Smart agricultural growth monitoring system based on the Internet of Things

Abstract

The present invention relates to the field of agricultural data processing technology, and in particular to a smart agricultural growth monitoring system based on the Internet of Things, the system comprising: a data acquisition module for acquiring multi-dimensional growth monitoring data of crops to be monitored; a model determination module for analyzing the multi-dimensional growth monitoring data to determine the dynamic growth model corresponding to the crops to be monitored at different growth stages, the dynamic growth model being an ellipse that changes with time, the ellipse including a transverse axis length and a longitudinal axis length; a display module for displaying the dynamic growth of the crops to be monitored based on the dynamic growth model. The present invention analyzes the multi-dimensional growth monitoring data of crops to determine the dynamic growth model corresponding to the crops at different growth stages, and intuitively displays the dynamic growth of the crops to be monitored based on the dynamic growth model, thereby effectively improving the reliability of crop growth assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural data processing, and in particular to a smart agricultural growth monitoring system based on the Internet of Things. Background Art

[0002] Smart agriculture is a form of modern agriculture that uses modern information technologies such as the Internet of Things, big data, and artificial intelligence to manage the entire agricultural production process in a refined and efficient manner. Smart agriculture provides suitable growth conditions for crops by real-time monitoring of various parameters in the agricultural production environment, thereby realizing intelligent and precise agricultural production. Using modern scientific and technological means to monitor and manage the growth process of crops in real time can effectively improve agricultural production efficiency and the quality of agricultural products.

[0003] In smart agriculture, it is usually necessary to evaluate the growth of crops. However, traditional crop growth evaluation often relies on subjective human judgment or analysis from a single dimension, which has large errors and one-sidedness, and cannot enable monitoring personnel to grasp the growth status of crops in real time and intuitively. The reliability of crop growth evaluation is low, which is not conducive to the intelligent management of agricultural production. Summary of the invention

[0004] In order to solve the above technical problem of low reliability in crop growth assessment, the purpose of the present invention is to provide a smart agricultural growth monitoring system based on the Internet of Things.

[0005] In order to solve the above technical problems, in the first aspect, the present invention provides a smart agricultural growth monitoring system based on the Internet of Things, the system comprising:

[0006] A data acquisition module, used to acquire multi-dimensional growth monitoring data of the crops to be monitored;

[0007] A model determination module is used to analyze the multi-dimensional growth monitoring data to determine a dynamic growth model corresponding to the monitored crop at different growth stages, wherein the dynamic growth model is an ellipse that changes over time and includes a transverse axis length and a longitudinal axis length;

[0008] The display module is used to display the dynamic growth of the crops to be monitored based on the dynamic growth model.

[0009] In combination with the above-mentioned first aspect, in some possible implementations, the different growth stages include an initial growth stage, a mid-growth stage and a late growth stage, and the late growth stage includes a fruit-bearing stage and a fruit-bearing stage; the multi-dimensional growth monitoring data includes at least: the emergence rate of the monitored crops at different times in the initial growth stage, the plant height and leaf area index at different times in the mid-growth stage, the fruit-bearing rate at different times in the fruit-bearing stage in the late growth stage, and the fruit-bearing rate at different times in the fruit-bearing stage in the late growth stage.

[0010] In combination with the first aspect above, in some possible implementations, the model determination module includes:

[0011] The initial growth model determination module is used to determine the horizontal axis length and the vertical axis length of the dynamic growth model in the initial growth stage according to the emergence rate of the monitored crop at different times in the initial growth stage;

[0012] The mid-growth model determination module is used to determine the horizontal axis length and the vertical axis length of the dynamic growth model in the mid-growth stage according to the plant height and the leaf area index of the monitored crop at different times in the mid-growth stage, and in combination with the final horizontal axis length and the vertical axis length of the dynamic growth model in the early growth stage;

[0013] The late growth model determination module is used to determine the lateral axis length and longitudinal axis length of the dynamic growth model in the late growth stage according to the fruit-bearing rate of the monitored crops at different moments in the fruit-bearing stage in the late growth stage, and the fruit-bearing rate at different moments in the fruit-bearing stage in the late growth stage, and in combination with the final lateral axis length and longitudinal axis length of the dynamic growth model in the mid-growth stage.

[0014] In combination with the first aspect above, in some possible implementations, the early growth model determination module includes:

[0015] A proportion determination module is used to obtain the proportion of the emergence rate according to the proportion of the emergence rate of the monitored crop at different times in the early growth stage to the benchmark emergence rate;

[0016] The axis length determination module is used to calculate the product value of the reference axis length and the proportion of the emergence rate, and use the product value as the horizontal axis length and the longitudinal axis length at different times of the dynamic growth model in the early growth stage.

[0017] In combination with the first aspect above, in some possible implementations, the mid-growth model determination module includes:

[0018] The first transverse axis length determination module is used to determine the transverse axis length of the dynamic growth potential model in the mid-growth stage at the target time according to the discreteness of the difference in plant heights of the monitored crop at all adjacent moments up to the target time in the mid-growth stage, and the plant height of the monitored crop at the target time in the mid-growth stage, and in combination with the final transverse axis length of the dynamic growth potential model in the early growth stage;

[0019] The first longitudinal axis length determination module is used to determine the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time based on the difference in leaf area index of the monitored crop at all adjacent times up to the target time in the mid-growth stage and combined with the final longitudinal axis length of the dynamic growth model in the early growth stage.

[0020] In combination with the first aspect above, in some possible implementations, the first transverse axis length determining module includes:

[0021] A discrete index determination module is used to determine the variance of the absolute value of the difference in plant heights of the monitored crop at all adjacent moments in the mid-growth stage up to the target moment, and obtain a discrete index of plant height difference;

[0022] A transverse axis length growth parameter determination module is used to determine the normalized value of the product of the plant height of the monitored crop at the target time in the mid-growth stage and the plant height difference discrete index to obtain a first transverse axis length growth parameter;

[0023] A transverse axis length growth value determination module, used to determine the product of the reference axis length and the first transverse axis length growth parameter to obtain a first transverse axis length growth value;

[0024] The transverse axis length acquisition module is used to determine the sum of the final transverse axis length of the dynamic growth model in the early growth stage and the first transverse axis length growth value to obtain the transverse axis length of the dynamic growth model in the middle growth stage at the target time.

[0025] In combination with the above first aspect, in some possible implementations, the first longitudinal axis length determining module includes:

[0026] A change slope determination module is used to determine the change slope of the leaf area index of the monitored crop at all adjacent moments in the mid-growth stage up to the target moment;

[0027] A first longitudinal axis length growth parameter determination module, used to determine a first longitudinal axis length growth parameter, if the change slope of the leaf area index of the monitored crop at the target time in the mid-growth stage is greater than or equal to 0, the first longitudinal axis length growth parameter is the normalized value of the change slope of the leaf area index of the monitored crop at the target time in the mid-growth stage; otherwise, the first longitudinal axis length growth parameter is the normalized value of the absolute value of the accumulated value of the change slope of the leaf area index of the monitored crop at the target time and the previous consecutive object time in the mid-growth stage, and the change slope of the leaf area index at the object time is less than 0;

[0028] A first longitudinal axis length growth value determination module, used to determine the product of the reference axis length and the first longitudinal axis length growth parameter to obtain a first longitudinal axis length growth value;

[0029] A longitudinal axis length acquisition module is used to determine the longitudinal axis length of the dynamic growth model in the mid-growth stage at a target time based on the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value. If the slope of the change of the leaf area index of the monitored crop in the mid-growth stage at the target time is greater than or equal to 0, the addition of the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value is used as the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time; otherwise, the difference between the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value is used as the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time.

[0030] In combination with the first aspect above, in some possible implementations, the late growth model determination module includes:

[0031] A second transverse axis length determination module is used to determine the product of the reference axis length and the fruit-bearing rate of the monitored crop at each moment in the fruit-bearing stage in the late growth stage to obtain a second transverse axis length growth value, determine the sum of the final transverse axis length of the dynamic growth model in the mid-growth stage and the second transverse axis length growth value, and obtain the transverse axis length of the dynamic growth model in the late growth stage at each moment in the fruit-bearing stage;

[0032] The second longitudinal axis length determination module is used to determine the ratio of the fruit-bearing rate of the monitored crops at each moment in the fruit-bearing stage in the late growth stage to the final fruit-bearing rate of the monitored crops in the fruit-bearing stage in the late growth stage, and obtain the fruit-bearing ratio; determine the normalized value of the fruit-bearing rate of the monitored crops at each moment in the fruit-bearing stage in the late growth stage and the fruit-bearing ratio, and obtain the second longitudinal axis length growth parameter; determine the product of the reference axis length and the second longitudinal axis length growth parameter, and obtain the second longitudinal axis length growth value; determine the final longitudinal axis length of the dynamic growth model in the mid-growth stage and the added value of the second longitudinal axis length growth value, and obtain the longitudinal axis length of the dynamic growth model in the late growth stage at each moment in the fruit-bearing stage.

[0033] In combination with the above-mentioned first aspect, in some possible implementations, the second transverse axis length determination module is also used to use the final transverse axis length of the dynamic growth model of the crops to be monitored in the late growth stage in the fruit-bearing stage as the transverse axis length of the dynamic growth model of the crops to be monitored in the late growth stage at each moment in the fruit-bearing stage; the second longitudinal axis length determination module is also used to use the final longitudinal axis length of the dynamic growth model of the crops to be monitored in the middle growth stage as the longitudinal axis length of the dynamic growth model of the crops to be monitored in the late growth stage at each moment in the fruit-bearing stage.

[0034] In combination with the first aspect above, in some possible implementations, the display module includes:

[0035] A comprehensive growth evaluation acquisition unit, used to determine the comprehensive growth evaluation of the crop to be monitored according to the difference between the final horizontal axis length and the longitudinal axis length of the dynamic growth model of the crop to be monitored in the late growth stage;

[0036] The display unit is used to display the shape of the dynamic growth model corresponding to the monitored crops at different growth stages, as well as the comprehensive growth evaluation of the monitored crops.

[0037] The present invention has the following beneficial effects: the present invention obtains multi-dimensional growth monitoring data of the crops to be monitored, and analyzes the long monitoring data to determine the dynamic growth model corresponding to the crops to be monitored at different growth stages, and the dynamic growth model is an ellipse that changes with time, and the ellipse includes a horizontal axis length and a longitudinal axis length; based on the dynamic growth model, the dynamic growth of the crops to be monitored is displayed. The present invention analyzes the multi-dimensional growth monitoring data of the crops to determine the dynamic growth model corresponding to the crops at different growth stages, and intuitively displays the dynamic growth of the crops through the shape characteristics reflected by the horizontal axis length and the longitudinal axis length of the ellipse corresponding to the dynamic growth model, avoiding the errors and one-sidedness of the traditional crop growth assessment that often relies on human subjective judgment or only analyzes from a single dimension, effectively improving the reliability of crop growth assessment, and thus improving the level of intelligent management of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 This is a structural schematic diagram of a smart agricultural growth monitoring system based on the Internet of Things according to an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the structure of a model determination module according to an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of a growth initial stage model determination module according to an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of the structure of a mid-growth model determination module according to an embodiment of the present invention;

[0043] Figure 5 This is a schematic structural diagram of a first transverse axis length determination module according to an embodiment of the present invention;

[0044] Figure 6 This is a schematic structural diagram of a first longitudinal axis length determination module according to an embodiment of the present invention;

[0045] Figure 7 This is a structural schematic diagram of a late growth model determination module according to an embodiment of the present invention;

[0046] Figure 8 FIG. 4 is a schematic diagram of the structure of a display module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to clearly illustrate the technical features of the present invention, the present invention is described in detail below through specific implementation methods in conjunction with the accompanying drawings.

[0048] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0049] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0050] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0051] It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0052] Although operations or steps are described in a specific order in the drawings in the embodiments of the present invention, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or that all the operations or steps shown must be performed to obtain the desired results. In the embodiments of the present invention, these operations or steps may be performed in series; these operations or steps may also be performed in parallel; or some of these operations or steps may be performed.

[0053] At the same time, it is understood that the data involved in the technical solution of the present invention (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of the relevant laws, regulations and relevant provisions. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those generally understood by technicians in the technical field of the present invention, and all parameters or indicators in the formulas involved in the present invention are normalized values ​​that eliminate the influence of dimensions.

[0054] In order to solve the problem that the existing reliability of crop growth assessment is low and is not conducive to the intelligent management of agricultural production, the embodiment of the present invention provides an intelligent agricultural growth monitoring system based on the Internet of Things. The system introduces a dynamic growth model to achieve comprehensive visualization of the growth conditions of crops at different growth stages in the growth cycle. It not only enables monitoring personnel to grasp the growth status of crops in real time and intuitively, but also enables a comprehensive and in-depth assessment of crop growth based on real-time data, effectively improving the reliability of crop growth assessment, greatly improving the scientificity and pertinence of crop management, ensuring the healthy growth of crops under optimal growth conditions, and ultimately promoting an increase in yield and improvement in quality.

[0055] A smart agriculture growth monitoring system based on the Internet of Things provided by an embodiment of the present invention will be introduced in detail below in conjunction with the accompanying drawings.

[0056] Figure 1 FIG. 1 shows a schematic diagram of a smart agricultural growth monitoring system based on the Internet of Things provided by an embodiment of the present invention, such as Figure 1 As shown, the system 10 specifically includes: a data acquisition module 101, a model determination module 102 and a display module 103. The implementation functions of the three modules will be specifically described below.

[0057] The data acquisition module 101 is used to acquire multi-dimensional growth monitoring data of the crops to be monitored.

[0058] The growth of crops is affected by a series of factors such as the complex external environment and human management measures. The embodiment of the present invention analyzes and evaluates the various characteristic performances of crops throughout the growth cycle by comprehensively monitoring them, determines a dynamic growth model for reflecting the status and trend of crop growth, and displays the dynamic growth of crops.

[0059] In an embodiment of the present invention, taking tomatoes as an example, by conducting sample surveys on planting areas at different stages of crop growth, multi-dimensional growth monitoring data of crops is obtained in real time, and the multi-dimensional growth monitoring data includes: the emergence rate of the monitored crops at different times in the early growth stage, the plant height and leaf area index at different times in the middle growth stage, the fruiting rate at different times in the fruiting stage in the late growth stage, and the fruiting rate at different times in the fruiting stage in the late growth stage. Since the embodiment of the present invention collects multi-dimensional growth monitoring data once a day, that is, collects multi-dimensional growth monitoring data once every 24 hours, each moment here corresponds to the collection moment at the same time point on different days.

[0060] Among them, optical sensors are used to measure the light transmittance of the crop canopy, and the Leaf Area Index (LAI) is calculated based on the relationship between light transmittance and leaf area. The leaf area index refers to the multiple of the total area of ​​crop leaves per unit land area to the land area. It is an important factor in determining the photosynthesis rate of crops. The larger the leaf area index, the stronger the photosynthesis. The leaf area index is the basis for monitoring crop growth.

[0061] In addition, each crop has a relatively obvious growth cycle stage, and the different growth stages of crops can be determined in advance based on experience, that is, the early growth stage, the middle growth stage and the late growth stage, as well as the fruiting stage and the fruiting stage in the late growth stage. For example, the different growth stages can be determined based on the number of days after the crop is sown.

[0062] The Internet of Things is used to transmit the multi-dimensional growth monitoring data of the monitored crops to the smart agriculture growth monitoring background, which analyzes the multi-dimensional growth monitoring data, determines the dynamic growth model corresponding to the monitored crops at different growth stages, and displays the dynamic growth of the monitored crops based on the dynamic growth model.

[0063] The model determination module 102 is used to analyze the multi-dimensional growth monitoring data to determine the dynamic growth model corresponding to the monitored crops at different growth stages. The dynamic growth model is an ellipse that changes with time, and the ellipse includes a horizontal axis length and a longitudinal axis length.

[0064] Construct a dynamic growth model of crops at different growth stages to accurately evaluate the comprehensive growth of crops. A dynamic growth model that reflects crop growth can be constructed using multidimensional data analysis and dynamic simulation methods. The dynamic growth model will be based on the initial emergence rate of crops as the starting point, and will continuously incorporate new data dimensions at different growth stages of crops to dynamically adjust the shape (such as the change from circular to elliptical) and size (area) of the model to reflect the growth status and health of crops. In the early stage of crop growth, the emergence rate of crops can be used to reflect the growth of crops; as time goes by and enters the mid-growth stage, the plant height of crops continues to change and gradually grows leaves. At this time, the overall leaf area index of crops continues to change. At this stage, by real-time monitoring of the fluctuations of plant height and leaf area index, combined with the final emergence rate of crops, the growth of crops can be effectively reflected; in the late growth stage, the fruiting rate and fruiting rate indicators can also provide important information about the growth status of crops. The specific data dimensions of crops at a specific stage can supplement the reflection of crop growth and more accurately evaluate the health status and production potential of crops.

[0065] In the embodiment of the present invention, Figure 2 As shown, the model determination module 102 includes:

[0066] The initial growth model determination module 1021 is used to determine the horizontal axis length and the vertical axis length of the dynamic growth model in the initial growth stage according to the emergence rate of the monitored crop at different times in the initial growth stage.

[0067] When constructing a dynamic growth model corresponding to crops at different growth stages, the initial emergence rate of the crops can be used as a starting point, and an ellipse can be introduced as the basic form of the model. In the early stage of the initial growth stage, the dynamic growth model can be presented as an ellipse with the same major and minor axes, that is, a circle. The emergence rate of the crops in the early growth stage can then be used to preliminarily determine the axis length of the ellipse to obtain the initial dynamic growth model.

[0068] In the early stages of crop growth, the higher the emergence rate of crops, the better the growth of crops. This should be significantly reflected in the model. The axis length of the ellipse represents the initial emergence rate of crops. The larger the axis length, the better the emergence. This will help to intuitively reflect the current better growth of crops.

[0069] In the embodiment of the present invention, Figure 3 As shown, the early growth model determination module 1021 includes:

[0070] The proportion determination module 10211 is used to obtain the proportion of the emergence rate according to the proportion of the emergence rate of the monitored crop at different times in the initial growth stage to the benchmark emergence rate;

[0071] The axis length determination module 10212 is used to calculate the product value of the reference axis length and the proportion of the emergence rate, and use the product value as the horizontal axis length and the longitudinal axis length at different times of the dynamic growth model in the initial growth stage.

[0072] As an example, a baseline emergence rate is set in advance , and a corresponding reference axis length , and combined with the actual emergence rate of crops at each moment in the early growth stage, the axis length of the dynamic growth model of crops in the early growth stage at different moments is determined:

[0073] ;

[0074] in, The dynamic growth model of crops in the early growth stage is The axis length at each moment, namely the horizontal axis length and the longitudinal axis length; Indicates the initial growth stage of crops. The germination rate at each moment; Indicates the initial growth stage of crops. The germination rate at the moment is within the benchmark germination rate The proportion in the germination rate is the proportion of germination rate.

[0075] Through the above method, the dynamic growth model of crops in the early growth stage can be determined. The dynamic growth model is an ellipse that changes with time. Since its horizontal axis length and vertical axis length are equal in the early growth stage, it appears as a circle, and the emergence rate at each moment in the early growth stage is within the benchmark emergence rate. The larger the proportion in the graph, the larger the corresponding circular area.

[0076] The mid-growth model determination module 1022 is used to determine the lateral axis length and longitudinal axis length of the dynamic growth model in the mid-growth stage based on the plant height and leaf area index of the monitored crops at different times in the mid-growth stage, and in combination with the final lateral axis length and longitudinal axis length of the dynamic growth model in the early growth stage.

[0077] After the initial growth stage of crops, they enter the mid-growth stage. In the mid-growth stage, crops will show a trend of growing taller, gradually growing leaves and continuing to grow densely. Therefore, in order to quantify the growth of crops in this growth stage, more dimensional data needs to be introduced, such as plant height, leaf area index, etc. Plant height can be reflected in the lateral changes of the dynamic growth model, and leaf area index can be reflected in the longitudinal changes of the dynamic growth model.

[0078] In the embodiment of the present invention, Figure 4 As shown, the mid-growth model determination module 1022 includes:

[0079] The first transverse axis length determination module 10221 is used to determine the transverse axis length of the dynamic growth potential model in the mid-growth stage at the target time according to the discreteness of the difference in plant heights of the monitored crop at all adjacent moments up to the target time in the mid-growth stage, and the plant height of the monitored crop at the target time in the mid-growth stage, and in combination with the final transverse axis length of the dynamic growth potential model in the early growth stage;

[0080] The first longitudinal axis length determination module 10222 is used to determine the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time based on the difference in leaf area index of the monitored crop at all adjacent times up to the target time in the mid-growth stage, and combined with the final longitudinal axis length of the dynamic growth model in the early growth stage.

[0081] As an example, in the mid-growth stage of crops, the higher the plant height, the better the growth of the crops. At the same time, the smaller the difference in plant height between adjacent moments before the current moment, the more regular the growth of the crop plants, which means that the growth of the crops at the current moment is better, which can be reflected in the longer horizontal axis length of the dynamic growth model.

[0082] Based on this, in the embodiment of the present invention, if Figure 5 As shown, the first transverse axis length determination module 10221 includes: a discrete index determination module 102211, which is used to determine the variance of the absolute value of the difference in plant height of the monitored crop at all adjacent moments up to the target moment in the mid-growth stage, and obtain a discrete index of plant height difference; a transverse axis length growth parameter determination module 102212, which is used to determine the normalized value of the product of the plant height of the monitored crop at the target moment in the mid-growth stage and the discrete index of plant height difference, and obtain a first transverse axis length growth parameter; a transverse axis length growth value determination module 102213, which is used to determine the product of the reference axis length and the first transverse axis length growth parameter, and obtain a first transverse axis length growth value; a transverse axis length acquisition module 102214, which is used to determine the sum of the final transverse axis length of the dynamic growth model in the early growth stage and the first transverse axis length growth value, and obtain the transverse axis length of the dynamic growth model in the mid-growth stage at the target moment.

[0083] Taking each moment in the mid-growth stage as a target moment, the final horizontal axis length based on the dynamic growth model in the early growth stage and the reference axis length , as well as the discreteness of the difference in plant height at all adjacent moments in the mid-growth stage up to the target moment, and the plant height of the crop at the target moment in the mid-growth stage, the formula for determining the horizontal axis length of the dynamic growth model in the mid-growth stage at the target moment is:

[0084] ;

[0085] in, The dynamic growth model of crops in the middle growth stage is moment (the The horizontal axis length of the target moment is the target moment; Indicates that crops are in the middle growth stage. Plant height at a certain moment; Indicates that crops are in the middle growth stage. The discrete index of plant height difference corresponding to each moment; Represents the standard normalization function.

[0086] In the above calculation formula, crops are in the middle growth stage. The higher the plant height at a certain moment, and the more regular the growth of the crop plants before that, the better the crop growth at the current moment, and the first horizontal axis length growth parameter If the final horizontal axis length of the dynamic growth model in the early growth stage is Based on the increase in the first transverse axis length The larger the value of The larger the value of .

[0087] Under normal circumstances, the leaves of crops usually fall off naturally after fruiting or when the fruit matures (i.e., in the late growth stage). However, under unfavorable environmental conditions or improper cultivation and management, the leaves may also fall off before fruiting or during fruiting (i.e., in the mid-growth stage). Therefore, the leaf area index of crops in the mid-growth stage should show a continuous growth first and then stabilize in a certain range. If the leaves fall off prematurely, it means that the growth of crops in the current growth stage is abnormal. Therefore, when the leaf area index is more ideal, the longitudinal axis length of the dynamic growth model is longer, indicating that the crop is in a healthy growth state. When the leaf area index shows a downward trend, the longitudinal axis length of the dynamic growth model will decrease, reflecting the decline in the growth of crops.

[0088] Based on this, in the embodiment of the present invention, if Figure 6As shown, the first longitudinal axis length determination module 10222 includes: a change slope determination module 102221, which is used to determine the change slope of the leaf area index of the monitored crop at all adjacent moments up to the target moment in the mid-growth stage; a first longitudinal axis length growth parameter determination module 102222, which is used to determine the first longitudinal axis length growth parameter. If the change slope of the leaf area index of the monitored crop at the target moment in the mid-growth stage is greater than or equal to 0, the first longitudinal axis length growth parameter is the normalized value of the change slope of the leaf area index of the monitored crop at the target moment in the mid-growth stage; otherwise, the first longitudinal axis length growth parameter is the normalized value of the absolute value of the accumulated value of the change slope of the leaf area index of the monitored crop at the target moment and its previous consecutive object moments in the mid-growth stage, and the change slope of the leaf area index at the object moment is less than 0; the first longitudinal axis The longitudinal growth value determination module 102223 is used to determine the product of the reference axis length and the first longitudinal axis length growth parameter to obtain the first longitudinal axis length growth value; the longitudinal axis length acquisition module 102224 is used to determine the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time according to the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value. If the slope of the change of the leaf area index of the monitored crop in the mid-growth stage at the target time is greater than or equal to 0, the sum of the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value is used as the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time; otherwise, the difference between the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value is used as the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time.

[0089] In order to better monitor and evaluate the growth status of crops, the leaf area index at each moment in the mid-growth stage after the initial growth stage is mapped in a two-dimensional coordinate system, where the horizontal axis is time and the vertical axis is the leaf area index, so that each data point can be obtained. The least squares method is used to fit all data points, so that the leaf area index fitting curve can be obtained. According to the fluctuation of the leaf area index fitting curve of crops in the mid-growth stage up to the target moment (any moment), the growth of crops in the mid-growth stage can be reflected. When the leaf area index shows a downward trend at the target moment, it should be reflected in the dynamic growth model in a timely manner so that the monitoring personnel can check the actual situation on the spot in a timely manner.

[0090] Therefore, according to the leaf area index fitting curve of crops in the middle growth stage up to the target moment (any moment), the change slope of the leaf area index corresponding to each moment in the leaf area index fitting curve is determined. The change slope is the slope of the straight line connecting the data points corresponding to each moment in the leaf area index fitting curve with the data points corresponding to the adjacent previous moment. Based on the distribution of the change slope of the leaf area index corresponding to each moment in the leaf area index fitting curve, combined with the final horizontal axis length of the dynamic growth model in the early growth stage and the reference axis length , determine the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time:

[0091] ;

[0092] in, The dynamic growth model of crops in the middle growth stage is moment (the The longitudinal axis length of the target moment is the target moment; Indicates that crops are in the middle growth stage. The slope of the leaf area index change at a certain moment; Indicates that crops are in the middle growth stage. The discrete index of plant height difference corresponding to each moment; Indicates that crops are in the middle growth stage. The cumulative value of the slope of the leaf area index change at the moment and the moments before that when the slope of the leaf area index change is less than 0 (i.e. the object moment); Represents the standard normalization function.

[0093] In the above calculation formula, The slope of the leaf area index change at each moment , indicating that the leaf area index is on the rise at the current moment, reflecting that the growth of crops is good. The final horizontal axis length of the dynamic growth model in the early growth stage is On the basis of The slope of the leaf area index change at each moment Normalize and get the first longitudinal axis length growth parameter , and calculate the reference axis length Growth parameter with first longitudinal axis length The product of the first longitudinal axis length is obtained. , and finally the final transverse axis length Increased value of first longitudinal axis length The sum of the values ​​is used as the dynamic growth model of crops in the middle growth stage. The longitudinal axis length at the moment. The slope of the leaf area index change at each moment , indicating that the leaf area index is showing a downward trend at the current moment, reflecting that the growth of crops is abnormal. If the leaf area index decreases continuously, in order to make the changes in the dynamic growth model easier to detect, it is necessary to enhance the performance of the continuous decrease. The sum of the slopes of the leaf area index at the moment and the moments before that whose slopes are less than 0 The smaller the negative number, the more serious the decrease in leaf area index. The absolute value of is normalized to obtain the first longitudinal axis length growth parameter , and then get the first longitudinal axis length growth value , and finally the final transverse axis length Subtract the first longitudinal axis length increase The longitudinal axis length of the dynamic growth model is obtained, and the shorter the longitudinal axis length of the corresponding dynamic growth model is, the more abnormal the growth of crops can be highlighted.

[0094] The late growth model determination module 1023 is used to determine the lateral axis length and longitudinal axis length of the dynamic growth model in the late growth stage according to the fruit-bearing rate of the monitored crops at different moments in the fruit-bearing stage in the late growth stage, and the fruit-bearing rate at different moments in the fruit-bearing stage in the late growth stage, and in combination with the final lateral axis length and longitudinal axis length of the dynamic growth model in the mid-growth stage.

[0095] In the late growth stage of crops, the contribution of plant height and leaf area index to the direct evaluation of crop growth is relatively weakened. At this time, crops enter the fruiting and fruiting stage. Therefore, the fruiting rate and fruiting rate of crops are used as the basis for judging the growth of crops. The final growth in the middle growth stage of crops will also affect the subsequent fruiting and fruiting. If the growth in the middle growth stage is good, the crop may show a higher fruiting rate in the later stage; otherwise, it may show a lower fruiting rate.

[0096] In the embodiment of the present invention, Figure 7 As shown, the late growth model determination module 1023 includes:

[0097] The second transverse axis length determination module 10231 is used to determine the product of the reference axis length and the fruit-bearing rate of the monitored crop at each moment in the fruit-bearing stage in the late growth stage, obtain the second transverse axis length growth value, determine the sum of the final transverse axis length of the dynamic growth model in the mid-growth stage and the second transverse axis length growth value, and obtain the transverse axis length of the dynamic growth model in the late growth stage at each moment in the fruit-bearing stage;

[0098] The second longitudinal axis length determination module 10232 is used to determine the ratio of the fruit-bearing rate of the monitored crop at each moment in the fruit-bearing stage in the late growth stage to the final fruit-bearing rate of the monitored crop in the fruit-bearing stage in the late growth stage, and obtain the fruit-bearing ratio; determine the normalized value of the fruit-bearing rate of the monitored crop at each moment in the fruit-bearing stage in the late growth stage and the fruit-bearing ratio, and obtain the second longitudinal axis length growth parameter; determine the product of the baseline axis length and the second longitudinal axis length growth parameter, and obtain the second longitudinal axis length growth value; determine the sum of the final longitudinal axis length of the dynamic growth model in the mid-growth stage and the second longitudinal axis length growth value, and obtain the longitudinal axis length of the dynamic growth model in the late growth stage at each moment in the fruit-bearing stage.

[0099] The second transverse axis length determination module 10231 is also used to use the final transverse axis length of the dynamic growth model of the crops to be monitored in the late growth stage at the fruit-bearing stage as the transverse axis length of the dynamic growth model of the crops to be monitored in the late growth stage at each moment in the fruit-bearing stage; the second longitudinal axis length determination module 10232 is also used to use the final longitudinal axis length of the dynamic growth model of the crops to be monitored in the mid-growth stage as the longitudinal axis length of the dynamic growth model of the crops to be monitored in the late growth stage at each moment in the fruit-bearing stage.

[0100] As an example, in the fruiting stage of the late growth stage, the fruiting rate can be used as the basis for determining the horizontal axis length of the dynamic growth model. The higher the fruiting rate, the more vigorous the crops grow in the current growth stage. Therefore, the horizontal axis length of the dynamic growth model can be increased accordingly. At this time, the vertical axis length of the dynamic growth model remains unchanged, that is, it is equal to the final vertical axis length of the dynamic growth model in the middle growth stage. The fruiting rate usually refers to the proportion of fruit trees that successfully bear fruit after flowering.

[0101] In this embodiment, the final lateral axis length of the dynamic growth model in the mid-growth stage is Based on the reference axis length , the horizontal axis length of the dynamic growth model is determined according to the fruit-bearing rate at each moment in the fruit-bearing stage in the late growth stage:

[0102] ;

[0103] in, The dynamic growth model of crops in the later growth stage is in the fruiting stage. The horizontal axis length at each moment; The dynamic growth model of crops in the later growth stage is in the fruiting stage. The fruit setting rate at each moment.

[0104] In the above calculation formula, in the late growth stage, when the crops begin to bear fruit but have not yet entered the fruiting stage, the horizontal axis length of the dynamic growth model will increase with the increase of the fruiting rate. At this time, the vertical axis length of the dynamic growth model remains unchanged, that is, it is always equal to the final vertical axis length of the dynamic growth model in the middle growth stage. .

[0105] When the fruiting stage of the late growth stage ends and enters the fruiting stage, the longitudinal axis length of the dynamic growth model is adjusted according to the final fruiting rate of the crop in the fruiting stage and the fruiting rate at each moment of the fruiting stage. When the fruiting rate is high, the longitudinal axis length should be appropriately increased to highlight the good fruiting of the crop; when the fruiting rate is low, the longitudinal axis length should be increased less to accurately show the poor fruiting of the crop. In addition, a high fruiting rate but a low fruiting rate indicates that although more fruits are formed, the fruiting effect is poor; conversely, a low fruiting rate but a high fruiting rate indicates that less fruits are formed but the fruiting effect is good. At the same time, the transverse axis length of the dynamic growth model remains unchanged, that is, it is equal to the final transverse axis length of the dynamic growth model in the late growth stage in the fruiting stage.

[0106] In this embodiment, the final longitudinal axis length of the dynamic growth model in the mid-growth stage is Based on the reference axis length and the final fruiting rate at the fruiting stage in the later growth stage , the longitudinal axis length of the dynamic growth model is determined according to the fruiting rate at each moment in the fruiting stage in the late growth stage:

[0107] ;

[0108] in, The dynamic growth model of crops in the later growth stage is the first The longitudinal axis length at each moment; Indicates the fruiting stage of crops in the late growth stage. The fruit setting rate at each moment; It indicates the final fruit-bearing rate of crops in the fruit-bearing stage in the late growth stage; Represents the normalization function.

[0109] In the above calculation formula, in the late growth stage, when the crop stops bearing fruit and enters the fruiting stage, the horizontal axis length of the dynamic growth visual model remains unchanged, that is, it is equal to the final horizontal axis length of the dynamic growth model in the late growth stage at the fruiting stage, and the vertical axis length will increase with the increase of the fruiting rate. It means that in the fruiting stage, the greater the ratio of the fruiting rate to the final fruiting rate in the fruiting stage, the higher the fruiting rate. The larger the value is and the greater the fruiting rate is, the better the crop growth is at this stage. At this time, the second longitudinal axis length growth parameter The larger the second longitudinal axis length, the longer the The larger it is, the longer the longitudinal axis length of the corresponding dynamic growth model will be.

[0110] The display module 103 is used to display the dynamic growth of the crops to be monitored based on the dynamic growth model.

[0111] According to the shape and size of the visual model of the crop's growth throughout the growth cycle, the comprehensive growth evaluation of the crop is determined. Specifically, in the mid-growth stage, the significant change in the height of the crop plant and the increase in the leaf area index should show a trend of synchronous growth. This is because as the plant grows taller, its leaf system is also expanding to capture more light energy for photosynthesis, thereby promoting the overall growth of the crop. Based on this, the synchronous growth of plant height and leaf area index usually indicates that the crop is in good growth condition. In the late stage of crop growth, the closer the fruiting rate is to the fruiting rate, it means that the crop uses more energy for the formation of fruits or seeds rather than unnecessary nutrient consumption, which usually indicates that the crop is growing well and the efficiency of fruiting is correspondingly improved. Therefore, at the end of the crop growth stage, the more similar the growth of the two dimensional data is, the better the overall growth of the crop is usually. This consistency can be presented in an intuitive way, that is, the smaller the difference between the major axis and the minor axis of the dynamic growth model, that is, the closer the shape of the dynamic growth model is to a circle, the more it shows that the crop is growing in a balanced and healthy way.

[0112] In the embodiment of the present invention, Figure 8 As shown, the display module 103 includes:

[0113] The comprehensive growth evaluation acquisition unit 1031 is used to determine the comprehensive growth evaluation of the crop to be monitored according to the difference between the final horizontal axis length and the vertical axis length of the dynamic growth model of the crop to be monitored in the late growth stage;

[0114] The display unit 1032 is used to display the shape of the dynamic growth model corresponding to the monitored crops at different growth stages and the comprehensive growth evaluation of the monitored crops.

[0115] As an example, the comprehensive growth evaluation acquisition unit 1031 calculates the final lateral axis length of the dynamic growth model of the crop in the late growth stage. and longitudinal axis length The difference between the two groups is used to determine the comprehensive growth evaluation of the crops to be monitored:

[0116] ;

[0117] in, Represents the comprehensive growth evaluation of the crops to be monitored.

[0118] In the above calculation formula, represents the absolute difference between the final transverse and longitudinal axis lengths of the dynamic growth model at the late growth stage, while Can be used as a normalization factor to ensure The value of is between 0 and 1, when the absolute difference between the final transverse and longitudinal axis lengths The smaller it is, the closer the dynamic growth model is to a circle, which means the better and more balanced the growth of the crop is. The larger the value, the faster the crop growth rate and the more obvious the growth effect of each growth cycle. The smaller, The larger it is, the higher the overall growth evaluation of the crop.

[0119] During the entire growth cycle of the monitored crops, the display unit 1032 displays the shape of the dynamic growth model corresponding to the monitored crops at different growth stages, and displays the comprehensive growth evaluation after the comprehensive growth evaluation acquisition unit 1031 obtains the comprehensive growth evaluation of the monitored crops.

[0120] Monitoring personnel can monitor the growth of crops in real time based on the displayed dynamic growth model, promptly detect and deal with abnormal growth problems, ensure the healthy growth of crops, and predict the future yield and quality of crops, providing decision support for agricultural production. The introduction of the dynamic growth model has completely revolutionized farmers' subjective judgment of crop growth, avoiding the problem of difficulty and inefficiency in capturing subtle growth differences in vast fields with the naked eye, greatly promoting the efficiency and accuracy of field management, and opening up a new path for intelligent and efficient agricultural production.

[0121] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A smart agricultural growth monitoring system based on the Internet of Things, characterized in that: The system includes: A data acquisition module, used to acquire multi-dimensional growth monitoring data of the crops to be monitored; A model determination module is used to analyze the multi-dimensional growth monitoring data to determine a dynamic growth model corresponding to the monitored crop at different growth stages, wherein the dynamic growth model is an ellipse that changes over time and includes a transverse axis length and a longitudinal axis length; A display module, used for displaying the dynamic growth of the crops to be monitored based on the dynamic growth model; The different growth stages include an initial growth stage, a mid-growth stage and a late growth stage, and the late growth stage includes a fruiting stage and a fruiting stage; the multi-dimensional growth monitoring data at least includes: the emergence rate of the monitored crop at different times in the initial growth stage, the plant height and leaf area index at different times in the mid-growth stage, the fruiting rate at different times in the fruiting stage in the late growth stage, and the fruiting rate at different times in the fruiting stage in the late growth stage; the model determination module includes: The initial growth model determination module is used to determine the horizontal axis length and the vertical axis length of the dynamic growth model in the initial growth stage according to the emergence rate of the monitored crop at different times in the initial growth stage; The mid-growth model determination module is used to determine the horizontal axis length and the vertical axis length of the dynamic growth model in the mid-growth stage according to the plant height and the leaf area index of the monitored crop at different times in the mid-growth stage, and in combination with the final horizontal axis length and the vertical axis length of the dynamic growth model in the early growth stage; A late growth model determination module is used to determine the horizontal axis length and the vertical axis length of the dynamic growth potential model in the late growth stage according to the fruit setting rate of the monitored crop at different moments in the fruit setting stage in the late growth stage, and the fruit setting rate at different moments in the fruiting stage in the late growth stage, and in combination with the final horizontal axis length and vertical axis length of the dynamic growth potential model in the mid-growth stage; The mid-growth model determination module comprises: The first transverse axis length determination module is used to determine the transverse axis length of the dynamic growth potential model in the mid-growth stage at the target time according to the discreteness of the difference in plant heights of the monitored crop at all adjacent moments up to the target time in the mid-growth stage, and the plant height of the monitored crop at the target time in the mid-growth stage, and in combination with the final transverse axis length of the dynamic growth potential model in the early growth stage; The first longitudinal axis length determination module is used to determine the longitudinal axis length of the dynamic growth potential model in the mid-growth stage at the target time according to the difference in leaf area index of the monitored crop at all adjacent moments up to the target time in the mid-growth stage and in combination with the final longitudinal axis length of the dynamic growth potential model in the early growth stage; The first transverse axis length determination module comprises: A discrete index determination module is used to determine the variance of the absolute value of the difference in plant heights of the monitored crop at all adjacent moments in the mid-growth stage up to the target moment, and obtain a discrete index of plant height difference; A transverse axis length growth parameter determination module is used to determine the normalized value of the product of the plant height of the monitored crop at the target time in the mid-growth stage and the plant height difference discrete index to obtain a first transverse axis length growth parameter; A transverse axis length growth value determination module, used to determine the product of the reference axis length and the first transverse axis length growth parameter to obtain a first transverse axis length growth value; A transverse axis length acquisition module is used to determine the sum of the final transverse axis length of the dynamic growth model in the early growth stage and the first transverse axis length growth value to obtain the transverse axis length of the dynamic growth model in the middle growth stage at the target time; The first longitudinal axis length determination module comprises: A change slope determination module is used to determine the change slope of the leaf area index of the monitored crop at all adjacent moments in the mid-growth stage up to the target moment; A first longitudinal axis length growth parameter determination module, used to determine a first longitudinal axis length growth parameter, if the change slope of the leaf area index of the monitored crop at the target time in the mid-growth stage is greater than or equal to 0, the first longitudinal axis length growth parameter is the normalized value of the change slope of the leaf area index of the monitored crop at the target time in the mid-growth stage; otherwise, the first longitudinal axis length growth parameter is the normalized value of the absolute value of the accumulated value of the change slope of the leaf area index of the monitored crop at the target time and the previous consecutive object time in the mid-growth stage, and the change slope of the leaf area index at the object time is less than 0; A first longitudinal axis length growth value determination module, used to determine the product of the reference axis length and the first longitudinal axis length growth parameter to obtain a first longitudinal axis length growth value; A longitudinal axis length acquisition module is used to determine the longitudinal axis length of the dynamic growth model in the mid-growth stage at a target time based on the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value. If the slope of the change of the leaf area index of the monitored crop in the mid-growth stage at the target time is greater than or equal to 0, the addition of the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value is used as the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time; otherwise, the difference between the final longitudinal axis length of the dynamic growth model in the early growth stage and the first longitudinal axis length growth value is used as the longitudinal axis length of the dynamic growth model in the mid-growth stage at the target time.

2. According to claim 1, a smart agricultural growth monitoring system based on the Internet of Things is characterized in that: The initial growth model determination module comprises: A proportion determination module is used to obtain the proportion of the emergence rate according to the proportion of the emergence rate of the monitored crop at different times in the early growth stage to the benchmark emergence rate; The axis length determination module is used to calculate the product value of the reference axis length and the proportion of the emergence rate, and use the product value as the horizontal axis length and the longitudinal axis length at different times of the dynamic growth model in the early growth stage.

3. According to the Internet of Things-based smart agricultural growth monitoring system of claim 1, it is characterized in that: The late growth model determination module comprises: A second transverse axis length determination module is used to determine the product of the reference axis length and the fruit-bearing rate of the monitored crop at each moment in the fruit-bearing stage in the late growth stage to obtain a second transverse axis length growth value, determine the sum of the final transverse axis length of the dynamic growth model in the mid-growth stage and the second transverse axis length growth value, and obtain the transverse axis length of the dynamic growth model in the late growth stage at each moment in the fruit-bearing stage; The second longitudinal axis length determination module is used to determine the ratio of the fruit-bearing rate of the monitored crops at each moment in the fruit-bearing stage in the late growth stage to the final fruit-bearing rate of the monitored crops in the fruit-bearing stage in the late growth stage, and obtain the fruit-bearing ratio; determine the normalized value of the fruit-bearing rate of the monitored crops at each moment in the fruit-bearing stage in the late growth stage and the fruit-bearing ratio, and obtain the second longitudinal axis length growth parameter; determine the product of the reference axis length and the second longitudinal axis length growth parameter, and obtain the second longitudinal axis length growth value; determine the final longitudinal axis length of the dynamic growth model in the mid-growth stage and the added value of the second longitudinal axis length growth value, and obtain the longitudinal axis length of the dynamic growth model in the late growth stage at each moment in the fruit-bearing stage.

4. The smart agricultural growth monitoring system based on the Internet of Things according to claim 3 is characterized in that: The second transverse axis length determination module is also used to use the final transverse axis length of the dynamic growth model of the crops to be monitored in the late growth stage at the fruit-bearing stage as the transverse axis length of the dynamic growth model of the crops to be monitored in the late growth stage at each moment in the fruit-bearing stage; the second longitudinal axis length determination module is also used to use the final longitudinal axis length of the dynamic growth model of the crops to be monitored in the middle growth stage as the longitudinal axis length of the dynamic growth model of the crops to be monitored in the late growth stage at each moment in the fruit-bearing stage.

5. The smart agricultural growth monitoring system based on the Internet of Things according to claim 1 is characterized in that: The display module comprises: A comprehensive growth evaluation acquisition unit, used to determine the comprehensive growth evaluation of the crop to be monitored according to the difference between the final horizontal axis length and the longitudinal axis length of the dynamic growth model of the crop to be monitored in the late growth stage; The display unit is used to display the shape of the dynamic growth model corresponding to the monitored crops at different growth stages, as well as the comprehensive growth evaluation of the monitored crops.

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