Matrix control based high throughput plant phenotyping system

Abstract

The application 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, a matrix plant phenotype detection environment is constructed, and to-be-detected plant samples are arranged in N plant cultivation units; each plant cultivation unit is respectively arranged with a corresponding environment regulation and control device, and is mapped into an addressable matrix node based on a logical addressing rule, and the matrix nodes form an environment control matrix through a distributed parallel communication link; a multi-factor monitoring module is arranged, a multi-factor collaborative monitoring network is built, a distributed sensor array arranged in the matrix nodes is arranged, a preset matrix sampling time sequence table is used, and each matrix node is uniformly triggered to synchronously collect SPAC parameters based on the preset matrix sampling time sequence table; the application breaks through the limitations of traditional plant phenotype collection means in terms of processing scale, regulation accuracy and data space-time consistency, and realizes automatic and intelligent management of the whole process of plant phenotype monitoring.

Description

Technical Field

[0001] This invention relates to the field of agricultural monitoring technology, and more specifically, to a high-throughput monitoring system for plant phenotypic characteristics based on matrix control. Background Technology

[0002] Patent publication number CN119046230A discloses a cross-platform plant phenotypic monitoring and control system, comprising: a control terminal, an industrial computer, sensor devices, and a storage medium. The control terminal is used to plan and manage experimental sites according to actual needs and to issue control commands to the industrial computer based on the data collection tasks. The industrial computer is used to issue operation commands to the sensor devices based on the received control commands. The sensor devices are used to capture plant phenotypic data, and the industrial computer uploads the captured data to the control terminal for processing and archiving. The control terminal then saves the processing results to the storage medium or uploads them to a data management and analysis platform. This invention can effectively improve experimental efficiency and ensure the accuracy and relevance of the data; therefore, this invention can be widely applied in the field of crop phenotypic monitoring.

[0003] Existing high-throughput plant phenotyping systems mainly suffer from the following problems:

[0004] In agricultural research and production, monitoring plant phenotypes is an important means of understanding plant growth status, physiological characteristics, and environmental responses. Traditional plant phenotype monitoring methods often rely on manual operation, which suffers from low efficiency, time and labor costs, low data accuracy, and inability to achieve high-throughput monitoring. At the same time, traditional monitoring systems are unable to comprehensively and synergistically monitor multiple factors in the soil, plants, and atmospheric continuum, nor can they achieve continuous data collection and precise control under different environmental conditions throughout the plant growth cycle. This greatly limits the depth and breadth of plant phenotype research and is detrimental to improving the efficiency of agricultural research and production.

[0005] In view of this, the present invention proposes a high-throughput monitoring system for plant phenotypes based on matrix control to solve the above problems. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a matrix-controlled high-throughput plant phenotypic monitoring system, comprising:

[0007] 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.

[0008] 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.

[0009] 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.

[0010] 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.

[0011] 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.

[0012] Preferably, the method for obtaining the matrix-style plant phenotypic detection environment includes:

[0013] 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.

[0014] Preferably, the method for obtaining the environmental control matrix includes:

[0015] 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.

[0016] Preferably, the method for constructing the multi-factor collaborative monitoring network includes:

[0017] 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.

[0018] Preferably, the SPAC parameters include:

[0019] 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.

[0020] Preferably, the method for obtaining the differentiated water and fertilizer instructions includes:

[0021] 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.

[0022] Preferably, the method for setting the time sequence of plant sample growth cycle monitoring includes:

[0023] 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.

[0024] 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.

[0025] Preferably, the method for obtaining the multidimensional phenotypic dataset includes:

[0026] 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.

[0027] Preferably, the method for obtaining the phenotypic response difference map includes:

[0028] 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.

[0029] 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.

[0030] Preferably, the quantitative evaluation results of plant growth performance include growth rate gradient, water use efficiency, and stress response sensitivity coefficient.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] This invention constructs a logically addressable environmental control matrix through matrix monitoring, which supports configuring multiple plant cultivation units as independent or combined treatment groups, enabling multivariate differentiated treatment designs for water, nutrients, and climate factors, and achieving high-throughput, highly controllable multi-treatment control experimental designs, thus significantly improving the throughput and flexibility of plant phenotypic research.

[0033] Based on real-time collected SPAC parameters and preset control criteria, differentiated water and fertilizer instructions are dynamically generated and accurately sent to the corresponding nodes, realizing refined irrigation and fertilization by "classification by treatment group, supply on demand, and control by node", effectively improving water and fertilizer utilization efficiency and treatment repeatability.

[0034] A two-dimensional indexing system of spatial matrix labels and time series labels was constructed to realize the systematic archiving and retrieval of phenotypic response data of different cultivation units throughout the entire plant growth cycle, supporting crop variety selection, environmental stress response assessment, and agronomic decision optimization. A dynamic penalty response function with an adjustable penalty index was introduced to characterize the phenotypic response process of plants under stress, more realistically reflecting the buffering and accelerating nonlinear change trend of plant phenotypes in response to stress stimuli, thereby improving the model's fitting accuracy and physiological interpretation ability.

[0035] By fitting and statistically analyzing parameters such as the response sensitivity coefficient and penalty index in the dynamic penalty response function, the response differences of different plant samples or treatment groups under specific stress conditions can be quantified, thereby generating a phenotypic response difference map with spatial distribution characteristics. This provides a visualized decision-making basis for applications such as plant stress tolerance evaluation, water and fertilizer management strategy optimization, and variety selection. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the structure of the matrix-controlled high-throughput plant phenotyping system of the present invention.

[0037] Figure 2 This is a schematic diagram of the high-throughput monitoring method for plant phenotypes based on matrix control according to the present invention.

[0038] Figure 3 This is a schematic diagram illustrating the acquisition path of the multidimensional phenotypic dataset of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1

[0041] Please see Figure 1 and Figure 3 As shown, this embodiment further illustrates the high-throughput plant phenotypic monitoring system based on matrix control proposed in this invention, including:

[0042] Traditional methods for obtaining plant phenotypic data rely on manual measurement and photographic analysis followed by software analysis. While these methods can yield indicators such as plant diameter and leaf length, they are time-consuming, inaccurate, and cumbersome, limiting the efficiency of large-scale genetic breeding and screening. Furthermore, traditional methods can only obtain partial phenotypic indicators, and the selection of superior plant types depends on researchers' experience, making statistical analysis difficult due to differing standards. Meanwhile, while existing high-throughput technologies have applications, they also have significant drawbacks.

[0043] With the deepening development of plant phenotyping research, how to rapidly, accurately, and in high throughput collect plant growth and physiological response data under diverse environmental conditions has become a key technological bottleneck in areas such as crop genetic improvement, efficient resource utilization, and the analysis of stress resistance mechanisms. Currently, most mainstream plant phenotyping monitoring systems rely on fixed acquisition paths or automated platforms to acquire image and environmental data. While they possess a certain degree of automation, they still have significant limitations in the following aspects:

[0044] Limited environmental control dimensions make it difficult to conduct multi-treatment control experiments: Existing systems usually rely on a unified environmental chamber or greenhouse environment, which has the characteristics of "overall control and local intervention" in the setting of treatment variables. They lack the ability to precisely regulate at the plant cultivation unit level and cannot meet the experimental design requirements of complex environmental factor combinations.

[0045] Poor spatiotemporal consistency and data acquisition bias and synchronization problems: Current systems mostly adopt linear mobile sensing architecture or fixed-point periodic inspection mode, which leads to time differences in data acquisition between individual plants. It is impossible to guarantee the consistency of data acquisition at the same time and similar scale of each processing unit, which reduces the timeliness and spatial comparison value of phenotypic data.

[0046] Insufficient multi-factor collaborative monitoring capabilities and limited dimensions of SPAC data acquisition: In actual cultivation environments, the water transport and physiological responses of plants need to comprehensively consider the linkage between multiple levels of soil, plant body and atmosphere. Existing systems do not fully cover or distribute SPAC parameters in an uneven manner, which limits the systematic study of water dynamics and stress response mechanisms.

[0047] Against this backdrop, the present invention proposes a matrix-controlled high-throughput plant phenotypic monitoring system, comprising:

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] Methods for obtaining the matrix-based plant phenotyping environment include:

[0054] 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. It integrates a soil container, environmental control equipment (such as a water and fertilizer sprinkler system, a local light source, and a temperature and humidity control device), and soil-plant-atmosphere continuum (SPAC) monitoring sensors, such as soil moisture sensors, air temperature and pressure detection modules, and leaf temperature sensors. It adopts a modular structure (such as a detachable tray or slot structure) to facilitate adjustment of different factor settings or replacement of equipment. Each plant cultivation unit is assigned a unique logical address label and 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, forming a matrix address mapping table. Each matrix node corresponds to one plant cultivation unit.

[0055] For example, a preset experimental area of ​​6 rows and 8 columns is set up according to the two-dimensional matrix structure of "row × column", with a total of 48 matrix nodes. Each matrix node is a plant cultivation unit, and the two-dimensional matrix coordinates are numbered from Node-1-1 to Node-6-8.

[0056] Methods for obtaining the environmental control matrix include:

[0057] 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 such as water, fertilizer, light, and temperature. Distributed parallel communication links are constructed, such as RS485 industrial bus networking or wireless Mesh networking, 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.

[0058] For example, during the system initialization phase, the matrix control terminal loads the control strategies for each matrix node according to the initial configuration file, including but not limited to moisture gradient settings, photoperiod adjustment, and temperature and humidity difference simulation schemes. Through control command distribution, differentiated microenvironmental configurations between matrix nodes can be achieved, ensuring logical independence between different plant cultivation units. To prevent environmental interference, each plant cultivation unit can be equipped with a physical isolation unit, including flexible foldable partitions, partially sealed shells, and adjustable vents, forming spatial isolation between matrix nodes and ensuring the accuracy of microenvironmental control.

[0059] Methods for constructing multi-factor collaborative monitoring networks include:

[0060] 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.

[0061] For example: Soil layer: Deploy soil temperature sensors, soil moisture (capacitive) sensors, and conductivity sensors to reflect the root environment;

[0062] Plant layer: Install a miniature stem flow meter on the plant stem, set an infrared thermal module or a non-contact leaf temperature sensor on the leaf surface, and can be configured with a stomatal conductance sensor and a chlorophyll fluorescence detector.

[0063] Atmosphere: Light intensity sensors and air temperature and humidity sensors are installed above the plant cultivation units. Concentration sensor.

[0064] 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.

[0065] SPAC parameters include:

[0066] SPAC parameters are key indicators characterizing the exchange and state of matter and energy between the soil, the plant itself, and the atmospheric environment in which the plant grows. They include soil parameters, plant parameters, and atmospheric environmental parameters; they reflect the interaction between plants and the environment and provide data support for phenotypic analysis and environmental regulation.

[0067] Soil parameters include physical, chemical, and water dynamics data, such as soil moisture content, soil temperature, electrical conductivity (EC, reflecting soil salinity), and water transfer rate; plant parameters include physiological, morphological, and growth dynamic data, such as plant height, stem diameter, leaf area index, plant weight, transpiration rate, stomatal conductance, and growth; atmospheric environmental parameters include micrometeorological data and energy exchange data, such as air temperature, relative humidity, and light intensity. Concentration, soil heat flux (reflecting energy transfer in the SPAC system), etc.

[0068] Methods for obtaining differentiated water and fertilizer instructions include:

[0069] The collected SPAC parameters are bound to the processing labels corresponding to the matrix nodes to construct the state parameter vectors of the matrix nodes. The preset microenvironmental difference control criteria are called to perform interval matching and deviation calculation on the state parameter vectors 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. The differentiated water and fertilizer instructions are bound to the matrix node addresses and sent to the corresponding matrix nodes through the communication link of the environmental control matrix to drive the output of the corresponding water and fertilizer irrigation devices (such as solenoid valves, drip pumps, fertilizer applicators).

[0070] Methods for setting the time series for monitoring the growth cycle of plant samples include:

[0071] 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.

[0072] 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.

[0073] Methods for obtaining multidimensional phenotypic datasets include:

[0074] 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.

[0075] Methods for obtaining phenotypic response differential maps include:

[0076] 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.

[0077] 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.

[0078] The dynamic penalty response function is: ;in, Indicates the plant at a certain point in time. Any phenotypic response value (such as growth rate gradient, water use efficiency, etc.). This represents the preset phenotypic response value without stress, indicating the maximum theoretical value that the plant should achieve at the same time point under ideal conditions (without stress). The stress intensity function represents the stress intensity at time point . The stress level experienced by plants can be a single indicator (such as VPD or soil water potential) or a weighted result of multiple indicators, such as the comprehensive water stress index. The stress response sensitivity coefficient controls the slope of the response curve and reflects the overall sensitivity of the plant to stress; the larger the value, the more severe the response and the faster the decline, and vice versa. This represents the penalty index, used to introduce a non-linear penalty effect to simulate the sluggish-accelerated response characteristics of plants to stress. Index representing a point in time;

[0079] This approach addresses the following problems in existing technologies: Most existing plant phenotypic data analyses are limited to comparisons at a single time point or in a single variable dimension, lacking dynamic response modeling methods that can cover the entire plant growth cycle and possess spatial positioning capabilities, making it difficult to characterize the spatiotemporal adaptation processes of plants to complex stress conditions. Traditional response functions often employ linear or logarithmic functions, failing to accurately simulate the buffering response of plants in the early stages of stress and the accelerated degradation process after stress intensifies, and cannot truly reflect the sluggishness and nonlinear collapse characteristics of plant physiological responses. Existing response analysis methods lack quantitative indicators with interpretable and comparable parameters, making it difficult to compare, classify, and screen key physiological performance characteristics such as stress response sensitivity, tolerance boundaries, and water use efficiency among large-scale treatment groups.

[0080] Compared to existing technologies, the advantages are as follows: By constructing a two-dimensional indexing system of spatial matrix labels and time series labels, a systematic archiving and retrieval of phenotypic response data from different cultivation units throughout the entire plant growth cycle is achieved, supporting in-depth analysis of environmental treatment and response processes. A dynamic penalized response function with an adjustable penalty index is introduced to characterize the phenotypic response process of plants under stress, more realistically reflecting the buffering and accelerating nonlinear changes in plant phenotypes in response to stress stimuli, thereby improving the model's fitting accuracy and physiological interpretability. Through fitting and statistical analysis of parameters such as the response sensitivity coefficient and penalty index in the dynamic penalized response function, the response differences of different plant samples or treatment groups under specific stress conditions can be quantified, generating a phenotypic response difference map with spatial distribution characteristics. This provides a visualized decision-making basis for applications such as plant stress tolerance evaluation, water and fertilizer management strategy optimization, and variety selection.

[0081] The quantitative assessment results of plant growth performance include growth rate gradient, water use efficiency, and stress response sensitivity coefficient.

[0082] In this embodiment, a logically addressable environmental control matrix is ​​constructed through matrix monitoring, which supports configuring multiple plant cultivation units as independent or combined treatment groups, realizing multivariate differentiated treatment design of water, nutrients and climate factors, and realizing high-throughput and highly controllable multi-treatment control experimental design, which greatly improves the throughput and flexibility of plant phenotypic research.

[0083] Based on real-time collected SPAC parameters and preset control criteria, differentiated water and fertilizer instructions are dynamically generated and accurately sent to the corresponding nodes, realizing refined irrigation and fertilization by "classification by treatment group, supply on demand, and control by node", effectively improving water and fertilizer utilization efficiency and treatment repeatability.

[0084] A two-dimensional indexing system of spatial matrix labels and time series labels was constructed to realize the systematic archiving and retrieval of phenotypic response data of different cultivation units throughout the entire plant growth cycle, supporting crop variety selection, environmental stress response assessment, and agronomic decision optimization. A dynamic penalty response function with an adjustable penalty index was introduced to characterize the phenotypic response process of plants under stress, more realistically reflecting the buffering and accelerating nonlinear change trend of plant phenotypes in response to stress stimuli, thereby improving the model's fitting accuracy and physiological interpretation ability.

[0085] By fitting and statistically analyzing parameters such as the response sensitivity coefficient and penalty index in the dynamic penalty response function, the response differences of different plant samples or treatment groups under specific stress conditions can be quantified, thereby generating a phenotypic response difference map with spatial distribution characteristics. This provides a visualized decision-making basis for applications such as plant stress tolerance evaluation, water and fertilizer management strategy optimization, and variety selection.

[0086] Example 2

[0087] Please see Figure 2 As shown, parts not described in detail in this embodiment are described in Embodiment 1. A high-throughput monitoring method for plant phenotypes based on matrix control is provided, including:

[0088] S1. Construct a matrix-style plant phenotypic detection environment by 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.

[0089] S2. Establish 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, trigger all matrix nodes to synchronously collect SPAC parameters.

[0090] S3. Based on the preset microenvironmental difference control criteria and SPAC parameters, generate differentiated water and fertilizer instructions for each matrix node, drive the corresponding water and fertilizer irrigation device to output, and form a differentiated treatment group.

[0091] S4. Set the monitoring time sequence of plant sample growth cycle through the matrix control terminal, and automatically execute 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.

[0092] S5. Perform spatiotemporal dual-dimensional indexing on the multidimensional phenotypic dataset, generate phenotypic response difference maps of different matrix nodes through matrix comparison, and output quantitative evaluation results of plant growth performance.

[0093] Since the electronic device described in this embodiment is the electronic device used in implementing the matrix-controlled high-throughput plant phenotyping system described in this application, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the matrix-controlled high-throughput plant phenotyping system described in this application. Therefore, how the electronic device implements the method in this application will not be described in detail here. Any electronic device used by those skilled in the art in implementing the matrix-controlled high-throughput plant phenotyping system described in this application falls within the scope of protection of this application.

[0094] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0095] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

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 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. 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 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 sequence 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. 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 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.

4. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 3, 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.

5. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 4, 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.

6. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 5, 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.

7. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 6, 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.

8. The high-throughput plant phenotypic monitoring system based on matrix control according to claim 7, 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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