the RGB image values ​​are converted to SPAD values ​​using conversion formula (1), the SPAD equivalent value SPAD1 of the RGB image values ​​at sensor installation position (A) is calculated to be 21.8. Furthermore, the SPAD equivalent value SPAD2 of the RGB image values ​​at sensor installation position (B) is calculated to be 28.8. Additionally, the SPAD equivalent value SPAD3 of the RGB image values ​​at position (C) where sensor 10 is not installed is calculated to be 24.5.

[0050] On the other hand, the SPAD value SPAD4 measured by sensor 10A at sensor installation location (A) on the first day is shown as (2) in the graph of SPAD values ​​shown by the thick solid line in Figure 7(b), and is assumed to be 30. Also, the SPAD value SPAD5 measured by sensor 10B at sensor installation location (B) on the first day is shown as (3) in the graph of SPAD values ​​shown by the thick dashed line in Figure 7(b), and is assumed to be 40.

[0051] Server 20 uses equation (2) to calculate the estimated SPAD6 for location (C) where sensor 10 is not installed on the first day. From equation (2), the estimated SPAD6 is calculated to be 33.9, as shown in (D) of Figure 7(b).

[0052] At this time, the growth stage of the rice plants at location (C) where sensor 10 is not installed on the first day can be determined from the relationship between the graphs of SPAD values ​​measured by sensors 10A and 10B.

[0053] Figure 8 illustrates a method for estimating the growth stage of rice plants at location (C) where the sensor 10 of this embodiment is not installed.

[0054] In the first example of Figure 8(a), in the graph of SPAD values ​​at sensor installation location (A), indicated by the thick solid line, the SPAD value SPAD4 at sensor installation location (A) on the first day is shown as (2), and the SPAD value at sensor installation location (A) on the second day is shown as (4). In the graph of SPAD values ​​at sensor installation location (B), indicated by the thick dashed line, the SPAD value SPAD5 at sensor installation location (B) on the first day is shown as (3), and the SPAD value at sensor installation location (B) on the second day is shown as (5). The estimated SPAD value SPAD6 at location (C) where sensor 10 is not installed on the first day is shown as (D). At this time, it is estimated that the growth stage of rice at sensor installation location (B) is slower than that of rice at sensor installation location (A), and the growth stage of rice at location (C) where sensor 10 is not installed is somewhere in between.

[0055] In the second example of Figure 8(b), in the graph of SPAD values ​​at sensor installation location (A), indicated by the thick solid line, the SPAD value SPAD4 at sensor installation location (A) on the first day is shown as (2), and the SPAD value at sensor installation location (A) on the second day is shown as (4). In the graph of SPAD values ​​at sensor installation location (B), indicated by the thick dashed line, the SPAD value SPAD5 at sensor installation location (B) on the first day is shown as (3), and the SPAD value at sensor installation location (B) on the second day is shown as (5). The estimated SPAD value SPAD6 at location (C) where sensor 10 is not installed on the first day is shown as (D). At this time, it is estimated that the growth stage of rice at sensor installation location (B) is slower than that of rice at sensor installation location (A), and the growth stage of rice at location (C) where sensor 10 is not installed is even slower.

[0056] In the third example of Figure 8(c), in the graph of SPAD values ​​at sensor installation location (A), indicated by a thick solid line, the SPAD value SPAD4 at sensor installation location (A) on the first day is shown as (2), and the SPAD value at sensor installation location (A) on the second day is shown as (4). In the graph of SPAD values ​​at sensor installation location (B), indicated by a thick dashed line, the SPAD value SPAD5 at sensor installation location (B) on the first day is shown as (3), and the SPAD value at sensor installation location (B) on the second day is shown as (5). The estimated SPAD value SPAD6 at location (C) where sensor 10 is not installed on the first day is shown as (D). At this time, it is estimated that the growth stage of rice at sensor installation location (B) is slower than that of rice at sensor installation location (A), and the growth stage of rice at location (C) where sensor 10 is not installed is faster.

[0057] In the fourth example of Figure 8(d), in the graph of SPAD values ​​at sensor installation location (A), indicated by the thick solid line, the SPAD value SPAD4 at sensor installation location (A) on the first day is shown as (2), and the SPAD value at sensor installation location (A) on the second day is shown as (4). In the graph of SPAD values ​​at sensor installation location (B), indicated by the thick dashed line, the SPAD value SPAD5 at sensor installation location (B) on the first day is shown as (3), and the SPAD value at sensor installation location (B) on the second day is shown as (5). The estimated SPAD value SPAD6 at location (C) where sensor 10 is not installed on the first day is shown as (D). At this time, it is estimated that the growth stage of the rice at sensor installation location (B) is earlier than that of the rice at sensor installation location (A), and the growth stage of the rice at location (C) where sensor 10 is not installed is somewhere in between.

[0058] In the fifth example of Figure 8(e), in the graph of SPAD values ​​at sensor installation location (A), indicated by a thick solid line, the SPAD value SPAD4 at sensor installation location (A) on the first day is shown as (2), and the SPAD value at sensor installation location (A) on the second day is shown as (4). In the graph of SPAD values ​​at sensor installation location (B), indicated by a thick dashed line, the SPAD value SPAD5 at sensor installation location (B) on the first day is shown as (3), and the SPAD value at sensor installation location (B) on the second day is shown as (5). The estimated SPAD value SPAD6 at location (C) where sensor 10 is not installed on the first day is shown as (D). At this time, it is estimated that the growth stage of rice at sensor installation location (B) is earlier than that of rice at sensor installation location (A), and the growth stage of rice at location (C) where sensor 10 is not installed is even earlier.

[0059] In the sixth example of Figure 8(f), in the graph of SPAD values ​​at sensor installation location (A), indicated by a thick solid line, the SPAD value SPAD4 at sensor installation location (A) on the first day is shown as (2), and the SPAD value at sensor installation location (A) on the second day is shown as (4). In the graph of SPAD values ​​at sensor installation location (B), indicated by a thick dashed line, the SPAD value SPAD5 at sensor installation location (B) on the first day is shown as (3), and the SPAD value at sensor installation location (B) on the second day is shown as (5). The estimated SPAD value SPAD6 at location (C) where sensor 10 is not installed on the first day is shown as (D). At this time, it is estimated that the growth stage of rice at sensor installation location (B) is earlier than that of rice at sensor installation location (A), and the growth stage of rice at location (C) where sensor 10 is not installed is later.

[0060] By applying the method for obtaining estimated SPAD values ​​at predetermined locations in the entire location F on the first day to all locations in the entire location F, it is possible to estimate the SPAD value distribution and growth stage of the entire location F on the first day, assuming that sensors 10 were installed and measured in the entire location F. Figure 9(a) shows an example of an estimated SPAD value distribution of the entire location F obtained in this way.

[0061] Furthermore, by correlating the graph of SPAD values ​​measured by sensor 10 with the graphs of grass height and stem values, it is possible to estimate the grass height and stem values ​​for a given day with a predetermined SPAD value. This makes it possible to estimate the distribution of grass height and stem values ​​on the first day of the entire area F, assuming that sensor 10 is installed and measurements are taken throughout the entire area F. Figures 9(b) and 9(c) show examples of estimated graphs of the distribution of grass height and stem values ​​for the entire area F obtained in this way.

[0062] By obtaining distribution information of F across the entire area, including not only SPAD values ​​but also plant height values ​​and stem values, it becomes possible to grasp the growth stage of rice more accurately and in detail, enabling more appropriate water management, fertilization, and pesticide application at the appropriate time.

[0063] In this embodiment, we have shown an example in which SPAD equivalent values ​​are obtained from RGB image values ​​and compared with SPAD measured values ​​from sensor 10. However, instead of SPAD values, color number values ​​from the leaf color scale may be used.

[0064] Furthermore, although this embodiment uses rice as an example of a crop in the field, the present invention can also be applied to other crops such as wheat, corn, cabbage, lettuce, and spinach. [Examples]

[0065] In this embodiment, as in Embodiment 1, it is assumed that a sensor 10 is installed in a predetermined field (for example, field FA in Figure 1) among several fields, while a sensor 10 is not installed in another field (for example, field FC in Figure 1). The basic configuration of the sensing system in this embodiment is the same as in Embodiment 1. In this embodiment, only the configurations that differ from Embodiment 1 will be described, and the common configurations will not be described.

[0066] Figure 10 is a schematic diagram illustrating the steps for acquiring (estimating) crop information in this embodiment. In this embodiment, the server 20 functions as a first acquisition unit that acquires the image capture results of the entire location F obtained by the device 100. The server 20 also functions as a second acquisition unit that acquires image information about crops based on the images. The server 20 also functions as a correction unit that corrects the acquired image information using at least one of the crop cover rate and the soil color of the field. Furthermore, the server 20 functions as a third acquisition unit that acquires information about crops at a second location using the corrected second image information.

[0067] Device 100 takes photographs of the area, including both field FA and FC, at predetermined times each day. The images taken by device 100 are transmitted from device 100 to server 20, and server 20 calculates the NDVI value for field FA and FC from these images. The calculated NDVI value is represented as "NDVI (provisional)" in Figure 10 because there is room for improvement in accuracy.

[0068] Sensor 10A, installed in field FA, takes pictures of field FA at predetermined times every day. The images of field FA taken by sensor 10A are transmitted to server 20, and server 20 calculates three indicator values ​​of crops (leaf color, plant height, and number of stems), vegetation cover, and soil color from the images within the range of field FA that sensor 10A can capture.

[0069] Here, we will explain the correction of the NDVI value calculated from the images captured by the device 100. First, the server 20 accesses a database configured internally, on another server, or on the cloud, and obtains estimated values ​​for soil color and vegetation cover in the field FC from the database on soil color and the database on vegetation cover stored in the server.

[0070] The database of soil color is, for example, a relationship formula between temperature and soil color in the field over the past 10 years in the area where sensor 10A is installed. This relationship formula may be in two forms: one for when there is water in the field and one for when there is no water, or it may be a relationship formula between temperature, water level, and soil color in the field. When server 20 obtains an estimated value of soil color, the user may be able to input whether there is water in field FC from user terminal 30, or sensor 10A may make this determination under the assumption that fields FA and FC are the same in terms of the presence or absence of water. In addition, the user may be able to input the water level from user terminal 30, or sensor 10A may have a water level sensor as one of its various sensors, and the result from that water level sensor may be used as the water level in field FC.

[0071] The database relating to vegetation cover is, for example, a formula showing the relationship between the number of days elapsed since planting (for rice, the number of days elapsed since transplanting) and the vegetation cover for the varieties planted in fields FA and FC over the past 10 years. The number of days elapsed since planting may be transmitted by the user to the server 20 via the user terminal 30, or the sensor 10A may count the number of days elapsed since planting and transmit the number of days elapsed since planting to the server 20.

[0072] Server 20 obtains estimated values ​​for soil color and vegetation cover in the field FC from the above relationship and corrects the NDVI calculated from the images taken by the device 100 ("NDVI (provisional)" in Figure 10) to a more accurate value ("NDVI (leaf)" in Figure 10).

[0073] Server 20 estimates (acquires) the SPAD value of the field FC using correlation and conversion formulas between NDVI (leaf) and SPAD value.

[0074] According to the configuration of this embodiment, more accurate monitoring can be performed even for field fuel cells (FCs) where the sensor 10 is not installed. [Other examples] The present invention can also be realized by supplying a program that implements one or more of the functions of the above-described embodiments to a system or device via a network or storage medium, and by having one or more processors in the computer of that system or device read and execute the program. It can also be realized by a circuit (e.g., an ASIC) that implements one or more functions.

[0075] This embodiment includes the following configurations and methods. (Composition 1) A first acquisition unit that acquires a first image obtained by photographing a location including a plant at a first timing, a second image obtained by photographing the location at a second timing prior to the first timing, first measurement information of the plant at a first position obtained by a sensor installed at the location at the first timing, and second measurement information of the plant at the first position obtained by the sensor at the second timing. A second acquisition unit that acquires first image information relating to the plant at a first position based on the first image, second image information relating to the plant at a first position based on the second image, and third image information relating to the plant at a predetermined position based on the first image, A processing apparatus characterized by having a third acquisition unit that acquires information about the plant at the predetermined position at the first timing using the first and second measurement information and the first to third image information. (Configuration 2) The second acquisition unit acquires fourth image information relating to the plant at the predetermined location based on the second image, The processing apparatus according to configuration 1, characterized in that the third acquisition unit acquires information about the plant at the predetermined position at the second timing using the first and second measurement information and the first, second, and fourth image information. (Composition 3) A first acquisition unit that acquires an image of the location obtained by photographing the location including plants, first measurement information of the plants at a first position obtained by a first sensor installed at the location, and second measurement information of the plants at a second position obtained by a second sensor installed at the location. A second acquisition unit that acquires first image information relating to the plant at the first position based on the image, second image information relating to the plant at the second position based on the image, and third image information relating to the plant at a predetermined position based on the image, A processing apparatus characterized by having a third acquisition unit that acquires information about the plant at the predetermined location using the first and second measurement information and the first to third image information. (Composition 4) The processing apparatus according to any one of the configurations 1 to 3, characterized in that the first and second measurement information is information relating to leaf color. (Composition 5) The processing apparatus according to any one of configurations 1 to 4, characterized in that the third acquisition unit acquires information regarding leaf color as information about the plant. (Composition 6) The processing apparatus according to any one of configurations 1 to 5, characterized in that the third acquisition unit acquires information relating to at least one of plant height and number of stems as plant information. (Composition 7) The processing apparatus according to any one of configurations 1 to 6, characterized in that the third acquisition unit acquires the distribution of information about the plants at the location. (Composition 8) The processing apparatus according to any one of configurations 1 to 7, characterized in that the first to second image information includes RGB values. (Composition 9) The first to third image information includes NDVI values, The processing apparatus according to any one of configurations 1 to 3, characterized in that the second acquisition unit corrects the second image information using at least one of the vegetation cover rate of the plants and the soil color of the location. (Composition 10) A first acquisition unit that acquires an image of the location obtained by photographing the location including plants, A second acquisition unit acquires image information relating to the plant at a second location different from the first location where a sensor for acquiring information about the plant is installed, based on the aforementioned image. A correction unit that corrects the image information using at least one of the vegetation cover rate of the plants and the soil color of the location, A processing apparatus characterized by having a third acquisition unit that acquires information about the plant at the second position using the corrected image information. (Composition 11) The processing apparatus according to configuration 10, characterized in that the first acquisition unit acquires information about the plant using information indicating the relationship between the corrected image information and the plant information. (Composition 12) The processing apparatus according to configuration 10 or 11, characterized in that the second acquisition unit acquires the soil color at the second position. (Composition 13) The processing apparatus according to any one of configurations 10 to 12, characterized in that the second acquisition unit acquires the vegetation cover rate at the second position. (Composition 14) The processing apparatus according to any one of configurations 10 to 13, characterized in that the second acquisition unit acquires information regarding the presence or absence of water at the second location. (Composition 15) A processing device described in any one of configurations 1 to 14, A device that acquires images by photographing a location, A processing system characterized by having a sensor that acquires information about plants. (Method 1) Steps include obtaining a first image obtained by photographing a location containing plants at a first timing, a second image obtained by photographing the location at a second timing prior to the first timing, first measurement information of the plants at a first position obtained by a sensor installed at the location at the first timing, and second measurement information of the plants at the first position obtained by the sensor at a second timing. Steps include obtaining first image information relating to the plant at a first position based on the first image, second image information relating to the plant at a first position based on the second image, and third image information relating to the plant at a predetermined position based on the first image, A processing method characterized by comprising the step of acquiring information about the plant at the predetermined position at the first timing using the first and second measurement information and the first to third image information. (Composition 16) A program characterized by causing a computer to execute the processing method described in Method 1.

[0076] Although preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of its gist. [Explanation of symbols]

[0077] 10 Growth Monitoring Sensors 20 Server (processing unit, first to third acquisition units)

Claims

1. A first acquisition unit that acquires a first image obtained by photographing a location including a plant at a first timing, a second image obtained by photographing the location at a second timing prior to the first timing, first measurement information of the plant at a first position obtained by a sensor installed at the location at the first timing, and second measurement information of the plant at the first position obtained by the sensor at the second timing. A second acquisition unit acquires first image information relating to the plant at a first position based on the first image, second image information relating to the plant at a first position based on the second image, and third image information relating to the plant at a predetermined position based on the first image. A processing apparatus characterized by having a third acquisition unit that acquires information about the plant at the predetermined position at the first timing using the first and second measurement information and the first to third image information.

2. The second acquisition unit acquires fourth image information relating to the plant at the predetermined position based on the second image, The processing apparatus according to claim 1, characterized in that the third acquisition unit acquires information about the plant at the predetermined position at the second timing using the first and second measurement information and the first, second, and fourth image information.

3. A first acquisition unit that acquires an image of the location obtained by photographing the location including plants, first measurement information of the plants at a first position obtained by a first sensor installed at the location, and second measurement information of the plants at a second position obtained by a second sensor installed at the location. A second acquisition unit that acquires first image information relating to the plant at the first position based on the image, second image information relating to the plant at the second position based on the image, and third image information relating to the plant at a predetermined position based on the image, A processing apparatus characterized by having a third acquisition unit that acquires information about the plant at the predetermined location using the first and second measurement information and the first to third image information.

4. The apparatus according to any one of claims 1 to 3, characterized in that the first and second measurement information is information relating to leaf color.

5. The processing apparatus according to any one of claims 1 to 3, characterized in that the third acquisition unit acquires information relating to leaf color as information relating to the plant.

6. The processing apparatus according to any one of claims 1 to 3, characterized in that the third acquisition unit acquires information relating to at least one of plant height and number of stems as plant information.

7. The processing apparatus according to any one of claims 1 to 3, characterized in that the third acquisition unit acquires the distribution of information on the plants at the location.

8. The processing apparatus according to any one of claims 1 to 3, characterized in that the first to second image information includes RGB values.

9. The first to third image information includes an NDVI value, The processing apparatus according to any one of claims 1 to 3, characterized in that the second acquisition unit corrects the second image information using at least one of the vegetation cover rate of the plants and the soil color of the location.

10. A first acquisition unit that acquires an image of the location obtained by photographing the location including plants, A second acquisition unit acquires image information relating to the plant at a second position different from the first position where a sensor for acquiring information about the plant is installed, based on the aforementioned image. A correction unit that corrects the image information using at least one of the vegetation cover rate of the plants and the soil color of the location, A processing apparatus characterized by having a third acquisition unit that acquires information about the plant at the second position using the corrected image information.

11. The processing apparatus according to claim 10, characterized in that the first acquisition unit acquires information about the plant using information indicating the relationship between the corrected image information and the plant information.

12. The apparatus according to claim 10 or 11, characterized in that the second acquisition unit acquires the soil color at the second position.

13. The processing apparatus according to claim 10 or 11, characterized in that the second acquisition unit acquires the vegetation cover rate at the second position.

14. The apparatus according to claim 10 or 11, characterized in that the second acquisition unit acquires information regarding the presence or absence of water at the second location.

15. A processing apparatus according to any one of claims 1 to 3, A device that acquires images by photographing a location, A processing system characterized by having a sensor that acquires information about plants.

16. Steps include obtaining a first image obtained by photographing a location including a plant at a first timing, a second image obtained by photographing the location at a second timing prior to the first timing, first measurement information of the plant at a first position obtained by a sensor installed at the location at the first timing, and second measurement information of the plant at the first position obtained by the sensor at a second timing. Steps include obtaining first image information relating to the plant at a first position based on the first image, second image information relating to the plant at a first position based on the second image, and third image information relating to the plant at a predetermined position based on the first image, A processing method characterized by comprising the step of acquiring information about the plant at the predetermined position at the first timing using the first and second measurement information and the first to third image information.

17. A program characterized by causing a computer to execute the processing method described in claim 16.

Images