Intelligent identification and evaluation method and system for tobacco plant topping operation

Abstract

The present invention relates to an intelligent identification and evaluation method and system for tobacco plant topping operations, belonging to the technical field of tobacco operations. The present invention obtains tobacco plant growth period data information according to the preprocessing results and the tobacco plant growth period identification model, further obtains historical topping operation data information of each tobacco plant type in each growth period through big data, obtains tobacco plant topping operation estimated operation information based on the historical topping operation data information, and evaluates the tobacco plant growth period data information and the tobacco plant topping operation estimated operation information to obtain an evaluation result, and finally makes personalized recommendations for tobacco plant topping operations according to the evaluation result. Through this method, the correlation between the topping operation and tobacco brown spot disease can be analyzed, so as to analyze the appropriate growth period of the topping operation according to the correlation, realize the effectiveness evaluation of the topping operation growth period, and improve the quality and yield of tobacco plants.

Description

Technical Field

[0001] The present invention relates to the technical field of tobacco operations, and in particular to an intelligent identification and evaluation method and system for tobacco plant topping operations. Background Art

[0002] Topping of flue-cured tobacco is an important means of regulating the nutritional level of tobacco leaves. The topping time and the number of leaves per plant are closely related to the yield and internal quality. Topping can promote the growth of the root system of tobacco plants, improve the ability of tobacco plants to absorb water and nutrients and synthesize nicotine, thereby changing the weight and size of the leaves, and changing the internal chemical composition of tobacco leaves, especially the upper tobacco leaves; topping is also an effective measure to remedy fertilization errors, or when tobacco plants grow abnormally under abnormal climatic conditions, to regulate the consumption of nutrients in the tobacco plants and reduce the adverse effects on quality. With the development of flue-cured tobacco production in my country, the cigarette industry has increasingly clear requirements for the internal chemical composition of tobacco raw materials, and the contradiction between tobacco yield, quality and efficiency and the coordination of internal chemical composition has become increasingly obvious. The problems of high nicotine content and poor availability of upper tobacco leaves are particularly prominent. However, since topping operations in different growth stages will have different effects, such as tobacco brown spot disease may be caused after topping operations, the existing technology is unable to analyze the correlation between topping operations and tobacco brown spot disease, and thus it is impossible to analyze the appropriate growth stage for topping operations based on the correlation, resulting in high rates of tobacco brown spot disease in tobacco plants, affecting the quality and yield of tobacco plants. Summary of the invention

[0003] The present invention overcomes the shortcomings of the prior art and provides an intelligent identification and evaluation method and system for tobacco plant topping operations.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides an intelligent identification and evaluation method for tobacco plant topping operations, comprising the following steps:

[0006] Acquire image data information of tobacco plants in a target area, and obtain a preprocessing result by preprocessing the image data information;

[0007] Constructing a tobacco plant growth period recognition model, and obtaining tobacco plant growth period data information according to the preprocessing result and the tobacco plant growth period recognition model;

[0008] Obtaining historical topping operation data information of each tobacco plant type in each growth period through big data, obtaining estimated operation information of tobacco plant topping operation based on the historical topping operation data information, and obtaining an evaluation result by evaluating the tobacco plant growth period data information and the estimated operation information of tobacco plant topping operation;

[0009] Based on the evaluation results, personalized recommendations are made for tobacco plant topping operations.

[0010] Furthermore, in a preferred embodiment of the present invention, it is characterized in that the image data information of the tobacco plants in the target area is obtained, and the image data information is preprocessed to obtain the preprocessing result, which specifically includes:

[0011] Acquire image data information of tobacco plants in a target area, and obtain image data of an area of ​​interest by cutting the tobacco plant image area from the image data information of tobacco plants in the target area;

[0012] Performing smoothing filtering on the image data of the region of interest by using a median filtering method to obtain a smoothing result of the image data of the region of interest;

[0013] The tobacco plant image region feature extraction is performed on the image data smoothing result of the region of interest using a canny operator to obtain a preprocessing result of the tobacco plant image.

[0014] Furthermore, in a preferred embodiment of the present invention, a tobacco plant growth period recognition model is constructed, and the tobacco plant growth period data information is obtained according to the preprocessing result and the tobacco plant growth period recognition model, which specifically includes:

[0015] Acquire a large number of preprocessing results of tobacco plant images of various tobacco plant types and corresponding growth stages, construct a database, and store the preprocessing results of the tobacco plant images of various tobacco plant types in the database in combination with a timestamp;

[0016] Constructing a tobacco plant growth stage recognition model based on a convolutional neural network, and inputting the preprocessing results of each tobacco plant type and tobacco plant images of corresponding growth stages in the database into the tobacco plant growth stage recognition model for training;

[0017] By adjusting each layer of data in the convolutional neural network to a preset distribution range, and making each layer of data tend to an identity function, after each layer of data tends to an identity function, saving model parameters;

[0018] The growth stage data information of each tobacco plant in the target area is obtained according to the tobacco plant growth stage recognition model.

[0019] Furthermore, in a preferred embodiment of the present invention, historical topping operation data information of each tobacco plant type in each growth period is obtained through big data, and estimated operation information of tobacco plant topping operation is obtained based on the historical topping operation data information, specifically including:

[0020] Obtain historical topping operation data information of each tobacco plant type at each growth stage and disease generation type data information related to the historical topping operation data information through big data;

[0021] Calculate the correlation degree information between the historical topping operation data information of each tobacco plant type at each growth period and the disease generation type data information related to the historical topping operation data information by using the grey correlation analysis method;

[0022] Acquire the growth period in which the correlation degree information is greater than the preset correlation degree information, and mark the growth period in which the correlation degree information is greater than the preset correlation degree information as an abnormal growth period for the tobacco plant topping operation;

[0023] A growth period other than the abnormal growth period of the tobacco plant topping operation is selected as an operation area of ​​the tobacco plant topping operation, and estimated operation information of the tobacco plant topping operation is generated according to the operation area of ​​the tobacco plant topping operation.

[0024] Furthermore, in a preferred embodiment of the present invention, the tobacco plant growth period data information and the tobacco plant topping operation estimated operation information are evaluated to obtain an evaluation result, which specifically includes:

[0025] Dividing the target area into several sub-areas, and classifying the growth period data according to the tobacco plant growth period data information, and obtaining the growth period data classification result of each sub-area;

[0026] Determine whether the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, and when the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, mark the relevant sub-region as an area where the tobacco plant topping operation can be performed;

[0027] When the classification result of the growth period data of each sub-region does not overlap with the estimated operation information of the tobacco plant topping operation, marking the relevant sub-region as an area where the tobacco plant topping operation cannot be performed;

[0028] An evaluation result is generated based on the areas where the tobacco plant topping operation can be performed and the areas where the tobacco plant topping operation cannot be performed.

[0029] Furthermore, in a preferred embodiment of the present invention, a personalized recommendation is made for the tobacco plant topping operation based on the evaluation result, specifically including:

[0030] If the evaluation result is that the area cannot be subjected to the tobacco plant topping operation, the data information of the current growth period of the tobacco plants in the corresponding sub-area is obtained, and the data information of the current growth period of the tobacco plants in the corresponding sub-area is estimated to obtain the estimated time information of the growth period when the tobacco plant topping operation can be performed;

[0031] Obtain weather condition information suitable for topping operation and weather condition information within a preset time through big data, and obtain a timestamp suitable for topping operation based on the weather condition information suitable for topping operation and weather condition information within the preset time;

[0032] Making personalized recommendations for the tobacco plant topping operation according to the timestamp suitable for the topping operation and the estimated time information of the growth period in which the tobacco plant topping operation can be performed, and displaying them in a certain manner;

[0033] If the evaluation result is that the area is suitable for tobacco plant topping operation, personalized recommendation for tobacco plant topping operation is made according to the timestamp suitable for topping operation and displayed in the recommended manner.

[0034] A second aspect of the present invention provides an intelligent identification and evaluation system for tobacco plant topping operations, the evaluation system comprising a memory and a processor, the memory comprising an intelligent identification and evaluation method program for tobacco plant topping operations, and when the intelligent identification and evaluation method program for tobacco plant topping operations is executed by the processor, the following steps are implemented:

[0035] Acquire image data information of tobacco plants in a target area, and obtain a preprocessing result by preprocessing the image data information;

[0036] Constructing a tobacco plant growth period recognition model, and obtaining tobacco plant growth period data information according to the preprocessing result and the tobacco plant growth period recognition model;

[0037] Obtaining historical topping operation data information of each tobacco plant type in each growth period through big data, obtaining estimated operation information of tobacco plant topping operation based on the historical topping operation data information, and obtaining an evaluation result by evaluating the tobacco plant growth period data information and the estimated operation information of tobacco plant topping operation;

[0038] Based on the evaluation results, personalized recommendations are made for tobacco plant topping operations.

[0039] In this embodiment, historical topping operation data information of each tobacco plant type in each growth period is obtained through big data, and estimated operation information of tobacco plant topping operation is obtained based on the historical topping operation data information, specifically including:

[0040] Obtain historical topping operation data information of each tobacco plant type at each growth stage and disease generation type data information related to the historical topping operation data information through big data;

[0041] Calculate the correlation degree information between the historical topping operation data information of each tobacco plant type at each growth period and the disease generation type data information related to the historical topping operation data information by using the grey correlation analysis method;

[0042] Acquire the growth period in which the correlation degree information is greater than the preset correlation degree information, and mark the growth period in which the correlation degree information is greater than the preset correlation degree information as an abnormal growth period for the tobacco plant topping operation;

[0043] A growth period other than the abnormal growth period of the tobacco plant topping operation is selected as an operation area of ​​the tobacco plant topping operation, and estimated operation information of the tobacco plant topping operation is generated according to the operation area of ​​the tobacco plant topping operation.

[0044] In this embodiment, the tobacco plant growth period data information and the estimated operation information of the tobacco plant topping operation are evaluated to obtain an evaluation result, which specifically includes:

[0045] Dividing the target area into several sub-areas, and classifying the growth period data according to the tobacco plant growth period data information, and obtaining the growth period data classification result of each sub-area;

[0046] Determine whether the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, and when the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, mark the relevant sub-region as an area where the tobacco plant topping operation can be performed;

[0047] When the classification result of the growth period data of each sub-region does not overlap with the estimated operation information of the tobacco plant topping operation, marking the relevant sub-region as an area where the tobacco plant topping operation cannot be performed;

[0048] An evaluation result is generated based on the areas where the tobacco plant topping operation can be performed and the areas where the tobacco plant topping operation cannot be performed.

[0049] In this embodiment, personalized recommendations are made on the tobacco plant topping operation based on the evaluation results, specifically including:

[0050] If the evaluation result is that the area cannot be subjected to the tobacco plant topping operation, the data information of the current growth period of the tobacco plants in the corresponding sub-area is obtained, and the data information of the current growth period of the tobacco plants in the corresponding sub-area is estimated to obtain the estimated time information of the growth period when the tobacco plant topping operation can be performed;

[0051] Obtain weather condition information suitable for topping operation and weather condition information within a preset time through big data, and obtain a timestamp suitable for topping operation based on the weather condition information suitable for topping operation and weather condition information within the preset time;

[0052] Making personalized recommendations for the tobacco plant topping operation according to the timestamp suitable for the topping operation and the estimated time information of the growth period in which the tobacco plant topping operation can be performed, and displaying them in a certain manner;

[0053] If the evaluation result is that the area is suitable for tobacco plant topping operation, personalized recommendation for tobacco plant topping operation is made according to the timestamp suitable for topping operation and displayed in the recommended manner.

[0054] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0055] The present invention obtains image data information of tobacco plants in a target area, and pre-processes the image data information to obtain pre-processing results, further constructs a tobacco plant growth period recognition model, obtains tobacco plant growth period data information according to the pre-processing results and the tobacco plant growth period recognition model, further obtains historical topping operation data information of each tobacco plant type in each growth period through big data, obtains tobacco plant topping operation estimated operation information based on the historical topping operation data information, and evaluates the tobacco plant growth period data information and the tobacco plant topping operation estimated operation information to obtain an evaluation result, and finally makes personalized recommendations for tobacco plant topping operations according to the evaluation results. The method can analyze the correlation between topping operations and tobacco brown spot disease, thereby analyzing the appropriate growth period of topping operations based on the correlation, realizing the effectiveness evaluation of the growth period of topping operations, and further improving the quality and yield of tobacco plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0057] Figure 1 An overall method flow chart of an intelligent identification and evaluation method for tobacco plant topping operation is shown;

[0058] Figure 2 A first method flow chart of an intelligent identification and evaluation method for tobacco plant topping operation is shown;

[0059] Figure 3 A second method flow chart of an intelligent identification and evaluation method for tobacco plant topping operation is shown;

[0060] Figure 4 A system block diagram of an intelligent identification and evaluation system for tobacco plant topping operations is shown. DETAILED DESCRIPTION

[0061] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0063] like Figure 1 As shown, the first aspect of the present invention provides an intelligent identification and evaluation method for tobacco plant topping operations, comprising the following steps:

[0064] S102: acquiring image data information of tobacco plants in the target area, and obtaining a preprocessing result by preprocessing the image data information;

[0065] S104: constructing a tobacco plant growth period recognition model, and acquiring tobacco plant growth period data information according to the preprocessing result and the tobacco plant growth period recognition model;

[0066] S106: obtaining historical topping operation data information of each tobacco plant type in each growth period through big data, obtaining estimated operation information of tobacco plant topping operation based on the historical topping operation data information, and obtaining an evaluation result by evaluating the tobacco plant growth period data information and the estimated operation information of tobacco plant topping operation;

[0067] S108: Making personalized recommendations on tobacco plant topping operations based on the evaluation results.

[0068] It should be noted that this method can analyze the correlation between topping operation and tobacco brown spot disease, and then analyze the appropriate growth period of topping operation based on the correlation, thereby realizing the effectiveness evaluation of the growth period of topping operation and further improving the quality and yield of tobacco plants.

[0069] Furthermore, in a preferred embodiment of the present invention, it is characterized in that the image data information of the tobacco plants in the target area is obtained, and the image data information is preprocessed to obtain the preprocessing result, which specifically includes:

[0070] Acquire image data information of tobacco plants in a target area, and obtain image data of an area of ​​interest by cutting the tobacco plant image area from the image data information of tobacco plants in the target area;

[0071] Performing smoothing filtering on the image data of the region of interest by using a median filtering method to obtain a smoothing result of the image data of the region of interest;

[0072] The tobacco plant image region feature extraction is performed on the image data smoothing result of the region of interest using a canny operator to obtain a preprocessing result of the tobacco plant image.

[0073] like Figure 2 As shown, further, in a preferred embodiment of the present invention, a tobacco plant growth period recognition model is constructed, and tobacco plant growth period data information is obtained according to the preprocessing result and the tobacco plant growth period recognition model, specifically including:

[0074] S202: obtaining a large number of preprocessing results of tobacco plant images of various tobacco plant types and corresponding growth stages, constructing a database, and storing the preprocessing results of the tobacco plant images of various tobacco plant types in the database in combination with a timestamp;

[0075] S204: constructing a tobacco plant growth stage recognition model based on a convolutional neural network, and inputting the preprocessing results of each tobacco plant type and the tobacco plant image of the corresponding growth stage in the database into the tobacco plant growth stage recognition model for training;

[0076] S206: adjusting each layer of data in the convolutional neural network to a preset distribution range, and making each layer of data tend to an identity function, and after each layer of data tends to an identity function, saving model parameters;

[0077] S208: Acquire growth stage data information of each tobacco plant in the target area according to the tobacco plant growth stage recognition model.

[0078] Exemplarily, the growth stages corresponding to the tobacco plant types include the initial flowering stage, the bud stage, the full flowering stage, etc. During the continuous training of the tobacco plant growth stage recognition model through a neural network, the parameters and data distribution will be continuously updated during the network model training process. For example, the input of the second layer is obtained by the input data and the first layer parameters, and the parameters of the first layer are constantly changing with the training, which will definitely cause the input data of the second layer to change. In order to make the loss function stable, it is necessary to adjust the data of each layer to a reasonable distribution range so that the data can approximate an identity function or residual function, thereby improving the accuracy of identifying the growth stage data information of each tobacco plant.

[0079] like Figure 3 As shown, further, in a preferred embodiment of the present invention, historical topping operation data information of each tobacco plant type in each growth period is obtained through big data, and estimated operation information of tobacco plant topping operation is obtained based on the historical topping operation data information, specifically including:

[0080] S302: Obtaining historical topping operation data information of each tobacco plant type at each growth period and disease generation type data information related to the historical topping operation data information through big data;

[0081] S304: Calculating the correlation degree information between the historical topping operation data information of each tobacco plant type in each growth period and the disease generation type data information related to the historical topping operation data information by grey correlation analysis method;

[0082] S306: Obtaining the growth period in which the correlation degree information is greater than the preset correlation degree information, and marking the growth period in which the correlation degree information is greater than the preset correlation degree information as an abnormal growth period for the tobacco plant topping operation;

[0083] S308: Selecting a growth period other than the abnormal growth period of the tobacco plant topping operation as an operation area of ​​the tobacco plant topping operation, and generating estimated operation information of the tobacco plant topping operation according to the operation area of ​​the tobacco plant topping operation.

[0084] For example, the grey correlation analysis method is a method to measure the degree of correlation between factors based on the similarity or difference in the development trends between factors, that is, the "grey correlation degree". For some types of tobacco plants, the incidence of tobacco brown spot disease varies greatly at different topping periods. For example, the tobacco brown spot disease index corresponding to topping during the flowering period of Yunyan is the highest, and the tobacco brown spot disease index corresponding to topping during the early flowering period is the lowest; for another type of tobacco plant, the tobacco brown spot disease index is the highest when topping is performed during the bud stage.

[0085] It should be noted that the grey correlation analysis method can analyze the correlation degree information between the historical topping operation data information of each tobacco plant type in each growth period and the disease generation type data information related to the historical topping operation data information; when the correlation degree information is greater than the preset correlation degree information, it means that when the plants in a certain growth period are toppled, the tobacco brown spot disease incidence index is high; otherwise, the tobacco brown spot disease incidence index is low. This method can screen out the growth period suitable for the topping operation of tobacco plants (estimated operation information of the topping operation of tobacco plants) according to different plant types.

[0086] Furthermore, in a preferred embodiment of the present invention, the tobacco plant growth period data information and the tobacco plant topping operation estimated operation information are evaluated to obtain an evaluation result, which specifically includes:

[0087] Dividing the target area into several sub-areas, and classifying the growth period data according to the tobacco plant growth period data information, and obtaining the growth period data classification result of each sub-area;

[0088] Determine whether the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, and when the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, mark the relevant sub-region as an area where the tobacco plant topping operation can be performed;

[0089] When the classification result of the growth period data of each sub-region does not overlap with the estimated operation information of the tobacco plant topping operation, marking the relevant sub-region as an area where the tobacco plant topping operation cannot be performed;

[0090] An evaluation result is generated based on the areas where the tobacco plant topping operation can be performed and the areas where the tobacco plant topping operation cannot be performed.

[0091] It should be noted that this method can be used to allocate topping operations to plants in a target area.

[0092] Furthermore, in a preferred embodiment of the present invention, a personalized recommendation is made for the tobacco plant topping operation based on the evaluation result, specifically including:

[0093] If the evaluation result is that the area cannot be subjected to the tobacco plant topping operation, the data information of the current growth period of the tobacco plants in the corresponding sub-area is obtained, and the data information of the current growth period of the tobacco plants in the corresponding sub-area is estimated to obtain the estimated time information of the growth period when the tobacco plant topping operation can be performed;

[0094] Obtain weather condition information suitable for topping operation and weather condition information within a preset time through big data, and obtain a timestamp suitable for topping operation based on the weather condition information suitable for topping operation and weather condition information within the preset time;

[0095] Making personalized recommendations for the tobacco plant topping operation according to the timestamp suitable for the topping operation and the estimated time information of the growth period in which the tobacco plant topping operation can be performed, and displaying them in a certain manner;

[0096] If the evaluation result is that the area is suitable for tobacco plant topping operation, personalized recommendation for tobacco plant topping operation is made according to the timestamp suitable for topping operation and displayed in the recommended manner.

[0097] It should be noted that weather condition information includes sunny days, rainy days, etc. This method can further improve the rationality of topping operations.

[0098] The second aspect of the present invention provides an intelligent identification and evaluation system 4 for tobacco plant topping operations, the evaluation system comprising a memory 41 and a processor 62, the memory 41 comprising an intelligent identification and evaluation method program for tobacco plant topping operations, and when the intelligent identification and evaluation method program for tobacco plant topping operations is executed by the processor 62, the following steps are implemented:

[0099] Acquire image data information of tobacco plants in a target area, and obtain a preprocessing result by preprocessing the image data information;

[0100] Constructing a tobacco plant growth period recognition model, and obtaining tobacco plant growth period data information according to the preprocessing result and the tobacco plant growth period recognition model;

[0101] Obtaining historical topping operation data information of each tobacco plant type in each growth period through big data, obtaining estimated operation information of tobacco plant topping operation based on the historical topping operation data information, and obtaining an evaluation result by evaluating the tobacco plant growth period data information and the estimated operation information of tobacco plant topping operation;

[0102] Based on the evaluation results, personalized recommendations are made for tobacco plant topping operations.

[0103] In this embodiment, historical topping operation data information of each tobacco plant type in each growth period is obtained through big data, and estimated operation information of tobacco plant topping operation is obtained based on the historical topping operation data information, specifically including:

[0104] Obtain historical topping operation data information of each tobacco plant type at each growth stage and disease generation type data information related to the historical topping operation data information through big data;

[0105] Calculate the correlation degree information between the historical topping operation data information of each tobacco plant type at each growth period and the disease generation type data information related to the historical topping operation data information by using the grey correlation analysis method;

[0106] Acquire the growth period in which the correlation degree information is greater than the preset correlation degree information, and mark the growth period in which the correlation degree information is greater than the preset correlation degree information as an abnormal growth period for the tobacco plant topping operation;

[0107] A growth period other than the abnormal growth period of the tobacco plant topping operation is selected as an operation area of ​​the tobacco plant topping operation, and estimated operation information of the tobacco plant topping operation is generated according to the operation area of ​​the tobacco plant topping operation.

[0108] In this embodiment, the tobacco plant growth period data information and the estimated operation information of the tobacco plant topping operation are evaluated to obtain an evaluation result, which specifically includes:

[0109] Dividing the target area into several sub-areas, and classifying the growth period data according to the tobacco plant growth period data information, and obtaining the growth period data classification result of each sub-area;

[0110] Determine whether the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, and when the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, mark the relevant sub-region as an area where the tobacco plant topping operation can be performed;

[0111] When the classification result of the growth period data of each sub-region does not overlap with the estimated operation information of the tobacco plant topping operation, marking the relevant sub-region as an area where the tobacco plant topping operation cannot be performed;

[0112] An evaluation result is generated based on the areas where the tobacco plant topping operation can be performed and the areas where the tobacco plant topping operation cannot be performed.

[0113] In this embodiment, personalized recommendations are made on the tobacco plant topping operation based on the evaluation results, specifically including:

[0114] If the evaluation result is that the area cannot be subjected to the tobacco plant topping operation, the data information of the current growth period of the tobacco plants in the corresponding sub-area is obtained, and the data information of the current growth period of the tobacco plants in the corresponding sub-area is estimated to obtain the estimated time information of the growth period when the tobacco plant topping operation can be performed;

[0115] Obtain weather condition information suitable for topping operation and weather condition information within a preset time through big data, and obtain a timestamp suitable for topping operation based on the weather condition information suitable for topping operation and weather condition information within the preset time;

[0116] Making personalized recommendations for the tobacco plant topping operation according to the timestamp suitable for the topping operation and the estimated time information of the growth period in which the tobacco plant topping operation can be performed, and displaying them in a certain manner;

[0117] If the evaluation result is that the area is suitable for tobacco plant topping operation, personalized recommendation for tobacco plant topping operation is made according to the timestamp suitable for topping operation and displayed in the recommended manner.

[0118] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0119] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0120] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0121] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0122] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0123] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent identification and evaluation method for tobacco plant topping operations, It is characterized in that The following steps are involved: Acquire image data information of tobacco plants in a target area, and obtain a preprocessing result by preprocessing the image data information; Constructing a tobacco plant growth period recognition model, and obtaining tobacco plant growth period data information according to the preprocessing result and the tobacco plant growth period recognition model; Obtaining historical topping operation data information of each tobacco plant type in each growth period through big data, obtaining estimated operation information of tobacco plant topping operation based on the historical topping operation data information, and obtaining an evaluation result by evaluating the tobacco plant growth period data information and the estimated operation information of tobacco plant topping operation; making personalized recommendations for tobacco plant topping operations based on the evaluation results; Based on the evaluation results, personalized recommendations are made for tobacco plant topping operations, including: If the evaluation result is that the area cannot be subjected to the tobacco plant topping operation, the data information of the current growth period of the tobacco plants in the corresponding sub-area is obtained, and the data information of the current growth period of the tobacco plants in the corresponding sub-area is estimated to obtain the estimated time information of the growth period when the tobacco plant topping operation can be performed; Obtain weather condition information suitable for topping operation and weather condition information within a preset time through big data, and obtain a timestamp suitable for topping operation based on the weather condition information suitable for topping operation and weather condition information within the preset time; Making personalized recommendations for the tobacco plant topping operation according to the timestamp suitable for the topping operation and the estimated time information of the growth period in which the tobacco plant topping operation can be performed, and displaying them in a certain manner; If the evaluation result is that the area is suitable for tobacco plant topping operation, a personalized recommendation for tobacco plant topping operation is made according to the timestamp suitable for topping operation.

2. The intelligent identification and evaluation method for tobacco plant topping according to claim 1, It is characterized in that Acquiring image data information of tobacco plants in a target area, and obtaining preprocessing results by preprocessing the image data information, specifically including: Acquire image data information of tobacco plants in a target area, and obtain image data of an area of ​​interest by cutting the tobacco plant image area from the image data information of tobacco plants in the target area; Performing smoothing filtering on the image data of the region of interest by a median filtering method to obtain a smoothing result of the image data of the region of interest; The tobacco plant image region feature extraction is performed on the image data smoothing result of the region of interest using a canny operator to obtain a preprocessing result of the tobacco plant image.

3. The intelligent identification and evaluation method for tobacco plant topping according to claim 1, It is characterized in that Constructing a tobacco plant growth period recognition model, and obtaining tobacco plant growth period data information according to the preprocessing result and the tobacco plant growth period recognition model, specifically including: Acquire a large number of preprocessing results of tobacco plant images of various tobacco plant types and corresponding growth stages, construct a database, and store the preprocessing results of the tobacco plant images of various tobacco plant types in the database in combination with a timestamp; Constructing a tobacco plant growth stage recognition model based on a convolutional neural network, and inputting the preprocessing results of each tobacco plant type and tobacco plant images of corresponding growth stages in the database into the tobacco plant growth stage recognition model for training; By adjusting each layer of data in the convolutional neural network to a preset distribution range, and making each layer of data tend to an identity function, after each layer of data tends to an identity function, saving model parameters; The growth stage data information of each tobacco plant in the target area is obtained according to the tobacco plant growth stage recognition model.

4. The intelligent identification and evaluation method for tobacco plant topping according to claim 1, It is characterized in that The historical topping operation data information of each tobacco plant type in each growth period is obtained through big data, and the estimated operation information of the tobacco plant topping operation is obtained based on the historical topping operation data information, specifically including: Obtain historical topping operation data information of each tobacco plant type at each growth stage and disease generation type data information related to the historical topping operation data information through big data; Calculate the correlation degree information between the historical topping operation data information of each tobacco plant type at each growth period and the disease generation type data information related to the historical topping operation data information by using the grey correlation analysis method; Acquire the growth period in which the correlation degree information is greater than the preset correlation degree information, and mark the growth period in which the correlation degree information is greater than the preset correlation degree information as an abnormal growth period for the tobacco plant topping operation; A growth period other than the abnormal growth period of the tobacco plant topping operation is selected as an operation area of ​​the tobacco plant topping operation, and estimated operation information of the tobacco plant topping operation is generated according to the operation area of ​​the tobacco plant topping operation.

5. The intelligent identification and evaluation method for tobacco plant topping operation according to claim 1, It is characterized in that By evaluating the tobacco plant growth period data information and the tobacco plant topping operation estimated operation information, an evaluation result is obtained, which specifically includes: Dividing the target area into several sub-areas, and classifying the growth period data according to the tobacco plant growth period data information, and obtaining the growth period data classification result of each sub-area; Determine whether the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, and when the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, mark the relevant sub-region as an area where the tobacco plant topping operation can be performed; When the classification result of the growth period data of each sub-region does not overlap with the estimated operation information of the tobacco plant topping operation, marking the relevant sub-region as an area where the tobacco plant topping operation cannot be performed; An evaluation result is generated based on the areas where the tobacco plant topping operation can be performed and the areas where the tobacco plant topping operation cannot be performed.

6. An intelligent recognition and evaluation system for the topping operation of tobacco plants, characterized in that, the evaluation system includes a memory and a processor. The memory contains a program for the intelligent recognition and evaluation method of the topping operation of tobacco plants. When the program for the intelligent recognition and evaluation method of the topping operation of tobacco plants is executed by the processor, the following steps are implemented: Obtain the image data information of tobacco plants in the target area, and obtain the preprocessing result by preprocessing the image data information; Construct a recognition model for the growth period of tobacco plants, and obtain the growth period data information of tobacco plants according to the preprocessing result and the recognition model for the growth period of tobacco plants; Obtain the historical topping operation data information of each tobacco plant type in each growth period through big data, obtain the estimated operation information of the topping operation of tobacco plants based on the historical topping operation data information, and obtain the evaluation result by evaluating the growth period data information of the tobacco plants and the estimated operation information of the topping operation of tobacco plants; Make a personalized recommendation for the topping operation of tobacco plants according to the evaluation result; Making a personalized recommendation for the topping operation of tobacco plants according to the evaluation result includes: If the evaluation result is that the area where the topping operation of tobacco plants cannot be carried out, obtain the current growth period data information of the tobacco plants in the corresponding sub-area, and estimate the current growth period data information of the tobacco plants in the corresponding sub-area to obtain the estimated time information for reaching the growth period when the topping operation of tobacco plants can be carried out; Obtain the weather condition information suitable for the topping operation and the weather condition information within the preset time through big data, and obtain the time stamp suitable for the topping operation according to the weather condition information suitable for the topping operation and the weather condition information within the preset time; Make a personalized recommendation for the topping operation of tobacco plants according to the time stamp suitable for the topping operation and the estimated time information for reaching the growth period when the topping operation of tobacco plants can be carried out, and display it in the following manner; If the evaluation result is an area where the topping operation of tobacco plants can be carried out, make a personalized recommendation for the topping operation of tobacco plants according to the time stamp suitable for the topping operation.

7. The intelligent recognition and evaluation system for the topping operation of tobacco plants according to claim 6, characterized in that, Obtaining the historical topping operation data information of each tobacco plant type in each growth period through big data, and obtaining the estimated operation information of the topping operation of tobacco plants based on the historical topping operation data information specifically includes: Obtain the historical topping operation data information of each tobacco plant type in each growth period and the data information of the disease generation types related to the generation of the historical topping operation data information through big data; Calculate the degree of association information between the historical topping operation data information of each tobacco plant type in each growth period and the data information of the disease generation types related to the generation of the historical topping operation data information through the grey relational analysis method; Obtain the growth period where the degree of association information is greater than the preset degree of association information, and mark the growth period where the degree of association information is greater than the preset degree of association information as the abnormal growth period of the topping operation of tobacco plants; A growth period other than the abnormal growth period of the tobacco plant topping operation is selected as an operation area of ​​the tobacco plant topping operation, and estimated operation information of the tobacco plant topping operation is generated according to the operation area of ​​the tobacco plant topping operation.

8. The intelligent identification and evaluation system for tobacco plant topping according to claim 6, It is characterized in that By evaluating the tobacco plant growth period data information and the tobacco plant topping operation estimated operation information, an evaluation result is obtained, which specifically includes: Dividing the target area into several sub-areas, and classifying the growth period data according to the tobacco plant growth period data information, and obtaining the growth period data classification result of each sub-area; Determine whether the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, and when the classification result of the growth period data of each sub-region coincides with the estimated operation information of the tobacco plant topping operation, mark the relevant sub-region as an area where the tobacco plant topping operation can be performed; When the classification result of the growth period data of each sub-region does not overlap with the estimated operation information of the tobacco plant topping operation, marking the relevant sub-region as an area where the tobacco plant topping operation cannot be performed; An evaluation result is generated based on the areas where the tobacco plant topping operation can be performed and the areas where the tobacco plant topping operation cannot be performed.

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