CN-121680544-B - Environment self-adaptive control method and system based on crop growth monitoring

Abstract

The invention provides an environment self-adaptive control method and system based on crop growth monitoring, which relate to the technical field of intelligent agriculture, and are characterized in that firstly crop growth state data and growth environment multi-element data are captured; the method comprises the steps of identifying crop growth potential characteristics from crop growth state data, constructing an environment evolution path based on the dynamic relation between the crop growth potential characteristics and environment multi-element data, generating an environment cooperative adjustment sequence according to the environment evolution path, adjusting the environment in stages by an execution sequence, synchronously and continuously capturing the adjusted data, updating the growth potential characteristics based on new data and optimizing the environment evolution path. The invention can adjust environmental parameters in real time according to the growth state of crops, realize environmental self-adaptive control, fully excite the growth potential of crops, improve the yield and quality and reduce the cost.

Inventors

• SHEN HUMING
• SHEN JIE
• LI LI
• MA XINXIN
• Du long
• WANG WEIJUN
• ZHANG JUN
• QIU JINMING
• YUAN HUI

Assignees

• 上海品上生活网络科技有限公司
• 上海清美农业科技有限公司

Dates

Publication Date

20260505

Application Date

20260211

Claims (8)

1. 1. An environmental adaptive control method based on crop growth monitoring, characterized in that the method comprises the following steps: Capturing crop growth state data and crop growth environment multi-element data, wherein the crop growth state data comprises crop organ morphological static parameters, physiological metabolism dynamic processes and growth rhythm fluctuation characteristics, and the crop growth environment multi-element data comprises environment composition data related to illumination, moisture, gas and temperature in a crop growth space; Identifying crop growth potential features from the crop growth status data, the crop growth potential features being core features related to an achievable growth upper limit and an evolution direction of a crop under suitable environmental conditions; Based on the dynamic action relation between the crop growth potential characteristics and the crop growth environment multi-element data, constructing an environment evolution path, wherein the environment evolution path is a stepwise environment change scheme for guiding the current environment to gradually transition to the direction adapting to the crop growth potential characteristics; Generating an environment cooperative regulation sequence according to the environment evolution path, wherein the environment cooperative regulation sequence comprises a regulation mode, a transition rhythm and inter-element linkage logic of environment elements in each environment evolution stage; Performing the environment cooperative regulation sequence to regulate the crop growth environment in stages, synchronously and continuously capturing regulated crop growth state data and regulated environment multi-element data, updating the crop growth potential characteristics based on the regulated crop growth state data, and optimizing the environment evolution path by utilizing the updated crop growth potential characteristics; the generating the environment cooperative regulation sequence according to the environment evolution path comprises the following steps: Analyzing each environment evolution stage in the environment evolution path, and extracting an environment element target state, an environment evolution stage duration and an environment element change rhythm in the environment evolution stage of each environment evolution stage; For each environment evolution stage, calculating the difference between the environment element target state of the environment evolution stage and the initial environment element state of the current environment evolution stage to obtain environment element state gap information; determining the adjustment direction of various environmental elements in each environmental evolution stage based on the environmental element state gap information; According to the duration of the environmental evolution stages and the change rhythm of the environmental elements, determining the adjustment rate of various environmental elements in each environmental evolution stage, wherein the adjustment rate is the change amplitude of the environmental elements in unit time; Aiming at each environment evolution stage, analyzing the mutual influence of various environment elements in the environment evolution stage, identifying the influence relationship of one environment element adjustment on the state change of other environment elements, and constructing an adjustment logic for avoiding the adverse interference among the elements based on the identified influence relationship; Based on the analysis results of the mutual influence, constructing an adjustment sequence of various environmental elements in each environmental evolution stage, wherein the adjustment sequence comprises the sequence of starting adjustment of different environmental elements; For each environment evolution stage, generating an environment regulation subsequence of the environment evolution stage according to the regulation direction, the regulation rate and the regulation sequence, wherein the environment regulation subsequence comprises a regulation scheme of various environment elements in the environment evolution stage; Analyzing the connection relation between the environment regulator sequences of adjacent environment evolution stages to obtain connection logic, and constructing a transition scheme between the environment evolution stages according to the connection logic; Based on the connection relation, supplementing an environment element buffer adjustment step between the environment adjustment subsequences of adjacent environment evolution stages to generate a final transition scheme; integrating the environment regulation subsequences of all environment evolution stages and buffer regulation steps among the environment evolution stages to form the environment cooperative regulation sequence; For each environmental evolution stage, analyzing the mutual influence of various environmental element adjusting processes in the environment evolution stage, identifying the influence relation of one environmental element adjustment on the state change of other environmental elements, and constructing an adjusting logic for avoiding the adverse interference among the elements based on the identified influence relation, wherein the adjusting logic comprises the following steps: Determining an environment evolution stage, acquiring the environment element type and the initial configuration state of the environment evolution stage, and defining various environment elements to be regulated in the environment evolution stage and respective regulation targets; Simulating the process of adjusting the environment elements according to the preset adjusting direction and adjusting speed aiming at each type of environment elements in the environment evolution stage to obtain a state change curve of the environment elements in the adjusting process, wherein the state change curve comprises element state values of different time nodes; Fixing the adjusting process of the environmental elements, simulating the state change curves of other various environmental elements when the environmental elements are independently adjusted, and identifying the difference between the state change curves when the environmental elements are independently adjusted and the common adjusting curve containing the fixed element adjustment by comparing the state change curves when the environmental elements are independently adjusted; Based on the difference, determining the influence property and direction of the environmental element adjustment on the state change of other various environmental elements; analyzing the influence property and the action mechanism behind the direction to obtain a specific mode of adjusting and changing the state change of other environmental elements by the environmental elements; partitioning an advantageous effect, which is an effect of facilitating the approach of other environmental elements to the adjustment target, and an adverse effect, which is an effect of blocking the approach of other environmental elements to the adjustment target; Obtaining interference information of conditions, expression forms and adjusting effects on other environment elements generated by the adverse effects; Repeatedly executing the simulation, comparison and analysis processes on all the environmental elements in the environmental evolution stage, and establishing a mutual influence relation table among various environmental elements; Screening out environmental element combinations with adverse effects based on the mutual influence relation table, and obtaining the conduction direction information of the adverse effects in each combination; Based on the mutual influence relation table and the screening result, outputting a mutual influence analysis result of the adjustment of the environmental elements in the environmental evolution stage, and constructing an adjustment logic for avoiding the adverse interference among the elements according to the mutual influence analysis result.
2. 2. The method of claim 1, wherein identifying crop growth potential characteristics from the crop growth status data comprises: Distinguishing organ form data, physiological metabolism data and growth rhythm data in the crop growth state data, wherein the organ form data records structural form and size related information of crop roots, stems, leaves, flowers and fruits, the physiological metabolism data records dynamic process information of crop nutrition synthesis, material transportation and energy conversion, and the growth rhythm data records growth activity degree and period fluctuation information of crops in different periods; Extracting structural integrity features and growth space layout features of each organ in the organ form data, wherein the structural integrity features reflect the completeness of development and functional soundness attributes of each organ, and the growth space layout features reflect the distribution form of each organ in a growth environment and the convenient attribute of resource acquisition; Extracting nutrition synthesis efficiency characteristics, substance transferring fluency characteristics and energy conversion stability characteristics from the physiological metabolism data, wherein the nutrition synthesis efficiency characteristics reflect the capability attribute of crops for synthesizing the nutrients required by the crops by utilizing environmental resources, the substance transferring fluency characteristics reflect the smooth attribute of the transmission of the nutrients among all organs, and the energy conversion stability characteristics reflect the continuous attribute of the crops for converting environmental energy into growth energy; Extracting growth activity period distribution characteristics and growth cycle fluctuation amplitude characteristics from the growth rhythm data, wherein the growth activity period distribution characteristics reflect growth active set attributes of crops in different periods of the day, and the growth cycle fluctuation amplitude characteristics reflect growth state change gentle attributes of the crops in continuous growth cycles; Based on the extracted features of the organ morphology data and the extracted features of the physiological metabolism data, establishing an association relationship between the extracted features of the organ morphology data and the extracted features of the physiological metabolism data, and obtaining an association relationship between the structural integrity features and the nutrition synthesis efficiency features and an association relationship between the growth space layout features and the substance transportation fluency features; Based on the association relation and the extracted characteristics of the growth rhythm data, establishing a constraint relation, and obtaining a constraint relation of growth activity period distribution characteristics on the association relation between the structural integrity characteristics and the nutrition synthesis efficiency characteristics and a constraint relation of growth period fluctuation amplitude characteristics on the association relation between the growth space layout characteristics and the material transportation fluency characteristics; Forming a feature association network based on the association relationship and the constraint relationship, screening out a core feature combination at a core topology position or a logic pivot position from the feature association network, tracking a change track of the core feature combination in the crop growth state data, and recording feature parameters and feature interaction change states of each feature in the core feature combination in different monitoring periods; Deducing an upper growth limit parameter of crops under the condition of no environmental limitation based on the change track of the core feature combination, wherein the upper growth limit parameter is an optimal development state which can be achieved by the morphology, the physiological metabolism and the growth rhythm of each organ of the crops; Integrating the variation track of the combination of the growth upper limit parameter and the core characteristic to form the crop growth potential characteristic.
3. 3. The method for adaptive environmental control based on crop growth monitoring according to claim 1, wherein the constructing an environmental evolution path based on the dynamic action relationship between the crop growth potential characteristics and the crop growth environment multi-element data comprises: splitting illumination element data, moisture element data, gas element data and temperature element data in the crop growth environment multi-element data, wherein the illumination element data records illumination intensity change and illumination duration distribution information in a growth environment, the moisture element data records moisture content and moisture replenishment frequency information in the growth environment, the gas element data records carbon dioxide concentration and oxygen concentration change information in the growth environment, and the temperature element data records temperature distribution and temperature fluctuation amplitude information in the growth environment; analyzing the action relation between each core feature in the crop growth potential features and the illumination element data to generate demand form information of an organ form optimal state in a growth upper limit parameter on illumination intensity change and illumination duration distribution and dependency mode information of a physiological metabolism optimal state on illumination elements; Analyzing the action relation between each core feature in the crop growth potential features and the water element data to generate the demand form information of the organ form optimal state on the water content and the water replenishment frequency, the dependence mode information of the physiological metabolism optimal state on the water element and the response form information of the growth rhythm optimal state on the water element change; analyzing the action relation between each core feature in the crop growth potential features and the gas element data to generate the requirement form information of the optimal state of nutrition synthesis efficiency on the change of carbon dioxide concentration and oxygen concentration and the dependency mode information of the optimal state of material transportation fluency on the gas element; Analyzing the action relation between each core feature in the crop growth potential features and the temperature element data to generate the demand form information of the optimal state of energy conversion stability on temperature distribution and temperature fluctuation amplitude and the response form information of the optimal state of growth cycle fluctuation on temperature element change; Integrating the demand form information, the dependence mode information and the response form information of each core feature and each environment element data to form an action relation network, wherein the action relation network comprises action priorities and interaction logics of various environment elements in the process of achieving the crop growth potential features; Dividing environmental evolution stages based on the action relation network to obtain environmental evolution stage division results, wherein each environmental evolution stage corresponds to a key development node in the crop growth potential feature achievement process, and the environmental elements of different environmental evolution stages are different in configuration key; aiming at each environment evolution stage, achieving requirements based on multi-element data of the current crop growth environment and potential of the environment evolution stage, and configuring an initial environment element combination; Based on the mutual coordination logic in the action relation network, a transition rule is constructed for the initial environment element combination of each environment evolution stage, wherein the transition rule comprises the change sequence and the mutual linkage mode of various environment elements in the environment evolution stage; And integrating the initial environment element combination of each environment evolution stage, the transition rule and the connection logic between the environment evolution stages to form the environment evolution path.
4. 4. The method of claim 1, wherein the executing the environmental collaborative adjustment sequence adjusts the crop growth environment in stages, capturing adjusted crop growth status data and adjusted environmental multi-element data synchronously and continuously, updating the crop growth potential characteristics based on the adjusted crop growth status data, and optimizing the environmental evolution path using the updated crop growth potential characteristics, comprising: Starting a corresponding environment regulating device according to the environment evolution stage division and regulation logic in the environment cooperative regulation sequence, firstly executing an environment regulation subsequence of a first environment evolution stage, and regulating various environment elements according to the regulation direction, the regulation rate and the regulation sequence in the environment regulation subsequence; Continuously capturing the regulated crop growth state data and the regulated environment multi-element data according to a set capturing interval in the execution process of the environment regulation subsequence of the first environment evolution stage; When the execution of the environmental regulation subsequence of the first environmental evolution stage is finished, the growth state data of the crops after the regulation of the first environmental evolution stage is based on the captured data, relevant characteristics of organ morphology, physiological metabolism and growth rhythm are re-extracted, a characteristic correlation network is established, a core characteristic combination is screened, new growth upper limit parameters are deduced, and crop growth potential characteristics after the regulation of the first environmental evolution stage are generated; Comparing the crop growth potential characteristics after the first environmental evolution stage and the crop growth potential characteristics before the first environmental evolution stage, and recording the change trend of the growth upper limit parameter and the change condition of the logic relationship between the core characteristic combinations to form excitation effect data of the crop growth potential; Based on the environment multi-element data after the first environment evolution stage is regulated, analyzing the actual execution effect of an environment regulation subsequence of the first environment evolution stage in the environment collaborative regulation sequence, and generating a fit parameter record of the actual environment change and the setting change of the environment regulation subsequence; combining the excitation effect data of the crop growth potential and the actual execution effect data of the environment regulation subsequence of the first environment evolution stage, and identifying a non-conforming part of a target state, a transition rule and an actual regulation effect set by the first environment evolution stage in the environment evolution path; Aiming at the inconsistent part, based on the crop growth potential characteristics after the adjustment of the first environmental evolution stage and the environmental multi-element data after the adjustment, adjusting an initial environmental element combination, a transition rule and an environmental evolution stage connection logic of a subsequent environmental evolution stage in the environmental evolution path to generate an optimized environmental evolution path; adjusting an environment adjustment subsequence of a subsequent environment evolution stage in the environment collaborative adjustment sequence and a buffer adjustment step between the environment evolution stages according to the optimized environment evolution path; starting an environment regulation subsequence of the optimized second environment evolution stage, and repeatedly executing the processes of capturing the regulated crop growth state data, updating the crop growth potential characteristics, identifying the inconsistent part, optimizing the environment evolution path and regulating the environment collaborative regulation sequence until the regulation of all the environment evolution stages is completed; after the execution of the environmental regulation subsequences of all environmental evolution stages is completed, generating final crop growth potential characteristics and an optimized environmental evolution path based on the finally captured regulated crop growth state data and regulated environmental multi-element data.
5. 5. The method according to claim 2, wherein the establishing a correlation between the extracted features based on the organ morphology data and the extracted features based on the physiological metabolic data to obtain a correlation between the structural integrity features and the nutrition synthesis efficiency features, and a correlation between the growth space layout features and the substance transport fluency features comprises: Taking the structural integrity characteristics and the growth space layout characteristics extracted from the organ morphology data as a first characteristic group, and taking the nutrition synthesis efficiency characteristics and the substance transportation fluency characteristics extracted from the physiological metabolism data as a second characteristic group, wherein both the two characteristic groups retain time dimension information and quantization parameters in the original data; Based on the crop growth state data, obtaining dynamic change data of the first feature group and the second feature group in a continuous monitoring period to form time series data of two groups of features, wherein each time node in the time series data corresponds to one group of feature parameters; Performing association analysis on the structural integrity feature time sequence in the first feature group and the nutrition synthesis efficiency feature time sequence in the second feature group to obtain a time lag relation and an accompanying change relation between structural integrity feature change and nutrition synthesis efficiency feature response change; Performing correlation analysis on the growth space layout feature time sequence in the first feature group and the material transportation fluency feature time sequence in the second feature group to obtain time lag relation and change correlation information between growth space layout feature change and material transportation fluency feature response change; Dividing a direct correlation, which is a change in one set of features directly resulting in a change in another set of features, and an indirect correlation, which is a change in one set of features resulting in another set of features by affecting other intermediate factors, based on the results of the two sets of correlation analyses; defining a leading feature and a subsequent feature for the directly associated feature pair, and obtaining a corresponding relation of response change trend of the subsequent feature when the leading feature generates a specific change trend, wherein the leading feature is a feature for inducing change, and the subsequent feature is a feature for generating response change; Identifying an intermediate influencing factor for the indirectly associated feature pair and obtaining a conduction path for the leading feature to act on the following feature through the intermediate influencing factor, the intermediate influencing factor being a crop growth related feature intermediate between the leading and following features; integrating the corresponding relation of the directly associated feature pairs and the conduction path of the indirectly associated feature pairs to form a preliminary feature association mapping frame, wherein the feature association mapping frame comprises association types and association information among groups of features; comparing the preliminary feature association mapping frame with a basic physiological mechanism of crop growth, and outputting a mapping result conforming to an internal action rule of organ morphology and physiological metabolism in the crop growth process; and supplementing triggering conditions and duration time information of each association relation in the mapping result to form an association mapping of the morphological characteristics and the physiological metabolism characteristics of the organ.
6. 6. The method for adaptively controlling environment based on crop growth monitoring according to claim 3, wherein the step of dividing the environmental evolution stage based on the action relation network to obtain the environmental evolution stage division result comprises the steps of: extracting growth upper limit parameters in the crop growth potential characteristics, defining the optimal development state of the crop in terms of organ morphology, physiological metabolism and growth rhythm, decomposing the optimal development state into a plurality of continuous development targets, wherein each development target corresponds to a key node in the crop growth potential characteristic achievement process; Determining the crop growth state requirement corresponding to each key node, and obtaining the organ morphological characteristics, physiological metabolism characteristics and growth rhythm characteristics of crops in the key node; Determining, based on the action relation network, a core environment element support required by each key node, wherein the core environment element support is an environment element configuration that plays a decisive role for the key node; Comparing the core environment element supports corresponding to the adjacent key nodes, and if the difference between the core environment element configurations of the two adjacent key nodes exceeds a preset configuration difference threshold value, determining the next key node as a change node; Dividing a transition process from a current environment to an optimal environment into a plurality of continuous environment evolution stages by taking the change nodes as boundaries, wherein each environment evolution stage comprises one or more key nodes, and the configuration of kernel environment elements of the environment evolution stages is kept relatively stable; Defining a core environment element type and a configuration priority in each environment evolution stage, wherein the configuration priority is set based on the dependence degree of the key node in the environment evolution stage on the environment element; Analyzing the interaction of the kernel environment elements and the non-core environment elements in each environment evolution stage to obtain auxiliary action information of the non-core environment elements on the core environment elements, and formulating a configuration scheme of the non-core environment elements according to the auxiliary action information; determining the duration of each environment evolution stage, wherein the duration is set based on the difficulty of achievement of key nodes in the environment evolution stage, the transition rhythm of the change of environment elements and the natural period of crop growth; setting a stage target for each environmental evolution stage, wherein the stage target is the state which is reached by the environmental elements at the end of the environmental evolution stage and the development degree which is reached by the crop growth potential, and the stage target corresponds to the key nodes in the environmental evolution stage; And integrating the core environment elements, the configuration priority, the duration and the stage targets of each environment evolution stage to form the environment evolution stage division result.
7. 7. The method according to claim 4, wherein the step of identifying the inconsistent portion of the target state, the transition rule, and the actual adjustment effect set in the first environmental evolution stage in the environmental evolution path by combining the excitation effect data of the crop growth potential and the actual execution effect data of the environmental adjustment subsequence in the first environmental evolution stage comprises: Extracting a growth upper limit parameter and a core feature combination in the crop growth potential feature regulated in the first environment evolution stage, and comparing the growth upper limit parameter and the core feature combination with the crop growth potential feature before regulation to obtain a change trend of the growth upper limit parameter and a change condition of a logic relationship between the core feature combination, wherein the change trend and the change condition form excitation effect data of the crop growth potential; Comparing the excitation effect data with an environment evolution target set for a first environment evolution stage in the environment evolution path to obtain difference information of an actual excitation effect and a target excitation effect; Extracting an actual environmental state in the environment multi-element data after the adjustment of the first environment evolution stage, and comparing the actual environmental state with a target environmental state set for the first environment evolution stage in the environment evolution path to obtain deviation values of the actual states and the target states of various environment elements, wherein the deviation values form actual execution effect data of an environment adjustment sub-sequence of the first environment evolution stage; Comparing the actual execution effect data with a transition rule set for a first environment evolution stage in the environment evolution path to obtain difference information among an actual environment element change rhythm, a linkage mode and a set rule; Judging the suitability of the target environment state set in the first environment evolution stage and the crop growth potential characteristics based on the difference information of the actual excitation effect and the target excitation effect, and if the suitability is insufficient, determining the target state as a non-conforming part; Based on the difference information between the actual environment element change and the set rule, judging the validity of the transition rule set in the first environment evolution stage, and if the transition rule cannot guide the environment element to change according to the expected change, determining the transition rule as a non-conforming part; analyzing the deviation cause of the actual state and the target state of the environmental element, and determining the target state or the transition rule corresponding to the deviation caused by the environmental evolution path setting problem as a non-compliant part after eliminating the influence of the non-external factor; Aiming at the determined inconsistent target state, analyzing the adaptation contradiction points of the determined inconsistent target state and the crop growth potential characteristics, and obtaining environment element configuration information which is not matched with the crop growth potential development requirement in the target state; aiming at the determined non-conforming transition rule, analyzing logic defects of the non-conforming transition rule in the process of guiding the change of the environment elements, and obtaining change rhythm or linkage mode information of the non-conforming transition rule, wherein the change rhythm or linkage mode information cannot realize the effective transition of the environment elements; and integrating the non-conforming target state, the adaptation contradiction point thereof, the non-conforming transition rule and the logic defect thereof to form a detailed recognition result of the non-conforming part in the environment evolution path.
8. 8. An environmental adaptive control system based on crop growth monitoring, comprising: A processor; a machine-readable storage medium storing machine-executable instructions for the processor; wherein the processor is configured to perform the crop growth monitoring-based environmental adaptive control method of any one of claims 1 to 7 via execution of the machine executable instructions.

Description

Environment self-adaptive control method and system based on crop growth monitoring Technical Field The invention relates to the technical field of intelligent agriculture, in particular to an environment self-adaptive control method and system based on crop growth monitoring. Background In the field of agricultural planting, accurate regulation and control of crop growth environments to achieve high yield and quality of crops is a long-sought goal. Conventional crop growth environmental control methods are mostly based on fixed environmental parameter settings, such as controlling the greenhouse environment according to predetermined values of temperature, humidity, illumination intensity, etc. However, crop growth is a dynamic process, the growth state of which varies over time, and the environmental demands of different growth stages vary. Moreover, crop growth is affected by a combination of environmental elements, which correlate and interact with each other to affect the growth and development of the crop. The existing environmental control technology usually only considers a single environmental element or a few environmental elements, and lacks comprehensive analysis on complex relations between the growth state of crops and the environmental elements. For example, the effect of illumination on crop water evaporation and gas exchange may not be considered when adjusting the intensity of illumination, and the effect of temperature on crop physiological metabolism and growth rhythms may be ignored when adjusting the temperature. The above-mentioned isolated environmental control method is difficult to meet the dynamic demands of crops in different growth stages, so that the growth potential of crops can not be fully exerted, and the yield and quality of crops are affected. Disclosure of Invention In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides an environmental adaptive control method based on crop growth monitoring, the method including: Capturing crop growth state data and crop growth environment multi-element data, wherein the crop growth state data comprises crop organ morphological static parameters, physiological metabolism dynamic processes and growth rhythm fluctuation characteristics, and the crop growth environment multi-element data comprises environment composition data related to illumination, moisture, gas and temperature in a crop growth space; Identifying crop growth potential features from the crop growth status data, the crop growth potential features being core features related to an achievable growth upper limit and an evolution direction of a crop under suitable environmental conditions; Based on the dynamic action relation between the crop growth potential characteristics and the crop growth environment multi-element data, constructing an environment evolution path, wherein the environment evolution path is a stepwise environment change scheme for guiding the current environment to gradually transition to the direction adapting to the crop growth potential characteristics; Generating an environment cooperative regulation sequence according to the environment evolution path, wherein the environment cooperative regulation sequence comprises a regulation mode, a transition rhythm and inter-element linkage logic of environment elements in each environment evolution stage; And executing the environment cooperative regulation sequence to regulate the crop growth environment in stages, synchronously and continuously capturing the regulated crop growth state data and the regulated environment multi-element data, updating the crop growth potential characteristics based on the regulated crop growth state data, and optimizing the environment evolution path by utilizing the updated crop growth potential characteristics. In still another aspect, an embodiment of the present invention further provides an environmental adaptive control system based on crop growth monitoring, which is characterized by including: The system comprises a processor, a machine-readable storage medium for storing machine-executable instructions of the processor, wherein the processor is configured to perform the crop growth monitoring-based environment adaptive control method described above via execution of the machine-executable instructions. In yet another aspect, an embodiment of the present invention further provides a computer program product including machine-executable instructions stored in a computer-readable storage medium, from which a processor of a crop growth monitoring-based environment adaptive control system reads the machine-executable instructions, the processor executing the machine-executable instructions, so that the crop growth monitoring-based environment adaptive control system performs the above-described crop growth monitoring-based environment adaptive control method. Based on the aspects