Method and system for monitoring plant movement

Abstract

The invention discloses a method for monitoring plant movement. The method comprises the following steps: S1, recording an initial posture of a plant; s2, a flexible clamp for a sensor is used, the sensor is fixed to the leaf stalk or stem of the plant, and the sensor collects motion data of the leaf in real time; s3, correcting the motion data, eliminating the measurement of the weight of the sensor on the motion of the plant, and obtaining processed data; and updating the posture of the previous moment based on the current processing data, obtaining the updated posture of the plant, and realizing the monitoring of the plant motion. The method provided by the invention can be used in an environment with poor light and shadow conditions to monitor the movement of the plant. And monitoring can be realized immediately only by fixing the sensor on the stem leaf or rhizome of the plant. The method greatly reduces the detection difficulty, and is suitable for behavior monitoring of various plants.

Description

Technical Field

[0001] The present invention relates to the field of agricultural biotechnology, and in particular to a method and system for monitoring plant movement. Background Art

[0002] The movement of plant leaves is not only an important reflection of their physiological activities and growth status, but also a key factor in plants' adaptation to changes in the external environment, such as light, temperature, and water. This dynamic behavior contains rich biological information and is of great significance for studying the growth and development laws of plants, the mechanism of response to adversity, and the precise management of agricultural production.

[0003] Traditional plant motion monitoring methods, especially those based on image analysis, can provide quantitative data of leaf motion to a certain extent, but their application faces many challenges. Such methods often require high-precision image acquisition equipment and subsequent huge data processing processes, which not only increases the cost of monitoring, but also places high demands on the professional quality of technicians. More importantly, in complex and changing natural environments, such as insufficient light, leaf occlusion or interlaced shadows, the accuracy of image analysis technology is often greatly reduced, limiting its wide application in the field or actual production environments. Given the limitations of traditional methods, it is particularly important to develop a plant leaf motion measurement system that is both simple and efficient, and has proper cost control. Summary of the invention

[0004] In view of the above-mentioned defects, the purpose of the present invention is to propose a method and system for monitoring plant movement, so as to solve the problems of difficulty in plant movement detection and complex equipment in the prior art.

[0005] To achieve this purpose, the present invention adopts the following technical solution: A method for monitoring plant movement, comprising the following steps:

[0006] Step S1: Record the initial posture of the plant;

[0007] Step S2: using a flexible sensor fixture to fix the sensor on the petiole or stem of the plant, the sensor collects the movement data of the leaf in real time;

[0008] Step S3: Correct the motion data to eliminate the effect of the sensor's own weight on the plant's motion measurement, and obtain processed data;

[0009] Based on the current processing data, the posture of the previous moment is updated to obtain the updated posture of the plant and realize the monitoring of plant movement.

[0010] Preferably, step A needs to be performed before performing step S3;

[0011] Step A: Perform Bonferroni correction on the motion data and remove the motion data whose probability of occurrence p is greater than the probability of rejection O′.

[0012] Preferably, after executing step A, the following steps are performed:

[0013] A low-pass filter is used to filter and denoise the remaining motion data;

[0014] The formula for filtering motion data is as follows:

[0015] y[n]=α·x[n]+(1-α)·y[n-1];

[0016] Where α is the filter coefficient, x[n] is the motion data of the current sensor, y[n] is the motion data after current filtering, and y[n-1] is the motion data after the previous filtering.

[0017] Preferably, the steps of correcting the motion data in step S3 are as follows:

[0018] Step S31: setting the value of the correction item in the vertical direction;

[0019] Step S32: obtaining the difference between the gravitational acceleration and the correction term as an error value;

[0020] Step S33: Correct the motion data using the error value and the correction weight to obtain processed data.

[0021] Preferably, the formula for obtaining the updated pose of the plant in step S3 is as follows:

[0022]

[0023] Where C(t) is the current position, C(t-Δt) is the previous position, ω f To process data.

[0024] A system for monitoring plant movement, using the method for monitoring plant movement, comprising:

[0025] The initial recording module is used to record the initial posture of the plant;

[0026] Sensor node module, used to collect blade motion data in real time;

[0027] The data concentration module is used to collect all the motion data, perform correction processing on the motion data, eliminate the effect of the sensor's own weight on the measurement of the plant's motion, and obtain processed data;

[0028] Based on the current processing data, the posture of the previous moment is updated to obtain the updated posture of the plant and realize the monitoring of plant movement.

[0029] Preferably, it also includes a correction module;

[0030] The correction module is used to perform Bonferroni correction on the motion data, and remove the motion data whose occurrence probability value p is greater than the rejection probability O′.

[0031] Preferably, it further comprises a denoising module, wherein the denoising module is used to filter and denoise the remaining motion data using a low-pass filter.

[0032] Preferably, the data concentration module performs the following steps:

[0033] Set the value of the correction item in the vertical direction;

[0034] Obtain the difference between the gravitational acceleration and the correction term as the error value;

[0035] The motion data is corrected by using the error value and the correction weight to obtain the processed data.

[0036] One of the above technical solutions has the following advantages or beneficial effects: the method provided by the present invention can be used in an environment with poor light and shadow conditions to monitor the movement of plants. Moreover, the monitoring can be realized immediately by only fixing the sensor on the stems, leaves or rhizomes of the plants. The detection difficulty is greatly reduced, and it is suitable for the behavior monitoring of various plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of an embodiment of the method of the present invention.

[0038] Figure 2 It is a schematic diagram of the structure of an embodiment of the system of the present invention.

[0039] Figure 3 is a motion data graph of a tomato leaf in one embodiment of the present invention. DETAILED DESCRIPTION

[0040] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0041] In the description of the embodiments of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0042] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, the meaning of "plurality" is two or more. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] like Figures 1 to 3 As shown, a method for monitoring plant movement comprises the following steps:

[0044] Step S1: Record the initial posture of the plant;

[0045] Step S2: using a flexible sensor fixture to fix the sensor on the petiole or stem of the plant, the sensor collects the movement data of the leaf in real time;

[0046] Step S3: Correct the motion data to eliminate the effect of the sensor's own weight on the plant's motion measurement, and obtain processed data;

[0047] Based on the current processing data, the posture of the previous moment is updated to obtain the updated posture of the plant and realize the monitoring of plant movement.

[0048] In order to solve the problem that the existing recording of plant trajectories requires a large amount of image data processing and is difficult to process, in the present invention, a sensor is used to obtain plant data, and then the posture of the plant is updated in conjunction with the previous posture, thereby realizing plant motion monitoring. The sensor can use the MPU6050 IMU sensor, which is a micro-electromechanical system including a three-axis gyroscope and a three-axis accelerometer. It depends on the change of the Coriolis force (Coriolis force) or the linear displacement to change the internal spring configuration, thereby obtaining the angular velocity and linear acceleration of the plant when the stems and leaves are in motion. In addition, the MPU6050 IMU sensor is light in weight and will not affect the normal movement of the plant stems and leaves.

[0049] However, the sensor still has a certain weight, and after being fixed to the plant, it will still have a certain impact on the plant's motion data. Therefore, before updating the posture, the motion data needs to be processed to eliminate the effect of the sensor's own weight on the plant's motion measurement and obtain processed data; after obtaining the processed data, the posture at the previous moment is updated based on the processed data, so as to continuously obtain the plant's posture and finally realize the monitoring and supervision of plant movement.

[0050] The method provided by the present invention can be used in an environment with poor light and shadow conditions to monitor the movement of plants. Moreover, the monitoring can be realized immediately by only fixing the sensor on the stems, leaves or rhizomes of the plants. The detection difficulty is greatly reduced, and it is suitable for the behavior monitoring of various plants.

[0051] Preferably, step A needs to be performed before performing step S3;

[0052] Step A: Perform Bonferroni correction on the motion data and remove the motion data whose probability of occurrence p is greater than the probability of rejection O′.

[0053] Since the present invention uses a sensor to obtain the angular velocity changes in the stems and leaves of plants as motion data, it is impossible to determine whether some motion data are generated under external interference due to the lack of external additional algorithm support (such as image algorithm). For example, the wind blows the leaves, causing the leaves to move, or other animals touch the large leaves. For this reason, in the present invention, Bonferroni is used to correct the motion data. First, the probability value P of the motion data is obtained, and the probability value P of the occurrence can be obtained through existing statistics. The probability of rejection O' can first set a fixed significance level O, and then use O / n, where n is the total amount of current motion data, to obtain the probability O'. The motion data obtained by non-normal plant movement can be screened out by the probability value p and the probability of rejection O', thereby improving the calculation accuracy of the posture during monitoring.

[0054] Preferably, after executing step A, the following steps are performed:

[0055] A low-pass filter is used to filter and denoise the remaining motion data;

[0056] The formula for filtering motion data is as follows:

[0057] y[n]=α·x[n]+(1-α)·y[n-1];

[0058] Where α is the filter coefficient, x[n] is the motion data of the current sensor, y[n] is the motion data after current filtering, and y[n-1] is the motion data after the previous filtering.

[0059] Preferably, the steps of correcting the motion data in step S3 are as follows:

[0060] Step S31: setting the value of the correction item in the vertical direction;

[0061] The correction items are set as follows: The value of r is between 0.1 and 2 and can be adjusted according to the weight of the sensor.

[0062] Step S32: obtaining the difference between the gravitational acceleration and the correction term as an error value;

[0063] Step S33: Correct the motion data using the error value and the correction weight to obtain processed data.

[0064] ω f =ω+k·e a ; where e a is the error value, k is the correction weight, and ω is the motion data (the angular velocity ω of the gyroscope obtained from the sensor).

[0065] Preferably, the formula for obtaining the updated pose of the plant in step S3 is as follows:

[0066]

[0067] Where C(t) is the current position, C(t-Δt) is the previous position, ω f To process data.

[0068] Example: According to the size of the tomato plant, the MPU6050 IMU sensor is fixed on the petiole of the tomato plant without affecting the growth of the plant. The relative distance between the sensors is kept at more than 3 cm. The IMU sensor is connected to the sensor node through a digital expansion board or an I2C expansion board. The data recording time is 3 days.

[0069] The leaf rhythmic change data obtained by sensors from each node are aggregated into the data concentrator and transmitted to the user end and cloud storage. The weight of the sensor itself will affect the plant motion measurement, and the direction cosine matrix (DCM) is used for data update: DCM is used to describe the posture of the sensor relative to the reference coordinate system.

[0070] At the initial moment, set the initial posture matrix to the identity matrix:

[0071] C(0) = I;

[0072] For each time step (e.g., Δt), the angular velocity ω of the gyroscope obtained from the sensor is used after Bonferroni correction, and a statistical test is performed on each set of sensor data, and a probability value p appears,

[0073] If: p<O′

[0074] Update the angular velocity data to DCM:

[0075]

[0076] Data analysis and processing: By applying Bonferroni correction to the measurements of the IMU's internal gyroscope and accelerometer in three directions and combining them with the digital motion processing algorithm (DMP), the angular direction can be accurately calculated. Analysis of the data shows that over a period of 3 days, the tomato leaves bend downward at 5-6° during the day and rise again at night. Results Figure 3 shown.

[0077] A system for monitoring plant movement, using the method for monitoring plant movement, comprising:

[0078] The initial recording module is used to record the initial posture of the plant;

[0079] Sensor node module, used to collect blade motion data in real time;

[0080] The data concentration module is used to collect all the motion data, perform correction processing on the motion data, eliminate the effect of the sensor's own weight on the measurement of the plant's motion, and obtain processed data;

[0081] Based on the current processing data, the posture of the previous moment is updated to obtain the updated posture of the plant and realize the monitoring of plant movement.

[0082] Preferably, it also includes a correction module;

[0083] The correction module is used to perform Bonferroni correction on the motion data, and remove the motion data whose occurrence probability value p is greater than the rejection probability O′.

[0084] Preferably, it further comprises a denoising module, wherein the denoising module is used to filter and denoise the remaining motion data using a low-pass filter.

[0085] Preferably, the data concentration module performs the following steps:

[0086] Set the value of the correction item in the vertical direction;

[0087] Obtain the difference between the gravitational acceleration and the correction term as the error value;

[0088] The motion data is corrected by using the error value and the correction weight to obtain the processed data.

[0089] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0090] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for monitoring plant movement, characterized in that: The steps include: Step S1: Record the initial posture of the plant; Step S2: using a flexible sensor fixture to fix the sensor on the petiole or stem of the plant, the sensor collects the movement data of the leaf in real time; Step S3: Correct the motion data to eliminate the effect of the sensor's own weight on the plant's motion measurement, and obtain processed data; Based on the current processing data, the posture of the previous moment is updated to obtain the updated posture of the plant and realize the monitoring of plant movement.

2. A method for monitoring plant movement according to claim 1, characterized in that: Step A needs to be executed before executing step S3; Step A: Perform Bonferroni correction on the motion data and remove the motion data whose probability of occurrence p is greater than the probability of rejection O′.

3. A method for monitoring plant movement according to claim 2, characterized in that: After executing step A, perform the following steps: A low-pass filter is used to filter and denoise the remaining motion data; The formula for filtering motion data is as follows: y[n]=α·x[n]+(1-α)·y[n-1]; Where α is the filter coefficient, x[n] is the motion data of the current sensor, y[n] is the motion data after current filtering, and y[n-1] is the motion data after the previous filtering.

4. A method for monitoring plant movement according to claim 1, characterized in that: The steps of correcting the motion data in step S3 are as follows: Step S31: setting the value of the correction item in the vertical direction; Step S32: obtaining the difference between the gravitational acceleration and the correction term as an error value; Step S33: Correct the motion data using the error value and the correction weight to obtain processed data.

5. A method for monitoring plant movement according to claim 4, characterized in that: The formula for obtaining the updated pose of the plant in step S3 is as follows: Where C(t) is the current position, C(t-Δt) is the previous position, ω f To process data.

6. A system for monitoring plant movement, characterized in that: The method for monitoring plant movement using any one of claims 1 to 5 comprises: The initial recording module is used to record the initial posture of the plant; Sensor node module, used to collect blade motion data in real time; The data concentration module is used to collect all the motion data, perform correction processing on the motion data, eliminate the effect of the sensor's own weight on the measurement of the plant's motion, and obtain processed data; Based on the current processing data, the posture of the previous moment is updated to obtain the updated posture of the plant and realize the monitoring of plant movement.

7. A system for monitoring plant movement according to claim 6, characterized in that: Also included is a correction module; The correction module is used to perform Bonferroni correction on the motion data, and remove the motion data whose occurrence probability value p is greater than the rejection probability O′.

8. A system for monitoring plant movement according to claim 6, characterized in that: It also includes a denoising module, which is used to filter and denoise the remaining motion data using a low-pass filter.

9. A system for monitoring plant movement according to claim 6, characterized in that: The data concentration module performs the following steps: Set the value of the correction item in the vertical direction; Obtain the difference between the gravitational acceleration and the correction term as the error value; The motion data is corrected by using the error value and the correction weight to obtain the processed data.

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