A system and method for growing seedlings

Abstract

This specification provides a seedling raising system and method. The system includes: at least one seedling tray, a conveying mechanism, an image acquisition mechanism, and a processor. The seedling tray contains soil and seeds. The conveying mechanism conveys the seedling tray through the image acquisition mechanism to acquire an image containing the seedling tray. The processor is configured to: acquire the image and identify the boundary of the seedling tray in the image; construct a target region based on the boundary of the seedling tray; divide the target region into at least one sub-region based on preset rules; determine the seed distribution information and soil information in each sub-region; and determine whether the seedling raising condition of the seedling tray is qualified based on the seed distribution information and soil information.

Description

Technical Field

[0001] This manual relates to the field of plant seedling cultivation, and in particular to a seedling cultivation system and method. Background Technology

[0002] Cultivating high-quality seedlings is crucial for agricultural production. Currently, seedling cultivation is generally carried out manually, with seeds and soil being added to empty seedling trays sequentially via a manual assembly line. This is a typical labor-intensive operation with low efficiency and high labor demand.

[0003] Existing seedling raising devices often have many problems, such as uneven distribution of seeds and soil when placing them into the seedling trays, easy jamming, and low level of intelligence, making it impossible to judge whether the seedling raising condition of the seedling trays is qualified, requiring frequent manual intervention and maintenance.

[0004] Therefore, it is necessary to provide a seedling raising system and method that automates seedling raising and determines whether the seedling raising condition is qualified, so as to improve production efficiency and quality. Summary of the Invention

[0005] This specification provides a seedling raising system according to one or more embodiments. The seedling raising system includes: at least one seedling tray, a conveying mechanism, an image acquisition mechanism, and a processor. The seedling tray contains soil and seeds. The conveying mechanism conveys the seedling tray through the image acquisition mechanism to acquire an image containing the seedling tray. The processor is configured to: acquire the image and identify the boundary of the seedling tray in the image; construct a target region based on the boundary of the seedling tray; divide the target region into at least one sub-region based on preset rules; determine seed distribution information and soil information within each sub-region, the seed distribution information including seed distribution density and uniformity, and the soil information including subsoil thickness; and determine whether the seedling raising condition of the seedling tray is qualified based on the seed distribution information and the soil information.

[0006] This specification provides one or more embodiments of a seedling raising method, which is executed by a seedling raising system. The method includes: acquiring the image and identifying the boundary of the seedling tray in the image; constructing a target region based on the boundary of the seedling tray; dividing the target region into at least one sub-region based on preset rules; determining seed distribution information and soil information in each sub-region, wherein the seed distribution information includes the distribution density and uniformity of the seeds, and the soil information includes the subsoil thickness; and determining whether the seedling raising condition of the seedling tray is qualified based on the seed distribution information and the soil information.

[0007] This specification provides one or more embodiments of a seedling raising device, including a processor for executing a seedling raising method.

[0008] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes a seedling cultivation method. Attached Figure Description

[0009] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0010] Figure 1 These are schematic diagrams of the modules of a seedling system according to some embodiments of this specification;

[0011] Figure 2 This is an exemplary flowchart of a seedling raising method based on some embodiments of this specification;

[0012] Figure 3 These are exemplary schematic diagrams of the estimation model shown in some embodiments of this specification. Detailed Implementation

[0013] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0014] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0015] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0016] Flowcharts are used in this specification to illustrate the operations performed by the system based on embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0017] Figure 1 This is a schematic diagram of the modules of a seedling system according to some embodiments of this specification.

[0018] In some embodiments, such as Figure 1 As shown, the seedling system 100 may include at least one seedling tray 110, a conveying mechanism 120, an image acquisition mechanism 130, and a processor 140.

[0019] The seedling tray 110 is a carrier that provides an environment for seedling growth. In some embodiments, the seedling tray contains soil and seeds. The seedling tray 110 can be placed on a conveying mechanism.

[0020] The conveying mechanism 120 is a device for conveying seedling trays. The conveying mechanism 120 may include a conveyor belt, a track, etc. In some embodiments, the conveying mechanism is configured to convey the seedling trays past an image acquisition mechanism.

[0021] The image acquisition mechanism 130 is a device for acquiring image information of the seedling tray. In some embodiments, the image acquisition mechanism may be a camera. In some embodiments, the image acquisition mechanism may be installed above a section of the conveyor belt of the conveying mechanism to acquire images containing the seedling tray.

[0022] In some embodiments, the seedling system 100 may further include a processor 140 and associated storage devices. The processor 140 is configured to coordinate and manage information transmission and collaboration between various devices, and to provide control and management functions for the seedling system 100.

[0023] In some embodiments, the processor is configured to acquire an image and identify the boundaries of the seedling trays in the image, construct a target region based on the boundaries of the seedling trays; divide the target region into at least one sub-region based on preset rules; determine the seed distribution information and soil information within each sub-region; and determine whether the seedling condition of the seedling trays is qualified based on the seed distribution information and soil information.

[0024] In some embodiments, the seedling system 100 further includes a replanting mechanism 150.

[0025] The reseeding mechanism 150 is a device for reseeding seedling trays. In some embodiments, the reseeding mechanism 150 includes a reseeding tray, a piercing assembly, and a reseeding feed assembly. When the number of seeds in a sub-region is insufficient, causing the seedling tray to be unqualified, the processor can control the reseeding mechanism 150 to perform reseeding.

[0026] In some embodiments, the replanting mechanism includes a replanting tray, a piercing assembly, and a replanting feed assembly. The replanting tray has at least one replanting grid, the size and number of which correspond to a sub-region. A replaceable intercepting sheet is laid inside the replanting tray, covering all the replanting grids. The replanting mechanism 150 can be installed directly above a section of the conveyor belt of the conveying mechanism 120, so that when the seedling tray 110 reaches the replanting mechanism 150 via the conveying mechanism 120, the planar projection of the replanting grids can completely coincide with the seedling tray 110.

[0027] For further information about the above-mentioned device, please refer to [link / reference]. Figures 2-3 And related content.

[0028] It should be noted that the above description of the seedling system 100 and its modules is for ease of description only and should not be construed as limiting this specification to the scope of the illustrated embodiments. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles. In some embodiments, Figure 1 The seedling tray 110, conveying mechanism 120, image acquisition mechanism 130, processor 140, and reseeding mechanism 150 disclosed herein can be different modules within a single system, or a single module can implement the functions of two or more of the aforementioned modules. For example, each module can share a storage module, or each module can have its own separate storage module. Such variations are all within the scope of protection of this specification.

[0029] Figure 2 This is an exemplary flowchart of a seedling cultivation method based on some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by a processor.

[0030] Step 210: Acquire the image and identify the boundaries of the seedling trays in the image, and construct the target region based on the boundaries of the seedling trays.

[0031] The image is a picture captured by the image acquisition mechanism 130 pairs of seedling trays. In some embodiments, the processor can identify the boundaries of the seedling trays in the image using an edge detection algorithm, and take the area within the boundaries of the seedling trays as the target area. The edge detection algorithm includes, but is not limited to, grayscale detection algorithms, neural network models, etc.

[0032] Step 220: Divide the target area into at least one sub-region based on preset rules.

[0033] Preset rules are methods used to divide sub-regions. In some embodiments, preset rules can be determined through various methods. For example, the processor can receive a division instruction input by the user and perform division according to the instruction. Alternatively, the processor can divide the target region into grids, where the grid shape and size can be determined based on the actual application scenario and requirements.

[0034] Step 230: Determine the seed distribution information and soil information within each sub-region.

[0035] Seed distribution information can reflect the spatial arrangement characteristics of seeds within a sub-region. In some embodiments, seed distribution information includes seed distribution density and distribution uniformity.

[0036] Distribution uniformity reflects the evenness of seed distribution within a sub-region. In some embodiments, the more uniform the seed distribution within a sub-region, the better the distribution uniformity and the larger the value; conversely, the more concentrated the seed distribution, the worse the distribution uniformity and the smaller the value.

[0037] In some embodiments, the processor can determine the distribution uniformity in a variety of ways.

[0038] As an example only, the processor can calculate the distribution uniformity using the following formula (1):

[0039] (1)

[0040] Where U represents the uniformity of seed distribution, This represents the actual variance of the seed within the current sub-region. This represents the maximum variance. This represents the variance if all seeds within the current sub-region are distributed along the boundary of that sub-region. Actual variance. This represents the variance of all seeds within the current sub-region. The processor can further mesh the sub-region and calculate the seed density within each mesh, determining the variance of the seed density across all meshes as the variance of the seeds within that sub-region.

[0041] Soil information can reflect soil-related characteristics within a subregion. In some embodiments, soil information includes subsoil thickness.

[0042] Subsoil thickness is used to represent the thickness characteristics of the soil within a subregion. Subsoil thickness can include the average subsoil thickness of the subregion. Subsoil refers to the soil layer located beneath the seed. For more information on subsoil, please refer to [link to relevant documentation]. Figure 3 Related explanations.

[0043] In some embodiments, the processor can determine the subsoil thickness using various methods. For example, the subsoil thickness can be determined using depth camera ranging.

[0044] In some embodiments, the processor can identify the seed size in each sub-region and determine the average seed size in each sub-region; based on the average seed size, the actual seed size, and camera parameters, it can determine the subsoil thickness in each sub-region.

[0045] The average seed size refers to the average size of seeds within each sub-region of an image. In some embodiments, after placing substrate soil in an empty seedling tray and sowing seeds on the substrate soil, the processor can filter seeds within a sub-region of the image that do not overlap with other seeds to perform size statistics and obtain the average seed size of each sub-region.

[0046] In some embodiments, since seeds that are too dark in color are not easy to identify, technicians can pre-dye the seeds to an easily identifiable color before identifying the seed size, in order to increase the contrast between the seeds and the soil in the image, making it easier for the image acquisition agency to identify the seeds more accurately.

[0047] The actual size of a seed refers to its actual length and width. The actual size of a seed can be obtained through measurement or image recognition.

[0048] Camera parameters can reflect the types of parameters a camera can have. For example, camera parameters can include pixels, field of view, etc.

[0049] In some embodiments, since the closer a seed is to the camera, the larger its size in the image, and the farther away a seed is from the camera, the smaller its size, the processor can determine the seed size in the image for seeds at different distances from the camera using camera parameters and the actual seed size. Based on the actual seed size in the image, the processor can determine the distance of the seed from the camera and infer the subsoil thickness. For example, for the same camera, i.e., a camera with the same camera parameters, a larger seed size in the image indicates that the seed is closer to the camera, suggesting a thicker subsoil layer beneath the seed; a smaller seed size in the image indicates that the seed is farther from the camera, suggesting a thinner subsoil layer beneath the seed.

[0050] In some embodiments, the processor can determine the subsoil thickness for each region based on the average seed size, the actual seed size, and camera parameters using a first preset table. The first preset table includes the correspondence between different average seed sizes, actual seed sizes, camera parameters, and different subsoil thicknesses. The processor can construct the first preset table through simulation experiments.

[0051] In one embodiment of this specification, the subsoil thickness of each sub-region is determined based on the average seed size, the actual seed size, and camera parameters. This enables precise measurement of soil thickness, improves seed germination rate and seedling growth quality, and ultimately enhances the overall precision and quality of seedling cultivation.

[0052] Step 240: Based on seed distribution information and soil information, determine whether the seedling raising condition of the seedling tray is qualified.

[0053] In some embodiments, the processor can determine whether the seedling raising condition of the seedling tray is qualified through various methods. For example, the seedling raising condition of the seedling tray is qualified when the seed distribution information or soil information meets preset conditions. Or, for example, the seedling raising condition of the seedling tray is qualified when both the seed distribution information and soil information meet preset conditions. The preset conditions corresponding to the seed distribution information and soil information can be determined according to the actual application scenario and requirements. For example, the preset condition corresponding to the seed distribution information could be: the number of seeds in each sub-region is within a preset quantity range; the preset condition corresponding to the soil information could be: the average subsoil thickness in each sub-region is within a preset average range, etc.

[0054] In some embodiments, the processor determines whether the seed distribution information of each sub-region meets a first preset condition; in response to the existence of a sub-region that does not meet the first preset condition, the seedling raising condition of the seedling tray is unqualified; in response to each sub-region meeting the first preset condition, the processor determines the uniformity of the subsoil thickness of the target area based on soil information; determines whether the uniformity of the subsoil thickness meets a second preset condition; in response to the target area not meeting the second preset condition, the seedling raising condition of the seedling tray is unqualified; in response to the target area meeting the second preset condition, the seedling raising condition of the seedling tray is qualified.

[0055] The first preset condition is used to determine whether the seed distribution is uniform. The first preset condition can be determined according to the actual application scenario and requirements. In some embodiments, the first preset condition may be that the seed uniformity is greater than a first threshold. The first threshold may be preset based on prior experience.

[0056] In some embodiments, if there is a sub-region in the seedling tray that does not meet the first preset condition, then the seedling raising condition of the seedling tray is unqualified.

[0057] In some embodiments, for seedling trays that do not meet the first preset conditions, replanting can be performed by a replanting mechanism. After the replanting is completed, the processor can re-determine whether the first preset conditions are met.

[0058] In some embodiments, the processor may determine at least one target sub-region based on the seed distribution information; control the piercing component to pierce the interceptor corresponding to at least one target sub-region, so that the seeds in the target replanting cell fall into the corresponding sub-region; and control the replanting feeding component to replenish an appropriate amount of seeds into the target replanting cell and replace the interceptor.

[0059] The target sub-region refers to the sub-region where seeds need to be added. The processor can determine the target sub-region in various ways. For example, the processor can determine a sub-region where the number of seeds is less than a preset replanting threshold as the target sub-region. Alternatively, the processor can determine a sub-region selected by the user as the target sub-region. The number of seeds within the sub-region can be determined using methods such as image recognition.

[0060] A reseeding mechanism is a device used to supplement seeding in areas with insufficient seeds. In some embodiments, the reseeding mechanism includes a seed tray, a piercing assembly, and a seed feeding assembly. For more information on reseeding mechanisms, see [link to relevant documentation]. Figure 1 Related explanations.

[0061] In some embodiments, the replanting tray has a flat disc-shaped structure, and the size of the replanting tray corresponds to the size of the seedling tray.

[0062] In some embodiments, the replanting tray is provided with at least one replanting grid, the size and number of which correspond to the sub-region. For example, if the seedling tray is divided into 50×10 sub-regions, then the replanting tray is provided with 50×10 replanting grids.

[0063] In some embodiments, a replaceable interceptor sheet is placed inside the reseeding tray to cover the opening of the reseeding grid and seal the seeds inside. The interceptor sheet is made of a puncture-resistant film, such as plastic film or paper.

[0064] A puncture assembly is a device used to puncture the intercepting plate, such as a puncture needle or a row of nails. When reseeding is required, the puncture assembly can evenly puncture the intercepting plate, allowing the seeds in the target reseeding grid to fall evenly into the corresponding sub-area, thus completing the reseeding operation.

[0065] A reseeding feed assembly is a device for storing and transporting seeds. In some embodiments, the reseeding feed assembly includes a seed storage bin, a feeder, and an interceptor plate changer, etc.

[0066] In some embodiments, in response to the completion of the reseeding operation, the processor can control the reseeding feed assembly to replenish seeds into the target reseeding cell and replace the punctured interceptor.

[0067] In some embodiments of this specification, replanting unqualified sub-regions using a replanting mechanism can effectively avoid material waste, improve seedling success rate, and reduce seedling costs.

[0068] In some embodiments, in response to the seed distribution information of each sub-region satisfying a first preset condition, the processor determines the uniformity of the subsoil thickness in the target detection area based on the soil information.

[0069] Subsoil thickness uniformity reflects the degree of uniformity of soil thickness within a sub-region. In some embodiments, the closer the soil thickness at different locations within a sub-region, the better the subsoil thickness uniformity; the greater the difference in soil thickness at different locations within a sub-region, the worse the subsoil thickness uniformity.

[0070] In some embodiments, the processor divides the subregion into standard subregions, low-lying subregions, or raised subregions based on the average seed size of each region and by using region division conditions; and determines the uniformity of subsoil thickness based on the distribution and area ratio of low-lying and raised subregions within the target region.

[0071] Region segmentation criteria are the conditions used to classify sub-regions. In some embodiments, the processor can determine the category of a sub-region based on a standard size range. For example, a sub-region is classified as standard if its average seed size is within the standard size range; a sub-region is classified as low-lying if its average seed size is less than the lower limit of the standard size range; and a sub-region is classified as raised if its average seed size is greater than the upper limit of the standard size range. The standard size range can be [average seed size...]. (100-α)%, Average seed size [(100+α)%], where the value of α can be determined based on prior experience.

[0072] In some embodiments, the processor can determine the uniformity of subsoil thickness based on the distribution and area ratio of low-lying and raised sub-regions within the target area. For example, if the total area ratio of low-lying and raised sub-regions exceeds a preset ratio threshold, the seedling raising condition is deemed unqualified.

[0073] In some embodiments of this specification, based on the average seed size of each sub-region, the sub-region is precisely divided into standard sub-regions, low-lying sub-regions, or raised sub-regions using region division conditions. Furthermore, the uniformity of subsoil thickness is determined based on the distribution and area ratio of low-lying and raised sub-regions within the target area. This can effectively avoid problems such as low seed germination rate and uneven seedling growth caused by uneven soil thickness, ensuring the smooth progress of seedling cultivation.

[0074] The second preset condition is used to determine whether the soil layer thickness distribution is uniform. In some embodiments, the second preset condition can be set by the user.

[0075] In some embodiments, the second preset condition may also be that the uniformity of the subsoil thickness is greater than a second threshold. The second threshold may be preset based on prior experience.

[0076] In some embodiments, if the target area simultaneously meets the first preset condition and the second preset condition, the seedling raising condition of the seedling tray is qualified; otherwise, the seedling raising condition of the seedling tray is unqualified.

[0077] In some embodiments, the processor can determine the standard size of the seed in the image based on the installation location of the image acquisition mechanism; determine the subsoil thickness of the low-lying area and the raised area based on the actual size of the seed and the standard size; and determine the topsoil thickness of each sub-area after covering with topsoil based on the total soil layer thickness and the subsoil thickness of each sub-area.

[0078] Subsoil thickness refers to the thickness of the soil layer located beneath the seed.

[0079] The standard seed size is the seed size corresponding to a subsoil thickness of standard thickness. The standard subsoil thickness can be preset based on prior experience. In some embodiments, the processor can determine the standard seed size in the image when the subsoil thickness is standard, based on the installation position of the image acquisition mechanism and through spatial modeling or other methods.

[0080] In some embodiments, the processor can determine the subsoil thickness of the low-lying and high-lying areas using a second preset table. The second preset table includes the correspondence between different actual seed sizes, standard seed sizes, standard subsoil thicknesses, and different subsoil thicknesses. The processor determines the standard seed size corresponding to different subsoil thicknesses and establishes the second preset table through simulation experiments.

[0081] The total soil thickness refers to the overall thickness of the soil layer within the seedling tray. The total soil thickness is the sum of the bottom soil thickness and the top soil thickness. In some embodiments, the total soil thickness can be preset based on experience.

[0082] Topsoil thickness refers to the thickness of the soil layer above the seed. In some embodiments, the processor determines the topsoil thickness of each subregion after covering with topsoil, based on the total soil layer thickness and the subsoil thickness.

[0083] In some embodiments, the processor can estimate the germination rate and germination time in various ways. For example, the closer the topsoil thickness is to the recommended topsoil thickness, the higher the germination rate and the shorter the germination time. The greater the deviation of the topsoil thickness from the recommended topsoil thickness, the lower the germination rate and the longer the germination time. The recommended topsoil thickness can be preset based on prior experience. For another example, the better the uniformity of the topsoil thickness, the higher the germination rate and the shorter the germination time. The worse the uniformity of the topsoil thickness, the lower the germination rate and the longer the germination time. The steps for determining the uniformity of the topsoil thickness are similar to those for determining the uniformity of the subsoil thickness; please refer to the relevant content above.

[0084] In some embodiments, the processor can estimate the seed germination rate and germination time for each sub-region based on a prediction model. For more information on the prediction model, see [link to relevant documentation]. Figure 3 And its related descriptions.

[0085] Growth expectation reflects the likelihood of healthy seedling growth within a sub-region. A higher growth expectation indicates a greater probability of successful seedling cultivation. In some embodiments, growth expectation is positively correlated with germination rate and negatively correlated with germination time.

[0086] As an example only, the relationship between growth expectation and germination rate and germination time can be expressed by formula (2):

[0087] (2)

[0088] in, Indicates growth expectation; and For coefficients greater than zero, Indicates germination rate. Indicates germination time; and It can be based on prior experience and pre-set.

[0089] In some embodiments, the processor can determine whether a seedling tray is qualified based on growth expectations and a third preset condition. The third preset condition can be determined according to the actual application scenario and / or user needs. For example, the third preset condition can be that the growth expectation is greater than a preset expectation threshold, or it can be that the growth expectation is greater than a preset expectation threshold, and the variance of the growth expectations between different sub-regions is less than a preset variance threshold. The preset expectation threshold and the preset variance threshold can be preset based on experience.

[0090] In some embodiments of this specification, the quality of the seedling tray is determined based on the growth expectation. This allows for the prediction of seedling growth in advance when making the seedling tray, avoiding the situation where seedlings are found to be substandard only after they have grown. This helps to save resources and ensure the quality of the seedling tray.

[0091] In some embodiments of this specification, standardized judgments based on seed distribution uniformity and soil thickness uniformity can more comprehensively and accurately assess the seedling raising status of the seedling tray, which helps to save labor costs while improving the seedling raising quality of the seedling tray.

[0092] Figure 3 These are exemplary schematic diagrams of the estimation model shown in some embodiments of this specification.

[0093] In some embodiments, such as Figure 3As shown, the processor can determine the seed germination rate 350 and germination time 360 ​​of each sub-region based on the subsoil thickness 310, topsoil thickness 320, and environmental parameters 330, through the prediction model 340.

[0094] A prediction model is a model used to determine seed germination rate and germination time. In some embodiments, the prediction can be a machine learning model.

[0095] In some embodiments, the inputs to the prediction model include the subsoil thickness, topsoil thickness, and environmental parameters for each sub-region; the outputs include the seed germination rate and germination time for each sub-region. The environmental parameters refer to parameters such as temperature, humidity, and light intensity in the seedling raising environment. For more information on seed germination rate and germination time, please refer to [link to relevant documentation]. Figure 2 Related Explanation. For more information on subsoil thickness, topsoil thickness, and environmental parameters, please refer to the related explanations above.

[0096] In some embodiments, the processor can train a prediction model based on multiple first training samples with first labels. The first training samples include the sample subsoil thickness, sample topsoil thickness, and sample environmental parameters. In some embodiments, the processor can acquire historical breeding results and determine the historical subsoil thickness, historical topsoil thickness, and historical environmental parameters of each sub-region as a set of first training samples. The historical seed germination rate and historical germination time of the corresponding sub-region are used as the first labels. The historical germination time can be the average germination time of all seeds within the sub-region.

[0097] In some embodiments, the processor can input the first training sample into the initial prediction model, construct a loss function based on the first label and the output of the initial prediction model, iteratively update the parameters of the initial prediction model based on the loss function, and terminate the iteration when the iteration termination condition is met, thus obtaining the trained prediction model. The iterative update method includes, but is not limited to, gradient descent, and the iteration termination condition can be the convergence of the loss function or the reaching of a threshold number of iterations.

[0098] In some embodiments of this specification, the germination rate and germination time of seeds in each sub-region can be accurately predicted through the prediction model, which can reduce the problem of poor seed germination caused by unsuitable environmental factors and soil conditions.

[0099] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0100] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0101] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0102] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0103] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A seedling cultivation system, characterized in that, include: At least one seedling tray, a conveying mechanism, an image acquisition mechanism, and a processor are provided. The seedling tray contains soil and seeds. The conveying mechanism conveys the seedling tray through the image acquisition mechanism to acquire an image containing the seedling tray. The processor is configured to: The image is acquired, and the boundaries of the seedling trays in the image are identified. A target region is constructed based on the boundaries of the seedling trays. The target area is divided into at least one sub-region based on preset rules; Determine seed distribution information and soil information for each sub-region. The seed distribution information includes seed distribution density and uniformity, and the soil information includes subsoil thickness, specifically including: Identify the seed size in each sub-region and determine the average seed size in each sub-region; Based on the average seed size, actual seed size, and camera parameters, the subsoil thickness of each sub-region is determined; Based on the seed distribution information and the soil information, determine whether the seedling raising condition of the seedling tray is qualified, specifically including: Determine whether the seed distribution information of each sub-region satisfies a first preset condition; If there is a sub-region that does not meet the first preset condition, then the seedling raising condition of the seedling tray is unqualified. In response to each of the sub-regions meeting the first preset condition: Based on the subsoil thickness, determining the uniformity of subsoil thickness in the target area specifically includes: Based on the average seed size of each sub-region, the sub-regions are divided into standard sub-regions, low-lying sub-regions, or raised sub-regions according to region partitioning conditions, specifically including: If the average seed size within the sub-region is within the standard size range, then it is determined to be a standard sub-region; If the average seed size within the sub-region is less than the lower limit of the standard size range, it is determined to be a low-lying sub-region. If the average seed size within the sub-region is greater than the upper limit of the standard size range, it is determined to be a raised sub-region; Based on the distribution and area ratio of the low-lying and high-lying sub-regions within the target area, the uniformity of the subsoil thickness is determined. Determine whether the uniformity of the bottom soil thickness meets the second preset condition; If the target area does not meet the second preset condition, then the seedling raising condition of the seedling tray is unqualified; In response to the target area meeting the second preset condition: Based on the installation location of the image acquisition mechanism, the standard size of the seed in the image is determined, and the standard size of the seed corresponds to the seed size at the standard subsoil thickness; Based on the actual size of the seed and the standard size of the seed, determine the thickness of the subsoil in the low-lying area and the raised area; Based on the total soil layer thickness and the subsoil thickness of each sub-region, the topsoil thickness of each sub-region after covering with topsoil is determined; Based on the topsoil thickness, the seed germination rate and germination time of each sub-region are estimated; Based on the seed germination rate and the germination time, the expected growth of seedlings in each sub-region is estimated; Based on the growth expectation, determine whether the seedling raising condition of the seedling tray is qualified.

2. The seedling system as described in claim 1, characterized in that, The step of determining whether the seedling raising condition of the seedling tray is qualified based on the growth expectation specifically includes: Determine whether the growth expectation meets the third preset condition; If the target area does not meet the third preset condition, then the seedling raising condition of the seedling tray is unqualified; If the target area meets the third preset condition, then the seedling raising condition of the seedling tray is qualified.

3. The seedling system as described in claim 1, characterized in that, The estimated seed germination rate and germination time for each sub-region include: Based on the subsoil thickness, topsoil thickness, and environmental parameters of each sub-region, the seed germination rate and germination time of each sub-region are determined by a prediction model, which is a machine learning model.

4. The seedling system according to any one of claims 1-3, characterized in that, The system also includes a replanting mechanism, which includes a replanting tray, a puncture assembly, and a replanting feed assembly; The replanting tray is provided with at least one replanting grid, the size and number of which correspond to the sub-region. The replanting tray is lined with a replaceable intercepting sheet that covers all the replanting grids. The processor is also configured to: Based on the seed distribution information, at least one target sub-region is determined; The piercing component is controlled to pierce the interceptor plate corresponding to at least one target sub-region, so that the seeds in the target replanting grid fall into the corresponding sub-region; as well as The control unit replenishes an appropriate amount of seeds into the target seeding cell and replaces the interceptor plate.

5. A seedling raising method, characterized in that, The seedling raising method is performed by the seedling raising system according to claim 1, and the method includes: The image is acquired, and the boundaries of the seedling trays in the image are identified. A target region is constructed based on the boundaries of the seedling trays. The target area is divided into at least one sub-region based on preset rules; Determine seed distribution information and soil information for each sub-region. The seed distribution information includes seed distribution density and uniformity, and the soil information includes subsoil thickness, specifically including: Identify the seed size in each sub-region and determine the average seed size in each sub-region; Based on the average seed size, actual seed size, and camera parameters, the subsoil thickness of each sub-region is determined; Based on the seed distribution information and the soil information, determine whether the seedling raising condition of the seedling tray is qualified, specifically including: Determine whether the seed distribution information of each sub-region satisfies a first preset condition; If there is a sub-region that does not meet the first preset condition, then the seedling raising condition of the seedling tray is unqualified. In response to each of the sub-regions meeting the first preset condition: Based on the subsoil thickness, determining the uniformity of subsoil thickness in the target area specifically includes: Based on the average seed size of each sub-region, the sub-regions are divided into standard sub-regions, low-lying sub-regions, or raised sub-regions according to region partitioning conditions, specifically including: If the average seed size within the sub-region is within the standard size range, then it is determined to be a standard sub-region; If the average seed size within the sub-region is less than the lower limit of the standard size range, it is determined to be a low-lying sub-region. If the average seed size within a sub-region is greater than the upper limit of the standard size range, it is determined to be a raised sub-region; Based on the distribution and area ratio of the low-lying and high-lying sub-regions within the target area, the uniformity of the subsoil thickness is determined. Determine whether the uniformity of the bottom soil thickness meets the second preset condition; If the target area does not meet the second preset condition, then the seedling raising condition of the seedling tray is unqualified; In response to the target area meeting the second preset condition: Based on the installation location of the image acquisition mechanism, the standard size of the seed in the image is determined, and the standard size of the seed corresponds to the seed size at the standard subsoil thickness; Based on the actual size of the seed and the standard size of the seed, determine the thickness of the subsoil in the low-lying area and the raised area; Based on the total soil layer thickness and the subsoil thickness of each sub-region, the topsoil thickness of each sub-region after covering with topsoil is determined; Based on the topsoil thickness, the seed germination rate and germination time of each sub-region are estimated; Based on the seed germination rate and the germination time, the expected growth of seedlings in each sub-region is estimated; Based on the growth expectation, determine whether the seedling raising condition of the seedling tray is qualified.

6. The seedling raising method as described in claim 5, characterized in that, The step of determining whether the seedling raising condition of the seedling tray is qualified based on the growth expectation specifically includes: Determine whether the growth expectation meets the third preset condition; If the target area does not meet the third preset condition, then the seedling raising condition of the seedling tray is unqualified; If the target area meets the third preset condition, then the seedling raising condition of the seedling tray is qualified.

7. A seedling raising device, characterized in that, The seedling raising device includes a processor for executing the seedling raising method according to any one of claims 5-6.

8. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer executes the seedling cultivation method as described in any one of claims 5-6.

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