Red date mature picking analysis method, system and terminal considering environmental factors

Abstract

The present invention discloses a red date mature picking analysis method, system and terminal considering environmental factors, which relates to the technical field of agricultural product maturity analysis. The key points of its technical solution are as follows: Curve fitting is performed on historical canopy light radiation data, historical temperature data and historical humidity data; The total gray information of all red dates in the target image is extracted, and the total gray information is divided into multiple hierarchical gray information according to the gray level interval; The mature time nodes of different hierarchical gray information are obtained by solving according to the light radiation fitting function, temperature fitting function, humidity fitting function and gray evolution model; The optimal time node for picking red dates in the target area is determined according to all the mature time nodes. The present invention considers the red date density distribution of different levels and the specific differences between levels, and obtains the optimal time node suitable for picking that can balance the red date quality and economic benefits, providing reference data for red date picking.

Description

Technical Field

[0001] The present invention relates to the technical field of analysis of the maturity of agricultural products, and more specifically, to a method, system and terminal for analyzing the mature picking of red dates considering environmental factors. Background Art

[0002] The picking time of agricultural products is affected by maturity. Premature harvesting will affect the taste and flavor, directly affecting the quality grade of the product. While relatively late picking will affect the transportation and sales of the product, directly affecting the economic benefits. Therefore, the agricultural product maturity analysis technology has been applied in the quality classification and maturity cycle prediction analysis of agricultural products.

[0003] Currently, the agricultural product maturity analysis technologies mainly include: (1) Analyzing the maturity of products based on the surface feature information of the products, mainly by training a classification model based on a large number of sample data for differentiation; if applied to the grade classification of batch products, it is required that the surface features are easy to identify and distinguish; if applied to the precise analysis of product maturity, it is required that the number of products is small, otherwise it is easy to have a large error problem, such as red dates; (2) Constructing a growth model for prediction based on the growth characteristics and environmental factors of the products, which is mainly applied to products with large morphological changes during the growth process, while for products with small morphological changes during the growth process, the prediction accuracy is effective, such as red dates.

[0004] Due to the large growth density of red dates, the morphological changes during the growth process are not particularly obvious, and affected by environmental factors such as temperature, humidity, and light, even the red dates on the same plant are prone to large differences in maturity. Therefore, it is difficult to accurately analyze the maturity of red dates on a large scale using the above-mentioned agricultural product maturity analysis technologies. Therefore, how to research and design a method, system and terminal for analyzing the mature picking of red dates considering environmental factors that can overcome the above defects is an urgent problem for us to solve at present. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the purpose of the present invention is to provide a method, system and terminal for analyzing the mature picking of red dates considering environmental factors, which hierarchically analyze the red dates in the target image based on grayscale information, can obtain the maturity time nodes corresponding to different levels, and finally consider the density distribution of red dates at different levels and the specific differences between levels to obtain the optimal time node suitable for picking that can balance the quality of red dates and economic benefits, providing reference data for red date picking.

[0006] The above technical object of the present invention is achieved through the following technical solutions:

[0007] In the first aspect, a method for analyzing the mature picking of red dates considering environmental factors is provided, including the following steps:

[0008] Obtain the historical canopy light radiation data, historical temperature data, and historical humidity data of the target area;

[0009] Use the least squares method to perform curve fitting on the historical canopy light radiation data, historical temperature data, and historical humidity data respectively to obtain the corresponding light radiation fitting function, temperature fitting function, and humidity fitting function;

[0010] Obtain the target image containing red dates in the target area, extract the total gray information of all red dates in the target image, and divide the total gray information into multiple hierarchical gray information according to the gray level interval;

[0011] Solve the maturity time nodes of different hierarchical gray information according to the light radiation fitting function, temperature fitting function, humidity fitting function, and gray evolution model;

[0012] Determine the optimal time node for picking red dates in the target area according to all the maturity time nodes to obtain the picking strategy.

[0013] Further, the calculation formula of the gray evolution model is specifically:

[0014]

[0015] Among them, y(t 0 +t) represents the maturity corresponding to the growth duration of red dates being t 0 +t; a is a constant determined by the growth characteristics of the red date variety in the target area; represents the maturity mapped by the gray value corresponding to the current time node t 0 ; t represents the time interval from the current time node; represents the average value of the environmental factor influence factors corresponding to the time interval t; y 0 represents the maturity reference value; t b represents the maturity time node.

[0016] Further, the calculation formula of the average value of the environmental factor influence factors is specifically:

[0017]

[0018] Among them, A(i) represents the influence factor of light radiation at time i; B(i) represents the influence factor of temperature at time i; C(i) represents the influence factor of humidity at time i.

[0019] Further, the calculation formula of the influence factor of light radiation at time i is specifically:

[0020]

[0021] Among them, RH(i) represents the light radiation fitting function; RH 0 represents the light radiation reference value;

[0022] The specific calculation formula for the influence factor of the temperature at time i is:

[0023]

[0024] Among them, T(i) represents the temperature fitting function; T 0 represents the temperature reference value;

[0025] The specific calculation formula for the influence factor of the humidity at time i is:

[0026]

[0027] Among them, S(i) represents the humidity fitting function; S 0 represents the humidity reference value.

[0028] Furthermore, if the influence factor calculated for the light radiation at time i is greater than the first threshold, then the first threshold is used as the actual influence factor of the light radiation at time i;

[0029] If the influence factor calculated for the temperature at time i is greater than the second threshold, then the second threshold is used as the actual influence factor of the temperature at time i;

[0030] If the influence factor calculated for the humidity at time i is greater than the third threshold, then the third threshold is used as the actual influence factor of the humidity at time i.

[0031] Furthermore, the specific calculation formula for the optimal time node is:

[0032]

[0033] Among them, n represents the number of layers of the total gray-scale information; t b (j) represents the maturity time node corresponding to the gray-scale information of the j-th layer; t d represents the optimal time node; δ j represents the weight coefficient of the gray-scale information of the j-th layer.

[0034] Furthermore, the weight coefficient of the maturity time node is allocated according to the proportion of red dates in the gray-scale information of different layers.

[0035] In the second aspect, a red date maturity picking analysis system considering environmental factors is provided, including:

[0036] A data acquisition module for obtaining historical canopy light radiation data, historical temperature data, and historical humidity data of the target area;

[0037] A curve fitting module, which is used to respectively perform curve fitting on historical canopy light radiation data, historical temperature data, and historical humidity data by using the least squares method to obtain corresponding light radiation fitting functions, temperature fitting functions, and humidity fitting functions;

[0038] A gray level stratification module, which is used to obtain a target image containing red dates in a target area, extract the total gray level information of all red dates in the target image, and divide the total gray level information into multiple hierarchical gray level information according to gray level intervals;

[0039] An evolution analysis module, which is used to solve the maturity time nodes of different hierarchical gray level information according to the light radiation fitting functions, temperature fitting functions, humidity fitting functions, and gray level evolution model;

[0040] A strategy generation module, which is used to determine the optimal time node for picking red dates in the target area according to all the maturity time nodes to obtain a picking strategy.

[0041] In a third aspect, a computer terminal is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the red date maturity picking analysis method considering environmental factors as described in any item of the first aspect.

[0042] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it can implement the red date maturity picking analysis method considering environmental factors as described in any item of the first aspect.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The red date maturity picking analysis method considering environmental factors provided by the present invention selects environmental factor data corresponding to time nodes from historical canopy light radiation data, historical temperature data, and historical humidity data, and performs hierarchical analysis on red dates in the target image according to gray level information, and can obtain the maturity time nodes corresponding to different levels. Finally, considering the red date density distribution of different levels and the specific differences between levels, the optimal time node suitable for picking that can balance red date quality and economic benefits is obtained, providing reference data for red date picking;

[0045] 2. The gray level evolution model in the present invention considers the differential influence of environmental factors on red date maturity at different growth time points, and can be adapted to the prediction and analysis of red date maturity at different growth time nodes, with a wide range of applications;

[0046] 3. The present invention effectively reduces the complexity of the maturity analysis process by performing fusion and accumulation processing on the influence factors of multiple environmental factors, and is applicable to batch analysis of a large number of red dates. Description of the Drawings

[0047] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0048] Figure 1 is a flowchart in an embodiment of the present invention;

[0049] Figure 2 is a system block diagram in an embodiment of the present invention. Detailed Embodiments

[0050] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and do not limit the present invention.

[0051] Embodiment 1: A method for analyzing the mature picking of red dates considering environmental factors, as Figure 1 shown, includes the following steps:

[0052] S1: Obtain historical canopy light radiation data, historical temperature data, and historical humidity data of the target area;

[0053] S1: Use the least squares method to perform curve fitting on the historical canopy light radiation data, historical temperature data, and historical humidity data respectively to obtain the corresponding light radiation fitting function, temperature fitting function, and humidity fitting function;

[0054] S1: Obtain a target image containing red dates in the target area, extract the total gray information of all red dates in the target image, and divide the total gray information into multiple hierarchical gray information according to the gray level interval;

[0055] S1: Solve the mature time nodes of different hierarchical gray information according to the light radiation fitting function, temperature fitting function, humidity fitting function, and gray evolution model;

[0056] S1: Determine the optimal time node for picking red dates in the target area according to all the mature time nodes to obtain a picking strategy.

[0057] It should be noted that the historical canopy light radiation data, historical temperature data, and historical humidity data mainly select historical annual or quarterly data in the same target area. To avoid obvious difference points in the light radiation fitting function, temperature fitting function, and humidity fitting function, interpolation correction processing can be performed on the light radiation fitting function, temperature fitting function, and humidity fitting function according to the correlation relationship between light radiation, temperature, and humidity.

[0058] The present invention selects environmental factor data corresponding to the corresponding time nodes from historical canopy light radiation data, historical temperature data, and historical humidity data, and analyzes the red dates in the target image hierarchically based on gray information, so as to obtain the corresponding maturity time nodes for different levels. Finally, considering the red date density distribution of different levels and the specific differences between levels, the optimal time node suitable for picking that can balance the red date quality and economic benefits is obtained, providing reference data for red date picking.

[0059] In this embodiment, the calculation formula of the gray evolution model is specifically:

[0060]

[0061] Among them, y(t 0 +t) represents the maturity corresponding to the red date growth duration of t 0 +t; a is a constant determined by the growth characteristics of the red date variety in the target area; represents the maturity mapped by the gray value corresponding to the current time node t 0 ; t represents the interval time from the current time node; represents the average value of the environmental factor influence factors corresponding to the interval time t; y 0 represents the maturity reference value; t b represents the maturity time node.

[0062] The calculation formula of the average value of the environmental factor influence factors is specifically:

[0063]

[0064] Among them, A(i) represents the influence factor of light radiation at the i-th moment; B(i) represents the influence factor of temperature at the i-th moment; C(i) represents the influence factor of humidity at the i-th moment.

[0065] The calculation formula of the influence factor of light radiation at the i-th moment is specifically:

[0066]

[0067] Among them, RH(i) represents the light radiation fitting function; RH 0 represents the light radiation reference value.

[0068] The calculation formula of the influence factor of temperature at the i-th moment is specifically:

[0069]

[0070] Among them, T(i) represents the temperature fitting function; T 0 represents the temperature reference value.

[0071] The calculation formula for the influence factor of humidity at time i is specifically as follows:

[0072]

[0073] Among them, S(i) represents the humidity fitting function; S 0 represents the humidity reference value.

[0074] In order to consider the rationality of the influence factor value, limits can be set by setting thresholds. Specifically, if the influence factor calculated for light radiation at time i is greater than the first threshold, then the first threshold is used as the actual influence factor of light radiation at time i; if the influence factor calculated for temperature at time i is greater than the second threshold, then the second threshold is used as the actual influence factor of temperature at time i; if the influence factor calculated for humidity at time i is greater than the third threshold, then the third threshold is used as the actual influence factor of humidity at time i.

[0075] The calculation formula for the optimal time node is specifically as follows:

[0076]

[0077] Among them, n represents the number of layers of the total gray-scale information; t b (j) represents the maturity time node corresponding to the gray-scale information of the j-th layer; t d represents the optimal time node; δ j represents the weight coefficient of the gray-scale information of the j-th layer.

[0078] It should be noted that the weight coefficients of the maturity time nodes are allocated according to the proportion of red dates in the gray-scale information of different layers.

[0079] Example 2: A red date maturity picking analysis system considering environmental factors, as Figure 2 shown, includes a data acquisition module, a curve fitting module, a gray-scale layering module, an evolution analysis module, and a strategy generation module.

[0080] Among them, the data acquisition module is used to obtain the historical canopy light radiation data, historical temperature data, and historical humidity data of the target area. The curve fitting module is used to perform curve fitting on the historical canopy light radiation data, historical temperature data, and historical humidity data respectively by using the least squares method to obtain the corresponding light radiation fitting function, temperature fitting function, and humidity fitting function. The gray level stratification module is used to obtain the target image containing red dates in the target area, extract the total gray level information of all red dates in the target image, and divide the total gray level information into multiple hierarchical gray level information according to the gray level interval. The evolution analysis module is used to solve the maturity time nodes of different hierarchical gray level information according to the light radiation fitting function, temperature fitting function, humidity fitting function, and gray level evolution model. The strategy generation module is used to determine the optimal time node for picking red dates in the target area based on all the maturity time nodes to obtain the picking strategy.

[0081] Working principle: The present invention selects the environmental factor data corresponding to the time nodes from the historical canopy light radiation data, historical temperature data, and historical humidity data, and performs hierarchical analysis on the red dates in the target image according to the gray level information, so as to obtain the maturity time nodes corresponding to different levels. Finally, considering the red date density distribution and specific differences between different levels, the optimal time node suitable for picking that can balance the red date quality and economic benefits is obtained, providing reference data for red date picking.

[0082] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0084] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the function.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the function.

[0086] The above specific implementation manners have further elaborated on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. Red date ripening and picking analysis method considering environmental factors, Characterized in that, It includes the following steps: Obtain the historical canopy light radiation data, historical temperature data, and historical humidity data of the target area; Use the least squares method to perform curve fitting on the historical canopy light radiation data, historical temperature data, and historical humidity data respectively to obtain the corresponding light radiation fitting function, temperature fitting function, and humidity fitting function; Obtain the target image containing red dates in the target area, extract the total gray information of all red dates in the target image, and divide the total gray information into multiple hierarchical gray information according to the gray level interval; Solve the ripening time nodes of different hierarchical gray information according to the light radiation fitting function, temperature fitting function, humidity fitting function, and gray evolution model; Determine the optimal time node for picking red dates in the target area according to all the ripening time nodes to obtain the picking strategy; The specific calculation formula of the gray evolution model is: Among them, y(t 0 +t) represents the maturity corresponding to the growth duration of t 0 +t of red dates; a is a constant determined by the growth characteristics of the red date variety in the target area; represents the maturity mapped by the gray value corresponding to the current time node t 0 ; t represents the time interval from the current time node; represents the average value of the environmental factor influence factors corresponding to the time interval t; y 0 represents the maturity reference value; t b represents the maturity time node; The specific calculation formula of the mean value of the environmental factor influence factor is: Among them, A(i) represents the influence factor of light radiation at time i; B(i) represents the influence factor of temperature at time i; C(i) represents the influence factor of humidity at time i; The specific calculation formula of the influence factor of light radiation at time i is: Among them, RH(i) represents the light radiation fitting function; RH 0 represents the light radiation reference value; The specific calculation formula of the influence factor of temperature at time i is: Among them, T(i) represents the temperature fitting function; T 0 represents the temperature reference value; The specific calculation formula of the influence factor of humidity at time i is: Among them, S(i) represents the humidity fitting function; S 0 represents the humidity reference value.

2. The red date ripening and picking analysis method considering environmental factors according to claim 1, Characterized in that, If the influence factor calculated by light radiation at time i is greater than the first threshold, then use the first threshold as the actual influence factor of light radiation at time i; If the influence factor calculated by temperature at time i is greater than the second threshold, then use the second threshold as the actual influence factor of temperature at time i; If the influence factor calculated by humidity at time i is greater than the third threshold, then use the third threshold as the actual influence factor of humidity at time i.

3. The red date ripening and picking analysis method considering environmental factors according to claim 1 or 2, Characterized in that, The specific calculation formula of the optimal time node is: Among them, n represents the number of layers of the total grayscale information; t b (j) represents the maturity time node corresponding to the grayscale information of the j-th layer; t d represents the optimal time node; δ j represents the weight coefficient of the grayscale information of the j-th layer.

4. The red date ripening and picking analysis method considering environmental factors according to claim 3, Characterized in that, The weight coefficient of the ripening time node is allocated according to the proportion of red dates in different hierarchical gray information.

5. Red date ripening and picking analysis system considering environmental factors, Characterized in that, This system is used to implement the red date ripening and picking analysis method considering environmental factors as described in any one of claims 1-4, including: A data acquisition module for obtaining the historical canopy light radiation data, historical temperature data, and historical humidity data of the target area; A curve fitting module for using the least squares method to perform curve fitting on the historical canopy light radiation data, historical temperature data, and historical humidity data respectively to obtain the corresponding light radiation fitting function, temperature fitting function, and humidity fitting function; A gray level stratification module for obtaining the target image containing red dates in the target area, extracting the total gray information of all red dates in the target image, and dividing the total gray information into multiple hierarchical gray information according to the gray level interval; An evolution analysis module, which is used to solve the maturity time nodes of gray-scale information at different levels according to the light radiation fitting function, temperature fitting function, humidity fitting function, and gray-scale evolution model; A strategy generation module, which is used to determine the optimal time node for red date picking in the target area based on all the maturity time nodes, and obtain the picking strategy.

6. A computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the red date maturity picking analysis method considering environmental factors according to any one of claims 1-4.

7. A computer-readable medium, on which a computer program is stored, characterized in that, when the computer program is executed by the processor, it can implement the red date maturity picking analysis method considering environmental factors according to any one of claims 1-4.

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