Method and device for detecting nutrients of blended fertilizer and computer storage medium

Abstract

The application discloses a kind of mixed fertilizer nutrient detection methods, comprising the following steps: obtaining the image including mixed fertilizer;Different categories of raw materials in mixed fertilizer are screened according to the image, to separate different categories of raw materials;Determine the nutrient content in mixed fertilizer according to separated raw materials.The application also discloses a kind of mixed fertilizer nutrient detection device and computer storage medium.The application separates different categories of raw materials in mixed fertilizer through the image of mixed fertilizer, and calculates the nutrient of mixed fertilizer according to separated raw materials, compared with chemical detection method, it needs shorter time for detection, and detection precision is also high, to improve the detection efficiency of fertilizer nutrient.

Description

Technical Field

[0001] This application relates to the field of fertilizer technology, and more particularly to the field of blended fertilizer technology, specifically to a nutrient detection method, apparatus, and computer storage medium for blended fertilizers. Background Technology

[0002] With the rapid development of my country's fertilizer industry, especially the increasing use of fertilizers, both the quantity and variety of fertilizers are rising daily. Blended fertilizers, a type of compound fertilizer, are made by simply mixing two or more granular, high-nutrient raw materials using mechanical methods, without significant chemical reactions during the mixing process. Blended fertilizers have simple production technology and flexible formulations, making them suitable for areas with large land areas, complex soil types, and diverse crop varieties.

[0003] Whether the composition and content of fertilizers meet the standard requirements is a key factor in determining the quality of fertilizers. Testing fertilizer nutrients is necessary to ensure the quality of fertilizers in circulation. Currently, fertilizer nutrient testing is usually done using chemical methods. However, chemical testing requires a long extraction time and is more complex, resulting in low efficiency in fertilizer nutrient testing. Summary of the Invention

[0004] This application provides a method, apparatus, and computer storage medium for nutrient detection in blended fertilizers, aiming to improve the efficiency of fertilizer nutrient detection.

[0005] This application provides a method for nutrient detection in blended fertilizers, which includes the following steps:

[0006] Acquire an image containing the blended fertilizer;

[0007] Based on the image, different categories of raw materials in the blended fertilizer are screened out to separate the different categories of raw materials;

[0008] The nutrient content in the blended fertilizer is determined based on the separated raw materials.

[0009] In some embodiments, the step of screening different categories of raw materials in the blended fertilizer based on the image to separate the different categories of raw materials includes:

[0010] In the blended fertilizer, target raw materials from multiple categories of raw materials are identified;

[0011] Obtain the optical characteristics of the target raw material;

[0012] In the image, the target raw material is screened out based on the optical features in order to separate the target raw material from the blended fertilizer.

[0013] In some embodiments, after the step of screening the target raw material based on the optical characteristics to separate the target raw material from the blended fertilizer, the method further includes:

[0014] The blended fertilizer after separating the target raw material is used as the updated blended fertilizer;

[0015] Using the updated blended fertilizer, return to the step of acquiring an image containing the blended fertilizer.

[0016] In some embodiments, after the step of screening the target raw material based on the optical characteristics to separate the target raw material from the blended fertilizer, the method further includes:

[0017] The purpose is to detect whether there are different types of raw materials in the blended fertilizer after the target raw material has been separated.

[0018] If present, then proceed with the step of using the blended fertilizer after separating the target raw material as the updated blended fertilizer.

[0019] In some embodiments, after the step of detecting whether there are raw materials of different categories in the blended fertilizer after the target raw material has been separated, the method further includes:

[0020] If not, then proceed with the step of determining the nutrient content in the blended fertilizer based on the separated raw materials.

[0021] In some embodiments, prior to the step of determining the target raw material among the multiple categories of raw materials in the blended fertilizer, the method further includes:

[0022] Obtain preset appearance images of the raw materials in each category;

[0023] The optical characteristics of the raw material are generated based on a preset appearance image of the raw material;

[0024] Store the optical characteristics of the raw materials described in each category.

[0025] In some embodiments, the step of determining the nutrient content in the blended fertilizer based on the separated raw materials includes:

[0026] The separated raw materials are weighed to obtain the weight of each category of raw materials;

[0027] The nutrient quality of the raw material of the corresponding category is determined based on the weight.

[0028] The nutrient content in the blended fertilizer is determined based on the nutrient quality of all categories of raw materials in the blended fertilizer.

[0029] In some embodiments, the step of acquiring an image containing the blended fertilizer includes:

[0030] An optical inspection system using a color sorter acquires images containing the blended fertilizer;

[0031] In some embodiments, the step of separating the different categories of raw materials includes:

[0032] The raw materials of different categories are separated using a color sorter separation system.

[0033] Furthermore, this application embodiment also provides a nutrient detection device for blended fertilizers. The nutrient detection device for blended fertilizers includes: a memory, a processor, and a nutrient detection program for blended fertilizers stored in the memory and executable on the processor. When the nutrient detection program for blended fertilizers is executed by the processor, it implements the steps of the nutrient detection method for blended fertilizers as described above.

[0034] Furthermore, this application embodiment also provides a computer storage medium storing a nutrient detection program for blended fertilizers, wherein when the nutrient detection program for blended fertilizers is executed by a processor, it implements the steps of the nutrient detection method for blended fertilizers as described in any one of the above descriptions.

[0035] The nutrient detection method, apparatus, and computer storage medium for blended fertilizers provided in this application embodiment acquire an image containing the blended fertilizer; different types of raw materials in the blended fertilizer are screened based on the image to separate them; and the nutrient content in the blended fertilizer is determined based on the separated raw materials. This application embodiment separates different types of raw materials in the blended fertilizer using an image of the blended fertilizer and calculates the nutrients in the blended fertilizer based on the separated raw materials. Compared with chemical detection methods, it requires less detection time and has higher detection accuracy, thereby improving the efficiency of fertilizer nutrient detection. Attached Figure Description

[0036] The technical solution and other beneficial effects of this application will become apparent from the following detailed description of specific embodiments in conjunction with the accompanying drawings.

[0037] Figure 1 This is a schematic flowchart of an embodiment of the nutrient detection method for blended fertilizers according to this application;

[0038] Figure 2 This is a schematic flowchart of another embodiment of the nutrient detection method for blended fertilizers of this application;

[0039] Figure 3 This is a schematic flowchart of another embodiment of the nutrient detection method for blended fertilizers of this application;

[0040] Figure 4 This is a schematic diagram of the nutrient detection device for blended fertilizers provided in an embodiment of this application.

[0041] Figure 5 This is a schematic diagram of the color sorter provided in the embodiments of this application;

[0042] Figure 6 This is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiments of this application. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0044] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0045] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0046] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0047] The following disclosure provides many different implementations or examples for carrying out different processes of this application. To simplify the disclosure, the steps of specific examples are described below. Of course, these are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or reference letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various implementations and / or steps discussed. In addition, this application provides examples of various specific processes and materials, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0048] Reference Figure 1 In one embodiment, the nutrient detection method for the blended fertilizer includes the following steps:

[0049] Step S10: Obtain an image containing the blended fertilizer;

[0050] In this embodiment, images containing blended fertilizers can be acquired by a camera device. For example, images containing blended fertilizers can be acquired by the optical detection system of a color sorter, which includes an optical sensor.

[0051] In some embodiments, the blended fertilizer is composed of numerous raw material particles; therefore, the image containing the blended fertilizer records the appearance and location of each raw material in the blended fertilizer. The appearance may include the shape, size, color, gloss, and transparency of the raw materials.

[0052] Step S20: Based on the image, different types of raw materials in the blended fertilizer are screened out to separate the different types of raw materials;

[0053] In this embodiment, different types of raw materials are identified based on the appearance of each raw material particle in the image, thus enabling the screening of different types of raw materials in blended fertilizers. The actual raw materials are separated based on the position of each raw material particle in the image, yielding the separated raw materials of different categories.

[0054] For example, color sorters can automatically separate discolored particles from granular materials based on differences in the optical properties of the materials using photoelectric detection technology. Therefore, the separation execution system of a color sorter can be used to sort different types of raw materials.

[0055] In some embodiments, different categories of raw materials in blended fertilizers refer to different components in blended fertilizers. For example, the raw material categories of blended fertilizers may include diammonium phosphate, 55% monoammonium phosphate, potassium chloride, urea, ammonium sulfate, core masterbatch, etc.

[0056] Step S30: Determine the nutrient content in the blended fertilizer based on the separated raw materials.

[0057] In this embodiment, the nutrient content of each category of raw material is calculated separately, and the combined nutrient content of all categories of raw materials is the nutrient content of the blended fertilizer.

[0058] In some embodiments, the nutrients in the blended fertilizer may include nitrogen, phosphorus, potassium, phosphorus pentoxide, potassium oxide, etc., and the nutrient content is the content of nitrogen, phosphorus, potassium, phosphorus pentoxide, potassium oxide, etc.

[0059] In some embodiments, since raw materials of the same category have the same composition, their nutrient mass can be directly calculated based on the mass percentage of each nutrient. For example, if potassium chloride contains 60% potassium oxide (K2O) in the separated raw materials, and the mass of potassium chloride is 100 grams, then its potassium oxide nutrient mass is 60 grams. If the blended fertilizer is 600 grams, and only potassium chloride contains potassium oxide nutrient, then the potassium oxide nutrient content in the blended fertilizer is 10%.

[0060] In the technical solution disclosed in this embodiment, different types of raw materials in the blended fertilizer are separated by images of the blended fertilizer, and the nutrients of the blended fertilizer are calculated based on the separated raw materials. Compared with chemical detection methods, the detection time is shorter and the detection accuracy is higher, thereby improving the detection efficiency of fertilizer nutrients.

[0061] In another embodiment, such as Figure 2 As shown above, in the above Figure 1 Based on the illustrated embodiment, step S20 includes:

[0062] Step S21: In the blended fertilizer, a target raw material among the multiple categories of raw materials is identified;

[0063] Step S22: Obtain the optical characteristics of the target raw material;

[0064] In this embodiment, optical features of all types of raw materials in the blended fertilizer are pre-set to identify the type of raw materials in the image based on the optical features.

[0065] In some embodiments, optical features may include characteristics of the shape, size, color, gloss, and transparency of the raw material. It is understood that optical features can be used to directly distinguish different categories of raw materials by appearance.

[0066] In some embodiments, prior to step S21, appearance images of each category of raw materials are acquired in advance and used as preset appearance images. Machine learning or training is performed based on the preset appearance images of the raw materials to obtain their optical characteristics, and the optical characteristics of each category of raw materials are stored.

[0067] In some embodiments, the operator can manually select the target raw material from multiple categories of raw materials in the blended fertilizer. In some embodiments, each category of raw materials in the blended fertilizer can be automatically set as the target raw material to automatically separate the different categories of raw materials one by one.

[0068] Step S23: In the image, the target raw material is screened out according to the optical features to separate the target raw material from the blended fertilizer.

[0069] In this embodiment, feature recognition is performed on the image based on the optical characteristics of the target raw material to determine the target raw material in the image and obtain its position in the image. Based on the position of the target raw material in the image, its actual position in three-dimensional space can be determined, allowing the target raw material to be separated from the blended fertilizer.

[0070] In some embodiments, after separating the target raw material from the blended fertilizer, the blended fertilizer after separating the target raw material can be used as the updated blended fertilizer. Using the updated blended fertilizer, the process returns to step S10 to redetermine the new target raw material, thereby achieving the separation of each type of raw material one by one.

[0071] In some embodiments, after separating the target raw material from the blended fertilizer, it can be further detected whether there are raw materials of different categories in the blended fertilizer after the target raw material has been separated. If so, the step of using the blended fertilizer after the target raw material has been separated as the updated blended fertilizer is executed to achieve the continued separation of different categories of raw materials. If not, it indicates that the separation of different categories of raw materials has been completed, and therefore step S30 can be executed.

[0072] In some embodiments, the operator may manually confirm whether there are different types of raw materials in the blended fertilizer after the target raw material has been separated, or the image recognition may be used to automatically determine whether there are different types of raw materials in the blended fertilizer after the target raw material has been separated.

[0073] In the technical solution disclosed in this embodiment, the optical characteristics of different types of raw materials are used to identify and separate them, classifying blended fertilizers into multiple single raw materials, making the detection of nutrient content in blended fertilizers more convenient.

[0074] In yet another embodiment, such as Figure 3 As shown, in Figures 1 to 2 Based on any embodiment, step S30 includes:

[0075] Step S31: Weigh the separated raw materials to obtain the weight of each category of raw materials;

[0076] Step S32: Determine the nutrient quality of the raw material of the corresponding category based on the weight;

[0077] In this embodiment, after classifying the blended fertilizer into multiple separate individual raw materials, each separate individual raw material is weighed to obtain the weight of each category of raw material. The nutrient content of the corresponding category of raw material can be determined based on its weight. For example, potassium chloride contains 60% potassium oxide, urea contains 46.2% nitrogen, ammonium sulfate contains 20.5% nitrogen, the core masterbatch contains 59% potassium oxide, diammonium phosphate contains 18% nitrogen and 46% phosphorus pentoxide, 55% monoammonium phosphate contains 11% nitrogen and 44% phosphorus pentoxide.

[0078] Step S33: Determine the nutrient content in the blended fertilizer based on the nutrient quality of all categories of raw materials in the blended fertilizer.

[0079] In this embodiment, the content of the same nutrient in the blended fertilizer is obtained by summing the masses of the same nutrient in all types of raw materials and dividing by the total mass of the blended fertilizer.

[0080] In one embodiment, to test the effectiveness of the nutrient detection method for blended fertilizers, the following experiment was conducted:

[0081] Experiment 1: 500g of blended fertilizer sample (N:P2O5:K2O = 23:18:10) was weighed. The raw materials were potassium chloride (K2O content 60%), urea (N content 46.2%), ammonium sulfate (N content 20.5%), core masterbatch (K2O content 59%), and diammonium phosphate (N content 18%, P2O5 content 46%). After screening with a color sorter, the separated materials were weighed: diammonium phosphate 200g, potassium chloride 50.1g, urea 149.3g, ammonium sulfate 65.6g, and core masterbatch 35g. The total nutrients in the sample were calculated to be 23.68% nitrogen, 18.4% phosphorus pentoxide, and 10.14% potassium oxide. Chemical analysis revealed 23.6% nitrogen, 18.21% phosphorus pentoxide, and 10.35% potassium oxide.

[0082] Experiment 2: 1000g of blended fertilizer sample (N:P2O5:K2O = 23:18:10) was weighed. The raw materials were potassium chloride (K2O content 60%), urea (N content 46.2%), ammonium sulfate (N content 20.5%), core masterbatch (K2O content 59%), and diammonium phosphate (N content 18%, P2O5 content 46%). After screening with a color sorter, the separated materials were weighed: diammonium phosphate 399.5g, potassium chloride 100.5g, urea 295.4g, ammonium sulfate 133.2g, and core masterbatch 71.4g. Finally, the total nutrients of the sample were calculated to be nitrogen content 23.57%, phosphorus pentoxide content 18.38%, and potassium oxide content 10.24%. The nutrient content, as determined by chemical analysis, is 23.5% nitrogen, 18.26% phosphorus pentoxide, and 10.18% potassium oxide.

[0083] Experiment 3: 500g of blended fertilizer sample (N:P2O5:K2O = 14:28:12) was weighed. The raw materials were potassium chloride (K2O content 60%), 55% monoammonium phosphate (N content 11%, P2O5 content 44%), ammonium sulfate (N content 20.5%), core masterbatch (K2O content 59%), and diammonium phosphate (N content 18%, P2O5 content 46%). After screening with a color sorter, the separated materials were weighed: diammonium phosphate 254.3g, potassium chloride 50.7g, 55% monoammonium phosphate 53.2g, ammonium sulfate 90.2g, and core masterbatch 51.6g. Finally, the total nutrients of the sample were calculated to be nitrogen content 14.02%, phosphorus pentoxide content 28.08%, and potassium oxide content 12.17%. The nutrient content, as determined by chemical analysis, was 14.1% nitrogen, 28.01% phosphorus pentoxide, and 12.2% potassium oxide.

[0084] Experiment 4: 1000g of blended fertilizer sample (N:P2O5:K2O = 14:28:12) was weighed. The raw materials were potassium chloride (K2O content 60%), 55% monoammonium phosphate (N content 11%, P2O5 content 44%), ammonium sulfate (N content 20.5%), core masterbatch (K2O content 59%), and diammonium phosphate (N content 18%, P2O5 content 46%). After screening with a color sorter, the separated materials were weighed: diammonium phosphate 510g, potassium chloride 100.5g, 55% monoammonium phosphate 104g, ammonium sulfate 183.2g, and core masterbatch 102.4g. Finally, the total nutrients of the sample were calculated to be nitrogen content 14.08%, phosphorus pentoxide content 28.04%, and potassium oxide content 12.07%. The nutrient content, as determined by chemical analysis, was 14.11% nitrogen, 28.08% phosphorus pentoxide, and 11.98% potassium oxide.

[0085] As can be seen from the above experiments, the nutrient detection method of the blended fertilizer in this embodiment has a faster detection time and higher detection accuracy compared with the chemical analysis method.

[0086] In the technical solution disclosed in this embodiment, the blended fertilizer is separated into multiple single raw materials, and the nutrient content of the blended fertilizer is calculated by weighing the single raw materials, which improves the detection efficiency and accuracy of nutrient content.

[0087] Furthermore, this application also proposes a nutrient detection device for blended fertilizers. The nutrient detection device for blended fertilizers includes: a memory, a processor, and a nutrient detection program for blended fertilizers stored in the memory and executable on the processor. When the nutrient detection program for blended fertilizers is executed by the processor, it implements the steps of the nutrient detection method for blended fertilizers as described in the above embodiments.

[0088] For example, refer to Figure 4 Nutrient detection devices for blended fertilizers may include Figure 4 The terminal structure is shown. In Figure 4 In this process, the blended fertilizer is fed from the magnetic feeder 11 and falls from the chute 12. The light source 13 provides illumination, the sensor 14 acquires an image containing the blended fertilizer, the background plate 15 provides an image of the target raw material to be screened, the air gun 16 sprays other raw materials into the rejects bin 17, while the target raw material enters the accepts bin 18, waiting for the next raw material separation.

[0089] For example, refer to Figure 5 Nutrient detection devices for blended fertilizers may include Figure 5The color sorter shown includes a ore bin 22, a conveyor belt 23, an optical detection system, a separation execution system, and a material bin 27. The optical detection system includes detection components 24, and the separation execution system includes a spray separation execution component 26. The mixed fertilizer in the ore bin 22 is conveyed and falls via the conveyor belt 23. The detection components 24 acquire images containing the mixed fertilizer. The image processing system 25 identifies the target material in the image. The computer 21 controls the spray separation execution component 26 to spray other raw materials into the defective material bin in the material bin 27, while the target raw material enters the qualified material bin in the material bin 27, awaiting the next material separation.

[0090] For example, Figure 6 This is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiments of this application. The nutrient detection device for blended fertilizers may include... Figure 6 The terminal structure shown.

[0091] like Figure 6 As shown, the terminal may include: a processor 1001, such as a CPU, DSP, or MCU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0092] Those skilled in the art will understand that Figure 6 The terminal structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0093] like Figure 6 As shown, the memory 1005, which serves as a computer storage medium, may include a network communication module, a user interface module, and a nutrient detection program for blended fertilizers.

[0094] exist Figure 6In the terminal shown, network interface 1004 is mainly used to connect to the backend server and communicate data with it; user interface 1003 is mainly used to connect to the client (user terminal) and communicate data with it; while processor 1001 can be used to call the nutrient detection program for blended fertilizer stored in memory 1005 and perform the following operations:

[0095] Acquire an image containing the blended fertilizer;

[0096] Based on the image, different categories of raw materials in the blended fertilizer are screened out to separate the different categories of raw materials;

[0097] The nutrient content in the blended fertilizer is determined based on the separated raw materials.

[0098] Furthermore, the processor 1001 can call the nutrient detection program for blended fertilizer stored in the memory 1005, and also perform the following operations:

[0099] In the blended fertilizer, target raw materials from multiple categories of raw materials are identified;

[0100] Obtain the optical characteristics of the target raw material;

[0101] In the image, the target raw material is screened out based on the optical features in order to separate the target raw material from the blended fertilizer.

[0102] Furthermore, the processor 1001 can call the nutrient detection program for blended fertilizer stored in the memory 1005, and also perform the following operations:

[0103] The blended fertilizer after separating the target raw material is used as the updated blended fertilizer;

[0104] Using the updated blended fertilizer, return to the step of acquiring an image containing the blended fertilizer.

[0105] Furthermore, the processor 1001 can call the nutrient detection program for blended fertilizer stored in the memory 1005, and also perform the following operations:

[0106] The purpose is to detect whether there are different types of raw materials in the blended fertilizer after the target raw material has been separated.

[0107] If present, then proceed with the step of using the blended fertilizer after separating the target raw material as the updated blended fertilizer.

[0108] Furthermore, the processor 1001 can call the nutrient detection program for blended fertilizer stored in the memory 1005, and also perform the following operations:

[0109] If not, then proceed with the step of determining the nutrient content in the blended fertilizer based on the separated raw materials.

[0110] Furthermore, the processor 1001 can call the nutrient detection program for blended fertilizer stored in the memory 1005, and also perform the following operations:

[0111] Obtain preset appearance images of the raw materials in each category;

[0112] The optical characteristics of the raw material are generated based on a preset appearance image of the raw material;

[0113] Store the optical characteristics of the raw materials described in each category.

[0114] Furthermore, the processor 1001 can call the nutrient detection program for blended fertilizer stored in the memory 1005, and also perform the following operations:

[0115] The separated raw materials are weighed to obtain the weight of each category of raw materials;

[0116] The nutrient quality of the raw material of the corresponding category is determined based on the weight.

[0117] The nutrient content in the blended fertilizer is determined based on the nutrient quality of all categories of raw materials in the blended fertilizer.

[0118] Furthermore, the processor 1001 can call the nutrient detection program for blended fertilizer stored in the memory 1005, and also perform the following operations:

[0119] An optical inspection system using a color sorter acquires images containing the blended fertilizer;

[0120] Furthermore, the processor 1001 can call the nutrient detection program for blended fertilizer stored in the memory 1005, and also perform the following operations:

[0121] The raw materials of different categories are separated using a color sorter separation system.

[0122] Furthermore, this application also proposes a computer storage medium storing a nutrient detection program for blended fertilizers. When the nutrient detection program for blended fertilizers is executed by a processor, it implements the steps of the nutrient detection method for blended fertilizers as described in the above embodiments.

[0123] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0124] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0125] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

Claims

1. A method for nutrient detection in blended fertilizers, characterized in that, The nutrient detection method for the blended fertilizer includes the following steps: Acquire an image containing the blended fertilizer; Based on the image, different categories of raw materials in the blended fertilizer are screened out to separate the different categories of raw materials; The nutrient content in the blended fertilizer is determined based on the separated raw materials; The step of filtering out different types of raw materials in the blended fertilizer based on the image to separate the different types of raw materials includes: In the blended fertilizer, target raw materials from multiple categories of raw materials are identified; The optical characteristics of the target raw material are obtained; the optical characteristics include the shape, size, color, gloss and transparency of the raw material, and the optical characteristics are used to directly distinguish different types of raw materials by appearance; In the image, the target raw material is screened out based on the optical features in order to separate the target raw material from the blended fertilizer.

2. The nutrient detection method for blended fertilizers as described in claim 1, characterized in that, After the step of screening the target raw material based on the optical characteristics to separate the target raw material from the blended fertilizer, the method further includes: The blended fertilizer after separating the target raw material is used as the updated blended fertilizer; Using the updated blended fertilizer, return to the step of acquiring an image containing the blended fertilizer.

3. The nutrient detection method for blended fertilizers as described in claim 2, characterized in that, After the step of screening the target raw material based on the optical characteristics to separate the target raw material from the blended fertilizer, the method further includes: The purpose is to detect whether there are different types of raw materials in the blended fertilizer after the target raw material has been separated. If present, then proceed with the step of using the blended fertilizer after separating the target raw material as the updated blended fertilizer.

4. The nutrient detection method for blended fertilizers as described in claim 3, characterized in that, After the step of detecting whether there are different types of raw materials in the blended fertilizer after the target raw material has been separated, the method further includes: If not, then proceed with the step of determining the nutrient content in the blended fertilizer based on the separated raw materials.

5. The nutrient detection method for blended fertilizers as described in claim 1, characterized in that, Prior to the step of determining the target raw material among multiple categories of raw materials in the blended fertilizer, the method further includes: Obtain preset appearance images of the raw materials in each category; The optical features of the raw material are generated based on a preset appearance image of the raw material; Store the optical characteristics of the raw materials described in each category.

6. The nutrient detection method for blended fertilizers as described in claim 1, characterized in that, The step of determining the nutrient content in the blended fertilizer based on the separated raw materials includes: The separated raw materials are weighed to obtain the weight of each category of raw materials; The nutrient quality of the raw material of the corresponding category is determined based on the weight. The nutrient content in the blended fertilizer is determined based on the nutrient quality of all categories of raw materials in the blended fertilizer.

7. The nutrient detection method for blended fertilizers as described in any one of claims 1 to 6, characterized in that, The step of acquiring an image containing the blended fertilizer includes: An optical inspection system using a color sorter acquires images containing the blended fertilizer; The step of separating the different categories of raw materials includes: The raw materials of different categories are separated using a color sorter separation system.

8. A nutrient detection device for blended fertilizers, characterized in that, The nutrient detection device for blended fertilizer includes: a memory, a processor, and a nutrient detection program for blended fertilizer stored in the memory and executable on the processor. When the nutrient detection program for blended fertilizer is executed by the processor, it implements the steps of the nutrient detection method for blended fertilizer as described in any one of claims 1 to 7.

9. A computer storage medium, characterized in that, The computer storage medium stores a nutrient detection program for blended fertilizers, which, when executed by a processor, implements the steps of the nutrient detection method for blended fertilizers as described in any one of claims 1 to 7.

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