Plant growth monitoring method and system

Abstract

The present disclosure provides a plant growth monitoring method and system, belonging to the field of plant growth monitoring, which comprises: obtaining first characteristic information and second characteristic information of a target plant; the first characteristic information is the characteristic information of the aboveground part of the target plant, and the second characteristic information is the characteristic information of the underground part of the target plant; calculating the growth index of the target plant based on the first characteristic information and the second characteristic information; determining the growth stage of the target plant based on the growth index. The present disclosure can monitor the data of plant growth, obtain the growth stage of the plant according to the growth characteristics of the plant, and change the monitoring frequency, which is beneficial to the research on the growth of the plant.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the field of plant growth monitoring, and more particularly to a plant growth monitoring method and system. BACKGROUND

[0002] With the progress of science and technology, the monitoring of plant growth has gradually been electronicized. However, at present, the monitoring of plant growth still requires a large amount of manual participation, and the experience of plant field technical personnel is required to be high, which is easy to miss the critical growth period of plants. SUMMARY

[0003] The present disclosure aims to provide a plant growth monitoring method and system to improve the monitoring reliability and accuracy of plant growth.

[0004] In a first aspect, the present disclosure provides a plant growth monitoring method, comprising:

[0005] obtaining first characteristic information and second characteristic information of a target plant; the first characteristic information is characteristic information of the aboveground part of the target plant, and the second characteristic information is characteristic information of the underground part of the target plant;

[0006] calculating a growth index of the target plant based on the first characteristic information and the second characteristic information;

[0007] determining a growth stage of the target plant based on the growth index.

[0008] In a second aspect, the present disclosure provides a plant growth monitoring system, comprising:

[0009] a data acquisition module configured to obtain first characteristic information and second characteristic information of a target plant; the first characteristic information is characteristic information of the aboveground part of the target plant, and the second characteristic information is characteristic information of the underground part of the target plant;

[0010] a data processing module configured to calculate a growth index of the target plant based on the first characteristic information and the second characteristic information;

[0011] a judgment module configured to determine a growth stage of the target plant based on the growth index.

[0012] In a third aspect, the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the plant growth monitoring method described above when executing the computer program.

[0013] In a fourth aspect, the present disclosure provides a computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the plant growth monitoring method described above.

[0014] The plant growth monitoring method and system provided by the embodiments of the present disclosure have the following beneficial effects:

[0015] By obtaining the feature information of the aboveground part and the underground part, the overall growth of the plant can be comprehensively and accurately evaluated. This multi-dimensional evaluation method can more accurately reflect the actual growth state of the plant and reduce the misjudgment caused by the fluctuation of a single index. The growth stage is determined based on the accurately calculated growth index, which helps to take targeted maintenance measures in time. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 The flowchart of the plant growth monitoring method provided by an embodiment of the present disclosure is shown in the figure.

[0018] Figure 2 The structural block diagram of the plant growth monitoring system provided by an embodiment of the present disclosure is shown in the figure.

[0019] Figure 3 The schematic block diagram of the electronic device provided by an embodiment of the present disclosure is shown in the figure. DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures, techniques, etc. are presented in order to thoroughly understand the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known devices, circuits and methods are omitted to avoid unnecessary details that hinder the description of the present disclosure.

[0021] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the following will be described by specific embodiments with reference to the accompanying drawings.

[0022] Reference should be made to Figure 1 , Figure 1 The flowchart of the plant growth monitoring method provided by an embodiment of the present disclosure is shown in the figure. The method comprises:

[0023] S101: Obtain first feature information and second feature information of the target plant; the first feature information is feature information of the aboveground part of the target plant, and the second feature information is feature information of the underground part of the target plant.

[0024] In this embodiment, the target plant can be corn, cotton, pea, rice and wheat, and the first feature information can be the size of the leaf, the number of leaves and the stem diameter of the plant, and the second feature information can be the root depth, the number of roots and the root diameter of the plant; the feature information of the aboveground part of the plant can be obtained by a mobile phone, a camera, a camera and the like, and the feature information of the underground part of the plant can be obtained by a root in-situ analyzer, a ground penetrating radar, a CT scan and the like.

[0025] For example, the root in-situ analyzer can obtain the image information of the underground part of the plant by high-resolution camera shooting, near-infrared light or laser scanning and the like.

[0026] The ground penetrating radar can emit high-frequency electromagnetic wave pulses to the underground. When these electromagnetic waves propagate in the soil, if they encounter plant roots, due to the physical and electrical properties of the roots being different from the surrounding soil, part of the energy of the electromagnetic waves will be reflected, refracted and scattered. By receiving and analyzing the intensity, propagation time and waveform characteristics of these echo signals, the position, depth, size, shape and distribution of the plant roots can be inferred.

[0027] The CT scan can use X-rays to irradiate the plant roots from multiple angles, and measure the degree of attenuation of the X-rays after passing through the plant roots. For plant roots, due to their tissue density and composition being different from the surrounding soil, the absorption and attenuation of X-rays are also different. During the scanning process, the X-ray source rotates around the plant sample, and the detector receives the X-rays passing through the sample on the other side. Then, the computer reconstructs a three-dimensional image of the plant roots according to the data obtained by the detector.

[0028] This embodiment can collect the feature information of the aboveground part of crops such as corn, cotton, pea, rice and millet, such as leaf size, leaf number and stem diameter, and the feature information of the underground part, such as root depth, root number and root diameter, to provide reliable data support for further data processing.

[0029] S102: Calculate the growth index of the target plant based on the first feature information and the second feature information.

[0030] In this embodiment, the target plant can be corn, the first feature information can be the feature information of the leaf size, the leaf number and the stem thickness of the corn aboveground part, and the second feature information can be the feature information of the root depth, the root number and the root thickness of the corn underground part; the growth index of the corn can be calculated based on the feature information of the leaf size, the leaf number and the stem thickness of the corn aboveground part and the feature information of the root depth, the root number and the root thickness of the corn underground part; the calculation method can be to obtain the growth index of the corn aboveground part according to the first feature information, obtain the growth index of the corn underground part according to the second feature information, average the growth index of the corn aboveground part and the growth index of the corn underground part to calculate the growth index of the corn; or, different weights are assigned to the growth index of the corn aboveground part and the growth index of the corn underground part, and the growth index of the corn is calculated according to the weights; the growth index of the corn aboveground part can be calculated according to the leaf size, the leaf number, the aboveground part height, the stem thickness and other information of the corn by assigning different weights, and the growth index of the corn underground part can be calculated according to the root depth, the root number, the root thickness and other information of the corn by assigning different weights.

[0031] In this embodiment, the feature information of the corn aboveground part and the feature information of the corn underground part collected can be used to calculate the growth index of the corn.

[0032] S103: Determine the growth stage of the target plant based on the growth index.

[0033] In this embodiment, the growth index can be the growth index of the apple tree, and the growth stage of the apple tree can be determined by the growth index of the apple tree. The growth stage can be the germination period, the seedling period, the growth period (including the rapid growth period and the slow growth period), the flowering period and the fruiting period. The relationship model between the growth stage and the growth index can be established according to the growth characteristics and biological laws of the apple tree. This model can be based on statistical and biological principles, and the model can reflect the change trend and characteristics of the growth index of the plant in different growth stages. After the relationship model between the growth stage and the growth index is established, the division standards of each growth stage need to be determined. These standards can be specific thresholds of the growth index, and the standards are determined based on a large amount of experimental data and biological knowledge.

[0034] In this embodiment, the growth index of the apple tree can be used to determine the growth stage of the apple tree, which reduces the labor cost and provides data reference for subsequent monitoring of the apple tree.

[0035] From the above, the present disclosure can comprehensively and accurately evaluate the overall growth of the plant by obtaining the feature information of the aboveground part and the underground part. This multi-dimensional evaluation method can more accurately reflect the actual growth state of the plant and reduce the misjudgment caused by the fluctuation of a single indicator. Determining the growth stage based on the accurately calculated growth index helps to take targeted maintenance measures in a timely manner.

[0036] In an embodiment of the present disclosure, the first feature information and the second feature information of the target plant are obtained, including:

[0037] The aboveground part of the target plant is monitored by using a first monitoring instrument to obtain first monitoring information;

[0038] The underground part of the target plant is monitored by using a second monitoring instrument to obtain second monitoring information;

[0039] The first monitoring information is subjected to feature extraction to obtain the first feature information;

[0040] The second monitoring information is subjected to feature extraction to obtain the second feature information.

[0041] In the present embodiment, the first monitoring instrument can be a camera, the target plant can be sorghum, and the first monitoring information can be image information of the aboveground part of the sorghum. The second monitoring instrument can be a root in-situ analyzer, and the second monitoring information can be image information of the underground part of the sorghum. The first monitoring information and the second monitoring information can be subjected to feature extraction by using a convolutional neural network that has been trained. The convolutional neural network that has been trained can be trained by using pictures containing feature information of various common plants (rice, wheat, sorghum, corn, soybeans, and cotton, etc.). The first feature information can be feature information such as leaf size, leaf number, and stem thickness of the aboveground part of the sorghum. The second feature information can be feature information such as root depth, root number, and root thickness of the underground part of the sorghum.

[0042] For example, the camera photographs the aboveground part of the sorghum to obtain pictures or videos, and the root in-situ analyzer scans or photographs the underground part of the sorghum to obtain pictures or videos. The pictures or videos of the aboveground part of the sorghum are subjected to feature extraction by using the convolutional neural network that has been trained by using pictures containing feature information of various common plants to obtain feature information such as leaf size, leaf number, and stem thickness of the aboveground part of the sorghum. The pictures or videos of the underground part of the sorghum are subjected to feature extraction by using the convolutional neural network that has been trained by using pictures containing feature information of various common plants to obtain feature information such as root depth, root number, and root thickness of the underground part of the sorghum.

[0043] Or, for example, the first monitor can be a camera, the target plant can be soybean, and the second monitor can be an ultrasonic monitor; the camera takes pictures or videos of the aboveground part of the soybean, and the ultrasonic monitor detects the underground part of the soybean by ultrasonic waves to obtain pictures; the pictures or videos of the aboveground part of the soybean are subjected to feature extraction by a convolutional neural network that has been trained by pictures of various common plants containing feature information, to obtain feature information such as leaf size, leaf number, and stem thickness of the aboveground part of the soybean; the pictures of the underground part of the soybean are subjected to feature extraction by a convolutional neural network that has been trained by pictures of various common plants containing feature information, to obtain feature information such as root depth, root number, and root thickness of the underground part of the soybean.

[0044] From the above, it can be concluded that the present disclosure can more accurately obtain feature information of each part by monitoring the aboveground and underground parts by the first monitor and the second monitor respectively, thereby improving the accuracy of monitoring; the data reliability is improved by extracting features from the first monitoring information and the second monitoring information by the trained convolutional neural network.

[0045] In an embodiment of the present disclosure, the first monitoring information includes aboveground image information of the target plant.

[0046] The first monitoring information is subjected to feature extraction to obtain first feature information, including:

[0047] The aboveground image information of the target plant is subjected to feature extraction to obtain a plurality of first plant features.

[0048] The matching degrees between the plurality of first plant features and a plurality of first standard plant feature libraries are calculated respectively.

[0049] A first number of first standard plant features are selected from the first standard plant feature library with the highest matching degree as the first feature information.

[0050] Different first standard plant feature libraries include plant aboveground features at different growth stages.

[0051] The matching degrees between the plurality of first plant features and a plurality of first standard plant feature libraries are calculated respectively, including:

[0052] The matching degrees between the plurality of first plant features and a plurality of first standard plant feature libraries are calculated respectively, including:

[0053] The first formula is:

[0054] ,

[0055] Wherein, representing matching degrees between the plurality of first plant features and the first feature library, representing the i-th first plant feature, n representing the number of first plant features, representing the j-th first standard plant feature in the first feature library, m representing the number of first standard plant features in the first feature library.

[0056] In the embodiment, the target plant can be corn, the first plant feature can be the number of leaves, the size of leaves, the first plant feature is a data vector value, the first standard plant feature library can be the standard growth of corn, the standard number of leaves, the standard size of leaves and the standard stem thickness at each growth stage, etc., and by calculating the matching degrees between the plurality of first plant features of the corn and the plurality of first standard plant feature libraries of the corn, the first number of first standard plant features with the highest matching degree is selected from the standard plant feature library as the first feature information of the corn.

[0057] For example, the target plant can be corn, the first plant feature can be the number of leaves, the size of leaves, the first standard plant feature library can be the standard growth of corn, the standard number of leaves, the standard size of leaves and the standard stem thickness at each growth stage, etc., and by the first formula, the matching degrees between the plurality of first plant features of the corn and the plurality of first standard plant feature libraries of the corn are calculated, and the one with the highest matching degree can be the A stage of the corn, at this time, the number of leaves, the size of leaves and the standard stem thickness of the corn at the A stage in the first standard plant feature library are combined (the first number at this time is 1) as the first feature information.

[0058] From the above, it can be concluded that the disclosure can more comprehensively and accurately obtain the key features of the above-ground part of the plant by detailed feature extraction on the above-ground image information, improve the accuracy of feature extraction, calculate the matching degrees between the plurality of first plant features and the standard plant feature library at different growth stages, more accurately judge the growth stage of the plant, and enrich the data of the stage, and reduce the error.

[0059] In an embodiment of the disclosure, the second monitoring information includes underground image information of the target plant;

[0060] The second monitoring information is subjected to feature extraction to obtain second feature information, including:

[0061] The underground image information of the target plant is subjected to feature extraction to obtain a plurality of second plant features;

[0062] The matching degrees between the plurality of second plant features and a plurality of second standard plant feature libraries are calculated, respectively;

[0063] The second number of second standard plant features with the highest matching degree is selected from the second standard plant feature library as the second feature information;

[0064] wherein the different second standard plant feature libraries comprise plant belowground features at different growth stages.

[0065] calculating a matching degree between the plurality of second plant features and the plurality of second standard plant feature libraries respectively, comprising:

[0066] calculating the matching degree between the plurality of second plant features and the second feature library by using a second formula, the second feature library being a feature library in the plurality of second standard plant feature libraries;

[0067] the second formula being:

[0068] ,

[0069] wherein, the matching degree between the plurality of second plant features and the second feature library, the zth second plant feature, and l represents the number of second plant features, the rth second standard plant feature in the second feature library, and k represents the number of second standard plant features in the second feature library.

[0070] In the embodiment, the target plant can be pea, the second plant features can be root depth and root number, the second plant features are data vector values, the second standard plant feature libraries can be standard growth stages of pea, standard root depth, root number, root branch number and root thickness at each growth stage, etc., and the matching degree between the plurality of second plant features and the plurality of second standard plant feature libraries is calculated, and the second number of second standard plant features in the standard plant feature library with the highest matching degree is selected as the second feature information of the pea.

[0071] For example, the target plant can be pea, the second plant features can be root depth and root number, the second standard plant feature libraries can be standard growth stages of pea, standard root depth, root number, root branch number and root thickness at each growth stage, etc., the matching degree between the plurality of second plant features of the pea and the plurality of second standard plant feature libraries of pea is calculated by using the second formula, the highest matching degree can be the B stage of the pea, and at this time, the root depth, root number and the standard root branch number and root thickness of the pea B stage in the second standard plant feature library of the pea are combined (at this time, the second number is 2) as the second feature information.

[0072] Or, for example, the target plant can be oats, the second plant feature can be root depth and root number, the second standard plant feature library can be the standard root depth, root number, root branch number and root thickness of oats at each growth stage, the matching degree between the multiple second plant features of the oats and the multiple second standard plant feature library of oats is calculated by the second formula, and the one with the highest matching degree can be the C stage of the oats, at this time, the root depth, root number and second standard plant feature library of the oats C stage of the standard root branch number and root thickness are combined as the second feature information.

[0073] From the above, it can be concluded that the disclosure can more comprehensively and accurately obtain the key features of the above-ground part of the plant by detailed feature extraction on the above-ground image information, improve the accuracy of feature extraction, calculate the matching degree of multiple second plant features and different growth stage standard plant feature libraries, more accurately judge the growth stage of the plant, and enrich the data of the stage, and reduce the error.

[0074] In an embodiment of the disclosure, the first feature information includes multiple first plant features and multiple first standard plant features, and the second feature information includes multiple second plant features and multiple second standard plant features.

[0075] The growth index of the target plant is calculated based on the first feature information and the second feature information, including:

[0076] The above-ground growth index of the target plant is calculated based on the multiple first plant features and the multiple first standard plant features.

[0077] The underground growth index of the target plant is calculated based on the multiple second plant features and the multiple second standard plant features.

[0078] The growth index of the target plant is determined based on the above-ground growth index and the underground growth index.

[0079] The growth index of the target plant is determined based on the above-ground growth index and the underground growth index, including:

[0080] The average of the above-ground growth index and the underground growth index is calculated as the growth index of the target plant.

[0081] The above-ground growth index of the target plant is calculated based on the multiple first plant features and the multiple first standard plant features, including:

[0082] The above-ground growth index of the target plant is calculated by the third formula;

[0083] The third formula includes:

[0084] ,

[0085] in, represents the aboveground growth index, represents the weight of the first plant feature, represents the ath first plant feature, represents the weight of the bth first standard plant feature, represents the bth first standard plant feature, x represents the number of first plant features, and y represents the number of first standard plant features.

[0086] Calculating an underground growth index of a target plant based on a plurality of second plant characteristics and a plurality of second standard plant characteristics includes:

[0087] Calculating the underground growth index of the target plant using the fourth formula;

[0088] The fourth formula includes:

[0089] ,

[0090] in, represents the aboveground growth index, represents the weight of the second plant feature, represents the cth second plant feature, represents the weight of the d-th second standard plant feature, represents the dth second standard plant feature, e represents the number of second plant features, and f represents the number of second standard plant features.

[0091] In this embodiment, the target plant can be rice, the first plant feature can be the number of leaves and the size of leaves, the first standard plant feature library can be the standard growth time of rice, the second plant feature can be the root depth and the number of roots, the second standard plant feature library can be the standard growth time of rice, the standard root depth, the number of roots, the number of root branches and the root diameter at each growth stage, etc., which can be obtained by , calculate the aboveground growth index of rice, where represents the aboveground growth index, Indicates the The weight of the first plant feature, Indicates the The first plant characteristic, Indicates the The weight of the first standard plant characteristics, Indicates the The first standard plant characteristics, represents the number of the first plant feature, Indicates the number of first standard plant characteristics; can be , calculate the underground growth index of rice, where, represents the underground growth index, Indicates the The weight of the second plant feature, Indicates the A second plant characteristic, Indicates the The weight of the second standard plant feature, Indicates the The second standard plant characteristics, represents the number of the second plant feature, Represents the quantity of the second standard plant characteristic; the average value of the aboveground growth index of rice and the underground growth index of rice is calculated as the growth index of the rice.

[0092] For example, the target plant may be rice, the first plant feature may be the number of leaves, the leaf size, the first standard plant feature library may be the standard growth time of rice, the standard number of leaves, the standard leaf size and the standard stem diameter at each growth stage, etc., the second plant feature may be the root depth, the second standard plant feature library may be the standard growth time of rice, the standard root depth, the number of roots, the number of leaves at each growth stage, etc. It can be 20, weight It can be 0.3, the leaf size It can be 15, weight It can be 0.4, the stem is thick It can be 5, weight Can be 0.3; standard value of blade number It can be 18, weight It can be 0.4, the standard value of leaf size Can be 12, weight It can be 0.5, the standard value of stem thickness Can be 4, weight Can be 0.1, calculate the aboveground growth index is 9.03; the root depth can be It can be 30, weight Can be 0.5, the number of roots It can be 10, weight It can be 0.5, and the standard value of root depth can be It can be 25, weight It can be 0.6, the standard value of the root number It can be 8, weight Can be 0.4, calculate the underground growth index is 19.1, and the growth index of the rice is calculated to be 14.065.

[0093] It can be concluded from the above that the disclosure comprehensively evaluates the growth status of the plant by simultaneously considering the features of the aboveground and underground parts, avoids one-sidedness caused by only focusing on a single part of the aboveground or underground part, and can more accurately reflect the overall growth trend of the plant; by specific formulas and calculation methods, the features of the plant are converted into a quantifiable growth index, thereby providing accurate numerical basis for the evaluation of plant growth.

[0094] In an embodiment of the disclosure, after determining the growth stage of the target plant based on the growth index, the method further comprises:

[0095] adjusting the monitoring frequency of the first monitoring instrument and adjusting the monitoring frequency of the second monitoring instrument based on the growth stage of the target plant; wherein the monitoring frequency of the first monitoring instrument is different at different growth stages, and the monitoring frequency of the second monitoring instrument is different at different growth stages.

[0096] In this embodiment, the target plant can be corn, the growth index can be calculated by the feature information of the aboveground part of the corn such as leaf size, leaf number and stem thickness, and the feature information of the underground part of the corn such as root depth, root number and root thickness, and the growth stage can be emergence stage, three-leaf stage, jointing stage, small trumpet stage, large trumpet stage, tasseling stage, flowering stage, grain filling stage, milk stage, dough stage and full maturity stage, etc. When the corn is in the three-leaf stage, the monitoring frequency of the first monitoring instrument can be once every 4 hours, and the monitoring frequency of the second monitoring instrument can be once every 4 hours. The monitoring frequency of the first monitoring instrument and the monitoring frequency of the second monitoring instrument should be consistent in order to comprehensively analyze the growth stage of the corn, and the monitoring frequency of the first monitoring instrument and the monitoring frequency of the second monitoring instrument should change with the change of the growth stage of the corn.

[0097] For example, the target plant can be corn, the growth stage corresponding to the growth index can be the emergence stage, the first monitoring instrument can be a camera, the monitoring frequency corresponding to the first monitoring instrument can be once every 8 hours, and the second monitoring instrument can be a root in-situ analyzer, the monitoring frequency corresponding to the second monitoring instrument can be once every 8 hours; when the growth stage corresponding to the growth index changes to the three-leaf stage, since the growth speed of the corn in the three-leaf stage is faster than that in the emergence stage, the monitoring frequency corresponding to the first monitoring instrument can be once every 4 hours, and the monitoring frequency corresponding to the second monitoring instrument can be once every 4 hours; when the growth stage corresponding to the growth index changes to the large trumpet stage, since the growth speed of the corn in the large trumpet stage is the fastest among all stages, the monitoring frequency corresponding to the first monitoring instrument can be once every 3 hours, and the monitoring frequency corresponding to the second monitoring instrument can be once every 3 hours.

[0098] Or, for example, the target plant can be cotton, the growth stages of cotton are: sowing and seedling stage, seedling stage, bud stage, flowering and boll stage, and boll opening stage, the growth stage corresponding to the growth index can be the seedling stage, the first monitoring instrument can be a camera, the monitoring frequency corresponding to the first monitoring instrument can be once every 12 hours, the second monitoring instrument can be an ultrasonic monitor, and the monitoring frequency corresponding to the second monitoring instrument can be once every 12 hours; when the growth stage corresponding to the growth index becomes the bud stage, since the growth speed of the bud stage of cotton is faster than that of the seedling stage, the monitoring frequency corresponding to the first monitoring instrument can be once every 10 hours, and the monitoring frequency corresponding to the second monitoring instrument can be once every 10 hours; when the growth stage corresponding to the growth index becomes the flowering and boll stage, since the growth speed of the flowering and boll stage of cotton is the fastest among all stages, the monitoring frequency corresponding to the first monitoring instrument can be once every 8 hours, and the monitoring frequency corresponding to the second monitoring instrument can be once every 8 hours.

[0099] Or, for example, the target plant can be winter wheat, the growth stages of winter wheat are: emergence stage, tillering stage, overwintering stage, green-up stage, emergence stage, jointing stage, booting stage, heading stage, flowering stage, grain filling stage, and mature stage, the growth stage corresponding to the growth index can be that the winter wheat is in the emergence stage, the first monitoring instrument can be a camera, the monitoring frequency corresponding to the first monitoring instrument can be once every 12 hours, the second monitoring instrument can be an ultrasonic monitor, and the monitoring frequency corresponding to the second monitoring instrument can be once every 12 hours; when the growth stage corresponding to the growth index becomes the overwintering stage, since the growth of the overwintering stage of winter wheat almost stops, the monitoring frequency corresponding to the first monitoring instrument can be once every 72 hours, and the monitoring frequency corresponding to the second monitoring instrument can be once every 72 hours; when the growth stage corresponding to the growth index becomes the jointing stage, since the growth speed of the jointing stage of winter wheat is the fastest among all stages, the monitoring frequency corresponding to the first monitoring instrument can be once every 6 hours, and the monitoring frequency corresponding to the second monitoring instrument can be once every 6 hours.

[0100] From the above, it can be concluded that the present disclosure reasonably allocates monitoring resources according to the importance and growth speed of different growth stages of plants, increases the monitoring frequency in the key growth period, can more timely obtain accurate information, and reduces the frequency in the non-key period, thereby saving manpower, material resources and time cost; the growth characteristics and needs of plants in different growth stages are different, and adjusting the monitoring frequency can more accurately capture key information.

[0101] The plant growth monitoring method corresponding to the above embodiment, Figure 2 A structural block diagram of a plant growth monitoring system provided by an embodiment of the present disclosure is shown. For ease of illustration, only parts related to the embodiments of the present disclosure are shown. Reference is made to the above embodiments of the plant growth monitoring method. Figure 2 The plant growth monitoring system 20 includes:

[0102] a data collection module 21, configured to obtain first characteristic information and second characteristic information of the target plant, the first characteristic information being characteristic information of an aboveground part of the target plant, and the second characteristic information being characteristic information of an underground part of the target plant;

[0103] a data processing module 22, configured to calculate a growth index of the target plant based on the first characteristic information and the second characteristic information;

[0104] a judgment module 23, configured to determine a growth stage of the target plant based on the growth index.

[0105] In an embodiment of the present disclosure, the data collection module 21 is specifically configured to:

[0106] monitor the aboveground part of the target plant by using a first monitoring instrument to obtain first monitoring information;

[0107] monitor the underground part of the target plant by using a second monitoring instrument to obtain second monitoring information;

[0108] perform feature extraction on the first monitoring information to obtain the first characteristic information;

[0109] perform feature extraction on the second monitoring information to obtain the second characteristic information.

[0110] In an embodiment of the present disclosure, the first monitoring information includes aboveground image information of the target plant;

[0111] The data collection module 21 includes a first feature extraction module and a first feature calculation module.

[0112] The first feature extraction module is configured to perform feature extraction on the aboveground image information of the target plant to obtain a plurality of first plant features.

[0113] The first feature calculation module is configured to calculate matching degrees between the plurality of first plant features and a plurality of first standard plant feature libraries respectively.

[0114] select a first number of first standard plant features from the first standard plant feature library with the highest matching degree as the first characteristic information;

[0115] Different first standard plant feature libraries include aboveground features of plants in different growth stages.

[0116] In an embodiment of the present disclosure, the second monitoring information includes underground image information of the target plant;

[0117] The data collection module 21 includes a second feature extraction module and a second feature calculation module.

[0118] The second feature extraction module is configured to extract features from the underground image information of the target plant to obtain a plurality of second plant features.

[0119] The second feature calculation module is configured to calculate matching degrees between the plurality of second plant features and a plurality of second standard plant feature libraries respectively.

[0120] The second quantity of second standard plant features with the highest matching degrees are selected from the second standard plant feature libraries as second feature information.

[0121] Different second standard plant feature libraries include plant underground features at different growth stages.

[0122] In an embodiment of the present disclosure, the first feature information includes a plurality of first plant features and a plurality of first standard plant features, and the second feature information includes a plurality of second plant features and a plurality of second standard plant features.

[0123] The data processing module 22 includes a first growth index calculation module, a second growth index calculation module, and a third growth index calculation module.

[0124] The first growth index calculation module is configured to calculate an aboveground growth index of the target plant based on the plurality of first plant features and the plurality of first standard plant features.

[0125] The second growth index calculation module is configured to calculate an underground growth index of the target plant based on the plurality of second plant features and the plurality of second standard plant features.

[0126] The third growth index calculation module is configured to determine a growth index of the target plant based on the aboveground growth index and the underground growth index.

[0127] In an embodiment of the present disclosure, the third growth index calculation module is specifically configured to:

[0128] Calculate an average value of the aboveground growth index and the underground growth index as the growth index of the target plant.

[0129] In an embodiment of the present disclosure, the plant growth monitoring system 20 further includes a monitoring module.

[0130] The monitoring module is configured to adjust a monitoring frequency of the first monitoring instrument and adjust a monitoring frequency of the second monitoring instrument based on a growth stage of the target plant, wherein the monitoring frequencies of the first monitoring instrument at different growth stages are different, and the monitoring frequencies of the second monitoring instrument at different growth stages are different.

[0131] Referring to Figure 3 , Figure 3 The schematic block diagram of the electronic device provided in an embodiment of the present disclosure is shown in FIG. 1. As shown in FIG. 1, the electronic device includes a processor 10, a memory 20, a communication interface 30, and a power supply 40. Figure 3The electronic device 300 in the embodiment shown can include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processor 301, the input device 302, the output device 303, and the memory 304 can communicate with each other through a communication bus 305. The memory 304 is configured to store a computer program including program instructions. The processor 301 is configured to execute the program instructions stored in the memory 304. The processor 301 is configured to invoke the program instructions to implement the functions of the modules / units in the above-described system embodiments, for example Figure 2 The functions of the modules 21 to 23.

[0132] It should be understood that, in the embodiments of the present disclosure, the processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0133] The input device 302 can include a touchpad, a fingerprint collection sensor (for collecting fingerprint information and direction information of a fingerprint of a user), a microphone, etc., and the output device 303 can include a display (LCD, etc.), a speaker, etc.

[0134] The memory 304 can include a read-only memory and a random access memory, and provide instructions and data for the processor 301. A portion of the memory 304 can also include a nonvolatile random access memory. For example, the memory 304 can also store device type information.

[0135] In specific implementations, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present disclosure can implement the implementation manners described in the first and second embodiments of the plant growth monitoring method provided by the embodiments of the present disclosure, and can also implement the implementation manners of the electronic device described in the embodiments of the present disclosure, which will not be described here.

[0136] In another embodiment of the present disclosure, a computer readable storage medium is provided, which stores a computer program. The computer program includes program instructions, which, when executed by a processor, implement all or part of the processes of the above-mentioned embodiment methods. The computer program can also instruct related hardware to complete the above-mentioned processes. The computer program can be stored in a computer readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0137] The computer readable storage medium can be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The computer readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of the electronic device. The computer readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.

[0138] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present disclosure.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic device and the units described above can refer to the corresponding processes in the above-mentioned method embodiments, which will not be described here.

[0140] In several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other manners. For example, the embodiments of the apparatus described above are merely illustrative. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the units can be indirect coupling or communication connection through some interfaces, or can be in electrical, mechanical or other forms.

[0141] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present disclosure.

[0142] In addition, each functional unit in the various embodiments of the present disclosure can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0143] The above is merely specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto, and any skilled person in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present disclosure, and these modifications or replacements should be covered in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A plant growth monitoring method, characterized in that: include: Using a first monitoring instrument to monitor the above-ground part of a target plant to obtain first monitoring information; monitoring the underground portion of the target plant using a second monitoring instrument to obtain second monitoring information; performing feature extraction on the first monitoring information to obtain first feature information; Extracting features from the second monitoring information to obtain second feature information; the first feature information is feature information of the above-ground part of the target plant, and the second feature information is feature information of the underground part of the target plant; Calculating a growth index of the target plant based on the first feature information and the second feature information; determining a growth stage of the target plant based on the growth index; The first monitoring information includes above-ground image information of the target plant; The extracting features from the first monitoring information to obtain first feature information includes: performing feature extraction on the above-ground image information of the target plant to obtain a plurality of first plant features; Calculating the matching degrees between the plurality of first plant features and a plurality of first standard plant feature libraries respectively; Selecting a first number of first standard plant features from the first standard plant feature library with the highest matching degree as first feature information; Among them, different first standard plant feature libraries include above-ground features of plants at different growth stages; Calculating the matching degrees between the plurality of first plant features and the plurality of first standard plant feature libraries, respectively, includes: Calculating the matching degree between the plurality of first plant features and a first feature library using a first formula, where the first feature library is a feature library in the plurality of first standard plant feature libraries; The first formula is: ， in, represents the matching degree between multiple first plant features and the first feature library, represents the i-th first plant feature, n represents the number of first plant features, represents the jth first standard plant feature in the first feature library, and m represents the number of first standard plant features in the first feature library; The second monitoring information includes underground image information of the target plant; The extracting features from the second monitoring information to obtain second feature information includes: performing feature extraction on the underground image information of the target plant to obtain a plurality of second plant features; Calculating the matching degrees between the plurality of second plant features and a plurality of second standard plant feature libraries respectively; selecting a second number of second standard plant features from the second standard plant feature library with the highest matching degree as second feature information; Among them, different second standard plant feature libraries include underground features of plants at different growth stages; Calculating the matching degrees between the plurality of second plant features and the plurality of second standard plant feature libraries, including: Calculating the matching degree between the plurality of second plant features and the second feature library using a second formula, where the second feature library is a feature library in the plurality of second standard plant feature libraries; The second formula is: ， in, represents the matching degree between multiple second plant features and the second feature library, represents the zth second plant feature, l represents the number of second plant features, represents the rth second standard plant feature in the second feature library, and k represents the number of second standard plant features in the second feature library.

2. The plant growth monitoring method according to claim 1, wherein: The first feature information includes a plurality of first plant features and a plurality of first standard plant features, and the second feature information includes a plurality of second plant features and a plurality of second standard plant features; The calculating the growth index of the target plant based on the first feature information and the second feature information includes: calculating an above-ground growth index of the target plant based on the plurality of first plant characteristics and the plurality of first standard plant characteristics; calculating an underground growth index of the target plant based on the plurality of second plant characteristics and the plurality of second standard plant characteristics; The growth index of the target plant is determined based on the above-ground growth index and the below-ground growth index.

3. The plant growth monitoring method according to claim 2, wherein: The determining the growth index of the target plant based on the aboveground growth index and the underground growth index comprises: An average value of the above-ground growth index and the underground growth index is calculated as the growth index of the target plant.

4. The plant growth monitoring method according to claim 1, wherein: After determining the growth stage of the target plant based on the growth index, the method further includes: The monitoring frequency of the first monitor is adjusted based on the growth stage of the target plant, and the monitoring frequency of the second monitor is adjusted; wherein the monitoring frequency of the first monitor is different in different growth stages, and the monitoring frequency of the second monitor is different in different growth stages.

5. A plant growth monitoring system, characterized in that: include: a data acquisition module, configured to monitor the above-ground part of the target plant using a first monitoring instrument to obtain first monitoring information; monitoring the underground portion of the target plant using a second monitoring instrument to obtain second monitoring information; performing feature extraction on the first monitoring information to obtain first feature information; Extracting features from the second monitoring information to obtain second feature information; the first feature information is feature information of the above-ground part of the target plant, and the second feature information is feature information of the underground part of the target plant; a data processing module, configured to calculate a growth index of the target plant based on the first characteristic information and the second characteristic information; a judgment module, configured to determine the growth stage of the target plant based on the growth index; The first monitoring information includes above-ground image information of the target plant; a data acquisition module, specifically configured to extract features from the above-ground image information of the target plant to obtain a plurality of first plant features; Calculating the matching degrees between the plurality of first plant features and a plurality of first standard plant feature libraries respectively; Selecting a first number of first standard plant features from the first standard plant feature library with the highest matching degree as first feature information; Among them, different first standard plant feature libraries include above-ground features of plants at different growth stages; Calculating the matching degrees between the plurality of first plant features and the plurality of first standard plant feature libraries, respectively, includes: Calculating the matching degree between the plurality of first plant features and a first feature library using a first formula, where the first feature library is a feature library in the plurality of first standard plant feature libraries; The first formula is: ， in, represents the matching degree between multiple first plant features and the first feature library, represents the i-th first plant feature, n represents the number of first plant features, represents the jth first standard plant feature in the first feature library, and m represents the number of first standard plant features in the first feature library; The second monitoring information includes underground image information of the target plant; a data acquisition module, specifically configured to extract features from the underground image information of the target plant to obtain a plurality of second plant features; Calculating the matching degrees between the plurality of second plant features and a plurality of second standard plant feature libraries respectively; selecting a second number of second standard plant features from the second standard plant feature library with the highest matching degree as second feature information; Among them, different second standard plant feature libraries include underground features of plants at different growth stages; Calculating the matching degrees between the plurality of second plant features and the plurality of second standard plant feature libraries, including: Calculating the matching degree between the plurality of second plant features and the second feature library using a second formula, where the second feature library is a feature library in the plurality of second standard plant feature libraries; The second formula is: ， in, represents the matching degree between multiple second plant features and the second feature library, represents the zth second plant feature, l represents the number of second plant features, represents the rth second standard plant feature in the second feature library, and k represents the number of second standard plant features in the second feature library.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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