A method for monitoring the growth state of garden trees

Abstract

The present application relates to the technical field of data processing, in particular to a kind of garden tree growth state monitoring method, the method comprises: determining the growth rate curve of tree;According to growth rate curve, precipitation, watering amount and fertilization amount determine the soil nutrient correlation degree of tree, and according to growth rate curve, humidity data and temperature data of tree in predetermined time period every day determine the environmental temperature and humidity correlation degree of tree;Determine the tree growth rate theoretical value of tree, determine the tree growth rate actual value of tree;Determine the growth abnormality degree of tree;According to the growth abnormality degree of tree and the growth abnormality degree of other trees in the same batch of tree species in garden, determine the growth monitoring index of tree in corresponding month;The growth state of tree is determined using the growth monitoring index of tree in each month.The present application improves the accuracy of the monitoring result of the growth state of tree in garden.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a garden tree growth state monitoring method. BACKGROUND

[0002] A garden is a natural and cultural landscape space created and maintained by artificial means. The garden includes a comprehensive layout of vegetation, water bodies, terrain, and buildings and other elements. Among them, when greening the garden, long-term tracking of the growth changes of trees can help researchers better understand the growth rules of trees. When the growth rate of the tree does not meet the growth rule, it means that the tree may be in an abnormal growth state.

[0003] In some scenarios, by monitoring the positive correlation between the growth rate of the trees in the garden and the watering amount and the fertilization amount, when the growth rate of the trees in the garden does not meet the positive correlation between the watering amount and the fertilization amount, it is considered that the monitored trees may be in an abnormal growth state such as disease. Among them, the watering amount and the fertilization amount essentially affect the soil moisture and soil conductivity of the tree roots. In actual situations, external environmental influences may also affect the soil moisture and soil conductivity of the tree roots, such as rainfall and snowfall, and the dependence of different growth stages of the trees on the watering amount and the fertilization amount varies, such as tree seedling stage mainly relies on environmental temperature and humidity to prevent seedling death, and tree growth stage relies more on soil nutrients to accelerate growth. Therefore, determining the growth state of the trees in the garden by only monitoring the positive correlation between the growth rate of the trees in the garden and the watering amount and the fertilization amount is not accurate, resulting in low accuracy of the monitoring result of the growth state of the trees in the garden. SUMMARY

[0004] In order to solve the technical problem of low accuracy of the monitoring result of the growth state of the trees in the garden, the purpose of the present application is to provide a garden tree growth state monitoring method, and the technical solution adopted is as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for monitoring growth state of a tree in a garden, comprising: determining a growth rate curve of the tree based on tree height data of the tree in the garden in each day in a predetermined time period; determining a soil nutrient related degree of the tree according to the growth rate curve, precipitation, watering amount and fertilization amount of the tree in each day in the predetermined time period, and determining an environmental temperature and humidity related degree of the tree according to the growth rate curve, humidity data and temperature data of the tree in each day in the predetermined time period; determining a theoretical value of the growth rate of the tree by using the soil nutrient related degree and the environmental temperature and humidity related degree, and determining an actual value of the growth rate of the tree from the growth rate curve; determining a growth anomaly degree of the tree according to the theoretical value of the growth rate of the tree and the actual value of the growth rate of the tree in a corresponding month; determining a growth monitoring index of the tree in the corresponding month according to the growth anomaly degree of the tree and growth anomaly degrees of other trees of the same batch as the tree in the garden; and determining a growth state of the tree by using the growth monitoring index of the tree in each month.

[0006] Optionally, the determining of the soil nutrient related degree of the tree according to the growth rate curve, the precipitation, the watering amount and the fertilization amount of the tree in each day in the predetermined time period comprises: determining a first ratio of the precipitation of a previous day and a maximum value of the precipitation as a groundwater storage degree of the tree in a current day; determining a soil moisture reflection degree of the tree in the current day according to the groundwater storage degree of the tree in the current day, the watering amount of the tree in the current day and a maximum watering amount of the tree in the predetermined time period; determining a soil conductivity reflection degree of the tree in the current day according to the groundwater storage degree of the tree in the current day, the fertilization amount of the tree in the current day and a maximum fertilization amount of the tree in the predetermined time period; determining a nutrient supply range of the tree in the current day by using the soil moisture reflection degree and the soil conductivity reflection degree; determining a nutrient supply range curve of the tree according to the nutrient supply range of each day in the predetermined time period; and segmenting the growth rate curve and the nutrient supply range curve by month respectively to obtain a plurality of growth rate sub-curves and a plurality of nutrient supply range sub-curves, and determining the soil nutrient related degree of the tree in a corresponding month according to a first Pearson correlation coefficient between the growth rate sub-curve and the nutrient supply range sub-curve of the corresponding month.

[0007] Optionally, the determining of the soil moisture reflection degree of the tree in the current day according to the groundwater storage degree of the tree in the current day, the watering amount of the tree in the current day and the maximum watering amount of the tree in the predetermined time period comprises: calculating a second ratio between the watering amount of the tree in the current day and the maximum watering amount of the tree in the predetermined time period; and determining a first product of the groundwater storage degree and the second ratio as the soil moisture reflection degree of the tree in the current day.

[0008] Optionally, the determining the soil moisture reflecting degree of the tree in the current day according to the groundwater storage degree of the tree in the current day, the fertilization amount of the tree in the current day, and the maximum fertilization amount of the tree in the predetermined time period comprises: calculating a third ratio between the fertilization amount of the tree in the current day and the maximum fertilization amount of the tree in the predetermined time period; and determining a second product of the groundwater storage degree and the third ratio as the soil moisture reflecting degree of the tree in the current day.

[0009] Optionally, the determining the soil nutrient correlation degree of the tree in the corresponding month according to the first Pearson correlation coefficient between the growth rate sub-curve and the nutrient supply amplitude sub-curve of the corresponding month comprises: calculating a first average coefficient of the first Pearson correlation coefficient between the growth rate sub-curve and the nutrient supply amplitude sub-curve of the corresponding month; and determining a fourth ratio of the first Pearson correlation coefficient between the growth rate sub-curve and the nutrient supply amplitude sub-curve of the corresponding month and the first average coefficient as the soil nutrient correlation degree of the tree in the corresponding month.

[0010] Optionally, the determining the environmental temperature and humidity correlation degree of the tree according to the growth rate curve, the humidity data and the temperature data of the tree in each day in the predetermined time period comprises: respectively determining an environmental humidity curve and an environmental temperature curve of the tree according to the humidity data and the temperature data of each day in the predetermined time period; segmenting the environmental humidity curve and the environmental temperature curve by month to obtain a plurality of environmental humidity sub-curves and a plurality of environmental temperature sub-curves; determining an environmental humidity correlation degree of the tree in the corresponding month according to a second Pearson correlation coefficient between the growth rate sub-curve and the environmental humidity sub-curve of the corresponding month; determining an environmental temperature correlation degree of the tree in the corresponding month according to a third Pearson correlation coefficient between the growth rate sub-curve and the environmental temperature sub-curve of the corresponding month; and determining an average value of the environmental humidity correlation degree and the environmental temperature correlation degree as the environmental temperature and humidity correlation degree of the tree in the corresponding month.

[0011] Optionally, the determining the tree growth rate theoretical value of the tree according to the soil nutrient correlation degree and the environmental temperature and humidity correlation degree comprises: determining a soil nutrient dependence degree of the tree in the corresponding month according to the soil nutrient correlation degree and the environmental temperature and humidity correlation degree; performing a least square fitting on the soil nutrient correlation degree of the tree in the corresponding month and the environmental temperature and humidity correlation degree of the tree in the corresponding month to obtain a first slope of a first fitting straight line of the soil nutrient correlation degree and a second slope of a second fitting straight line of the environmental temperature and humidity correlation degree; determining a maturity degree of the tree in the corresponding month according to the first slope, the second slope, and the soil nutrient dependence degree of the tree in the corresponding month; and determining the tree growth rate theoretical value of the tree in the corresponding month according to the environmental temperature and humidity correlation degree of the tree in the corresponding month and the maturity degree.

[0012] Optionally, the soil nutrient dependence degree of the tree in the corresponding month is determined according to the soil nutrient correlation degree and the environmental temperature and humidity correlation degree, and comprises: calculating a fifth ratio between the soil nutrient correlation degree and the environmental temperature and humidity correlation degree; and performing normalization processing on the fifth ratio to obtain the soil nutrient dependence degree of the tree in the corresponding month.

[0013] Optionally, the maturity degree of the tree in the corresponding month is determined according to the first slope, the second slope and the soil nutrient dependence degree of the tree in the corresponding month, and comprises: calculating a sixth ratio between the first slope and the second slope; performing normalization processing on the sixth ratio to obtain a normalized ratio; and determining a third product between the normalized ratio and the soil nutrient dependence degree as the maturity degree of the tree in the corresponding month.

[0014] Optionally, the growth monitoring index of the tree in the corresponding month is determined according to the growth abnormality degree of the tree and the growth abnormality degrees of other trees of the same batch of the tree species in the garden, and comprises: calculating an average value of a first difference between the theoretical tree growth rate value and the actual tree growth rate value of the tree in each month; determining a seventh ratio between the first difference and the average value between the theoretical tree growth rate value and the actual tree growth rate value of the tree in the corresponding month as the growth abnormality degree of the tree in the corresponding month; calculating an average abnormality degree of the growth abnormality degrees of the other trees of the same batch of the tree species in the garden; calculating a second difference between the growth abnormality degree of the tree in the corresponding month and the average abnormality degree; and performing normalization processing on the second difference to obtain the growth monitoring index of the tree in the corresponding month.

[0015] The present application has the following beneficial effects: firstly, the growth rate curve of the tree is determined based on the tree height data of the tree in the garden every day in a predetermined period of time; then, the soil nutrient correlation degree of the tree is determined according to the growth rate curve, the precipitation, the watering amount and the fertilization amount of the tree every day in the predetermined period of time, and the environmental temperature and humidity correlation degree of the tree is determined according to the humidity data and the temperature data of the tree every day in the predetermined period of time; the theoretical tree growth rate value of the tree is determined by using the soil nutrient correlation degree and the environmental temperature and humidity correlation degree, the actual tree growth rate value of the tree is determined from the growth rate curve, and the growth abnormality degree of the tree is determined according to the theoretical tree growth rate value and the actual tree growth rate value of the tree in the corresponding month; secondly, the growth monitoring index of the tree in the corresponding month is determined according to the growth abnormality degree of the tree and the growth abnormality degrees of other trees of the same batch of the tree species in the garden; and finally, the growth state of the tree is determined by using the growth monitoring index of the tree in each month.

[0016] Therefore, the embodiment of the present application can determine the tree growth rate theoretical value in combination with the precipitation, watering amount, fertilization amount, and temperature and humidity data in the growth environment of the tree in the garden, determine the tree growth abnormality degree in combination with the actual tree growth rate, monitor the growth abnormality degrees of other trees of the same batch and same species as the tree in the garden, and determine the tree growth monitoring index based on the growth abnormality degrees. Therefore, the growth state of the tree is determined according to the growth monitoring index, and the accuracy of the monitoring result of the growth state of the tree in the garden is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings required to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A flow chart of a garden tree growth state monitoring method provided by an embodiment of the present application is shown in

[0019] Figure 2 A tree growth rate curve schematic diagram provided by an embodiment of the present application is shown in DETAILED DESCRIPTION

[0020] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purpose, the specific embodiments, structures, features and effects of the garden tree growth state monitoring method according to the present application are described in detail as follows in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0022] The specific scheme of the garden tree growth state monitoring method provided by the present application is specifically described below in combination with the drawings.

[0023] Embodiment one:

[0024] Please refer to Figure 1 which shows a flow chart of a garden tree growth state monitoring method provided by an embodiment of the present application, including:

[0025] S101, determining a tree growth rate curve of the tree in the garden based on the tree height data of the tree in the garden every day in a predetermined time period.

[0026] Specifically, the predetermined time period can be determined according to actual conditions, and in the embodiment of the present application, the value is 1 year. When measuring the tree height of the trees every day, a laser range finder can be used for measurement. The laser range finder is aimed at the top of the tree and emits laser light, and the laser range finder receives the laser signal returned by the top of the tree and calculates the tree height data. The tree height data of the trees in the garden every day is monitored and recorded.

[0027] Further, the difference between the tree height of the trees every day and the tree height of the trees the previous day is taken as the growth rate of the trees every day. Then, the growth rate curve of the trees in the predetermined time period is drawn. Among them, as shown in Figure 2 Figure 2 is a tree growth rate curve diagram provided by an embodiment of the present application. As can be seen from Figure 2 , the growth rate curve of the trees changes in real time with the monitoring time.

[0028] Further, while collecting the growth rate of the trees, the watering amount and the fertilizing amount of the trees in the garden every day are recorded, and if the trees are not watered or fertilized that day, the watering amount or the fertilizing amount is recorded as 0. In addition, temperature data and humidity data in the environment where the trees grow can also be collected by using temperature sensors and humidity sensors, and the amount of weather precipitation in the growth time of the trees can be read by using a weather system. Among them, the amount of precipitation includes the amount of rainfall and the amount of snowfall.

[0029] S102, according to the growth rate curve, the amount of precipitation of the trees every day in the predetermined time period, the watering amount and the fertilizing amount, determine the soil nutrient related degree of the trees, and according to the growth rate curve, the humidity data and the temperature data of the trees every day in the predetermined time period, determine the environment temperature and humidity related degree of the trees.

[0030] Specifically, watering and fertilizing the trees in the garden can effectively ensure the good quality of soil moisture and conductivity, and the amount of weather precipitation can also affect soil nutrients. When there is a large amount of precipitation weather, the roots of the trees will store a large amount of underground water, which can improve the soil moisture, and at the same time, there will be a large amount of trace elements in the rainwater, which can improve the soil conductivity. Therefore, the watering amount and the fertilizing amount of the trees in the garden combined with the amount of rainfall can reflect the soil moisture and conductivity, and then the soil nutrient related degree of the trees can be determined.

[0031] ​Further, in determining the soil nutrient correlation degree of the tree, as an optional embodiment of the present application, first, a first ratio of the rainfall of the previous day and the maximum rainfall is determined as the groundwater storage degree of the tree in the current day; then, according to the groundwater storage degree of the tree in the current day, the watering amount of the tree in the current day, and the maximum watering amount of the tree in a predetermined time period, the soil moisture reflection degree of the tree in the current day is determined; and according to the groundwater storage degree of the tree in the current day, the fertilization amount of the tree in the current day, and the maximum fertilization amount of the tree in the predetermined time period, the soil conductivity reflection degree of the tree in the current day is determined; secondly, the nutrient supply range of the tree in the current day is determined by using the soil moisture reflection degree and the soil conductivity reflection degree; and the nutrient supply range curve of the tree is determined according to the nutrient supply range of each day in the predetermined time period; thirdly, the growth rate curve and the nutrient supply range curve are segmented according to months respectively to obtain a plurality of growth rate sub-curves and a plurality of nutrient supply range sub-curves; and finally, the soil nutrient correlation degree of the tree in the corresponding month is determined according to the first Pearson correlation coefficient between the growth rate sub-curve and the nutrient supply range sub-curve of the corresponding month.

[0032] Specifically, in the embodiment of the present application, the rainfall data of the tree in a predetermined time period is read, wherein the rainfall of the i-th day is denoted as P i , and the maximum rainfall in the predetermined time period is denoted as P max . The greater the rainfall of the tree in the garden, the more groundwater is reserved in the root of the tree. Since there is a process lag from the rainfall reservation to the root of the tree, the rainfall of the i-1-th day is used to reflect the groundwater storage degree of the i-th day. The embodiment of the present application specifically calculates the groundwater storage degree of the i-th day by using the following formula:

[0033]

[0034] In the formula, U i represents the groundwater storage degree of the i-th day. P i-1 represents the rainfall of the i-1-th day. P max represents the maximum rainfall in the predetermined time period.

[0035] It should be noted that, in order to ensure that the calculation result is meaningful, when performing fractional operation, a parameter adjustment factor greater than 0 needs to be added to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to the actual situation, and the present application does not make special limitations.

[0036] Further, for a certain tree, the greater the watering amount of the tree, the greater the groundwater reserve degree of the tree root per day, which indicates that the soil moisture of the tree root is greater. Therefore, when determining the soil moisture reflection degree of the tree in the current day, as an optional embodiment of the present application, first, a second ratio between the watering amount of the tree in the current day and the maximum watering amount of the tree in the predetermined period is calculated; and then a first product of the groundwater reserve degree and the second ratio is determined as the soil moisture reflection degree of the tree in the current day.

[0037] Specifically, the present embodiment records any day as the i-th day, and the present embodiment calculates the soil moisture reflection degree of the tree in the current day by using the following formula:

[0038]

[0039] In the above formula, IY i represents the soil moisture reflection degree of the tree in the i-th day. U i represents the groundwater reserve degree in the i-th day. J i represents the watering amount of the tree in the i-th day. J max represents the maximum watering amount of the tree in the predetermined period.

[0040] Further, when determining the soil conductivity reflection degree of the tree in the current day, as an optional embodiment of the present application, first, a third ratio between the fertilization amount of the tree in the current day and the maximum fertilization amount of the tree in the predetermined period is calculated; and then a second product of the groundwater reserve degree and the third ratio is determined as the soil conductivity reflection degree of the tree in the current day.

[0041] Specifically, the present embodiment records any day as the i-th day, and the present embodiment calculates the soil conductivity reflection degree of the tree in the current day by using the following formula:

[0042]

[0043] In the above formula, IX i represents the soil conductivity reflection degree of the tree in the i-th day. U i represents the groundwater reserve degree in the i-th day. F i represents the fertilization amount of the tree in the i-th day. F max represents the maximum fertilization amount of the tree in the predetermined period.

[0044] Further, since the soil moisture and the soil conductivity are both main nutrients of the soil of the tree, the nutrient supply range of the tree in the i-th day can be calculated according to the soil moisture reflection degree in combination with the soil conductivity reflection degree, and the present embodiment calculates the nutrient supply range of the tree in the i-th day by using the following formula:

[0045] G i= IY i × IX i

[0046] In the above formula, G i represents the nutrient supply range of the tree on the i-th day. IX i represents the soil conductivity reflection degree of the tree on the i-th day. IY i represents the soil moisture reflection degree of the tree on the i-th day.

[0047] Thus, the nutrient supply range curve of the tree is drawn by taking time as the abscissa and taking the nutrient supply range as the ordinate, by calculating the nutrient supply range of the tree every day in the predetermined time period according to the above embodiment of the present application.

[0048] Further, considering that the demand degree of the tree for the same environmental factor also differs in different growth stages, such as the seedling stage, the tree is more dependent on the environmental temperature and humidity than the soil nutrient to ensure the survival of the seedling. While the mature tree is more dependent on the soil nutrient than the environmental temperature and humidity to ensure the rapid growth of the tree, it is necessary to judge the growth stage in which the tree is located. Then, the theoretical value of the growth rate of the tree is obtained by combining the nutrient supply range, and the real-time growth abnormality degree of the tree is obtained according to the difference between the theoretical value and the actual value of the growth rate.

[0049] Further, the nutrient supply range curve and the growth rate curve are segmented by month, and the Pearson correlation coefficient between each segment of the nutrient supply range sub-curve and the growth rate sub-curve in each month is calculated. In determining the soil nutrient correlation degree of the tree in the corresponding month, as an optional embodiment of the present application, first, the first average coefficient of the first Pearson correlation coefficient between the growth rate sub-curve and the nutrient supply range sub-curve in the corresponding month is calculated; and then the fourth ratio of the first Pearson correlation coefficient between the growth rate sub-curve and the nutrient supply range sub-curve in the corresponding month to the first average coefficient is determined as the soil nutrient correlation degree of the tree in the corresponding month.

[0050] Specifically, the soil nutrient correlation degree of the tree in the corresponding month is calculated by the following formula in the embodiment of the present application:

[0051]

[0052] In the above formula, IA represents the soil nutrient correlation degree of the real-time month. PCC represents the first Pearson correlation coefficient of the nutrient supply range sub-curve and the growth rate sub-curve in the real-time month. represents the average of the first Pearson correlation coefficients of the nutrient supply range sub-curve and the growth rate sub-curve in all months in the predetermined time period, i.e., the first average coefficient.

[0053] Further, in determining the environmental temperature and humidity correlation degree of the tree, as an optional embodiment of the present application, the environmental humidity curve and the environmental temperature curve of the tree are determined according to the humidity data and the temperature data of each day in a predetermined time period respectively; the environmental humidity curve and the environmental temperature curve are segmented by month respectively to obtain a plurality of environmental humidity sub-curves and a plurality of environmental temperature sub-curves; the environmental humidity correlation degree of the tree in the corresponding month is determined according to the second Pearson correlation coefficient between the growth rate sub-curve and the environmental humidity sub-curve of the corresponding month; the environmental temperature correlation degree of the tree in the corresponding month is determined according to the third Pearson correlation coefficient between the growth rate sub-curve and the environmental temperature sub-curve of the corresponding month; and the average of the environmental humidity correlation degree and the environmental temperature correlation degree is determined as the environmental temperature and humidity correlation degree of the tree in the corresponding month.

[0054] Specifically, the environmental humidity curve and the environmental temperature curve of the tree are plotted with time as the horizontal coordinate and temperature data and humidity data as the vertical coordinate respectively. Then the environmental humidity curve and the environmental temperature curve are segmented by month respectively to obtain a plurality of environmental humidity sub-curves and a plurality of environmental temperature sub-curves.

[0055] Further, in determining the environmental humidity correlation degree of the tree in the corresponding month, the following formula can be used for calculation:

[0056]

[0057] In the above formula, IF represents the environmental humidity correlation degree of the tree in the real-time month. PCC2 represents the second Pearson correlation coefficient between the environmental humidity sub-curve and the growth rate sub-curve of the real-time month. PCC2 represents the average of the second Pearson correlation coefficients between the environmental humidity sub-curve and the growth rate sub-curve of all months in the predetermined time period.

[0058] Further, in determining the environmental temperature correlation degree of the tree in the corresponding month, the following formula can be used for calculation:

[0059]

[0060] In the above formula, IE represents the environmental temperature correlation degree of the tree in the corresponding month. PCC1 represents the third Pearson correlation coefficient between the growth rate sub-curve and the environmental temperature sub-curve of the real-time month. PCC1 represents the average of the third Pearson correlation coefficients between the growth rate sub-curve and the environmental temperature sub-curve of all months.

[0061] Further, the average of IF and IE is taken as the environmental temperature and humidity correlation degree of the tree in the corresponding month.

[0062] S103, determining the theoretical value of the tree growth rate of the tree according to the soil nutrient correlation degree and the environmental temperature and humidity correlation degree, and determining the actual value of the tree growth rate of the tree from the growth rate curve.

[0063] Specifically, the greater the soil nutrient correlation degree of the real-time month of the tree compared with the environmental temperature and humidity correlation degree, the more the tree depends on the soil nutrient, and the more mature the growth stage of the current tree. Therefore, when determining the actual value of the tree growth rate of the tree, as an optional embodiment of the present application, first, the soil nutrient dependence degree of the tree in the corresponding month is determined according to the soil nutrient correlation degree and the environmental temperature and humidity correlation degree; then, the soil nutrient correlation degree of the tree in the corresponding month and the environmental temperature and humidity correlation degree of the tree in the corresponding month are fitted by the least square method to obtain a first slope of a first fitting straight line of the soil nutrient correlation degree and a second slope of a second fitting straight line of the environmental temperature and humidity correlation degree; then, the maturity degree of the tree in the corresponding month is determined according to the first slope, the second slope and the soil nutrient dependence degree of the tree in the corresponding month; and finally, the theoretical value of the tree growth rate of the tree in the corresponding month is determined according to the environmental temperature and humidity correlation degree of the tree in the corresponding month and the maturity degree.

[0064] In the determination of the soil nutrient dependence degree of the tree in the corresponding month, as an optional embodiment of the present application, first, a fifth ratio between the soil nutrient correlation degree and the environmental temperature and humidity correlation degree is calculated; and then, the soil nutrient dependence degree of the tree in the corresponding month is obtained by normalizing the fifth ratio.

[0065] Specifically, the soil nutrient dependence degree of the tree in the corresponding month can be calculated by the following formula:

[0066]

[0067] In the above formula, IC represents the soil nutrient dependence degree of the tree in the corresponding month. IB represents the environmental temperature and humidity correlation degree of the tree in the corresponding month. IA represents the soil nutrient correlation degree of the real-time month. norm represents a normalization function, which is used to perform linear normalization processing on the fifth ratio, and is not limited to the maximum and minimum value normalization. The linear normalization processing can be the maximum and minimum value normalization.

[0068] Further, the sample points of the soil nutrient correlation degrees of the tree in each month are placed in a two-dimensional coordinate system, i.e., time is taken as the horizontal coordinate and the soil nutrient correlation degree is taken as the vertical coordinate, a first fitting straight line is obtained by fitting the multiple sample points of the soil nutrient correlation degree by using the least square straight line fitting method, and a first slope K of the first fitting straight line is extracted. Then, the environmental temperature and humidity correlation degree is fitted in the same way to obtain a second fitting straight line, and a second slope of the second fitting straight line is extracted.

[0069] Further, the more the tree tends to grow into the mature stage, the more the degree of demand for soil nutrients will increase in the time domain, and the more the degree of demand for environmental temperature and humidity will decrease in the time domain, the greater K is and the smaller IB is, and the higher the confidence of the growth reflection is. Therefore, in determining the maturity degree of the tree in the corresponding month, as an optional embodiment of the present application, a sixth ratio between the first slope and the second slope is first calculated; then the sixth ratio is normalized to obtain a normalized ratio; and finally a third product between the normalized ratio and the soil nutrient dependence degree is determined as the maturity degree of the tree in the corresponding month.

[0070] Specifically, the maturity degree of the tree in the corresponding month is calculated by the following formula in the embodiment of the present application:

[0071]

[0072] In the above formula, G represents the maturity degree of the tree in the corresponding month. K represents the first slope. represents the second slope. IC represents the soil nutrient dependence degree of the tree in the corresponding month. norm represents a normalization function, which is used to normalize the sixth ratio.

[0073] Further, the more the tree tends to grow into the mature stage, the more the tree depends on soil moisture, and the more the tree in the seedling stage depends on the temperature and humidity of the environment other than soil nutrients. Therefore, the tree growth rate theoretical value of the tree in the corresponding month is calculated according to the maturity degree of the monitoring tree growth stage and the environmental temperature and humidity dependence degree of the soil, and specifically calculated by the following formula:

[0074] v = IB x G

[0075] In the above formula, v represents the tree growth rate theoretical value in the corresponding month. G represents the maturity degree of the tree in the corresponding month. IB represents the environmental temperature and humidity dependence degree of the tree in the corresponding month.

[0076] In S104, the growth abnormality degree of the tree is determined according to the tree growth rate theoretical value of the tree in the corresponding month and the actual tree growth rate of the tree.

[0077] Specifically, the actual tree growth rate of the tree is read from the growth rate curve. The tree growth rate theoretical value of the tree in each month and the actual tree growth rate of the tree are subtracted to obtain a growth rate difference degree. If the growth rate difference degree of the tree in a certain month is much greater than the average growth rate difference degree of the tree in other months, it means that the growth abnormality degree of the tree in the month is greater.

[0078] ​​Further, in the calculation of the growth abnormality degree of the tree, as an optional embodiment of the present application, the average of the first difference between the theoretical value of the growth rate of the tree in each month and the actual value of the growth rate of the tree is calculated; the seventh ratio of the first difference between the theoretical value of the growth rate of the tree in the corresponding month and the actual value of the growth rate of the tree to the average is determined as the growth abnormality degree of the tree in the corresponding month.

[0079] S105, determining the growth monitoring index of the tree in the corresponding month according to the growth abnormality degree of the tree and the growth abnormality degrees of other trees of the same batch and the same species in the garden.

[0080] Specifically, there are multiple brands and batches of trees in the garden, and the environmental influence, soil nutrients and irrigation of these trees are kept substantially the same. Therefore, the growth monitoring index of the tree can be obtained according to the difference between the growth abnormality degree of the tree and the growth abnormality degrees of other trees of the same species and the same batch in the current garden.

[0081] Further, the growth abnormality degree Q of the jth tree of the same batch and the same species as the tree to be monitored in the garden is calculated in the manner described in the above embodiments of the present application. j Suppose that there are M trees of the same batch and the same species as the tree to be monitored in the garden. If the growth abnormality degree of the tree to be monitored is significantly higher than that of other trees of the same batch and the same species, the probability that the tree to be monitored is in an abnormal growth state is higher. Therefore, in the determination of the growth monitoring index of the tree in the corresponding month, as an optional embodiment of the present application, the average abnormality degree of the growth abnormality degrees of other trees of the same batch and the same species in the garden is first calculated; then the second difference between the growth abnormality degree of the tree in the corresponding month and the average abnormality degree is calculated; and finally the second difference is normalized to obtain the growth monitoring index of the tree in the corresponding month.

[0082] Specifically, the growth monitoring index σ of the tree is calculated by the following formula in the embodiments of the present application:

[0083]

[0084] In the above formula, σ represents the growth monitoring index of the tree. Q represents the growth abnormality degree of the tree in the corresponding month. Q j represents the growth abnormality degree of the jth tree of the same batch and the same species as the tree to be monitored. M represents the number of trees of the same batch and the same species as the tree to be monitored in the garden. norm represents a normalization function for normalizing .

[0085] S106, determining the growth state of the tree by using the growth monitoring index of the tree in each month.

[0086] Specifically, the growth monitoring index σ of the tree in the last 10 months closest to the current time of the tree can be selected. The growth monitoring index σ of the tree in the last 10 months closest to the current monitoring garden is placed in a two-dimensional coordinate system, in which the horizontal coordinate is time and the vertical coordinate is the growth monitoring index σ. The least square linear fitting method is used to linearly fit the growth monitoring index σ in the two-dimensional coordinate system to obtain a fitting straight line. The greater the slope of the fitting straight line, the greater the possibility that the growth monitoring index σ of the tree in the monitoring garden in the last 10 months shows an upward trend. Further, if the slope of the fitting straight line of the growth monitoring index σ of the tree in the last 10 months of the currently monitored garden is greater than a first threshold value and the growth monitoring index σ of the last 10 months is greater than a second threshold value, it is considered that the tree is in a higher probability of abnormal growth state such as disease, and the tree needs to be diagnosed by the garden plant medical staff to a deeper degree. The specific values of the first threshold value and the second threshold value can be determined according to the actual situation, and in the embodiment of the present application, the first threshold value can be 0.5, and the second threshold value can be 0.88.

[0087] The embodiment of the present application can determine the tree growth rate theoretical value in combination with the precipitation, watering amount, fertilization amount, temperature and humidity data in the tree growth environment in the garden, and then determine the tree growth abnormality degree in combination with the actual value of the tree growth rate, and monitor the growth abnormality degree of other trees of the same batch and same species as the tree in the garden, and determine the growth monitoring index of the tree in this way. In this way, the growth state of the tree is determined according to the growth monitoring index, which improves the accuracy of the monitoring result of the growth state of the tree in the garden.

[0088] It should be noted that the above-mentioned embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0089] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. A method for monitoring the growth state of an ornamental tree, characterized by, The garden tree growth state monitoring method comprises: determining a growth rate curve of the tree based on daily tree height data of the tree in a garden in a predetermined time period; determining a soil nutrient related degree of the tree according to the growth rate curve, daily precipitation, watering amount and fertilization amount of the tree in the predetermined time period, and determining an environment temperature and humidity related degree of the tree according to the growth rate curve, daily humidity data and temperature data of the tree in the predetermined time period; the determination of the soil nutrient related degree of the tree comprises: determining a first ratio of the precipitation of the previous day and the maximum value of the precipitation as the groundwater storage degree of the tree in the current day; determining a soil moisture reflection degree of the tree in the current day according to the groundwater storage degree of the tree in the current day, the watering amount of the tree in the current day and the maximum watering amount of the tree in the predetermined time period, comprising: calculating a second ratio between the watering amount of the tree in the current day and the maximum watering amount of the tree in the predetermined time period; determining a first product of the groundwater storage degree and the second ratio as the soil moisture reflection degree of the tree in the current day; determining a soil conductivity reflection degree of the tree in the current day according to the groundwater storage degree of the tree in the current day, the fertilization amount of the tree in the current day and the maximum fertilization amount of the tree in the predetermined time period, comprising: calculating a third ratio between the fertilization amount of the tree in the current day and the maximum fertilization amount of the tree in the predetermined time period; determining a second product of the groundwater storage degree and the third ratio as the soil conductivity reflection degree of the tree in the current day; determining a nutrient supply range of the tree in the current day by using the soil moisture reflection degree and the soil conductivity reflection degree; the calculation formula of the nutrient supply range is: In the above formula, represents the degree of nutrient supply of the tree on the i-th day; represents the degree of soil conductivity reflection of the tree on the i-th day; represents the degree of soil moisture reflection of the tree on the i-th day; determining a nutrient supply range curve of the tree according to the nutrient supply range of each day in the predetermined time period; segmenting the growth rate curve and the nutrient supply range curve by months respectively to obtain a plurality of growth rate sub-curves and a plurality of nutrient supply range sub-curves; determining a soil nutrient related degree of the tree in a corresponding month according to a first Pearson correlation coefficient between the growth rate sub-curve and the nutrient supply range sub-curve of the corresponding month; the calculation formula of the soil nutrient related degree is: In the above formula, a soil nutrient correlation degree for a real-time month; a first Pearson correlation coefficient of the nutrient supply amplitude sub-curve and the growth rate sub-curve for a real-time month; a mean value of the first Pearson correlation coefficient of the nutrient supply amplitude sub-curve and the growth rate sub-curve for all months in a predetermined time period; the determination of the environment temperature and humidity related degree of the tree comprises: determining an environment humidity curve and an environment temperature curve of the tree according to the humidity data and the temperature data of each day in the predetermined time period respectively; segmenting the environment humidity curve and the environment temperature curve by months respectively to obtain a plurality of environment humidity sub-curves and a plurality of environment temperature sub-curves; determining an environment humidity related degree of the tree in a corresponding month according to a second Pearson correlation coefficient between the growth rate sub-curve and the environment humidity sub-curve of the corresponding month; the calculation formula of the environment humidity related degree is: In the above formulae, represents the degree of correlation of the tree to the environmental humidity in the real-time month; represents the second Pearson correlation coefficient between the environmental humidity sub-curve and the growth rate sub-curve of the real-time month; represents the average of the second Pearson correlation coefficients between the environmental humidity sub-curve and the growth rate sub-curve of all months in the predetermined time period; determine the environmental temperature correlation degree of the tree in the corresponding month according to the third Pearson correlation coefficient between the growth rate sub-curve and the environmental temperature sub-curve of the corresponding month; the calculation formula of the environmental temperature correlation degree is: in the above formulae, denotes the degree of correlation of the tree with the ambient temperature in the corresponding month; denotes the third Pearson correlation coefficient between the growth rate sub-curve and the ambient temperature sub-curve for the real-time month; denotes the mean of the third Pearson correlation coefficients between the growth rate sub-curve and the ambient temperature sub-curve for all months. determine the average value of the environmental humidity correlation degree and the environmental temperature correlation degree as the environmental temperature and humidity correlation degree of the tree in the corresponding month; determine the tree growth rate theoretical value of the tree by using the soil nutrient correlation degree and the environmental temperature and humidity correlation degree, including: determining the soil nutrient dependence of the tree in the corresponding month according to the soil nutrient correlation degree and the environmental temperature and humidity correlation degree, and the acquisition of the soil nutrient dependence includes: calculating the fifth ratio between the soil nutrient correlation degree and the environmental temperature and humidity correlation degree; the fifth ratio is normalized to obtain the soil nutrient dependence of the tree in the corresponding month; perform least square fitting on the soil nutrient correlation degree of the tree in the corresponding month and the environmental temperature and humidity correlation degree of the tree in the corresponding month to obtain the first slope of the first fitting straight line of the soil nutrient correlation degree and the second slope of the second fitting straight line of the environmental temperature and humidity correlation degree; determine the maturity degree of the tree in the corresponding month according to the first slope, the second slope and the soil nutrient dependence of the tree in the corresponding month, and the acquisition of the maturity degree includes: calculating the sixth ratio between the first slope and the second slope; the sixth ratio is normalized to obtain a normalized ratio; and determining the third product between the normalized ratio and the soil nutrient dependence as the maturity degree of the tree in the corresponding month; determine the tree growth rate theoretical value of the tree in the corresponding month according to the environmental temperature and humidity correlation degree of the tree in the corresponding month and the maturity degree; the calculation formula of the tree growth rate theoretical value is: In the above formula, represents a theoretical value of the growth rate of the tree in the corresponding month; represents the degree of maturity of the tree in the corresponding month; represents the degree of correlation of the environmental temperature and humidity of the tree in the corresponding month; determine the tree growth rate actual value of the tree from the growth rate curve; determine the growth anomaly degree of the tree according to the tree growth rate theoretical value and the tree growth rate actual value of the tree in the corresponding month; determine the growth monitoring index of the tree in the corresponding month according to the growth anomaly degree of the tree and the growth anomaly degrees of other trees of the same batch of the tree species in the garden, including: calculating the average value of the first difference between the tree growth rate theoretical value and the tree growth rate actual value of the tree in each month; determine the seventh ratio between the first difference and the average value between the tree growth rate theoretical value and the tree growth rate actual value of the tree in the corresponding month as the growth anomaly degree of the tree in the corresponding month; calculate the average anomaly degree of the growth anomaly degrees of other trees of the same batch of the tree species in the garden; calculate the second difference between the growth anomaly degree of the tree in the corresponding month and the average anomaly degree; normalize the second difference to obtain the growth monitoring index of the tree in the corresponding month; determine the growth state of the tree by using the growth monitoring index of the tree in each month.

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