Patent Description:
In the past, a normalized difference vegetation index (NDVI) has been used an index indicative of a distribution condition or degree of activity of plants.

In PTL <NUM>, for example, an information processing system that calculates a growth index of crops has been disclosed on the basis of an NDVI image obtained from an RGB image and near-infrared ray image in which crops are captured.

<NPL> discusses the complex feedback relationship between climate variability and vegetation dynamics as being a subject of intense investigation for its implications in furthering our understanding of the global biogeochemical cycle. This document by White et al. addresses the question in this context: "How does topography influence the vegetation's response to natural climate fluctuations?" This document by White et al. explores this issue through the analysis of interannual vegetation variability over a very large area (continental United States) using long-term (<NUM>-year period of <NUM>-<NUM>), monthly averaged, biweekly maximum value composite normalized difference vegetation index (NDVI) data. These data are obtained from satellite remote sensing at <NUM>-km resolution. Through the implementation of data mining techniques, this document by White et al. aims to show that the Northern Pacific climate oscillation and the ENSO phenomena influence the year-to-year vegetation variability over an extensive geographical domain. Further, the vegetation response to these fluctuations depends on a variety of topographic attributes such as elevation, slope, aspect, and proximity to moisture convergence zones, although the first two are the predominant controls. Therefore, the dynamic response of terrestrial vegetation to climate fluctuations, which shows tremendous spatial heterogeneity, is claimed to be closely linked to the variability induced by the topography. These findings suggest according to this document by White et al. that the representation of vegetation dynamics in existing climate models, which do not incorporate such dependencies, may be inadequate. Therefore, this document by White et al. suggests that climate models that are regularly employed to guide policy decisions need to better incorporate these dependencies for the assessment of terrestrial carbon sequestration under evolving climate scenarios.

<CIT> discloses an automated irrigation control comprising crop sensor physically attached to a crop and a light sensitive sensor having a photo-detector for monitoring light intensity of a crop, an irrigation conduit extending along the span of the irrigation zone and adapted to carry fluid, with one or more controllable valves and sensors, growth sensors placed in close proximity of the crop sensors, a computer control system, an irrigation controller, and a communications link between the computer control system, the one or more crop sensor, the three or more growth sensors, and the irrigation controller.

However, for example, in a case where an environmental condition is changed when performing an inspection of vegetation, an NDVI fluctuates. Therefore, in some cases, it is difficult to confirm growth conditions of vegetation in a conventional NDVI image.

The present disclosure has been made in a consideration of such a circumstance and is aimed at enabling the growth conditions of vegetation to be easily confirmed.

The problem is solved by the subject matter of the independent claims. A signal processing apparatus according to one aspect of the present disclosure includes among others a relative value calculation section configured to calculate a relative value to an average of an index from the index indicative of a state of an inspection object, which is calculated on the basis of a sensing signal; and a display processing section configured to perform processing to allow an image indicative of the state of the inspection object on the basis of the relative value to be displayed.

A signal processing method or a program according to another aspect of the present disclosure includes among others the steps of: calculating a relative value to an average of a vegetation index from the vegetation index indicative of a state of an inspection object, which is calculated on the basis of a sensing signal; and performing processing to allow an image indicative of the state of the inspection object on the basis of the relative value to be displayed.

According to another aspect of the present disclosure, there is calculated a relative value to an average of a vegetation index from a state indicative of growth conditions of an inspection object, which is calculated on the basis of a sensing signal; and there is performed processing to allow an image indicative of the state of the inspection object on the basis of the relative value to be displayed.

According to an aspect of the present disclosure, growth conditions of vegetation can be easily confirmed.

Hereinafter, a specific embodiment to which the present technology is applied will be described in detail with reference to the drawings.

<FIG> is a block diagram illustrating a configuration example of an embodiment of a vegetation inspection apparatus to which the present technology is applied.

As illustrated in <FIG>, a vegetation inspection apparatus <NUM> performs an inspection of grass as an inspection object in a state in which environmental light such as sunlight is radiated. Then, the vegetation inspection apparatus <NUM> displays an image (e.g., a relative NDVI image described below) indicative of growth conditions such as a grass state or a degree of activity on a display section <NUM>.

For example, the vegetation inspection apparatus <NUM> includes an optical system <NUM>, an aperture diaphragm <NUM>, a spectral sensor <NUM>, a signal processing block <NUM>, and a control block <NUM>. Further, the spectral sensor <NUM> has a spectroscope <NUM> and a sensing device <NUM>.

The optical system <NUM> has one or plural pieces of lenses. For example, the optical system <NUM> collects light incident to the vegetation inspection apparatus <NUM>, such as reflected light in which the environmental light is reflected on the grass. Further, the optical system <NUM> provides an image of a photographic subject on a detector plane of the sensing device <NUM> of the spectral sensor <NUM>.

The aperture diaphragm <NUM> controls amount of light collected into the spectral sensor <NUM> via the optical system <NUM>. Thereby, the aperture diaphragm <NUM> adjusts an exposure of an image acquired by the spectral sensor <NUM>.

The spectral sensor <NUM> detects components in plural different wavelength regions of the reflected light in which the environmental light is reflected on the grass. Specifically, the spectral sensor <NUM> separates the reflected light into light in plural wavelength regions by using the spectroscope <NUM>. Further, the spectral sensor <NUM> provides a detection signal in which brightness of light (spectrally split component) of respective wavelength regions is detected in each pixel of the sensing device <NUM> for the signal processing block <NUM>.

The spectroscope <NUM> has a configuration in which a plurality of optical filters that transmit light of predetermined wavelength regions are arranged in each pixel of the sensing device <NUM>. Then, spectroscope <NUM> separates light radiated on the detector plane of the sensing device <NUM> by using respective optical filters. Note that the above optical filter is referred to as a color filter that separates visible light into each color.

In <FIG>, an example of the optical filter that is arranged as the spectroscope <NUM> is illustrated. For example, eight pixels such that the number of vertical pixels × the number of horizontal pixels is <NUM> × <NUM> is defined as a measurement unit. Further, eight kinds of optical filters that transmit light in respective different wavelength regions are arranged in a manner corresponding to each pixel that configures the measurement unit. Specifically, in the order corresponding to a short wavelength in a manner corresponding to eight pixels of the measurement unit, an optical filter B1 that transmits first blue light, an optical filter B2 that transmits second blue light, an optical filter G1 that transmits first green light, an optical filter G2 that transmits second green light, an optical filter R1 that transmits first red light, an optical filter R2 that transmits second red light, an optical filter IR1 that transmits first infrared light, and an optical filter IR2 that transmits second infrared light are arranged.

While using as one measurement unit the above optical filter of eight pixels, the spectroscope <NUM> has a configuration in which the optical filter for n measurement units (n is a natural number equal to or greater than <NUM>) is continuously arranged in the entire plane of the detector plane of the sensing device <NUM>. Note that the measurement unit of the optical filter is not limited to a configuration in which eight pixels are defined as one measurement unit. In the measurement unit of the optical filter, other modes such as a configuration in which four pixels (R, G, B, IR) are defined as one measurement unit can be adopted.

For example, in a configuration that is a so-called Bayer arrangement and in which an R pixel, a G pixel, and a B pixel are arranged, the optical filter of an arrangement example in which a portion of pixels are replaced with IR pixels may be used. Specifically, in the G pixel in which the number of pixels is plentiful in the Bayer arrangement, as illustrated in <FIG>, the optical filter in which the G pixels arranged in a row of the R pixels are replaced with the IR pixels can be used. Further, the optical filter in which the B pixel in which visibility is low is replaced with the IR pixel can be used. In this case, as illustrated in <FIG>, a configuration in which all the B pixels are not replaced with the IR pixels but a portion of B pixels are replaced with the IR pixels can be used.

The sensing device <NUM> is, for example, an image pickup device having a configuration in which a plurality of pixels are arranged in a matrix pattern in the detector plane. Further, the sensing device <NUM> detects brightness of the spectrally split component spectrally split by each optical filter of the spectroscope <NUM> in each pixel. Further, the sensing device <NUM> outputs the detection signal (sensing signal) in accordance with the brightness of each spectrally split component.

Note that, in addition to an area sensor that gets an object by a plane, as the sensing device <NUM>, a line sensor that gets the object by a line can be used. Further, even in the case where the R pixel and the IR pixel are arranged only one by one in the sensing device <NUM>, a mechanism for moving a sensor or a measurement object is provided to thereby scan the object.

The signal processing block <NUM> performs signal processing on the detection signal (that is, a sensing image sensed by the sensing device <NUM>) output from the spectral sensor <NUM>. Thereby, the signal processing block <NUM> generates an image indicative of a result in which the growth conditions of the grass are inspected and displays the image on the display section <NUM>. Note that a detailed configuration of the signal processing block <NUM> will be described below with reference to <FIG>.

The control block <NUM> performs control for each block that configures the vegetation inspection apparatus <NUM> such as the sensing device <NUM> and the signal processing block <NUM> so that the growth conditions of the grass can be preferably inspected in the vegetation inspection apparatus <NUM>.

The vegetation inspection apparatus <NUM> configured as described above can inspect the growth conditions of the grass by using a normalized difference vegetation index NDVI that is numerically indicative of the growth conditions. Further, the vegetation inspection apparatus <NUM> can acquire an NDVI image constructed by the normalized difference vegetation index NDVI as a result in which the growth conditions of the grass are inspected. The normalized difference vegetation index NDVI is obtained by calculating the following formula (<NUM>) by using a pixel value R of a pixel in which red light is detected in the sensing device <NUM> and a pixel value IR of a pixel in which near-infrared light is detected. <NUM>] <MAT>.

Here, the normalized difference vegetation index NDVI is used as an index of growth of stems and leaves. Note that a reflectance (pixel value IR) of the near-infrared light and a reflectance (pixel value R) of the red light are calculated by obtaining as an incident light intensity a red light intensity and near-infrared ray intensity in an area such as sky and by obtaining as a reflected light intensity the red light intensity and near-infrared ray intensity in an object area in an RGB image and a near-infrared ray image in an area that is not the object area. Further, in the reflectance of the near-infrared light and that of the red light, the incident light intensity may be measured by using a diffusion plate having a known reflectance as a reference. Further, a reflection coefficient may be calculated on the basis of a ratio between the incident light intensity and reflection luminance of an object and then the reflectance of the near-infrared light and that of the red light may be obtained by converting the reflection coefficient into a reflectance. Further, the vegetation inspection apparatus <NUM> calculates the NDVI image by using an average, dispersion, high-order dispersion, or the like of NDVI only in the object area. Through the process, the NDVI image is calculated from only information obtained from pixels in the object area. Thereby, the NDVI image can be calculated with higher accuracy.

Incidentally, the vegetation inspection apparatus <NUM> performs an inspection of the grass in an outdoor environment. Thereby, for example, the detection signal detected by the spectral sensor <NUM> may fluctuate, for example, in accordance with a change in environmental conditions such as a position of sun, weather (fine/cloudy), or a direction of grass grain. In the result, the NDVI image is generated by using the detection signal output from the spectral sensor <NUM> directly. In such a case, it is assumed that, in some cases, it is difficult to determine the growth conditions of the grass by an influence in which the fluctuation of the detection signal is given to the NDVI image.

To solve the above problem, in the vegetation inspection apparatus <NUM>, the signal processing is performed on the detection signal by the signal processing block <NUM> so that the influence given to the NDVI image is suppressed by the fluctuation in the detection signal due to the change in the environmental conditions. The process permits the growth conditions of vegetation to be easily confirmed. For example, it is possible to easily make a distinction between a location in which the grass is preferably grown or a location in which the grass is not preferably grown.

<FIG> is a block diagram illustrating a configuration example of the signal processing block <NUM>.

As illustrated in <FIG>, the signal processing block <NUM> includes an NDVI average calculation section <NUM>, a correlation coefficient calculation section <NUM>, an NDVI relative value calculation section <NUM>, and a display processing section <NUM>.

By using the detection signal acquired by actually sensing the grass by the sensing device <NUM> illustrated in <FIG>, the NDVI average calculation section <NUM> calculates an NDVI average Na obtained by averaging the normalized difference vegetation index NDVI of the entire grass and provides the NDVI average Na for the correlation coefficient calculation section <NUM>.

The NDVI average calculation section <NUM> calculates an average Ra of all the pixel values R of the red light detected in a grass area in the pixel value R of the red light included in the detection signal output from the spectral sensor <NUM>. Similarly, the NDVI average calculation section <NUM> calculates an average IRa of all the pixel values IR of the near-infrared light detected in the grass area in the pixel value IR of the near-infrared light included in the detection signal output from the spectral sensor <NUM>. Further, the NDVI average calculation section <NUM> obtains the NDVI average Na to the entire grass from the average Ra of the red light and the average IRa of the near-infrared light on the basis of the following formula (<NUM>). <NUM>] <MAT>.

The correlation coefficient calculation section <NUM> calculates a correlation coefficient α by which the NDVI average Na obtained by the NDVI average calculation section <NUM> is matched with a predetermined NDVI specified value Nd and provides the correlation coefficient α for the NDVI relative value calculation section <NUM>. Here, it is assumed that in the vegetation inspection apparatus <NUM>, an average of the normalized difference vegetation indexes NDVI to the entire grass is the NDVI specified value Nd determined to be previously a specified value. Further, as represented in the following formula (<NUM>), the NDVI specified value Nd is represented by using the average Ra of the red light, the average IRa of the near-infrared light, and the correlation coefficient α. <NUM>] <MAT>.

Further, from formula (<NUM>), the correlation coefficient α that allows the average of the normalized difference vegetation indexes NDVI that is originally the NDVI average Na to be matched with the NDVI specified value Nd is represented by the following formula (<NUM>). <NUM>] <MAT>.

In each measurement unit of the detection signal acquired by actually sensing the grass by the sensing device <NUM> illustrated in <FIG> (for example, on the basis of the sensing signal for each pixel and not the entire NDVI image), the NDVI relative value calculation section <NUM> applies the correlation coefficient α calculated by the correlation coefficient calculation section <NUM> and calculates an NDVI relative value Nr relative to the NDVI average Na. Note that the NDVI average Na is used in order to describe the correlation coefficient a and is not required for an operation to obtain the NDVI relative value Nr. Specifically, as represented in the following formula (<NUM>), the NDVI relative value calculation section <NUM> can obtain the NDVI relative value Nr (x, y) on the basis of the pixel value R (x, y) of the red light and pixel value IR (x, y) of the infrared light for each measurement unit (x, y) of the sensing device <NUM>. <NUM>] <MAT>.

The display processing section <NUM> generates the NDVI image based on the NDVI relative value Nr calculated by the NDVI relative value calculation section <NUM> and performs display processing in which the NDVI image is displayed on the display section <NUM>. For example, the display processing section <NUM> maps a color set in accordance with the NDVI relative value Nr (x, y) in each measurement unit (x, y) like a heat map to thereby generate the NDVI image. Note that, as described above, the NDVI relative value Nr (x, y) is relative to the NDVI average Na. Hereinafter, the NDVI image generated from the NDVI relative value Nr (x, y) is appropriately referred to as the relative NDVI image.

As described above, the vegetation inspection apparatus <NUM> is configured and the relative NDVI image indicative of a result in which the growth conditions of the grass are inspected is displayed on the display section <NUM>.

Further, in the vegetation inspection apparatus <NUM>, for example, the NDVI specified value Nd can be set (for example, set to <NUM> to <NUM> when the inspection object is the grass) in a portion in which a change in the normalized difference vegetation index NDVI is large. Thereby, the NDVI relative value Nr is calculated by using the NDVI specified value Nd as a reference. Therefore, the relative NDVI image in which it is easy to confirm a portion in which a change in the normalized difference vegetation index NDVI is large, that is, a portion in which a change in the growth conditions of the grass is large can be generated. The above relative NDVI image is generated, and thereby it is possible for the vegetation inspection apparatus <NUM> to confirm the growth condition of the grass more easily than a conventional NDVI image in which the normalized difference vegetation index NDVI is directly used.

Further, the vegetation inspection apparatus <NUM> can realize quantization and visualization by using the NDVI relative value Nr for the growth conditions of the grass in a level that is incapable of being visually confirmed in the conventional NDVI image. That is, the vegetation inspection apparatus <NUM> matches the NDVI average Na with the NDVI specified value Nd (to be always constant), and thereby subjects the detection signal output from the sensing device <NUM> to a signal normalization. Thereby, the vegetation inspection apparatus <NUM> can suppress an influence that is given to the detection signal due to a change in the environmental conditions and generate the relative NDVI image in which it is easy to confirm the growth conditions of the grass.

Next, the signal processing that is performed in the signal processing block <NUM> will be described with reference to a flowchart illustrated in <FIG>.

In step S11, for example, the detection signal for a piece of relative NDVI image is provided from the sensing device <NUM> to the signal processing block <NUM>. Then, the NDVI average calculation section <NUM> acquires the detection signal. In step S12, the NDVI average calculation section <NUM> obtains the average Ra of the red light and the average IRa of the near-infrared light from the detection signal provided from the sensing device <NUM>. Further, the NDVI average calculation section <NUM> calculates the NDVI average Na on the basis of formula (<NUM>) described above.

In step S13, the correlation coefficient calculation section <NUM> calculates the correlation coefficient α that matches the NDVI average Na obtained by the NDVI average calculation section <NUM> in step S11 with the NDVI specified value Nd on the basis of formula (<NUM>) described above.

In step S14, the NDVI relative value calculation section <NUM> applies the correlation coefficient α obtained by the correlation coefficient calculation section <NUM> in step S12 and calculates the NDVI relative value Nr on the basis of formula (<NUM>) described above in each measurement unit of the detection signal provided from the sensing device <NUM>.

In step S15, the display processing section <NUM> generates the relative NDVI image on the basis of the NDVI relative value Nr obtained in each measurement unit by the NDVI relative value calculation section <NUM> in step S13 and performs display processing in which the relative NDVI image is displayed on the display section <NUM>.

After the process of step S15, the signal processing in the signal processing block <NUM> is completed.

As described above, the signal processing block <NUM> can suppress an influence that is given to the detection signal due to a change in the environmental conditions and generate the relative NDVI image in which the growth conditions of the grass are confirmed more easily than the conventional NDVI image.

In <FIG>, for example, an example of the conventional NDVI image and an example of the relative NDVI image are illustrated.

On the upper side of <FIG>, the conventional NDVI image is illustrated, and on the lower side of <FIG>, the relative NDVI image is illustrated. Both the conventional NDVI image and the relative NDVI image illustrated in <FIG> are generated by using the same detection signal.

For example, the vegetation inspection apparatus <NUM> may display only the relative NDVI image on the display section <NUM>. Alternatively, the vegetation inspection apparatus <NUM> may simultaneously display the conventional NDVI image and the relative NDVI image on the display section <NUM> while disposed to thereby compare them. Further, the vegetation inspection apparatus <NUM> may switch a display between the conventional NDVI image and the relative NDVI image through the user operation. Further, the conventional NDVI image and the relative NDVI image are not limited to a simultaneous display of them or a switching display of them. For example, the vegetation inspection apparatus <NUM> can display a live image photographed by a normal image pickup apparatus, other images, or the like simultaneously or while switching.

The vegetation inspection apparatus <NUM> calculates the normalized difference vegetation index NDVI from an image obtained through sensing. Thereby, the vegetation inspection apparatus <NUM> can display a distribution or degree of activity of the grass as the heat map as illustrated in the upper side of <FIG>. As described above, the vegetation inspection apparatus <NUM> further obtains the NDVI relative value Nr so as to be relative to the NDVI average Na. Thereby, as illustrated in the lower side of <FIG>, the vegetation inspection apparatus <NUM> can generate the relative NDVI image that emphatically indicates a portion in which the growth conditions of the grass are satisfactory and a portion in which the growth conditions of the grass are unsatisfactory. For example, the vegetation inspection apparatus <NUM> appropriately sets the NDVI specified value Nd to thereby generate the relative NDVI image such that the portion in which the growth conditions of the grass are unsatisfactory is noticeable.

Here, with regard to a color display of the heat map, as a highlight of the satisfactory portion and unsatisfactory portion of the growth conditions of the grass, for example, the satisfactory portion of the growth conditions of the grass can be displayed so as to have a blue color. The unsatisfactory portion of the growth conditions of the grass can be displayed so as to have a red color. Further, in accordance with a predetermined standard, the color display may be performed only on the satisfactory portion or unsatisfactory portion of the growth conditions of the grass. Note that the highlight is not limited to the color display, and further shading, luminance, or saturation of an image may be changed to thereby present the highlight. A degree of the display modes can be switched through setting or operations of the user. Particularly, the user changes the NDVI specified value Nd or multiplies the NDVI specified value Nd by the correlation coefficient a to thereby change how to highlight the unsatisfactory portion of the growth conditions of the grass.

Incidentally, in <FIG>, the relative NDVI image is generated over the entire grass. Further, the vegetation inspection apparatus <NUM> may generate the relative NDVI image in each specified area of interest while paying attention to the specified area.

As illustrated in <FIG>, for example, the vegetation inspection apparatus <NUM> can specify a sunny area (area without hatching) in which sunlight is radiated on the grass and a shadow area (area with hatching) in which sunlight is not radiated on the grass. Further, the vegetation inspection apparatus <NUM> can individually generate the respective relative NDVI images of the sunny area and the shadow area.

Specifically, from the entire grass sensed by the spectral sensor <NUM>, the vegetation inspection apparatus <NUM> can set the area of interest on the basis of the brightness and allow the signal processing block <NUM> to perform the signal processing in each area of interest. In this case, the NDVI average calculation section <NUM> calculates the NDVI average Na concerning the area of interest of the grass. The correlation coefficient calculation section <NUM> calculates the correlation coefficient a concerning the area of interest of the grass. Further, the NDVI relative value calculation section <NUM> can calculate the NDVI relative value Nr relative to the NDVI average Na in each area of interest of the grass.

Through the process, the vegetation inspection apparatus <NUM> can inspect the growth conditions of the grass more particularly in each area of interest, for example, in each of the sunny area and the shadow area. Note that specification of the area of interest is not limited to the sunny area and the shadow area. An arbitrary area can be specified, for example, a half of a tournament-quality court is specified, and the like.

Further, as illustrated in <FIG>, the vegetation inspection apparatus <NUM> can distinguish the grass area (area without hatching) and an area other than the grass (area with hatching) in the entire image. Further, the vegetation inspection apparatus <NUM> can specify only the grass area as the area of interest and generate the relative NDVI image of the grass area. At this time, the specification of the grass area can be performed on the basis of a normal image recognition or on the basis of the normalized difference vegetation index NDVI. As described above, the vegetation inspection apparatus <NUM> can automatically determine and specify the area of interest. In addition, the vegetation inspection apparatus <NUM> can specify an arbitrary area in the entire image as the area of interest, for example, in accordance with setting of the user.

Note that, in the present embodiment, descriptions will be made with reference to the normalized difference vegetation index NDVI. Further, in the vegetation inspection apparatus <NUM>, the vegetation index (for example, an RVI (Ratio Vegetation Index), a GNDVI (Green NDVI), and the like) other than the normalized difference vegetation index NDVI may be used. Further, the vegetation inspection apparatus <NUM> can cover a forest, agricultural crops, and the like in addition to the grass as described above as an inspection object to be inspected on the growth conditions.

As other vegetation indexes, for example, the ratio vegetation index (RVI) calculated by performing an operation of the following formula (<NUM>), a difference vegetation index (DVI) calculated by performing an operation of the following formula (<NUM>), or the like can be used. <NUM>] <MAT> [Math. <NUM>] <MAT>.

Note, however, that, in formulas (<NUM>) and (<NUM>), IR represents a reflectance (pixel value of a pixel that detects the near-infrared light) of a near-infrared area and R represents a reflectance (pixel value of a pixel that detects the red light) of red in a visible area. Note that, here, only the vegetation index in which IR and R are used as a parameter is exemplified. Further, it is as a matter of course possible to measure other vegetation indexes by using, as a parameter, reflectances, etc. of other light other than red in the visible area. In addition, a spectral ratio is not limited to a combination of R and IR.

Further, the present technology can be applied to a vegetation inspection system connected through a network, for example, in addition to an apparatus that is configured by an apparatus alone like the vegetation inspection apparatus <NUM>.

As illustrated in <FIG>, for example, a vegetation inspection system <NUM> has a configuration in which an image pickup apparatus <NUM> and a signal processing apparatus <NUM> for vegetation inspection are connected through a network <NUM>.

The image pickup apparatus <NUM> includes the spectral sensor <NUM> illustrated in <FIG>. The image pickup apparatus <NUM> detects components in a plurality of different wavelength regions regarding reflected light in which the environmental light is reflected on the grass. Further, the image pickup apparatus <NUM> transmits a detection signal indicative of detection results to the signal processing apparatus <NUM> through the network <NUM>.

The signal processing apparatus <NUM> has the function similar to that of the signal processing block <NUM> illustrated in <FIG>. The signal processing apparatus <NUM> receives the detection signal that is transmitted through the network <NUM> from the image pickup apparatus <NUM> for vegetation inspection. Further, the signal processing apparatus <NUM> performs the signal processing in which the relative NDVI image as described above is generated. Then, the signal processing apparatus <NUM> accumulates the relative NDVI images in an accumulation apparatus (not illustrated) connected to the network <NUM> to thereby observe a change in the growth conditions of the grass.

As described above, according to the present technology, the grass in a remote location can be inspected through the network <NUM> and a manager of the grass can perform a management of the grass everywhere. Although not illustrated in the figure, a plurality of the image pickup apparatuses <NUM> are connected to the network <NUM>, and thereby the manager can one-dimensionally manage the grass in a plurality of locations. Further, the manager can observe the growth conditions of the grass by using a multi-camera in which the plurality of the image pickup apparatuses <NUM> link up with one another. Further, the manager can observe the growth conditions of the grass while moving by using a UAV (Unmanned Aerial Vehicle) such as a so-called drone.

Note that, in the present embodiment, a vegetation index based on reflection of light by plants is described by using plants such as the grass as the inspection object. According to the present technology, an object other than plants may be used as the inspection object. The present technology can be applied to an inspection of various inspection objects by using indexes other than the vegetation index. Further, the vegetation index is an index that indicates the growth conditions of plants, and additionally, for example, can be used to make a contribution indirectly to the growth conditions and grasp plant conditions even if conditions are not directly regarded as the growth conditions like conditions of photosynthesis or the like.

Note that each processing described with reference to the flowchart described above need not always be sequentially executed in time series described in the flowchart. Thus, the processing includes the processes that are executed in parallel or discretely (parallel processes or a process based on objects, for example). Further, a program may be processed by a single CPU or by a plurality of CPUs in a distributed manner. Further, in the present specification, a system represents the entire apparatus configured by a plurality of apparatuses.

The above series of processing (signal processing method) may be executed by hardware or may be executed by software. When the series of processing is executed by software, programs constituting the software are installed to the computer from a program recording medium on which programs are recorded. In this case, the computer includes a computer embedded into dedicated hardware and a general-purpose personal computer capable of executing various kinds of functions by installing various kinds of programs.

<FIG> is a block diagram illustrating a configuration example of hardware of a computer which executes the series of processes described above under programs.

In the computer, a CPU (Central Processing Unit) <NUM>, a ROM (Read Only Memory) <NUM>, and a RAM (Random Access Memory) <NUM> are interconnected via a bus <NUM>.

An input/output interface <NUM> is further connected to the bus <NUM>. An input section <NUM>, an output section <NUM>, a storage section <NUM>, a communication section <NUM>, and a drive <NUM> are connected to the input/output interface <NUM>. The input section <NUM> is constituted by a keyboard, a mouse, a microphone, or the like. The output section <NUM> is constituted by a display, a speaker, or the like. The storage section <NUM> is constituted by a hard disk, a non-volatile memory, or the like. The communication section <NUM> is constituted by a network interface or the like. The drive <NUM> drives a removable medium <NUM> such as a magnetic disk, an optical disk, a magneto optical disk, or a semiconductor memory.

In the computer configured as described above, the CPU <NUM> loads, on the RAM <NUM>, a program stored in the storage section <NUM> through the input/output interface <NUM> and the bus <NUM> and executes the program to thereby execute the series of processes, for example.

The program executed by the computer (CPU <NUM>) may be recorded, for example, in the removable medium <NUM> that is a package medium including a magnetic disk (including a flexible disk), an optical disk (CD-ROM (Compact Disc-Read Only Memory), DVD (Digital Versatile Disc), or the like), a magneto optical disk, a semiconductor memory, or the like and provided. Further, alternatively, the program may be provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital satellite broadcasting.

In the computer, the program can be installed into the storage section <NUM> via the input/output interface <NUM> when the removable medium <NUM> is mounted on the drive <NUM>. Also, the program may be received by the communication section <NUM> via a wired or wireless transmission medium and be installed into the storage section <NUM>. In addition, the program may be installed beforehand into the ROM <NUM> or the storage section <NUM>.

In addition, the embodiment of the present disclosure is not limited to the above described embodiment and can be variously modified without departing from the scope of the present disclosure.

Claim 1:
A signal processing apparatus (<NUM>) comprising:
a relative value calculation section (<NUM>) configured to calculate a relative value to an average of an index from the index indicative of a state of an inspection object, which is calculated on basis of a sensing signal; and
a display processing section (<NUM>) configured to perform processing to allow an image indicative of the state of the inspection object on basis of the relative value to be displayed,
wherein the sensing signal represents a sensing image sensed by a sensing device,
wherein the sensing signal includes at least a detection value according to brightness of near-infrared light and red light, the signal processing apparatus (<NUM>) further comprising:
an average calculation section configured to calculate the average of the index, which is obtained by averaging the index in an entire inspection object;
wherein calculating the average of the index comprises using an average of all the detection values of the red light in the entire inspection object and using an average of all the detection values of the near-infrared light in the entire inspection object, characterized in that
the average calculation section (<NUM>) is configured to calculate the average Na of the index according to <MAT>
wherein Ra denotes the average of all the detection values of the red light in the entire inspection object and IRa denotes the average of all the detection values pixel values of the near-infrared light in the entire inspection object;
the signal processing apparatus (<NUM>) further comprising:
a correlation coefficient calculation section (<NUM>) configured to calculate a correlation coefficient α that matches the average Na of the index with a predetermined specified value Nd <MAT> according to <MAT>
wherein the relative value calculation section (<NUM>) is configured to apply the correlation coefficient and to calculate the relative value Nr(x,y) of the index relative to the average of the index in each measurement unit (x,y) in which a measurement is performed on the inspection object.