KR20190042690A - Soil diagnosis method and soil condition improvement method - Google Patents

Abstract

The soil diagnosis method includes a growth distribution detection step, a cloud point determination step, and a soil diagnosis step. In the growth distribution detecting step, the distribution of the growth state of the crop in the package is detected. In the determination of the cloudy point, on the basis of the detection results obtained in the above-mentioned growth rate distribution step, one or more cloudy points (P1 to P4) including cloudy points (P1 to P4) included in a relatively non- (P1 to P5). In the soil diagnosis process, at least one of the soil nutrient condition or the soil physical condition situation in each of the cloud point (P1 to P5) is diagnosed based on the soil sample collected at the cloud point (P1 to P5).

Description

Soil diagnosis method and soil condition improvement method

The present invention relates to a soil diagnosis method and a soil condition improvement method.

Conventionally, it is widely practiced to carry out soil diagnosis in order to know the nutrient condition and the physical condition of the soil in a field (field). Patent Document 1 discloses a soil analysis system that performs soil diagnosis (soil analysis) of this kind.

In this patent document 1, the sampling for collecting the soil sample is carried out at a plurality of points appropriately dispersed so that the necessary data collection is performed from the whole of the packaging (farm) so as to obtain accurate analysis results. The four corners and diagonal lines It is preferable to set the sampling point to a total of 6 points of arbitrary two points on the image plane. It is also disclosed that the package may be divided into a plurality of quadrangular sections to perform sampling for each section.

Japanese Patent Publication No. 5351325

However, in the case where the sampling point is a total of six points of a total of four corners of the entire package and two arbitrary points on the diagonal line, the soil natures and physical conditions are good at any of these six points, There may be cases where the soil nutrient condition or physical condition is not good. In such a case, there is a risk of overlooking an area where the nutrient situation or physical condition of the soil in the package is not good, and the opportunity to perform appropriate measures such as soil preparation or basification in the area is missed.

In addition, when the packaging is divided into a plurality of rectangular compartments and sampling is performed for each classification, it is necessary to divide the compartments into as many detailed compartments as possible in order not to overlook areas where the nutrient condition or physical condition of the soil is not good Therefore, there is a problem that the sampling number is increased, and the analysis of the soil takes a great deal of time and time.

SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances, and an object of the present invention is to provide soil diagnosis results in areas where the soil nutrient condition or physical condition is not good, It is to be able to utilize it for making and supporting.

The problems to be solved by the present invention are as described above, and means for solving this problem and effects thereof will be described below.

According to a first aspect of the present invention, the following soil diagnosis method is provided. That is, the soil diagnosis method includes a growth distribution detection step, a cloud point determination step, and a soil diagnosis step. In the growth distribution detecting step, the distribution of the growth state of the crop in the package is detected. In the cloud point determination step, based on the detection results obtained in the above described cloud point distribution detection step, one or more cloud point points are determined, including cloud point points included in a relatively poorly grown region. The soil diagnosis process diagnoses at least one of a soil nutrient condition or a soil physical condition condition at each of the cloud point based on the soil sample collected at the cloud point.

As a result, the soil diagnosis result can be obtained at the soil point included in the region where the growing condition is relatively poor, and the result can be utilized for the subsequent soil making and grazing.

In the above soil diagnosis method, it is preferable to carry out the following method. That is, the growth distribution detecting step includes an imaging step and a growth distribution image generation step. In the imaging step, the growth state of the crop is picked up by the image pickup means. In the above-mentioned growth distribution image generation step, the image obtained by imaging by the image pickup means is converted into a growth distribution image showing a deviation of the growth state of the crop.

This makes it possible to more accurately obtain the distribution of the growth state of the crops in the package by converting the image obtained by the image pick-up by the image pickup means into the growth distribution image, thereby making it possible to obtain a soil diagnosis result for a region where the growth state is relatively poor .

In the above soil diagnosis method, it is preferable to carry out the following method. That is, the imaging means is a multispectral camera. The above-mentioned growth distribution image is an image showing the distribution of normalized difference vegetation index.

Thus, by referring to the distribution image showing the distribution of the normalized difference vegetation index, the distribution of the growth state of the crop in the package can be grasped accurately, and the region where the necessity of soil diagnosis is high can be appropriately determined. It is possible to realize an efficient soil diagnosis.

In the soil diagnosis method, it is preferable that the imaging means is mounted on a flying object.

Thereby, it is possible to pick up the growth state of the crop in the package in a plane in the air, and it is possible to carry out imaging with high accuracy with less distortion. Therefore, the distribution of the growth state of the crops in the package can be obtained more accurately, and the region where the necessity of soil diagnosis is high can be grasped clearly. It is possible to realize an efficient soil diagnosis.

According to a second aspect of the present invention, there is provided the following soil condition improvement method. That is, the soil condition improvement method includes a growth distribution detection step, a cloud point determination step, a soil diagnosis step, and a soil making step. In the growth distribution detecting step, the distribution of the growth state of the crop in the package is detected. In the cloud point determination step, based on the detection results obtained in the above described cloud point distribution detection step, one or more cloud point points are determined, including cloud point points included in a relatively poorly grown region. The soil diagnosis process diagnoses at least one of a soil nutrient condition or a soil physical condition situation at each of the cementation points based on a soil sample collected at the cementation point after the crop in the package is broken. In the soil making process, at least one of the soil making or grazing of the package is carried out based on the diagnosis result obtained in the soil diagnosis process, before the crop of the next cycle is planted in the package.

As a result, the soil diagnosis result can be obtained at the soil point included in the area where the growth condition of the crop is not relatively good, and the soil making or grazing can be performed based on the diagnosis result. Thus, the yield and quality of the crop in the next cycle can be expected to improve.

In the method for improving the soil condition, it is preferable to carry out the following procedure. That is, the growth distribution detecting step includes an imaging step and a growth distribution image generation step. In the imaging step, the growth state of the crop is picked up by the image pickup means. In the above-mentioned growth distribution image generation step, the image obtained by imaging by the image pickup means is converted into a growth distribution image showing a deviation of the growth state of the crop.

Thereby, the distribution of the growth state of the crop in the package can be grasped more accurately by converting the image obtained by picking up the image by the image pickup means into the growth distribution image. Therefore, the soil diagnosis result for the region where the growth state is not relatively good can be reliably obtained, and soil making or grafting can be efficiently and efficiently carried out on the basis of the soil diagnosis result. Thus, the yield and quality of the crop in the next cycle can be expected to improve.

BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a diagram showing a method for detecting a distribution of a growing state of a crop in a packaging in a growth distribution detection step included in a soil diagnosis method according to an embodiment of the present invention, FIG.
Fig. 2 is a diagram schematically showing a distribution image obtained by converting the captured image obtained in Fig. 1; Fig.
Fig. 3 is a diagram showing an example in which a plurality of fish trough points are determined based on a growth distribution image. Fig.
4 is a block diagram showing a time series of steps included in the soil diagnosis method according to an embodiment of the present invention.
5 is a block diagram showing a time series of processes included in the soil condition improvement method according to an embodiment of the present invention.
6 is a diagram showing an example of a top-up fertilizer map.
Fig. 7 (a) is a diagram showing an example of the distribution of nitrogen absorption amount in packaging in the case where grains are added and conventionally carried out by a conventional method. Fig. 7 (b) is a diagram showing an example of the distribution of the nitrogen absorption amount in the packaging in the case where various maps are generated by utilizing the image pickup result by the image pickup means, and grafting and extrusion are carried out.

Next, an embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a schematic diagram showing a multi-copter 10 for detecting distribution of the growth state of a crop 2 in a package 1 in a growth distribution detection step included in a soil diagnosis method according to an embodiment of the present invention. Fig. 2 is a side view showing a state in which the package 1 is picked up from above. Fig.

The multi-copter (air vehicle) 10 shown in Fig. 1 is used for imaging (photographing) the package 1 in the sky in the soil diagnosis method of the present embodiment. The multi-copter (10) mounts a multispectral camera (imaging means) (20).

The multi-copter 10 is configured as an unmanned multi-copter in which a plurality of (for example, six) propellers (rotors) 11 are mounted, and remote control by radio is possible. Further, the multi-copter 10 is provided with a driving source (for example, an electric motor) for driving the propeller 11, thereby allowing the aircraft to fly.

The multispectral camera 20 is configured as a digital camera capable of simultaneously capturing images of, for example, two bands (visible red light and near infrared light). The multispectral camera 20 is attached to the lower portion of the multi-copter 10 with the lens facing downward, and an image can be obtained by picking up the package 1 in a planar manner in the air.

The multi-copter 10 is provided with a positioning device such as a GPS (not shown). When the package 1 is picked up by the multispectral camera 20, the position of the gas can be stored have. This makes it possible to easily grasp the correspondence relationship between the image captured by the multi-copter 10 and the position of the package 1.

In the soil diagnosis method of the present embodiment, the image of the package 1 obtained by imaging with the multispectral camera 20 is converted into a distribution image (NDVI image in this embodiment) indicating the deviation of the growth state of the crop 2, Is carried out.

The NDVI is publicly known, but will be briefly described below. It is generally known that green leaf of a plant absorbs light of a wavelength in the region of visible red light and strongly reflects light of a wavelength in the region of near infrared light. As the crop grows, the green leaf becomes darker, absorbs more visible red light, and more strongly reflects near infrared light. If the crop does not grow very much, the green leaf becomes thin, does not absorb the visible red light much, and reflects the near infrared light weakly. NDVI is the Normalized Difference Vegetation Index (NDVI) using this.

Further, the value of NDVI is calculated by the equation NDVI = (IR - R) / (IR + R). Here, R is an observed value of visible red light, and IR is an observed value of near infrared light. The value of NVDI is normalized to take a value of -1 to 1, and an NDVI image (growth distribution image) can be generated by associating this value with a color such as red to blue. That is, in this NDVI image, the closer to the good growth state of the crop 2, the closer to the red color, and the closer to the poor growth state of the crop 2, the closer the color to blue. By referring to the color of the NDVI image, the deviation of the growth state of the crop 2 in the package 1 can be known.

The user flew the multi-copter 10 over the package 1 to be subjected to the soil diagnosis and picks up the package 1 in the sky by the multispectral camera 20 Imaging step). Images of two bands (visible red light and near infrared light) obtained by imaging are stored in a removable external memory attached to the multispectral camera 20, respectively. The image stored in the removable external memory is read into an appropriate computer for image processing and converted into an NVDI image by appropriately processing using the above calculation formula (growth distribution image forming step in the growth distribution detection step).

In the soil diagnosis method of the present embodiment, a plurality of cloudy points for sampling the soil based on the NVDI image are determined (cloudy point determination step). The plurality of glue points are preferably arranged such that the growth state of the crop (2) includes a relatively poor area, preferably the soil condition of the soil condition in the area where the growth condition of the crop (2) is not good, , And the NVDI image is visually observed by the user. 3 is a diagram showing an example in which a plurality of grid points P1 to P5 are determined based on an NVDI image. In addition, in the example shown in Fig. 3, four points included in the region where the growth state of the crop 2 is relatively poor are defined as the glue points P1 to P4, and the growth state of the crop 2 is And one point included in a relatively good area is defined as a glue point P5. This glue point P5 is used to collect a soil sample to be compared (more specifically, for obtaining a target value of the soil modification).

After the cropping points P1 to P5 have been determined and the crop 2 has grown and finished in the package 1, the user can select the soil at points P1 to P5 in the package 1, The soil is cut at a predetermined depth by using a known boring stick or the like, and this is used as a soil sample. At that time, at least one of the measurement of soil depth by a boring stick and the measurement of soil hardness by a soil hardness meter or the like is carried out, and a part of the soil physical condition of the soil point is recorded.

Subsequently, the user can obtain, for the soil samples taken from each of the cloud points P1 to P5, the soil samples obtained from the above, such as, for example, dry soil mass, dry soil volume, In addition to physical properties other than soil physical properties, it is necessary to measure the pH, phosphate absorption coefficient, and available free iron oxide, free iron oxide, The contents of the permissible lacquer acid, the permissible tauric acid, the displacable calcium, and the substituted lime are measured. As a result, soil diagnosis is carried out (soil diagnosis process).

The soil diagnosis can be carried out by any known method as long as it can grasp at least one of (preferably both) the soil nutrient condition and the soil physical condition of each soil sample. That is, diagnosis may be made using a commercially available soil analysis kit or by using a high-precision measuring apparatus (for example, an ion chromatograph) used in a laboratory.

Thus, the result of the soil diagnosis at the cloud point P1 included in the region where the growth state of the crop 2 is not relatively good can be obtained. The result of this soil diagnosis can be utilized for the subsequent soil making and grazing (soil making process).

Hereinafter, this will be described in detail. For example, if the drainage is diagnosed as poor and the pH is low as a result of the soil diagnosis of the soil point P1, the area containing the soil point P1 may be subjected to a light crushing using a subsoiler, Input can be planned as subsequent soil-making. By carrying out this plan (the soil making process), the area including the cloud point P1 of the package 1 is subjected to the light crushing by the sub-soiler to crush the impervious layer, can do. In addition, by adding the lime material, the pH can be increased.

For example, if the soil layer is found to be shallow and the pH is high as a result of the soil diagnosis at the glue point P2, the area containing the glue point P2 may be deeply cultured using the plow, And the use of fertilizers near the acidic side can be planned as soil preparation and grafting thereafter. By executing this plan (the soil making process), the area containing the cloud point P2 of the package 1 is subjected to carding by the plow to deepen the planting, The root zone can be enlarged. In addition, the pH can be lowered by using a fertilizer near the acidic side.

In the case where the soil is diagnosed as having a high soil density (high density) and a high electric conductivity as a result of the soil diagnosis at the cloud point P3, for example, The use of fertilizer that does not increase the concentration of crust and crushing of soil using soiller can be planned as the subsequent soil making and grazing. By carrying out this plan (soil making process), the area including the cloud point P3 of the package 1 is subjected to lightning crushing by the plasticizer to lower the groundwater level, find the lowered groundwater, The roots of the crop (2) planted in the cycle can be made to extend deeply. By using a fertilizer which does not increase the salt concentration, the electric conductivity can be lowered. Alternatively, in the next cycle, the crop 2 may not be planted in (the peripheral region of at least the coal point P3 of) the packaging 1, but instead the cleaning crop (in this embodiment, the plant preferably absorbing the salt) You can also plan to plant. It is possible to lower the concentration of the salt in the peripheral region of the cloud point P3 by providing a period for planting the cleaning crop and to adjust the salt concentration suitable for the cultivation of the crop 2 in the subsequent cycle.

Also, for example, if the soil diagnosis at the chrome point (P4) indicates that the soil is hard and the corrosion content is low (due to the low organic matter), the area containing the chrome point (P4) , The mixing of organics with plows and the application of a few more composts can be planned as subsequent soil preparation and grafting. By carrying out this plan (soil making process), organic matter such as green foliage is mixed with the area containing the cloud point P4 of the package 1, and the activation of microorganisms decomposing the organic matter So as to form a monolayer. As a result, the soil is expanded, and water permeability and drainage properties can be improved.

Further, when planning the subsequent soil making or grazing based on the result of the soil diagnosis in the soil diagnosis process as described above, it is possible to prevent the soil from being dispersed in the soil at the grain boundary (in this embodiment, (P5)) is set as a target value, and a plan for making a local soil or grazing in a region where the growth condition of the crop was not good (the hatching region shown in Fig. 3) You can. As a result, it is possible to expect that the growth state of the crop 2 in the next cycle is uniform over the entire area of the package 1, and further, the yield and quality of the crop 2 can be expected to be improved.

As described above, in the present embodiment, the soil diagnosis is carried out by a soil diagnosis method including a growth distribution detection step, a cloud point determination step, and a soil diagnosis step (see FIG. 4). In the growth distribution detecting step, the distribution of the growth state of the crop 2 in the package 1 is detected. In the cloud point determination step, the cloud point (P1 to P4) included in the region where the growth state is not relatively good is included based on the detection result (NVDI image in this embodiment) obtained in the above growth rate distribution detection step One or more cloud points P1 to P5 are determined. In the soil diagnosis process, at least one of the soil nutrient condition or the soil physical condition situation in each of the cloud point (P1 to P5) is diagnosed based on the soil sample collected at the cloud point (P1 to P5).

As a result, soil diagnosis results can be obtained at the tillage points (P1 to P4) in which the growth state of the crop (2) is not relatively good, so that the soil can be utilized for the subsequent soil making and grazing. In other words, the soil diagnosis can be performed mainly on the area where the growth state of the crop 2 in the package 1 is not good, and the subsequent soil making, grazing, etc. can be performed using the result .

In the soil diagnosis method of the present embodiment, the growth distribution detection step includes an imaging step and a growth distribution image generation step. In the imaging step, the growth state of the crop 2 is picked up by the image pickup means (multispectral camera 20). In the above-mentioned growth distribution image generation step, an image obtained by imaging by the image pickup means (multispectral camera 20) is converted into a distribution distribution image (NDVI image) representing the deviation of the growth state of the crop 2.

Thereby, the distribution of the growth state of the crop 2 in the package 1 can be grasped more accurately by converting the image obtained by the image pickup means (multispectral camera 20) into the growth distribution image (NDVI image) Therefore, the soil diagnosis result for the region where the growth state of the crop 2 is relatively poor can be reliably obtained.

In the soil diagnosis method of the present embodiment, the imaging means is a multispectral camera 20. The above-mentioned growth distribution image is an NDVI image showing the distribution of the normalization difference vegetation index.

By referring to the NDVI image representing the distribution of the normalized difference vegetation index, the distribution of the growth state of the crop 2 in the package 1 can be grasped accurately, and the region where the necessity of performing the soil diagnosis is appropriately Can be determined. In addition, it becomes possible to realize the soil diagnosis efficiently and centrally with respect to the necessary points.

In the soil diagnosis method of the present embodiment, the multispectral camera 20 is mounted on a multi-copter (flying object) 10.

As a result, the growth state of the crop 2 in the package 1 in a plane in the air can be picked up, and imaging with high accuracy with less distortion can be performed. Therefore, the distribution of the growth state of the crop 2 in the package 1 can be more accurately obtained, and the area where the necessity of soil diagnosis is high can be grasped precisely. It is possible to realize an efficient soil diagnosis.

Further, in the present embodiment, it is possible to improve the soil condition in the package 1 (soil condition) by the soil condition improvement method including the growth distribution detection step, the cloud point determination step, the soil diagnosis step, Improvement) is performed. In the growth distribution detecting step, the distribution of the growth state of the crop 2 in the package 1 is detected. In the determination of the cloudy point, on the basis of the detection results obtained in the above-mentioned growth rate distribution step, one or more cloudy points (P1 to P4) including cloudy points (P1 to P4) included in a relatively non- (P1 to P5). In the soil diagnosis process, the soil nutrient situation (P 1 to P 5) at each of the cloud points P1 to P5 is calculated based on the soil samples collected at the cloud point P1 to P5 after the crop 2 in the package 1 is cut- Or at least one of the soil physical conditions. In the soil making process, at least one of the soil making or grazing of the package 1 is carried out based on the diagnosis result obtained in the soil diagnosis process before the crop 2 of the next cycle is planted in the package 1 .

This makes it possible to obtain the soil diagnosis results at the cloud point P1 to P4 included in the region where the growth state of the crop 2 was not relatively good and to make soil or graft based on the result of the diagnosis have. Thus, improvement in the yield and quality of the crop 2 in the next cycle can be expected.

In the soil condition improvement method of the present embodiment, the growth distribution detection step includes an imaging step and a growth distribution image generation step. In the imaging step, the growth state of the crop 2 is photographed by the multispectral camera (imaging means) 20. In the above-mentioned growth distribution image generation step, an image obtained by imaging with the multispectral camera (image pickup means) 20 is converted into an NDVI image (growth distribution image) showing a variation in the growth state of the crop.

Thereby, the distribution of the growth state of the crop 2 in the package 1 can be obtained more accurately by converting the image obtained by imaging with the multispectral camera (imaging means) 20 into the NDVI image (grown distribution image) . Therefore, the soil diagnosis result for the area in which the growth state of the crop 2 was not relatively good can be surely obtained, and the soil 1 can be efficiently or efficiently produced on the basis of the soil diagnosis result have. Thus, improvement in the yield and quality of the crop 2 in the next cycle can be expected.

In the soil condition improving method according to the present embodiment, in the soil making step, before the crop 2 of the next cycle is planted in the package 1, based on the diagnosis result obtained in the soil diagnosis step, 1) or at least a part of the area of the package 1 (specifically, the area where the growth condition of the crop 2 is not good).

Thereby, at least one of soil making or grazing can be performed only in a necessary area in the package 1 to prepare for planting of the crop 2 in the next cycle. Therefore, it is possible to realize efficient agriculture work.

While the preferred embodiments of the present invention have been described, the above configuration can be modified as follows, for example.

In the above embodiment, the multispectral camera 20 as the image pickup means is mounted on the unmanned multicoperator 10, but the present invention is not limited to this. For example, instead of this, an image capturing means may be mounted on an unmanned helicopter or an unmanned airplane. Alternatively, instead of these, an imaging means may be mounted on a satellite. Alternatively, the imaging means may be mounted on a manned vehicle.

A GNDV image represented by an image corresponding to the area of the package (1) with GNDVI (Green Normalized Difference Vegetation Index) as the same index as the NVDI may be used as the growth distribution image. Further, the value of GNDVI is calculated by the equation GDNVI = (IR - G) / (IR + G). Herein, IR is the observed value of the near-infrared light, and G is the observed value of the reflected light intensity of the green light.

In the above embodiment, the distribution of the growth state of the crop 2 in one package 1 is shown as an NVDI image in the growth distribution detecting step. However, the present invention is not limited to this. For example, Instead, a plurality of packages 1, 1, ... are superimposed and picked up in the air to show the distribution (deviation) of the growth state among the plurality of packages 1, 1, ... as NVDI images, It is also possible to determine a cloud point.

(Amount of fertilizer) to be further applied to the crop 2 may be calculated using the NVDI image generated in the above-described growth distribution detecting step.

In the above-described embodiment, four cluster points are defined from within a region (the region indicated by hatching in FIG. 3) in which the growth state of the crop 2 is not relatively good, but the number of cluster points may be larger Or at least.

In the above embodiment, in order to compare the soil nutrient status and the soil physical status of the soil samples (samples collected from the cloud points P1 to P4), the cloud point P5 ). However, sampling from a region where the growth state is relatively good is not essential.

The various working machines and materials used for soil making and grafting in the soil making process shown in the above embodiment are merely illustrative and may be used to make soil using other working machines or to use other materials, Other materials may be combined to perform the gravitation.

In the above embodiment, it has been stated that a distribution image representing the deviation of the growth state of the crop 2 in the package 1 is generated on the basis of the NDVI, which is an index indicating the degree of the green leaf of the crop 2 , The distribution of the growth state of the crop 2 can not always be obtained on the basis of only the degree of the green leaf of the crop 2. For example, in addition to or in place of the degree of foliage of the green leaf of the crop 2 (specifically, NVDI), the crop 2 in the package 1 obtained from the image pickup result of the multispectral camera 20 And the distribution of the growth state of the crop 2 is taken into consideration in consideration of the soil power of the package 1 estimated by calculating the product of the density distribution of the green leaf of the crop 2 and the density distribution .

In the above embodiment, the user has to visually observe the growth distribution image (NVDI image) in the clouding point determination step so as to appropriately determine the cloud point so as to include the region where the growth state of the crop 2 is relatively poor . However, the determination of the cloud point is not necessarily performed by visual observation. For example, instead of this, a plurality of blue points of the NVDI image or a portion close to the blue point of the NVDI image may be automatically determined by the computer do. In this case, it is preferable to determine the fishing ground point so that a plurality of fishing ground points are disposed at a distance from each other.

Hereinafter, a method for collectively managing the operation of the package 1 by using an image obtained by imaging with the multispectral camera 20 will be schematically described.

First, the package 1 is photographed in a vertical plane using the multispectral camera 20 mounted on the air vehicle.

Subsequently, based on the image obtained by imaging with the multispectral camera 20, a leaf color map (concretely, the NVDI image), which is an image showing the degree of livability of the crop 2 in the package 1, . Also, based on an image obtained by imaging with the multispectral camera 20, a hardness map, which is an image showing the density distribution of the crop 2 in the package 1, is acquired. Further, by taking the product of the leaf color value obtained in the leaf color map and the hard water (density) obtained in the hard water map, the intelligence map as the distribution of the nitrogen absorption amount in the package 1 is acquired. In this manner, a leaf color map, a water-quality map, and an intelligence map are generated based on the image obtained by imaging with the multispectral camera 20.

Based on the leaf color map obtained as described above, a fertilization map for determining the amount of the fertilizer to be additionally distributed to the crop 2 planted in the package 1 in this cycle is generated. In this map, the packaging surface of the package 1 is divided into an area (for example, a unit section) having an appropriate width, and the amount of the fertilizer to be distributed is determined for each divided area. An example of the dominant map is shown in Fig.

The information of this overflow map is inputted to a control unit of a flying object (for example, an unmanned helicopter) on which fertilizer is mounted, and fertilizer is applied to the package 1 in the overflow while varying the amount of fertilizer based on the overflow map . As a result, for example, fertilizer can be added to a region where the growth condition of the crop (2) is good in a small amount and to a region where the growth condition is poor. As a result, the yield and quality of the crop (2) in this cycle can be expected to be improved.

On the other hand, on the basis of the thus obtained intelligence map, a stomach map for determining the amount of fertilizer to be (preliminarily) sprinkled on the package 1 in the next cycle before planting the crop 2 is generated. Also in this pyramid map, the packaging surface of the package 1 is divided into an area (for example, a unit division) having an appropriate area, and the amount of the fertilizer to be distributed is determined for each divided area .

The fertilizer map is inputted to the fertilizer apparatus, and the fertilizer is preliminarily sprayed to the package 1 by the fertilizer apparatus while varying the amount of fertilizer based on the fertilizer map. As a result, for example, the package 1 can exhibit a small amount in a region with high intelligence and a lot in a region with low intelligence. As a result, improvement in the yield and quality of the crop 2 in the next cycle can be expected.

7A shows an example of the distribution of the nitrogen absorption amount in the package 1 in the case where the image obtained by imaging with the multispectral camera 20 is not utilized and the staring and drawing are performed by a conventional method . In this example, since the distribution of the intellectual power in the package 1 could not be grasped and the distribution of the growth conditions could not be grasped, the distribution of the growth conditions was uniformly applied to the entire area of the package 1, The distribution of nitrogen uptake was locally deviated.

Fig. 7 (b) is a graph showing the relationship between the nitrogen absorption amount in the packaging 1 when the images obtained by imaging with the multispectral camera 20 are used to generate the stomach map and the density map, As shown in Fig. In this example, after the distribution of the intellectual power in the package 1 was grasped, and after the distribution of the growth conditions was grasped, the distribution of the nitrogen uptake amount was suppressed.

1: Packing
2: Crops
10: Multicopter (flying)
20: Multispectral camera (image pickup means)
P1 ~ P5:

Claims (6)

A growth distribution detecting step of detecting a distribution of the growth state of the crop in the package,
A cloud point determination step of determining one cloud cloud point or a plurality of cloud point points including a cloud point included in a relatively poorly growing area based on the detection result obtained in the above step
And a soil diagnosis process for diagnosing at least one of a soil nutrient condition or a soil physical condition situation at each of said cloud point based on the soil sample collected at said cloud point. The method according to claim 1,
The growth distribution detecting step may include:
An image pickup step of picking up the growth state of the crop by the image pickup means,
And a growth distribution image generation step of converting the image obtained by imaging by the imaging means into a growth distribution image showing a deviation of a growing state of the crop. 3. The method of claim 2,
Wherein the imaging means is a multispectral camera,
Wherein the growth distribution image is an image showing a distribution of the normalized difference vegetation index. The method according to claim 2 or 3,
Wherein the imaging means is mounted on a flying object. A growth distribution detecting step of detecting a distribution of the growth state of the crop in the package,
A cloud point determination step of determining one cloud cloud point or a plurality of cloud point points including a cloud point included in a relatively poorly growing area based on the detection result obtained in the above step
A soil diagnosis process for diagnosing at least one of a soil nutrient condition or a soil physical condition situation at each of said fishing grounds based on a soil sample collected at said fishing ground after a crop in said package is cut off;
And a soil making process for carrying out at least one of soil making or grazing of the package based on the diagnosis result obtained in the soil diagnosis process before the crop of the next cycle is planted in the package. Way. 6. The method of claim 5,
The growth distribution detecting step may include:
An image pickup step of picking up the growth state of the crop by the image pickup means,
And a growth distribution image generation step of converting the image obtained by imaging by the imaging means into a growth distribution image showing a deviation of a growing state of the crop.

Images