Method, apparatus and processor for determining crop leaf area index

Abstract

An embodiment of the present application provides a method, device and processor for determining the leaf area index of crops. The method includes: obtaining three-dimensional point cloud data of a to-be-measured area in a planting area where the crops are located; selecting a simulated observation point in the to-be-measured area and determining a simulated observation area; obtaining all the three-dimensional point cloud data of the simulated observation area and performing preprocessing to divide a plurality of grids; determining voxel data of the crops corresponding to each grid according to the three-dimensional point cloud data included in each grid; controlling an image acquisition device carried on a drone device to scan the simulated observation area at a plurality of observation angles at the simulated observation point to obtain target voxel data of a target grid of the simulated observation area at each observation angle; for each observation angle, determining a gap ratio between the target voxel data corresponding to the observation angle; and determining the leaf area index of the crops in the to-be-measured area according to the gap ratio, so as to greatly improve the efficiency and accuracy of determining the leaf area index.

Description

Technical Field

[0001] The present application relates to the field of agriculture, and particularly to a method, apparatus, storage medium and processor for determining the leaf area index of crops. Background Art

[0002] Currently, traditional leaf area index is mainly measured by using leaf surface scanning equipment and optical equipment. Among them, when obtaining the leaf area index through the page scanning equipment, it is necessary to remove all the leaves of the crops, which consumes a large amount of labor costs and is easy to damage the farmland. When determining the leaf area index through optical equipment, it is generally determined by measuring the light penetration intensity of the bottom or upper canopy of the crops. Among them, common optical equipment includes canopy analyzers and fisheye cameras, etc. However, when using this method to determine the leaf area index of crops, it is necessary for operators to carry equipment to conduct on-site measurements in the planting area, which is difficult to avoid manual damage to the crops in the planting area, reducing the accuracy of the determined leaf area index. The labor, time and equipment usage costs required are relatively large, and it is difficult to improve the efficiency of determining the leaf area index. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, apparatus, storage medium and processor for determining the leaf area index of crops.

[0004] To achieve the above purpose, the first aspect of the present application provides a method for determining the leaf area index of crops, including:

[0005] Obtaining three-dimensional point cloud data of a to-be-measured area in a planting area where the crops are located;

[0006] Selecting a simulated observation point in the to-be-measured area and determining a simulated observation area where the simulated observation point is located;

[0007] Obtaining all the three-dimensional point cloud data of the simulated observation area and performing preprocessing to divide all the three-dimensional point cloud data of the simulated observation area into multiple grids;

[0008] Determining voxel data of the crops corresponding to each grid according to the three-dimensional point cloud data included in each grid;

[0009] Controlling an image acquisition device carried on a drone device to scan the simulated observation area at multiple observation angles at the simulated observation point to obtain a target grid of the simulated observation area and target voxel data corresponding to the target grid at each observation angle;

[0010] For each observation angle, determining the gap ratio between the target voxel data corresponding to the observation angle;

[0011] Determining the leaf area index of the crops in the to-be-measured area according to the gap ratio.

[0012] In an embodiment of the present application, determining the leaf area index of crops in a region to be measured according to the gap ratio includes: selecting a plurality of specific observation angles from all the observation angles; sorting the plurality of specific observation angles in ascending order, and determining a plurality of observation angle rings according to the angle intervals formed by any two adjacent specific observation angles; for any one observation angle ring, determining the total gap ratio of the observation angle ring according to the gap ratios of all the observation angles included in the observation angle ring; and performing a weighted sum of the total gap ratios of the plurality of observation angle rings to determine the leaf area index.

[0013] In an embodiment of the present application, the leaf area index is determined by formula (1):

[0014]

[0015] where LAI refers to the leaf area index, i refers to the i-th observation angle ring, n refers to the total number of observation angle rings, θ i refers to the observation angle included in the i-th observation angle ring, and P(θ i ) refers to the total gap ratio of the i-th observation angle ring, and w i refers to the weight of the i-th observation angle ring.

[0016] In an embodiment of the present application, the observation angle rings are sorted according to the interval size, and the weights of the first M observation angle rings sorted in front are all greater than the weights of the observation angle rings sorted behind, where M is a natural number, and M is determined according to the product of the number of observation angle rings and a preset percentage.

[0017] In an embodiment of the present application, the method further includes: after determining the leaf area index of the crops in the region to be measured according to the gap ratio, determining the growth stage of the crops; determining the aggregation index corresponding to the growth stage; and determining the true leaf area index of the crops according to the aggregation index and the leaf area index.

[0018] In an embodiment of the present application, selecting a simulated observation point in the region to be measured and determining the simulated observation region where the simulated observation point is located includes: determining the canopy region of the crops in the region to be measured, where the canopy region refers to a planar region located above the region to be measured, spaced from the canopy of the crops by a first value and having an area equal to that of the region to be measured; determining the center point of the canopy region as the simulated observation point; and determining the circular region formed with the projection point of the simulated observation point in the region to be measured as the origin and a radius of a second value as the simulated observation region.

[0019] In an embodiment of the present application, obtaining the three-dimensional point cloud data of the area to be measured in the planting area where the crop is located includes: controlling the drone device to fly along a preset path and at a preset height, and controlling the image acquisition device to perform image acquisition operations at a preset inclination angle and a preset resolution to obtain the area image of the area to be measured; generating the three-dimensional point cloud data for the area to be measured according to the area image.

[0020] In an embodiment of the present application, the method further includes: sending the leaf area index and / or the true leaf area index to a display device for display.

[0021] The second aspect of the present application provides a machine-readable storage medium, on which instructions are stored, and when the instructions are executed by a processor, the processor is configured to execute the above-mentioned method for determining the leaf area index of crops.

[0022] The third aspect of the present application provides a processor configured to execute the above-mentioned method for determining the leaf area index of crops.

[0023] The fourth aspect of the present application provides a device for determining the leaf area index of crops, including the above-mentioned processor.

[0024] Through the above technical solutions, it is possible to obtain the three-dimensional point cloud data of the area to be measured of the crop, construct the three-dimensional scene of the area to be measured, and scan the simulated observation area at multiple observation angles in the constructed three-dimensional scene. Determine the gap ratio at each observation angle according to the target voxel data corresponding to the target grid at multiple observation angles, and thus determine the leaf area index of the crop in the area to be measured according to the gap ratio, which can greatly reduce the labor cost and time cost, and there is no need for manual carrying of collection equipment into the planting area to avoid damaging the crops in the planting area, and greatly improve the efficiency and accuracy of determining the leaf area index.

[0025] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. They are used together with the following specific implementation to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the drawings:

[0027] Figure 1 Schematically shows a flowchart of a method for determining the leaf area index of crops according to an embodiment of the present application;

[0028] Figure 2 Schematically shows the internal structure diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the specific embodiments described herein are only for the purpose of illustration and explanation of the embodiments of the present application, and are not intended to limit the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0030] Figure 1 Schematically shown is a flowchart of a method for determining the crop leaf area index according to an embodiment of the present application. As Figure 1 shown, in an embodiment of the present application, a method for determining the crop leaf area index is provided, including the following steps:

[0031] Step 101, obtaining three-dimensional point cloud data of a to-be-measured area in a planting area where the crop is located.

[0032] Step 102, selecting a simulated observation point in the to-be-measured area and determining a simulated observation area where the simulated observation point is located.

[0033] Step 103, obtaining all the three-dimensional point cloud data of the simulated observation area and performing preprocessing to divide all the three-dimensional point cloud data of the simulated observation area into multiple grids.

[0034] Step 104, determining voxel data of the crop corresponding to each grid according to the three-dimensional point cloud data included in each grid.

[0035] Step 105, controlling an image acquisition device carried on a drone device to scan the simulated observation area at multiple observation angles at the simulated observation point to obtain a target grid of the simulated observation area and target voxel data corresponding to the target grid at each observation angle.

[0036] Step 106, for each observation angle, determining a gap ratio between the target voxel data corresponding to the observation angle.

[0037] Step 107, determining the leaf area index of the crop in the to-be-measured area according to the gap ratio.

[0038] Crops may refer to various plants cultivated in agriculture. For example, crops may refer to field crops such as rice, wheat, and corn. Crops may be planted in a planting area suitable for their growth. The planting area may refer to farmland. The leaf area index may refer to the ratio of the total leaf area of crops in a certain area to the area occupied by the area. When determining the crop leaf area index, first, the processor may first obtain the three-dimensional point cloud data of the area to be measured of the crop in the planting area. Among them, the area to be measured may refer to multiple sub-areas included in the planting area.

[0039] The processor can further select a simulated observation point in the area to be measured, and can determine the simulated observation area where the simulated observation point is located. The processor can obtain all three-dimensional point cloud data of the simulated observation area and can pre-process all the three-dimensional point cloud data to divide all the three-dimensional point cloud data of the simulated observation area into multiple grids. Among them, the pre-processing may include classification processing, cutting processing and resampling processing. The types of three-dimensional point cloud data may include multiple types, for example, it may include three-dimensional point cloud data of vegetation and three-dimensional point cloud data of soil. Thus, the classification processing can classify the three-dimensional point cloud data of vegetation and the three-dimensional point cloud data of soil. The resampling processing can keep the resolution of each grid consistent with the resolution of the image acquisition device. When multiple grids are determined, the processor can determine the voxel data of the crop corresponding to each grid based on the three-dimensional point cloud data included in each grid.

[0040] The processor may send a scanning signal to the drone device. The drone device may refer to a drone device whose cost is lower than a preset value. The drone device may be equipped with an image acquisition device. The image acquisition device may include a camera, a video camera, a camera, a recorder, and other devices with image acquisition functions. After the drone device receives the signal, the drone device may fly to a simulated observation point of the simulated observation area. Afterwards, the processor may further control the image acquisition device it carries to scan the simulated observation area at multiple observation angles at the simulated observation point to obtain a target grid of the simulated observation area at each observation angle and target voxel data corresponding to the target grid.

[0041] Among them, the observation angle may refer to the observation inclination angle of the image acquisition device. The observation angle may be preset in advance, or a signal for setting the observation angle may be sent to the image acquisition device before the processor controls the image acquisition device of the drone device to scan, so that the image acquisition device scans according to the observation angle set by the processor. For example, if the viewing angle of the image acquisition device is directly below the drone device, it can be determined that the observation angle of the image acquisition device at this time is 0°. The scanning method of the image acquisition device may include circular scanning. That is, the position of the drone device at the simulated observation point remains unchanged, and the image acquisition device carried by the drone device can perform a 360° rotation scan according to its observation angle.

[0042] When determining the target grid of the simulation area and the target voxel data corresponding to the target grid at each observation angle, the processor can determine the gap ratio between the target voxel data corresponding to each observation angle. When determining the gap ratio corresponding to each observation angle, the processor can further determine the leaf area index of the crops in the area to be measured.

[0043] Through the above technical solution, it is possible to obtain the three-dimensional point cloud data of the area to be measured of the crops, construct the three-dimensional scene of the area to be measured, and scan the simulation observation area at multiple observation angles in the constructed three-dimensional scene. Determine the gap ratio at each observation angle according to the target voxel data corresponding to the target grid at multiple observation angles, so as to determine the leaf area index of the crops in the area to be measured according to the gap ratio, which can greatly reduce the labor cost and time cost, and there is no need for manual carrying of acquisition equipment into the planting area, avoiding damage to the crops in the planting area, and greatly improving the efficiency and accuracy of determining the leaf area index.

[0044] In one embodiment, obtaining the three-dimensional point cloud data of the area to be measured in the planting area where the crops are located includes: controlling the drone device to fly according to a preset path and a preset height, and controlling the image acquisition device to perform image acquisition operations according to a preset inclination angle and a preset resolution to obtain the area image of the area to be measured; generating the three-dimensional point cloud data for the area to be measured according to the area image.

[0045] The processor can send an image acquisition signal to the drone device. When the drone device receives the image acquisition signal, the drone device can fly according to a preset path and a preset height, and can control the image acquisition device to perform image acquisition operations according to a preset inclination angle and a preset resolution to obtain the area image of the area to be measured. Among them, the preset path can be a flight route preset for the area to be measured in the planting area, and the double-flight overlap degree of this flight route can be set to 90%. In order to better generate the three-dimensional point cloud data according to the area image in the future, the image resolution of the area image needs to be set to 3 ± error value mm. Thus, the preset height of the drone device can be set to any value between 20m and 30m, and the preset resolution of the image acquisition device carried by it can be set to 4K. The preset inclination angle can be set to 60°. After obtaining the area image of the area to be measured, the processor can generate the three-dimensional point cloud data for the area to be measured according to the area image.

[0046] In one embodiment, a simulated observation point is selected in the area to be measured, and the simulated observation area where the simulated observation point is located is determined, including: determining the canopy area of the crop in the area to be measured, where the canopy area refers to a planar area located above the area to be measured, spaced from the canopy of the crop by a first value and having an area equal to that of the area to be measured; determining the center point of the canopy area as the simulated observation point; and determining the circular area formed with the projection point of the simulated observation point in the area to be measured as the origin and a radius of a second value as the simulated observation area.

[0047] The processor can determine the canopy area of the crop in the area to be measured. Among them, the canopy area refers to a planar area located above the area to be measured, spaced from the canopy of the crop by a first value and having an area equal to that of the area to be measured. The first value can be 1m. The processor can determine the center point of the canopy area as the simulated observation point. Further, the processor can determine the circular area formed with the projection point of the simulated observation point in the area to be measured as the origin and a radius of a second value as the simulated observation area. Among them, the second value can be 2.5m.

[0048] In one embodiment, determining the leaf area index of the crop in the area to be measured according to the gap ratio includes: selecting a plurality of specific observation angles from all the observation angles; sorting the plurality of specific observation angles in ascending order, and determining a plurality of observation angle rings according to the angle intervals formed by any two adjacent specific observation angles; for any one observation angle ring, determining the total gap ratio of the observation angle ring according to the gap ratios of all the observation angles included in the observation angle ring; and performing a weighted sum of the total gap ratios of the plurality of observation angle rings to determine the leaf area index.

[0049] The processor can select a plurality of specific observation angles from all the observation angles. Among them, the specific observation angles can be customized according to the actual situation. In the case of determining a plurality of characteristic observation angles, the processor can sort the plurality of specific observation angles in ascending order, and can determine a plurality of observation angle rings according to the angle intervals formed by any two adjacent characteristic observation angles. For example, if the specific observation angles include 0°, 15°, 30°, 45°, 60°, 75°, then it can be determined that the observation angle rings include (0°, 15°], (15°, 30°], (30°, 45°], (45°, 60°], (60°, 75°], where “(” refers to an open interval and “]” refers to a closed interval. For any one observation angle ring, the processor can determine the total gap ratio of the observation angle ring according to the gap ratios of all the observation angles included in the observation angle ring. Then, the processor can perform a weighted sum of the total gap ratios of the plurality of observation angle rings to determine the leaf area index. Specifically, the processor can first determine the weight of each observation angle ring, and then the processor can perform a weighted sum of the total gap ratios of each observation angle ring to determine the leaf area index.

[0050] In one embodiment, the observation angle rings are sorted according to the interval size, and the weights of the first M observation angle rings in the sorting are all greater than the weights of the observation angle rings behind in the sorting, where M is a natural number and M is determined according to the product of the number of observation angle rings and a preset percentage.

[0051] After determining the observation angle rings, the processor can sort the observation angle rings according to the interval size. Among them, the interval size can correspond to the observation angles included in each observation angle ring. For example, any observation angle in the observation angle ring of (0°, 15°] is less than any observation angle in the observation angle ring of (15°, 30°]. At this time, the observation angle ring of (0°, 15°] can be arranged before the observation angle ring of (15°, 30°]. After sorting all the observation angle rings, the processor can determine the number of observation angle rings in the front of the sorting according to the product of the number of observation angle rings and the preset percentage. For example, if there are 5 observation angle rings and the preset percentage is 0.4, then the number of observation angle rings in the front of the sorting can be 2.

[0052] After determining the number of observation angle rings in the front of the sorting, the weights of all the observation angle rings in the front of the sorting can be set to be greater than the weights of the observation angle rings behind in the sorting. For example, for the 5 observation angle rings of (0°, 15°], (15°, 30°], (30°, 45°], (45°, 60°], and (60°, 75°], if the number of observation angle rings in the front of the sorting is 2, then the processor can increase the weights of the two observation angle rings of (0°, 15°] and (15°, 30°] so that the weights of these two observation angle rings are both greater than the weights of the three observation angle rings of (30°, 45°], (45°, 60°], and (60°, 75°]. Specifically, the weight of the observation angle ring of (0°, 15°] can be set to 2.21, and the weight of the observation angle ring of (15°, 30°] can be set to 1.27. By increasing the weights of the preset number of observation angle rings in the front of the sorting, the accuracy of subsequently determining the leaf area index of the crop can be greatly improved.

[0053] In one embodiment, the leaf area index is determined by formula (1):

[0054]

[0055] where LAI refers to the leaf area index, i refers to the i-th observation angle ring, n refers to the total number of observation angle rings, θ i refers to the observation angle included in the i-th observation angle ring, P(θ i ) refers to the total gap ratio of the i-th observation angle ring, w iRefers to the weight of the i-th observation angle ring.

[0056] In one embodiment, the method further includes: after determining the leaf area index of the crop in the area to be measured according to the gap ratio, determining the growth stage of the crop; determining the aggregation index corresponding to the growth stage; and determining the true leaf area index of the crop according to the aggregation index and the leaf area index.

[0057] The aggregation index is an important parameter characterizing the aggregation characteristics of the spatial distribution of canopy elements. The leaf area index and the true leaf area index of the crop are affected by the aggregation index of the crop. Due to different crop types, the value range of the corresponding aggregation index is different, and the aggregation index of each growth stage will also be correspondingly different. For example, if the crop is rice, the value range of its aggregation index from the initial growth stage to the mature stage is 0.5 to 0.7. According to this value range, the aggregation index of each growth stage of rice can be determined. After determining the leaf area index of the crop, the processor can first determine the growth stage of the crop at this time. Then, the processor can further determine the aggregation index corresponding to the growth stage of the crop. After that, the processor can determine the true leaf area index of the crop according to the aggregation index and the leaf area index.

[0058] In one embodiment, the true leaf area index can be determined according to formula (2):

[0059]

[0060] Where Cl refers to the aggregation index of the growth stage of the crop, LAI refers to the true leaf area index, and LAI refers to the leaf area index.

[0061] In one embodiment, the method further includes: sending the leaf area index and / or the true leaf area index to a display device for display.

[0062] After determining the leaf area index and the true leaf area index of the crop, the processor can send the leaf area index and / or the true leaf area index to a display device for display. Among them, the display device can be installed in a field workstation. The display device can include devices with display functions such as a display screen.

[0063] Through the above technical solution, it is possible to obtain the three-dimensional point cloud data of the area to be measured of the crop, construct the three-dimensional scene of the area to be measured, and scan the simulated observation area at multiple observation angles in the constructed three-dimensional scene. According to the target voxel data corresponding to the target grid at multiple observation angles, the gap ratio at each observation angle is determined, and thus the leaf area index of the crop in the area to be measured is determined according to the gap ratio. This can greatly reduce the labor cost and time cost, without the need for manual carrying of collection equipment into the planting area, avoiding damage to the crops in the planting area, and greatly improving the efficiency and accuracy of determining the leaf area index. At the same time, by increasing the weight of the preset number of observation angle rings ranked in the front, the accuracy of subsequently determining the leaf area index of the crop can be greatly improved. According to the determined aggregation index of the crop growth period and the crop leaf area index, the true leaf area index of the crop is further determined, greatly improving the accuracy of determining the true leaf area index of the crop.

[0064] Figure 1 FIG. is a schematic flowchart of a method for determining the leaf area index of a crop in an embodiment. It should be understood that although Figure 1 the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in

[0065] include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0066] In one embodiment, a storage medium is provided, on which a program is stored, and when the program is executed by a processor, the above method for determining the leaf area index of a crop is implemented.

[0067] In one embodiment, a processor is provided, and the processor is used to run a program, wherein when the program runs, the above method for determining the leaf area index of a crop is executed.

[0068] In one embodiment, a device for determining the leaf area index of a crop is provided, including the above-mentioned processor. Figure 2As shown in the figure. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data such as leaf area index. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, it implements a method for determining the crop leaf area index.

[0069] Those skilled in the art can understand that Figure 2 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0070] An embodiment of this application provides a device. The device includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining three-dimensional point cloud data of a to-be-measured area in a planting area where a crop is located; selecting a simulated observation point in the to-be-measured area and determining a simulated observation area where the simulated observation point is located; obtaining all the three-dimensional point cloud data of the simulated observation area and performing preprocessing to divide all the three-dimensional point cloud data of the simulated observation area into multiple grids; determining voxel data of the crop corresponding to each grid according to the three-dimensional point cloud data included in each grid; controlling an image acquisition device carried on a drone device to scan the simulated observation area at multiple observation angles at the simulated observation point to obtain a target grid of the simulated observation area and target voxel data corresponding to the target grid at each observation angle; for each observation angle, determining a gap ratio between the target voxel data corresponding to the observation angle; determining the leaf area index of the crop in the to-be-measured area according to the gap ratio.

[0071] In one embodiment, determining the leaf area index of the crops in the area to be measured according to the gap ratio includes: selecting a plurality of specific observation angles from all the observation angles; sorting the plurality of specific observation angles in ascending order, and determining a plurality of observation angle rings according to the angle intervals formed by any two adjacent specific observation angles; for any one observation angle ring, determining the total gap ratio of the observation angle ring according to the gap ratios of all the observation angles included in the observation angle ring; performing a weighted sum of the total gap ratios of the plurality of observation angle rings to determine the leaf area index.

[0072] In one embodiment, the leaf area index is determined by formula (1):

[0073]

[0074] where LAI refers to the leaf area index, i refers to the i-th observation angle ring, n refers to the total number of observation angle rings, θ i refers to the observation angles included in the i-th observation angle ring, P(θ i ) refers to the total gap ratio of the i-th observation angle ring, and w i refers to the weight of the i-th observation angle ring.

[0075] In one embodiment, the observation angle rings are sorted according to the interval sizes, and the weights of the first M observation angle rings in the sorting are all greater than the weights of the observation angle rings behind, where M is a natural number and M is determined according to the product of the number of observation angle rings and a preset percentage.

[0076] In one embodiment, the method further includes: after determining the leaf area index of the crops in the area to be measured according to the gap ratio, determining the growth stage of the crops; determining the aggregation index corresponding to the growth stage; and determining the true leaf area index of the crops according to the aggregation index and the leaf area index.

[0077] In one embodiment, selecting a simulated observation point in the area to be measured and determining the simulated observation area where the simulated observation point is located includes: determining the canopy area of the crops in the area to be measured, where the canopy area is a planar area located above the area to be measured, spaced from the canopy of the crops by a first value and having an area equal to that of the area to be measured; determining the center point of the canopy area as the simulated observation point; and determining the circular area formed with the projection point of the simulated observation point in the area to be measured as the origin and a radius of a second value as the simulated observation area.

[0078] In one embodiment, obtaining the three-dimensional point cloud data of the area to be measured in the planting area where the crops are located includes: controlling the drone device to fly according to a preset path and a preset height, and controlling the image acquisition device to perform image acquisition operations according to a preset inclination angle and a preset resolution to obtain the area image of the area to be measured; and generating the three-dimensional point cloud data for the area to be measured according to the area image.

[0079] In one embodiment, the method further includes: sending the leaf area index and / or the true leaf area index to a display device for display.

[0080] The present application also provides a computer program product which, when executed on a data processing device, is adapted to execute a program initialized with method steps for determining a crop leaf area index.

[0081] Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0083] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0085] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0086] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0087] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0088] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0089] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining the leaf area index of crops, characterized in that, The method includes: Obtaining three-dimensional point cloud data of a to-be-measured area in a planting area where crops are located; Selecting simulated observation points in the to-be-measured area and determining a simulated observation area where the simulated observation points are located; Obtaining all three-dimensional point cloud data of the simulated observation area and performing preprocessing to divide all three-dimensional point cloud data of the simulated observation area into multiple grids; Determining voxel data of crops corresponding to each grid according to the three-dimensional point cloud data included in each grid; Controlling an image acquisition device carried on a drone device to scan the simulated observation area at multiple observation angles at the simulated observation points to obtain target grids of the simulated observation area and target voxel data corresponding to the target grids at each observation angle; For each observation angle, determining a gap ratio between the corresponding target voxel data; Determining the leaf area index of the crops in the to-be-measured area according to the gap ratio; 2. The method for determining the crop leaf area index according to claim 1, wherein The determining the leaf area index of the crops in the to-be-measured area according to the gap ratio includes: Selecting multiple specific observation angles from all the observation angles; Sorting the multiple specific observation angles in ascending order and determining multiple observation angle rings according to an angle interval formed by any two adjacent specific observation angles; For any one observation angle ring, determining a total gap ratio of the observation angle ring according to the gap ratios of all the observation angles included in the observation angle ring; Performing weighted summation on the total gap ratios of the multiple observation angle rings to determine the leaf area index; 3. The method for determining the crop leaf area index according to claim 2, characterized in that, The leaf area index is determined by formula (1): Among them, refers to the leaf area index, i refers to the i-th observation angle ring, and n refers to the total number of observation angle rings. refers to the observation angle included in the i-th observation angle ring. refers to the total gap ratio of the i-th observation angle ring. refers to the weight of the i-th observation angle ring.

4. The method for determining the crop leaf area index according to claim 2, characterized in that, Sorting the observation angle rings according to the interval sizes, and the weights of the first M observation angle rings in the sorting are all greater than the weights of the observation angle rings sorted later, where M is a natural number and M is determined according to the product of the number of the observation angle rings and a preset percentage; 5. The method for determining the crop leaf area index according to claim 1, characterized in that The method further includes: After determining the leaf area index of the crops in the to-be-measured area according to the gap ratio, determining the growth stage of the crops; Determining an aggregation index corresponding to the growth stage; Determining the true leaf area index of the crops according to the aggregation index and the leaf area index; 6. The method for determining the crop leaf area index according to claim 1, wherein The selecting the simulated observation points in the to-be-measured area and determining the simulated observation area where the simulated observation points are located includes: Determining a canopy area of the crops in the to-be-measured area, where the canopy area is a planar area located above the to-be-measured area, spaced from the canopy of the crops by a first value and having an area equal to that of the to-be-measured area; Determining the center point of the canopy area as the simulated observation point; Determining a circular area formed with the projection point of the simulated observation point in the to-be-measured area as the origin and a second value as the radius as the simulated observation area; 7. The method for determining the crop leaf area index according to claim 1, characterized in that, The obtaining the three-dimensional point cloud data of the to-be-measured area in the planting area where the crops are located includes: Controlling the drone device to fly according to a preset path and a preset height, and controlling the image acquisition device to perform image acquisition operations according to a preset inclination angle and a preset resolution to obtain a regional image of the to-be-measured area; Generating three-dimensional point cloud data for the to-be-measured area according to the regional image.

8. The method for determining the crop leaf area index according to any one of claims 1 to 7, characterized in that, The method further includes: sending the leaf area index and / or the true leaf area index to a display device for display.

9. A machine-readable storage medium having instructions stored thereon, characterized in that, When executed by a processor, the instruction causes the processor to be configured to execute the method for determining a crop leaf area index according to any one of claims 1 to 8.

10. An apparatus for determining the leaf area index of crops, characterized in that, The apparatus includes a processor configured to execute the method for determining a crop leaf area index according to any one of claims 1 to 8.

Images