CN112801535A - Orchard growth assessment method based on multi-source remote sensing data - Google Patents
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Abstract
The invention discloses an orchard growth assessment method based on multi-source remote sensing data, which comprises the steps of obtaining remote sensing image data, calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data, carrying out threshold division based on the vegetation index data to obtain growth information, assessing the orchard growth situation according to the growth information, replacing single point source data in the prior art with the multi-source remote sensing data, using freely-obtained land satellite data and sentinel second satellite images as basic data sources to replace artificial ground investigation and a large number of sensor arrangement in the traditional method to achieve the purpose of reducing labor cost and material cost, dividing ratio threshold into five grades according to a normal distribution principle, sequentially carrying out extreme difference, normal, slightly good and excellent five grades, and visually outputting in a five-color chart form, so that the manager can quickly have a rough understanding of the current growth conditions of the garden.
Description
Technical Field
The invention relates to the field of image recognition, in particular to an orchard growth evaluation method based on multi-source remote sensing data.
Background
When large-scale orchard management is carried out, time and labor are consumed for arranging sensors and networking, point source data are often used for replacing surface source data, the more far the sensors deviate, the worse the precision is, the more serious the deviation condition is, and the positions needing attention can not be accurately positioned in the geographic space.
Most of data used by the existing monitoring method utilizing the remote sensing technology is single images, and spatial resolution, temporal resolution and spectral resolution cannot be taken into consideration, and the three are restricted with each other to cause great limitation.
On the basis of the defects of the prior art, the orchard growth assessment method based on the multi-source remote sensing data is provided.
Disclosure of Invention
The invention provides an orchard growth evaluation method based on multi-source remote sensing data, and aims to solve the problems that in the prior art, single-source data is adopted, so that errors are large, a large amount of manpower and material resources are needed to lay sensors to obtain data, and the data cannot be updated in real time.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses an orchard growth evaluation method based on multi-source remote sensing data, which comprises the following steps:
acquiring remote sensing image data;
calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data;
and performing threshold division based on the vegetation index data to obtain growth information, and evaluating the orchard growth condition according to the growth information.
The method comprises the steps of obtaining remote sensing image data, conducting primary processing on different remote sensing image data to facilitate data processing, calculating the processed remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data, dividing the vegetation index data into 5 division areas according to normal distribution, presenting the division areas in a form of a five-color diagram, and evaluating the growth condition of an orchard according to the five-color diagram.
Preferably, the acquiring remote sensing image data includes:
acquiring sentinel second satellite data and land satellite data;
performing atmospheric correction on the sentinel second satellite data and the land satellite data;
and resampling the land satellite data to the same resolution as the sentinel second satellite data to obtain remote sensing image data.
Preferably, the calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data comprises:
calculating the remote sensing image data according to a vegetation index calculation formula, wherein the vegetation index calculation formula is as follows: NDVI ═ NIR-RED ÷ (NIR + RED), where NDVI denotes the vegetation index, NIR denotes the reflection value of the near infrared band, and RED denotes the reflection value of the infrared band;
and calculating the remote sensing image data in the T year according to the vegetation index calculation formula by months and calculating an average value to obtain the average value of the vegetation indexes in the previous month, wherein T is an integer larger than 1.
Preferably, threshold division is performed based on the vegetation index data to obtain growth information, and the orchard growth condition is evaluated according to the growth information, and the method comprises the following steps:
calculating the growth ratio of each month according to a growth ratio calculation formula, wherein the growth ratio is NDVI in the current month/NDVI in the past year;
setting four thresholds based on the normal distribution;
dividing the growth ratio according to four threshold values to obtain a five-color chart, and evaluating the growth condition of the orchard according to the five-color chart.
An orchard growth assessment device based on multi-source remote sensing data comprises:
an acquisition module: the remote sensing image acquisition device is used for acquiring remote sensing image data;
a calculation module: the remote sensing image data are calculated according to a normalized vegetation index algorithm to obtain vegetation index data;
an evaluation module: and the method is used for carrying out threshold division on the basis of the vegetation index data to obtain growth information and evaluating the orchard growth condition according to the growth information.
Preferably, the acquiring module specifically includes:
an acquisition subunit: the system is used for acquiring sentinel second satellite data and land satellite data;
a correction unit: the system is used for carrying out atmospheric correction on the sentinel second satellite data and the land satellite data;
a sampling unit: and the system is used for resampling the land satellite data to the same resolution as the sentinel second satellite data to obtain remote sensing image data.
Preferably, the calculation module specifically includes:
the first calculation unit: the vegetation index calculation formula is used for calculating the remote sensing image data according to the vegetation index calculation formula: NDVI ═ NIR-RED ÷ (NIR + RED), where NDVI denotes the vegetation index, NIR denotes the reflection value of the near infrared band, and RED denotes the reflection value of the infrared band;
a second calculation unit: and the vegetation index calculation module is used for calculating the remote sensing image data in the T year according to the vegetation index calculation formula by months and calculating an average value to obtain the average value of the vegetation indexes in the previous year and every month, wherein T is an integer greater than 1.
Preferably, the evaluation module comprises:
a third calculation unit: the system is used for calculating the growth ratio of each month according to a growth ratio calculation formula, wherein the growth ratio is NDVI in the month/NDVI in the past year;
threshold unit: for setting four thresholds based on the normal distribution;
dividing a unit: and the orchard growth ratio is divided according to four threshold values to obtain a five-color chart, and the orchard growth condition is evaluated according to the five-color chart.
An electronic device comprising a memory and a processor, the memory for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a method for orchard growth assessment based on multi-source remote sensing data as described in any of the above.
A computer-readable storage medium storing a computer program which, when executed, causes a computer to implement a method for orchard growth assessment based on multi-source remote sensing data as described in any one of the above.
The invention has the following beneficial effects:
1. multi-source remote sensing data is used for replacing single point source data in the prior art, land satellite data which can be obtained freely and sentinel second satellite images are used as basic data sources, artificial ground investigation and a large number of sensors in the traditional method are replaced, and the purposes of reducing labor cost and material cost are achieved;
2. the multi-source remote sensing data are combined, the space-time spectral resolution is improved, the land satellite data are relatively comprehensive, the problem that the quantity of the second-number data of the sentinel is insufficient is solved, and meanwhile, the problem that the data resolution of the second-number data of the sentinel is insufficient is solved;
3. according to the normal distribution principle, the ratio threshold is divided into five grades, namely five grades of extreme difference, poor, normal, slightly good and excellent in sequence, and the five grades are visually output in a five-color chart form, so that a manager can quickly and approximately know the current growth condition of the garden.
Drawings
FIG. 1 is a first flowchart of a method for orchard growth assessment based on multi-source remote sensing data according to an embodiment of the present invention;
FIG. 2 is a second flowchart of the orchard growth evaluation method based on multi-source remote sensing data according to the embodiment of the invention;
FIG. 3 is a third flowchart of the orchard growth evaluation method based on multi-source remote sensing data according to the embodiment of the invention;
FIG. 4 is a fourth flowchart of the orchard growth evaluation method based on multi-source remote sensing data according to the embodiment of the invention;
FIG. 5 is a flowchart for implementing a method for orchard growth assessment based on multi-source remote sensing data according to an embodiment of the present invention.
FIG. 6 is a schematic diagram of an orchard growth evaluation device based on multi-source remote sensing data according to an embodiment of the invention;
FIG. 7 is a schematic diagram of an obtaining module of an orchard growth evaluating device based on multi-source remote sensing data according to an embodiment of the invention;
FIG. 8 is a schematic diagram of a processing module of an orchard growth evaluation device based on multi-source remote sensing data according to an embodiment of the invention;
FIG. 9 is a schematic diagram of an output module of an orchard growth evaluation device based on multi-source remote sensing data according to an embodiment of the invention;
FIG. 10 is a flowchart illustrating an embodiment of an orchard growth assessment apparatus based on multi-source remote sensing data according to the present invention;
fig. 11 is a schematic diagram of an electronic device for implementing an orchard growth evaluation device based on multi-source remote sensing data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in FIG. 1, the orchard growth assessment method based on multi-source remote sensing data comprises the following steps:
s110, obtaining remote sensing image data;
s120, calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data;
s130, threshold division is carried out on the basis of the vegetation index data to obtain growth information, and the orchard growth condition is evaluated according to the growth information.
By acquiring multi-source remote sensing image data, calculating the remote sensing image data according to a normalized vegetation index algorithm, fully reflecting the growth and nutrition information of the orchard, then carrying out threshold division based on the vegetation index data to obtain the monthly growth condition of the orchard, fully evaluating the growth condition of the orchard according to the growth information, and carrying out targeted fertilization and maintenance.
Example 2
As shown in fig. 2, an orchard growth assessment method based on multi-source remote sensing data includes:
s210, acquiring sentinel second satellite data and land satellite data;
s220, performing atmospheric correction on the sentinel second satellite data and the land satellite data;
s230, resampling the land satellite data to the resolution which is the same as that of the sentinel second satellite data, and obtaining remote sensing image data;
s240, calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data;
and S250, carrying out threshold division based on the vegetation index data to obtain growth information, and evaluating the orchard growth condition according to the growth information.
As can be seen from embodiment 2, the multisource remote sensing data is used to replace single point source data in the prior art, and land satellite data and sentinel second satellite images which can be freely acquired are used as basic data sources to replace artificial ground survey and a large number of sensor layout in the conventional method, so that the purposes of reducing labor cost and material cost are achieved; the combination of multi-source remote sensing data improves the space-time spectral resolution, the land satellite data is relatively comprehensive, the problem of insufficient data quantity of the sentinel second is solved, and meanwhile, the problem of insufficient data resolution of the land satellite is solved due to high data resolution of the sentinel second.
Example 3
As shown in fig. 3, an orchard growth assessment method based on multi-source remote sensing data includes:
s310, obtaining remote sensing image data;
s320, calculating the remote sensing image data according to a vegetation index calculation formula, wherein the vegetation index calculation formula is as follows: NDVI ═ NIR-RED ÷ (NIR + RED), where NDVI denotes the vegetation index, NIR denotes the reflection value of the near infrared band, and RED denotes the reflection value of the infrared band;
s330, calculating the remote sensing image data in the T year according to the vegetation index calculation formula and calculating an average value to obtain the average value of the vegetation indexes in the previous year every month, wherein T is an integer larger than 1;
s340, threshold division is carried out on the basis of the vegetation index data to obtain growth information, and the orchard growth condition is evaluated according to the growth information.
In embodiment 3, the red light band and the near infrared band in the three types of basic remote sensing data are obtained, the corresponding reflection values are taken to participate in the calculation of the normalized vegetation index NDVI, the results are divided into 12 groups according to the month, the average value of the NDVI in each group is calculated, the average value image of the NDVI in each month in the past year is obtained, and the average value image participates in the calculation of the growth ratio.
Example 4
As shown in fig. 4, an orchard growth assessment method based on multi-source remote sensing data includes:
s410, obtaining remote sensing image data;
s420, calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data;
s430, calculating the growth ratio of each month according to a growth ratio calculation formula, wherein the growth ratio is NDVI in the current month/NDVI in the past year;
s440, setting four thresholds based on normal distribution;
s450, dividing the growth ratio according to four threshold values to obtain a five-color chart, and evaluating the growth condition of the orchard according to the five-color chart.
In embodiment 4, the average monthly NDVI value in the past decade is obtained to evaluate the growth condition of the current year, the average monthly NDVI value in the current year is compared with the average monthly NDVI value in the past, the growth condition of the current year in each month compared with the previous year is obtained, the growth ratio is divided into five segments, the five segments are presented in the form of a color map, and the fertilization and cultivation can be performed according to the color map.
Example 5
As shown in fig. 5, one specific embodiment may be:
downloading Sentinel second data serving as the most main basic data through an official website of the European and space administration, acquiring terrestrial satellite data through a geographic data space cloud for data before 16 years, downloading satellite image data of 2010-2019 in total, and dividing the satellite image data in the ten years into Landsat7, Landsat8 and Sentinel-2, wherein Landsat7 needs to additionally perform strip repair work due to sensor failure; due to the relation of data organization modes, the Sentinel-2 needs to additionally carry out wave band synthesis; the Landsat data is not subjected to radiometric calibration, so that the step needs to be carried out, and the Landsat data, the Landsat data and the Landsat data need to be subjected to atmospheric correction together, wherein the Landsat data is subjected to atmospheric correction by using a flash method; the Sentinel-2 is completed by utilizing a Sen2Cor tool provided by an official, after the steps are completed, the land satellite No. seven data Landsat7 and the land satellite No. eight data Landsat8 are resampled to 10 m resolution, the resolution is consistent with that of the Sentinel No. two data Sentinel-2, the downloaded original satellite image has larger breadth and is far beyond the garden range needing to be monitored, the calculation efficiency and the occupied storage space are considered, the clipping to the garden range is favorable for improving the data processing efficiency, then the red light wave band and the near infrared wave band in the three types of basic remote sensing data are obtained, the corresponding reflection value is taken to participate in the calculation of the normalized vegetation index NDVI, the result is divided into 12 groups according to the month, the average value of the NDVI of each group is obtained, the average value image of each month in the previous year is obtained, then the ratio value is obtained by the month data and the month data in the previous year, the long-potential five-color chart is output in a grading manner, the long potential ratio value is obtained, for the growth ratio, 4 thresholds are set according to normal distribution, the calculation result is divided into 5 grades (4 thresholds are 0.25, 0.5, 1 and 1.5 respectively), and after reclassification is carried out according to the thresholds, a five-color chart result is output and is visually output in a five-color chart form, so that a manager can quickly and roughly know the current growth condition of the garden.
Example 6
As shown in fig. 6, an orchard growth assessment device based on multi-source remote sensing data comprises:
the acquisition module 10: the remote sensing image acquisition device is used for acquiring remote sensing image data;
the calculation module 20: the remote sensing image data are calculated according to a normalized vegetation index algorithm to obtain vegetation index data;
the evaluation module 30: and the method is used for carrying out threshold division on the basis of the vegetation index data to obtain growth information and evaluating the orchard growth condition according to the growth information.
One embodiment of the above apparatus may be: the obtaining module 10 obtains multi-source remote sensing image data, the calculating module 20 calculates the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data, and the evaluating module 30 performs threshold division according to the vegetation index data obtained by the calculating module 20 to obtain a five-color diagram and evaluates the five-color diagram.
Example 7
As shown in fig. 7, an obtaining module 10 of the orchard growth evaluating device based on multi-source remote sensing data includes:
the acquisition subunit 12: the system is used for acquiring sentinel second satellite data and land satellite data;
the correction unit 14: the system is used for carrying out atmospheric correction on the sentinel second satellite data and the land satellite data;
the sampling unit 16: and the system is used for resampling the land satellite data to the same resolution as the sentinel second satellite data to obtain remote sensing image data.
One embodiment of the acquisition module 10 of the above apparatus may be: the acquiring subunit 12 acquires the sentinel second satellite data and the land satellite data, the correcting unit 14 performs atmospheric correction on the sentinel second satellite data and the land satellite data acquired by the acquiring subunit 12, and the sampling unit 16 re-samples the land satellite data corrected by the correcting unit 14 until the resolution is consistent with the sentinel second satellite data.
Example 8
As shown in fig. 8, a computing module 20 of the orchard growth evaluating device based on multi-source remote sensing data comprises:
the first calculation unit 22: the vegetation index calculation formula is used for calculating the remote sensing image data according to the vegetation index calculation formula: NDVI ═ NIR-RED ÷ (NIR + RED), where NDVI denotes the vegetation index, NIR denotes the reflection value of the near infrared band, and RED denotes the reflection value of the infrared band;
the second calculation unit 24: and the vegetation index calculation module is used for calculating the remote sensing image data in the T year according to the vegetation index calculation formula by months and calculating an average value to obtain the average value of the vegetation indexes in the previous year and every month, wherein T is an integer greater than 1.
One embodiment of the processing module 20 of the above apparatus may be: the first calculating unit 22 calculates the remote sensing image data according to a vegetation index calculation formula, wherein the reflection value of a near infrared band and the reflection value of an infrared band in the remote sensing image data are involved in calculation, the second calculating unit 24 calculates the NDVI value calculated by the first calculating unit 22 as a value of the past decade, sums the NDVI values according to the month and calculates an average value, and the data is calculated according to the month.
Example 9
As shown in fig. 9, an evaluation module 30 of the orchard growth evaluation device based on multi-source remote sensing data comprises:
third calculation unit 32: the system is used for calculating the growth ratio of each month according to a growth ratio calculation formula, wherein the growth ratio is NDVI in the month/NDVI in the past year;
threshold unit 34: for setting four thresholds based on the normal distribution;
the dividing unit 36: and the orchard growth ratio is divided according to four threshold values to obtain a five-color chart, and the orchard growth condition is evaluated according to the five-color chart.
One embodiment of the evaluation module 30 of the above apparatus may be: the third calculating unit 32 participates in the calculation of the obtained monthly NDVI average value according to the growth ratio calculation formula to obtain an average value of the current month except the month corresponding to the past year to obtain a growth ratio, the threshold unit 34 sets four thresholds based on normal distribution, the normal distribution is that the data at the head end and the tail end are lower or higher, the total number is less, the intermediate data are moderate, the total number is more, the division unit 36 divides the growth ratio according to the four thresholds to obtain a five-color chart, and the growth condition is evaluated according to the five-color chart.
Example 10
As shown in fig. 10, one specific implementation may be:
s1010, obtaining remote sensing image data;
s1020, calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data;
and S1030, performing threshold division based on the vegetation index data to obtain growth information, and evaluating the orchard growth condition according to the growth information.
Example 11
As shown in fig. 11, an electronic device includes a memory 1101 and a processor 1102, where the memory 1101 is configured to store one or more computer instructions, where the one or more computer instructions are executed by the processor 1102 to implement a method for orchard growth assessment based on multi-source remote sensing data as described above.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the electronic device described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
A computer-readable storage medium storing a computer program which, when executed, causes a computer to implement a method for orchard growth assessment based on multi-source remote sensing data as described above.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 1101 and executed by the processor 1102 to implement the present invention. One or more modules/units may be a series of computer program instruction segments capable of performing certain functions, the instruction segments being used to describe the execution of a computer program in a computer device.
The computer device may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The computer device may include, but is not limited to, a memory 1101, a processor 1102. Those skilled in the art will appreciate that the present embodiments are merely exemplary of a computing device and are not intended to limit the computing device, and may include more or fewer components, or some of the components may be combined, or different components, e.g., the computing device may also include input output devices, network access devices, buses, etc.
The processor 1102 may be a Central Processing Unit (CPU), other general purpose processor 1102, a digital signal processor 1102 (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general purpose processor 1102 may be a microprocessor 1102 or the processor 1102 may be any conventional processor 1102 or the like.
The storage 1101 may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 1101 may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (FlashCard), etc. provided on the computer device. Further, the memory 1101 may also include both an internal storage unit and an external storage device of the computer device. The memory 1101 is used to store computer programs and other programs and data required by the computer device. The memory 1101 may also be used to temporarily store data that has been output or is to be output.
The above description is only an embodiment of the present invention, but the technical features of the present invention are not limited thereto, and any changes or modifications within the technical field of the present invention by those skilled in the art are covered by the claims of the present invention.
Claims (10)
1. The orchard growth assessment method based on multi-source remote sensing data is characterized by comprising the following steps:
acquiring remote sensing image data;
calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data;
and performing threshold division based on the vegetation index data to obtain growth information, and evaluating the orchard growth condition according to the growth information.
2. The orchard growth assessment method based on multi-source remote sensing data according to claim 1, characterized in that the remote sensing image data acquisition comprises:
acquiring sentinel second satellite data and land satellite data;
performing atmospheric correction on the sentinel second satellite data and the land satellite data;
and resampling the land satellite data to the same resolution as the sentinel second satellite data to obtain remote sensing image data.
3. The method for orchard growth assessment based on multi-source remote sensing data according to claim 1, wherein calculating the remote sensing image data according to a normalized vegetation index algorithm to obtain vegetation index data comprises:
calculating the remote sensing image data according to a vegetation index calculation formula, wherein the vegetation index calculation formula is as follows: NDVI ═ NIR-RED ÷ (NIR + RED), where NDVI denotes the vegetation index, NIR denotes the reflection value of the near infrared band, and RED denotes the reflection value of the infrared band;
and calculating the remote sensing image data in the T year according to the vegetation index calculation formula by months and calculating an average value to obtain the average value of the vegetation indexes in the previous month, wherein T is an integer larger than 1.
4. The method for orchard growth assessment based on multi-source remote sensing data according to claim 3, wherein threshold division is performed based on the vegetation index data to obtain growth information, and orchard growth conditions are assessed according to the growth information, and the method comprises the following steps:
calculating the growth ratio of each month according to a growth ratio calculation formula, wherein the growth ratio is NDVI in the current month/NDVI in the past year;
setting four thresholds based on the normal distribution;
dividing the growth ratio according to four threshold values to obtain a five-color chart, and evaluating the growth condition of the orchard according to the five-color chart.
5. The utility model provides an orchard growth evaluation device based on multisource remote sensing data which characterized in that includes:
an acquisition module: the remote sensing image acquisition device is used for acquiring remote sensing image data;
a calculation module: the remote sensing image data are calculated according to a normalized vegetation index algorithm to obtain vegetation index data;
an evaluation module: and the method is used for carrying out threshold division on the basis of the vegetation index data to obtain growth information and evaluating the orchard growth condition according to the growth information.
6. The orchard growth assessment device based on multi-source remote sensing data according to claim 5, wherein the obtaining module specifically comprises:
an acquisition subunit: the system is used for acquiring sentinel second satellite data and land satellite data;
a correction unit: the system is used for carrying out atmospheric correction on the sentinel second satellite data and the land satellite data;
a sampling unit: and the system is used for resampling the land satellite data to the same resolution as the sentinel second satellite data to obtain remote sensing image data.
7. The orchard growth assessment device based on multi-source remote sensing data according to claim 5, wherein the calculation module specifically comprises:
the first calculation unit: the vegetation index calculation formula is used for calculating the remote sensing image data according to the vegetation index calculation formula: NDVI ═ NIR-RED ÷ (NIR + RED), where NDVI denotes the vegetation index, NIR denotes the reflection value of the near infrared band, and RED denotes the reflection value of the infrared band;
a second calculation unit: and the vegetation index calculation module is used for calculating the remote sensing image data in the T year according to the vegetation index calculation formula by months and calculating an average value to obtain the average value of the vegetation indexes in the previous year and every month, wherein T is an integer greater than 1.
8. The orchard growth assessment device based on multi-source remote sensing data according to claim 5, wherein the assessment module specifically comprises:
a third calculation unit: the system is used for calculating the growth ratio of each month according to a growth ratio calculation formula, wherein the growth ratio is NDVI in the month/NDVI in the past year;
threshold unit: for setting four thresholds based on the normal distribution;
dividing a unit: and the orchard growth ratio is divided according to four threshold values to obtain a five-color chart, and the orchard growth condition is evaluated according to the five-color chart.
9. An electronic device, comprising a memory and a processor, wherein the memory is used for storing one or more computer instructions, and the one or more computer instructions are executed by the processor to realize the orchard growth assessment method based on multi-source remote sensing data according to any one of claims 1-4.
10. A computer-readable storage medium storing a computer program, wherein the computer program is configured to enable a computer to execute the method for orchard growth assessment based on multi-source remote sensing data according to any one of claims 1 to 4.
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