Plant phenotype high-throughput monitoring system based on matrix control

Abstract

The invention belongs to the technical field of agricultural monitoring, and discloses a plant phenotype high-throughput monitoring system based on matrix control, which comprises a matrix monitoring module for constructing a matrix plant phenotype detection environment, and configuring plant samples to be detected in N plant cultivation units; each plant cultivation unit is provided with corresponding environment regulation and control equipment and is mapped into addressable matrix nodes based on a logic addressing rule, and each matrix node forms an environment control matrix through a distributed parallel communication link; the multi-factor monitoring module is used for building a multi-factor collaborative monitoring network, and uniformly triggering each matrix node to synchronously collect SPAC parameters based on a preset matrix sampling time sequence table through a distributed sensor array arranged in the matrix nodes; the limitation of a traditional plant phenotype acquisition means on the aspects of processing scale, regulation and control accuracy and data space-time consistency is broken through, and automatic and intelligent management of the whole plant phenotype monitoring process is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural monitoring, more particularly, the present application relates to a plant phenotype high-throughput monitoring system based on matrix control. BACKGROUND

[0002] The patent with the patent publication number CN119046230A discloses a cross-platform plant phenotype monitoring control system, which comprises a control terminal, an industrial computer, a sensor device and a storage medium. The control terminal is used to plan and manage the experimental site according to actual needs, and to issue control instructions to the industrial computer according to the collection task. The industrial computer is used to issue operation commands to the sensor device according to the received control instructions. The sensor device is used to capture plant phenotype data and upload the captured data to the control terminal for processing and archiving. The processing results are saved to the storage medium or uploaded to the data management and analysis platform by the control terminal. The present application can effectively improve the experimental efficiency and ensure the accuracy and relevance of the data. Therefore, the present application can be widely applied in the field of crop phenotype monitoring.

[0003] The existing plant phenotype high-throughput monitoring system mainly has the following problems: In agricultural research and production, monitoring plant phenotype is an important means to understand plant growth conditions, physiological characteristics and response to the environment. Traditional plant phenotype monitoring methods often rely on manual operation, which has low efficiency, consumes time and effort, and has low data accuracy, and cannot achieve high-throughput monitoring. At the same time, the traditional monitoring system cannot comprehensively and cooperatively monitor multiple factors in the soil, plants and atmosphere continuum, and cannot continuously collect and accurately control data under different environments during the plant growth cycle, which greatly limits the depth and breadth of plant phenotype research and is not conducive to the efficiency improvement of agricultural research and production.

[0004] In view of the above problems, the present application provides a plant phenotype high-throughput monitoring system based on matrix control. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned purposes, the present application provides the following technical scheme: a plant phenotype high-throughput monitoring system based on matrix control, comprising: A matrix monitoring module is used to construct a matrix plant phenotype detection environment, and the plant samples to be tested are arranged in N plant cultivation units. Each plant cultivation unit is respectively configured with a corresponding environment control device, and is mapped to an addressable matrix node based on a logical addressing rule. Each matrix node forms an environment control matrix through a distributed parallel communication link. A multi-factor monitoring module is configured to build a multi-factor collaborative monitoring network. A distributed sensor array is arranged in a matrix node. Based on a preset matrix sampling time sequence table, the multi-factor monitoring module triggers the matrix node to synchronously collect SPAC parameters. A differential control module generates differential water and fertilizer instructions for each matrix node based on preset micro-environment difference regulation criteria and SPAC parameters, drives the corresponding water and fertilizer irrigation device to output, and forms a differential processing group. A whole-cycle control module sets a plant sample growth cycle monitoring time sequence through a matrix control terminal, automatically performs periodic data collection of each matrix node according to a time axis, and forms a multi-dimensional phenotype data set covering the plant sample growth cycle. A phenotype response analysis module indexes the multi-dimensional phenotype data set in time and space dimensions, generates a phenotype response difference map of different matrix nodes through matrix comparison, and outputs a quantitative evaluation result of plant growth performance.

[0006] Preferably, the method for obtaining the matrix plant phenotype detection environment comprises: A two-dimensional matrix structure is established in a preset experimental area, and the area is divided into plant cultivation units; each cultivation unit constitutes an independently controllable plant cultivation experimental micro-environment, and is internally integrated with a soil container, an environment regulation device, and a soil-plant-atmosphere continuum monitoring sensor; each plant cultivation unit is assigned a unique logical address tag, and is mapped to a two-dimensional matrix coordinate number according to a preset logical addressing rule, all two-dimensional matrix coordinate numbers are coded as matrix node addresses, a matrix address mapping table is formed, and each matrix node corresponds to a plant cultivation unit.

[0007] Preferably, the method for obtaining the environment control matrix comprises: The environment control matrix is composed of distributed parallel communication links between matrix nodes. Each matrix node is internally provided with an embedded micro-control unit for receiving differential water and fertilizer instructions and driving the environment regulation device to perform environment parameter regulation operations. A distributed parallel communication link is constructed to realize communication between matrix nodes. A master-slave structure is used to poll the state of each matrix node and send corresponding control instructions through a matrix control terminal, thereby forming an environment control matrix.

[0008] Preferably, the method for building the multi-factor collaborative monitoring network comprises: The multi-factor collaborative monitoring network relies on a distributed sensor array arranged in a matrix node to realize synchronized collection of SPAC parameters; the distributed sensor array is arranged vertically in each matrix node according to a spatial structure, and each plant sample is monitored in terms of multiple factors from the soil layer, the plant layer and the atmosphere layer; according to a preset matrix sampling time sequence table, the SPAC parameters are synchronously collected by each matrix node at a sampling time point, with a time axis as a main dimension and a matrix node address as a secondary dimension.

[0009] Preferably, the SPAC parameters include: The SPAC parameters are key indexes representing material and energy exchange and states among the soil, the plant itself and the atmospheric environment where the plant grows, including soil parameters, plant body parameters and atmospheric environment parameters.

[0010] Preferably, the method for obtaining the differentiated water and fertilizer instructions includes: The collected SPAC parameters are bound to the processing labels corresponding to the matrix nodes to construct a state parameter vector of the matrix nodes; a preset microenvironment difference regulation criterion is called to perform interval matching and deviation calculation on the state parameter vector of each matrix node; and based on the deviation calculation result, a PID algorithm is used to generate differentiated water and fertilizer instructions including irrigation opening time length, irrigation amount, fertilizer concentration and water and fertilizer ratio.

[0011] Preferably, the method for setting the plant sample growth cycle monitoring time sequence includes: The matrix control terminal sets the growth cycle start and end time and different phased time intervals of the plant sample, and configures corresponding monitoring parameter sets and monitoring frequencies for different growth stages; the matrix control terminal generates a monitoring task schedule table covering the entire growth cycle according to the set time intervals and parameter requirements, and the monitoring task schedule table includes different monitoring time nodes and monitoring task instructions corresponding to each time node; The monitoring task instructions include sensor types to be activated, sampling channel numbers, sampling frequencies and effective time windows, the matrix control terminal maps the monitoring task instructions to corresponding matrix node addresses through logical addressing, and schedules the monitoring tasks to each matrix node in time sequence to form a monitoring time sequence arrangement covering the entire growth cycle of the plant sample.

[0012] Preferably, the method for obtaining the multi-dimensional phenotype data set includes: Each matrix node maintains a unified time reference with the system according to the monitoring task schedule set by the matrix control terminal, and automatically triggers the collection task of the SPAC parameter when reaching the preset monitoring time node; each matrix node completes the real-time synchronous collection of the SPAC parameter within the task time window according to the preset sampling frequency and channel configuration through the internally integrated distributed sensor array; after the collection is completed, each item of data is automatically attached with corresponding timestamp information and matrix node address as metadata identifier, and is subjected to format standardization processing and archival management, so as to form a multi-dimensional phenotype data set covering the growth cycle of the plant sample.

[0013] Preferably, the method for obtaining the phenotype response difference map comprises: Based on the multi-dimensional phenotype data set, the multi-dimensional phenotype data set is subjected to spatio-temporal two-dimensional index management according to a preset data structure format, the spatio-temporal two-dimensional index comprises a spatial matrix label index and a time sequence label index, and the phenotype responses of the matrix nodes under different environmental treatments are compared and analyzed; A dynamic penalty type response function is introduced to quantify the plant stress response, the dynamic penalty type response function is fitted, the stress response sensitivity coefficient distribution of the matrix nodes under different environmental treatments is calculated, the phenotype response difference map of different matrix nodes is generated, and the quantitative evaluation result of the plant growth performance is output.

[0014] Preferably, the quantitative evaluation result of the plant growth performance comprises a growth rate gradient, a water use efficiency and a stress response sensitivity coefficient.

[0015] Compared with the prior art, the present application has the following beneficial effects: The present application constructs a logically addressable environmental control matrix through matrix monitoring, supports configuring multiple plant cultivation units as independent or combined treatment groups, realizes multivariate differentiated treatment design of water, nutrients and climate factors, realizes high-throughput and controllable multi-treatment control experiment design, and greatly improves the throughput and flexibility of plant phenotype research; According to the real-time collected SPAC parameters and the preset regulation criteria, the differentiated water and fertilizer instructions are dynamically generated and accurately issued to the corresponding nodes, the fine irrigation and fertilization of "classification according to treatment group, on-demand supply, node control" is realized, and the water and fertilizer utilization efficiency and the treatment repeatability are effectively improved; The two-dimensional index system of the space matrix label and the time sequence label is constructed, the phenotypic response data of different cultivation units in the whole plant growth cycle is systematically archived and retrieved, and the crop variety screening, environmental stress response evaluation and agronomic decision optimization are supported; the dynamic penalty type response function with an adjustable penalty index is introduced to characterize the phenotypic response process of plants under stress environment, so that the fitting accuracy and the biological interpretation ability of the model are improved.

[0016] Through the fitting and statistical analysis of the response sensitivity coefficient and the penalty index and other parameters in the dynamic penalty type response function, the response difference of different plant samples or treatment groups under specific stress conditions can be quantified, so that a phenotypic response difference atlas with spatial distribution characteristics is generated, and visual decision basis is provided for plant stress tolerance evaluation, water and fertilizer management strategy optimization and variety screening. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 It is a structure schematic view of the plant phenotype high-throughput monitoring system based on matrix control of the present application. Figure 2 It is a process schematic view of the plant phenotype high-throughput monitoring method based on matrix control of the present application. Figure 3 It is a process schematic view of the acquisition path of the multi-dimensional phenotype data set of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] Embodiment 1 Please refer to Figure 1 and Figure 3 The plant phenotype high-throughput monitoring system based on matrix control of the present application is further illustrated, which comprises: The plant phenotype data acquisition depends on manual measurement and software analysis after photography, and the plant diameter, leaf length and other indicators can be obtained, but a large amount of time is consumed, the accuracy is low, the work is tedious, and the large-scale genetic breeding screening efficiency is limited. Moreover, the traditional method can only obtain part of the phenotype indicators, and the excellent plant type selection depends on the experience of scientific researchers, and it is difficult to count due to different standards. At the same time, although the existing high-throughput technology has been applied, there are obvious defects.

[0020] With the in-depth development of plant phenomics research, how to quickly, accurately and high-throughput collect plant growth and physiological response data under various environmental conditions has become a key technical bottleneck for crop genetic improvement, resource efficient utilization and stress resistance mechanism analysis. At present, the mainstream plant phenotype monitoring system relies on fixed acquisition path or automatic platform to obtain image and environmental data. Although it has certain automation capability, it still has obvious limitations in the following aspects: Limited environmental control dimension, difficult to realize multi-processing condition control experiment: the existing system usually relies on uniform environment cabin or greenhouse environment, which has the characteristics of "overall control and local intervention" in processing variable setting, lacks precise control ability at the level of plant cultivation unit, and is difficult to meet the experimental design requirements of complex environmental factor combination processing; Poor spatiotemporal consistency, acquisition data exists deviation and synchronization problem: the current system mostly adopts linear moving type sensing architecture or fixed point periodic inspection mode, which leads to time difference in data acquisition between plant individuals, and cannot guarantee the acquisition consistency of each processing unit at the same time point and similar scale, reducing the timeliness and spatial comparison value of phenotype data; Insufficient multi-factor collaborative monitoring capability, single dimension of SPAC data acquisition: in actual cultivation environment, the water transmission and physiological response of plants need to consider the linkage relationship of soil, plant body and atmosphere at multiple levels, and the coverage of SPAC parameters in the existing system is not complete or uneven, which limits the systematic study of water dynamics and stress response mechanism; Under this background, the present application proposes a plant phenotype high-throughput monitoring system based on matrix control, which comprises: Matrix monitoring module, construct a matrix type plant phenotype detection environment, configure the plant samples to be tested in N plant cultivation units; each plant cultivation unit is configured with corresponding environment control equipment, and is mapped to an addressable matrix node based on a logical addressing rule, and each matrix node forms an environment control matrix through a distributed parallel communication link; Multi-factor monitoring module, build a multi-factor collaborative monitoring network, trigger the synchronous acquisition of SPAC parameters of each matrix node based on the preset matrix sampling time sequence table through the distributed sensor array arranged in the matrix node; Differential control module, generate differential water and fertilizer instructions for each matrix node according to the preset microenvironment difference regulation criterion combined with SPAC parameters, drive the corresponding water and fertilizer irrigation device to output, and form a differential processing group; Full-cycle control module, set the plant sample growth cycle monitoring time sequence through the matrix control terminal, automatically execute the periodic data acquisition of each matrix node according to the time axis, and form a multi-dimensional phenotype data set covering the growth cycle of the plant sample; The phenotype response analysis module indexes the multidimensional phenotype dataset in time and space dimensions, generates a phenotype response difference atlas of different matrix nodes through matrix comparison, and outputs a quantitative evaluation result of plant growth performance.

[0021] The acquisition method of the matrix plant phenotype detection environment includes: A two-dimensional matrix structure is established in a preset experimental area, and the area is divided into plant cultivation units; each cultivation unit constitutes an independently controllable plant cultivation experiment microenvironment, and internally integrates a soil container, an environment regulation and control device (such as a water and fertilizer sprinkling irrigation system, a local light source, a temperature and humidity control device), and a soil-plant-atmosphere continuum (SPAC) monitoring sensor, such as a soil humidity sensor, an air temperature and pressure detection module, and a leaf surface temperature sensor, and adopts a modular structure (such as a detachable tray or slot structure) to facilitate adjustment of different factor settings or replacement of equipment; a unique logical address tag is assigned to each plant cultivation unit, and is mapped to a two-dimensional matrix coordinate number according to a preset logical addressing rule, all two-dimensional matrix coordinate numbers are encoded as matrix node addresses, a matrix address mapping table is formed, and each matrix node corresponds to a plant cultivation unit; For example, a preset experimental area with a two-dimensional matrix structure of “row x column” is set as 6 rows and 8 columns, a total of 48 matrix nodes, each matrix node is a plant cultivation unit, and the two-dimensional matrix coordinate numbers are Node-1-1 to Node-6-8.

[0022] The acquisition method of the environment control matrix includes: The environment control matrix is composed of distributed parallel communication links between the matrix nodes, and each matrix node is internally provided with an embedded micro control unit for receiving differentiated water and fertilizer instructions and driving the environment regulation and control device to perform water and fertilizer, light, temperature, and other environment parameter regulation and control operations; a distributed parallel communication link, such as an RS485 industrial bus network or a wireless Mesh network, is constructed to realize communication between the matrix nodes, and a master-slave structure is used to poll the state of each matrix node and send corresponding control instructions by a matrix control terminal, thereby forming the environment control matrix. For example, in the system initialization stage, the matrix control terminal loads the regulation and control strategies of each matrix node according to the initial configuration file, including but not limited to water gradient setting, photoperiod regulation, and temperature and humidity difference simulation scheme. Through control instruction distribution, the microenvironment differentiation configuration between the matrix nodes can be realized, and the logical independence between different plant cultivation units is ensured; in order to prevent environmental interference, a physical isolation unit, including a flexible foldable partition, a local sealed shell, and an adjustable air vent, can be arranged in each plant cultivation unit to form spatial isolation between the matrix nodes and ensure the accuracy of the microenvironment regulation and control.

[0023] The construction method of the multi-factor collaborative monitoring network includes: The multi-factor collaborative monitoring network relies on a distributed sensor array arranged in a matrix node to realize the synchronized collection of SPAC parameters; the distributed sensor array is arranged vertically in each matrix node according to a spatial structure, and each plant sample is monitored in terms of multiple factors from the soil layer, the plant layer and the atmospheric layer; according to a preset matrix sampling time sequence table, the matrix node address is taken as a secondary dimension, and each matrix node is triggered to synchronously collect the SPAC parameters at the sampling time point.

[0024] For example: soil layer: deploy soil temperature sensors, soil moisture content (capacitive) sensors, and conductivity sensors to reflect the root environment; Plant layer: install a micro stem flow meter on the plant stem and set an infrared thermal module or a non-contact leaf temperature sensor on the leaf surface, and configure a stomatal conductance sensor and a chlorophyll fluorescence detector; Atmospheric layer: deploy light intensity sensors, air temperature and humidity sensors, Concentration sensors above the plant cultivation unit.

[0025] The SPAC parameters collected by each matrix node are packaged in the format of “two-dimensional matrix coordinate number + data type + timestamp + value” and uploaded to the matrix control terminal through a distributed parallel communication link.

[0026] The SPAC parameters include: The SPAC parameters are key indicators representing the material and energy exchange and state between the soil, the plant itself and the atmospheric environment in which the plant grows, including soil parameters, plant body parameters and atmospheric environment parameters; they reflect the interaction between the plant and the environment and provide data support for phenotype analysis and environmental regulation; The soil parameters include the physical properties, chemical properties and water dynamics data of the soil, such as soil moisture content, soil temperature, conductivity (EC, reflecting soil salinity), water conductivity, etc.; the plant body parameters include the physiological data, morphological structure and growth dynamic data of the plant body, such as plant height, stem diameter, leaf area index, plant weight, transpiration rate, stomatal conductance, growth amount, etc.; the atmospheric environment parameters include microclimate data and energy exchange data, such as air temperature, relative humidity, light intensity, Concentration, soil heat flux (reflecting the energy transfer in the SPAC system), etc.

[0027] The method for obtaining the differentiated water and fertilizer instructions includes: The collected SPAC parameters are bound to the processing labels corresponding to the matrix nodes to construct a state parameter vector of the matrix nodes; a preset microenvironment difference regulation criterion is called to perform interval matching and deviation calculation on the state parameter vector of each matrix node; based on the deviation calculation result, a PID algorithm is used to generate a differentiated water and fertilizer instruction including irrigation opening duration, irrigation amount, fertilizer concentration, and water and fertilizer ratio; the differentiated water and fertilizer instruction is bound to the matrix node address, and is sent to the corresponding matrix node through the communication link of the environment control matrix to drive the corresponding water and fertilizer irrigation device (such as an electromagnetic valve, a drip irrigation pump, and a fertilizer applicator) to output.

[0028] The setting method of the plant sample growth cycle monitoring time sequence includes: The growth cycle start and end time and different stage time intervals of the plant sample are set through the matrix control terminal, and corresponding monitoring parameter sets and monitoring frequencies are configured for different growth stages; the matrix control terminal generates a monitoring task schedule table covering the entire growth cycle according to the set time intervals and parameter requirements, and the monitoring task schedule table includes different monitoring time nodes and monitoring task instructions corresponding to each time node; The monitoring task instruction includes the sensor type to be activated, the sampling channel number, the sampling frequency, and the effective time window, the matrix control terminal maps the monitoring task instruction to the corresponding matrix node address through logical addressing, and schedules the monitoring task to each matrix node in time sequence to form a monitoring time sequence arrangement covering the entire growth cycle of the plant sample.

[0029] The method for obtaining the multi-dimensional phenotype data set includes: Each matrix node maintains a unified time reference with the system according to the monitoring task schedule table set by the matrix control terminal, and automatically triggers the collection task of the SPAC parameters when reaching the preset monitoring time node; each matrix node completes the real-time synchronous collection of the SPAC parameters within the task time window according to the preset sampling frequency and channel configuration through the integrated distributed sensor array; after the collection is completed, each item of data is automatically attached with corresponding timestamp information and matrix node address as metadata identifier, and is subjected to format standardization processing and archival management, thereby forming a multi-dimensional phenotype data set covering the growth cycle of the plant sample.

[0030] The method for obtaining the phenotype response difference atlas includes: Based on the multi-dimensional phenotype data set, the multi-dimensional phenotype data set is subjected to spatio-temporal two-dimensional index management according to a preset data structure format, the spatio-temporal two-dimensional index includes a spatial matrix label index and a time sequence label index, and the phenotype responses of each matrix node under different environmental treatments are compared and analyzed; The dynamic penalty type response function is introduced to quantify the stress response of the plant. The stress response sensitivity coefficient distribution of each matrix node under different environmental treatments is calculated by fitting the dynamic penalty type response function, a phenotype response difference atlas of different matrix nodes is generated, and a quantitative evaluation result of the growth performance of the plant is output.

[0031] The dynamic penalty type response function is: ; wherein, represents any one of the phenotype response values (such as growth rate gradient, water use efficiency, etc.) of the plant at the time point ; represents a preset non-stress phenotype response value, which represents the maximum theoretical value that the phenotype of the plant should reach at the same time point under an ideal environment (non-stress); represents a stress intensity function, which represents the stress level borne by the plant at the time point , and can be a single index (such as VPD, soil water potential) or a multi-index weighted result, such as a comprehensive water stress index; represents a stress response sensitivity coefficient, which controls the descending slope of the response curve and reflects the overall sensitivity of the plant to stress; the greater the value, the more intense the response and the faster the decline, and vice versa, indicating greater tolerance; represents a penalty index, which is used to introduce a nonlinear penalty effect to simulate the sluggish accelerated response characteristics of the plant to stress; represents an index of the time point; The following problems existing in the prior art are solved: In the prior art, most plant phenotype data analysis is limited to single time point or single variable dimension comparison, and there is a lack of dynamic response modeling means capable of covering the entire growth cycle of the plant and having spatial positioning capability, making it difficult to depict the spatiotemporal adaptability process of the plant under complex stress conditions. Traditional response functions mostly use linear or logarithmic functions, which cannot accurately simulate the buffering reaction of the plant in the early stage of stress and the accelerated degradation process after the stress intensifies, and cannot truly reflect the sluggish and collapse nonlinear characteristics of the physiological response of the plant. The existing response analysis means lack quantitative indicators with parameter interpretability and comparability, making it difficult to compare, classify and screen the stress response sensitivity, tolerance boundary, water use efficiency and other key physiological performances between large-scale treatment groups.

[0032] The beneficial effects of the prior art are: by constructing a two-dimensional index system of spatial matrix label and time sequence label, the phenotypic response data of different cultivation units in the whole plant growth cycle is systematically archived and retrieved, supporting in-depth analysis of environmental treatment and response process; a dynamic penalty type response function with adjustable penalty index is introduced to characterize the phenotypic response process of plants under stress environment, more truly reflecting the buffering and accelerated nonlinear change trend of plant phenotype under stress stimulation, thereby improving the fitting accuracy and biological interpretation ability of the model. Through fitting and statistical analysis of the response sensitivity coefficient and penalty index and other parameters in the dynamic penalty type response function, the response difference of different plant samples or treatment groups under specific stress conditions can be quantified, thereby generating a phenotypic response difference atlas with spatial distribution characteristics, providing a visual decision basis for plant stress tolerance evaluation, water and fertilizer management strategy optimization and variety selection and other applications.

[0033] The quantitative evaluation results of plant growth performance include growth rate gradient, water use efficiency and stress response sensitivity coefficient.

[0034] In this embodiment, a logically addressable environmental control matrix is constructed through matrix monitoring, supporting the configuration of multiple plant cultivation units as independent or combined treatment groups, realizing multivariate differential treatment design of water, nutrients and climate factors, realizing high-throughput and controllable multi-treatment control experiment design, greatly improving the throughput and flexibility of plant phenotype research; According to the real-time collected SPAC parameters combined with the preset control criteria, differential water and fertilizer instructions are dynamically generated and accurately issued to the corresponding nodes, realizing fine irrigation and fertilization of "classification by treatment group, on-demand supply, node control", effectively improving water and fertilizer utilization efficiency and treatment repeatability; A two-dimensional index system of spatial matrix label and time sequence label is constructed, realizing systematic archiving and retrieval of phenotypic response data of different cultivation units in the whole plant growth cycle, supporting crop variety selection, environmental stress response evaluation and agronomic decision optimization; a dynamic penalty type response function with adjustable penalty index is introduced to characterize the phenotypic response process of plants under stress environment, more truly reflecting the buffering and accelerated nonlinear change trend of plant phenotype under stress stimulation, thereby improving the fitting accuracy and biological interpretation ability of the model.

[0035] Through fitting and statistical analysis of the response sensitivity coefficient and penalty index and other parameters in the dynamic penalty type response function, the response difference of different plant samples or treatment groups under specific stress conditions can be quantified, thereby generating a phenotypic response difference atlas with spatial distribution characteristics, providing a visual decision basis for plant stress tolerance evaluation, water and fertilizer management strategy optimization and variety selection and other applications.

[0036] Embodiment 2 Please refer to Figure 2 As shown in the embodiment, the part not described in detail is described in the embodiment 1, the matrix control based plant phenotype high-throughput monitoring method is provided, including: S1, constructing a matrix plant phenotype detection environment, configuring the plant samples to be detected in N plant cultivation units; each plant cultivation unit is configured with a corresponding environment control device, and is mapped to an addressable matrix node based on a logical addressing rule, and each matrix node forms an environment control matrix through a distributed parallel communication link; S2, building a multi-factor collaborative monitoring network, triggering the synchronous collection of SPAC parameters of each matrix node based on a preset matrix sampling time sequence table through a distributed sensor array arranged in the matrix node; S3, generating the differential water and fertilizer instructions of each matrix node according to the preset microenvironment difference regulation criterion combined with the SPAC parameters, driving the corresponding water and fertilizer irrigation device to output, and forming a differential processing group; S4, setting the plant sample growth cycle monitoring time sequence through the matrix control terminal, automatically executing the periodic data collection of each matrix node according to the time axis, and forming a multi-dimensional phenotype data set covering the growth cycle of the plant sample; S5, indexing the multi-dimensional phenotype data set in time and space, generating a phenotype response difference map of different matrix nodes through matrix comparison, and outputting the quantitative evaluation result of plant growth performance.

[0037] Since the electronic device introduced in the embodiment is the electronic device used in the matrix control based plant phenotype high-throughput monitoring system in the embodiment, the specific implementation of the electronic device and its various changes can be understood by those skilled in the art based on the matrix control based plant phenotype high-throughput monitoring system introduced in the embodiment, so the implementation of the electronic device in the method in the embodiment will not be introduced in detail. As long as the electronic device used in the matrix control based plant phenotype high-throughput monitoring system in the embodiment is implemented by those skilled in the art, it belongs to the scope protected by the present application.

[0038] The above formulas are dimensionless numerical calculations, the formula is obtained by collecting a large amount of data to simulate the formula of the nearest real situation, and the preset parameters and threshold values in the formula are set by those skilled in the art according to the actual situation.

[0039] The above merely describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary technical operators in the technical field, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A high-throughput plant phenotypic monitoring system based on matrix control, characterized in that, include: The matrix monitoring module constructs a matrix-style plant phenotypic detection environment, configuring the plant samples to be tested in N plant cultivation units; each plant cultivation unit is configured with corresponding environmental control equipment, and mapped to addressable matrix nodes based on logical addressing rules. Each matrix node forms an environmental control matrix through distributed parallel communication links. The multi-factor monitoring module establishes a multi-factor collaborative monitoring network. Through a distributed sensor array deployed in the matrix nodes, based on a preset matrix sampling time series table, it uniformly triggers each matrix node to synchronously collect SPAC parameters. The differentiated control module generates differentiated water and fertilizer instructions for each matrix node based on preset microenvironmental difference regulation criteria and SPAC parameters, driving the corresponding water and fertilizer irrigation devices to output and form a differentiated treatment group. The full-cycle management module sets the monitoring time sequence of plant sample growth cycle through the matrix control terminal, and automatically executes the periodic data collection of each matrix node according to the time axis to form a multidimensional phenotypic dataset covering the plant sample growth cycle. The phenotypic response analysis module performs spatiotemporal dual-dimensional indexing on the multidimensional phenotypic dataset, generates phenotypic response difference maps of different matrix nodes through matrix comparison, and outputs quantitative evaluation results of plant growth performance.

2. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 1, characterized in that, The method for obtaining the matrix-style plant phenotypic detection environment includes: A two-dimensional matrix structure is established in the preset experimental area, and the area is divided into... Each plant cultivation unit constitutes an independent and controllable experimental microenvironment for plant cultivation, integrating a soil container, environmental control equipment, and a soil-plant-atmosphere continuum monitoring sensor. Each plant cultivation unit is assigned a unique logical address tag and mapped to a two-dimensional matrix coordinate number according to a preset logical addressing rule. All two-dimensional matrix coordinate numbers are encoded into matrix node addresses, forming a matrix address mapping table, with each matrix node corresponding to a plant cultivation unit.

3. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 2, characterized in that, The method for obtaining the environmental control matrix includes: The environmental control matrix is ​​composed of distributed parallel communication links between matrix nodes. Each matrix node has an embedded microcontroller unit to receive differentiated water and fertilizer instructions and drive environmental control equipment to perform environmental parameter adjustment operations. The distributed parallel communication links are constructed to realize communication between matrix nodes. The matrix control terminal polls the status of each matrix node through a master-slave structure and sends corresponding control instructions to form the environmental control matrix.

4. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 3, characterized in that, The method for constructing the multi-factor collaborative monitoring network includes: The multi-factor collaborative monitoring network relies on a distributed sensor array deployed in matrix nodes to achieve synchronized acquisition of SPAC parameters. Distributed sensor arrays are vertically deployed in each matrix node according to the spatial structure to conduct multi-factor collaborative monitoring of each plant sample from the soil, plant and atmospheric levels. According to the preset matrix sampling time series table, with the time axis as the main dimension and the matrix node address as the secondary dimension, the synchronous acquisition of SPAC parameters of each matrix node is triggered uniformly at the sampling time point.

5. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 4, characterized in that, The SPAC parameters include: SPAC parameters are key indicators characterizing the exchange and state of matter and energy between the soil in which plants grow, the plants themselves, and the atmospheric environment. They include soil parameters, plant parameters, and atmospheric environmental parameters.

6. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 5, characterized in that, The method for obtaining the differentiated water and fertilizer instructions includes: The collected SPAC parameters are bound to the processing labels corresponding to the matrix nodes to construct the state parameter vector of the matrix nodes; the preset microenvironment difference control criteria are called to perform interval matching and deviation calculation on the state parameter vector of each matrix node; based on the deviation calculation results, the PID algorithm is used to generate differentiated water and fertilizer instructions including irrigation start time, irrigation volume, fertilizer concentration and water-fertilizer ratio.

7. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 6, characterized in that, The method for setting the time sequence for monitoring the growth cycle of plant samples includes: The matrix control terminal sets the start and end times of the plant sample's growth cycle and different time intervals for each stage, and configures the corresponding set of monitoring parameters and monitoring frequency for each growth stage. Based on the set time intervals and parameter requirements, the matrix control terminal generates a monitoring task plan that covers the entire growth cycle. The monitoring task plan includes different monitoring time nodes and the monitoring task instructions corresponding to each time node. The monitoring task instructions include the type of sensor to be activated, the sampling channel number, the sampling frequency, and the effective time window. The matrix control terminal maps the monitoring task instructions to the corresponding matrix node address through logical addressing and schedules the monitoring tasks to each matrix node in chronological order, forming a monitoring time sequence that covers the entire growth cycle of the plant sample.

8. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 7, characterized in that, The methods for obtaining the multidimensional phenotypic dataset include: Each matrix node maintains a unified time reference with the system locally according to the monitoring task plan set by the matrix control terminal, and automatically triggers the SPAC parameter acquisition task when the preset monitoring time node is reached. Each matrix node completes real-time synchronous acquisition of SPAC parameters within the task time window through its internally integrated distributed sensor array, based on the preset sampling frequency and channel configuration. After acquisition, each data is automatically appended with corresponding timestamp information and matrix node address as metadata identifiers, and undergoes format standardization processing and archiving management, thereby forming a multidimensional phenotypic dataset covering the growth cycle of plant samples.

9. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 8, characterized in that, The method for obtaining the phenotypic response difference map includes: Based on the multidimensional phenotypic dataset, according to the preset data structure format, the multidimensional phenotypic dataset is managed by a spatiotemporal dual-dimensional index. The spatiotemporal dual-dimensional index includes a spatial matrix label index and a time series label index, and the phenotypic response of each matrix node under different environmental processing is compared and analyzed. A dynamic penalty response function is introduced to quantify plant stress response. By fitting the dynamic penalty response function and calculating the distribution of stress response sensitivity coefficients of each matrix node under different environmental treatments, a phenotypic response difference map of different matrix nodes is generated, and the quantitative evaluation results of plant growth performance are output.

10. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 9, characterized in that, The quantitative evaluation results of plant growth performance include growth rate gradient, water use efficiency, and stress response sensitivity coefficient.

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