A method and system for optimizing deep placement of fertilizer for root zone matching

Abstract

This application discloses a method and system for optimizing deep fertilization locations for root zone matching. By acquiring root zone evolution field data, nutrient release field data, water migration field data, staged root zone nutrient absorption threshold data, and deep fertilization pose constraint data, the method performs temporal field registration of the root zone evolution process and nutrient release process to establish a root-fertilizer temporal coupling relationship. Based on this, it performs mapping analysis on the nutrient propagation range using water migration paths to form nutrient reachability domain data, and performs adaptation discrimination of nutrient reachable regions according to root zone absorption thresholds to obtain root zone absorption adaptation data. Furthermore, it combines fertilization pose constraints to reverse-engineer the fertilization location, forming a set of root zone-matched fertilization points. This achieves reverse-engineering of fertilization locations based on root zone evolution, water-fertilizer migration relationships, and root zone absorption capacity, thereby forming a set of deep fertilization points that match the crop root zone absorption range and improving the matching degree between fertilization locations and root zone spatial distribution.

Description

Technical Field

[0001] This invention relates to the field of agricultural fertilization technology, specifically to a method and system for optimizing deep fertilization locations for root zone matching. Background Technology

[0002] Nutrient absorption by crops primarily occurs within the root zone during growth. The degree of matching between fertilization location and the spatial distribution of the root zone directly affects nutrient absorption efficiency and crop growth. With the development of agricultural production towards refined management, ensuring effective nutrient absorption within the crop root zone during fertilization has become a crucial research direction for improving fertilizer utilization efficiency and reducing nutrient loss. Particularly in deep fertilization operations, the spatial location of the fertilization point within the soil profile not only influences nutrient diffusion and migration in the soil but also affects the effectiveness of nutrient absorption by the root zone at different growth stages. Therefore, rationally determining the fertilization location is of great significance.

[0003] Currently, to achieve deep fertilization, the fertilization depth and location are typically set based on crop type, soil type, and past planting experience. For example, fertilizer may be applied to a certain depth in the soil using agricultural machinery before sowing, or applied in strips or holes at fixed depths and lateral distances. In practice, these methods often rely on empirical rules or simple agronomic parameters, setting fixed fertilization depth ranges and spacings to complete the fertilization layout, thereby meeting the basic nutrient requirements of crops during their growth process to a certain extent.

[0004] However, the above methods rely mainly on experience to determine the fertilization location, making it difficult to comprehensively consider the coupling relationship between the crop root zone growth and evolution process, the fertilizer nutrient release process, and the soil moisture migration process. This results in a low degree of matching between the fertilization location and the root zone nutrient absorption range, and insufficient nutrient utilization efficiency. Summary of the Invention

[0005] In view of the above-mentioned actual situation, this application proposes a method and system for optimizing deep fertilization location for root zone matching, in order to solve the problem that the fertilization location in the prior art mainly relies on experience to set, which makes it difficult to comprehensively consider the coupling relationship between crop root zone growth and evolution process, fertilizer nutrient release process and soil moisture migration process, resulting in low matching degree between fertilization location and root zone nutrient absorption range and insufficient nutrient utilization efficiency.

[0006] A method for optimizing deep fertilization location for root zone matching, the method comprising the following steps: S1, acquire data to be processed, the data to be processed includes root zone evolution field data, nutrient release field data, water migration field data, staged root zone nutrient absorption threshold data, and deep fertilization posture constraint data. S2, perform time-series field registration processing on the root zone evolution field data and the nutrient release field data to establish a time-series coupling relationship between root zone action and nutrient supply, thereby obtaining root fertilizer time-series coupling data. S3, perform water and fertilizer carrying capacity mapping processing on the root-fertilizer time-series coupling data and the water migration field data to determine the water migration path in the soil profile based on the water migration field data, and combine the transfer relationship between nutrient release and water migration represented by the root-fertilizer time-series coupling data to form nutrient reachability domain data that represents the reachable range of nutrients in the soil profile. S4, perform absorption domain discrimination processing on the nutrient reachability domain data and the staged root zone nutrient absorption threshold data, so as to determine the range of root zone effect on nutrients in different growth stages based on the staged root zone nutrient absorption threshold data, and combine the nutrient reachability domain data to perform adaptation discrimination on the nutrient reachable area, forming root zone absorption adaptation data that characterizes the correspondence between the nutrient reachable area and the root zone absorption range; S5, perform fertilization pose back-calculation processing on the root zone absorption adaptation data and the deep fertilization pose constraint data to determine the location distribution of nutrient action areas based on the root zone absorption adaptation data, and combine the depth range of fertilization points in the soil profile and the lateral spacing relative to the root zone boundary defined by the deep fertilization pose constraint data to form a set of root zone matching fertilization points for pre-sowing layout.

[0007] Furthermore, step S2 includes the following sub-steps: S201, the root zone evolution field data is subjected to root zone time window decomposition processing to obtain root zone action time window data. The root zone time window decomposition processing is to use the root zone evolution field data to segmentally analyze the root zone expansion range, root zone stratum migration status and root zone boundary evolution relationship of crops in different growth stages, and determine the root zone action start interval, continuous coverage interval and boundary transfer interval in each growth period to form root zone action time window data that characterizes the stage action range of the root zone. S202, the root zone action window data and the nutrient release field data are subjected to segmental coupling processing to obtain root fertilizer time-series coupling data. The segmental coupling processing is based on the root zone action window data to determine the time-series demand of the root zone for nutrient action segments under each growth period, and uses the nutrient release field data to determine the nutrient release effective segment, continuous supply segment and attenuation segment under the corresponding time conditions after the application of controlled-release fertilizer and urea. Then, the time-series demand is segmentally matched with the nutrient release segments to form a coupling relationship between the root zone action window and the nutrient release window under each growth period.

[0008] Furthermore, step S3 includes the following sub-steps: S301, perform migration path analysis processing on the water migration field data to obtain water carrying capacity path data. The migration path analysis processing involves using the water migration field data to identify the infiltration, lateral expansion, and retention sections of water along the depth and lateral directions in the soil profile, and determining the water transfer path and migration boundary in the profile structure based on the identification results, so as to form water carrying capacity path data that characterizes the water carrying capacity direction and its migration range. S302, perform migration mapping processing on the water carrying path data and the root-fertilizer time-series coupling data to obtain nutrient reachability domain data. The migration mapping processing determines the migration direction of nutrients in the soil profile based on the water carrying path data, and uses the root-fertilizer time-series coupling data to determine the nutrient release segments in each growth period. Then, the nutrient release segments are propagated and mapped along the water carrying path to determine the spatial distribution range that nutrients can reach in the corresponding growth period, thereby forming nutrient reachability domain data.

[0009] Furthermore, step S4 includes the following sub-steps: S401, the absorption segment analysis processing is performed on the staged root zone nutrient absorption threshold data to obtain absorption discrimination interval data. The absorption segment analysis processing is to use the staged root zone nutrient absorption threshold data to divide the root zone nutrient action distance, response range and absorption limit of the crop in different growth stages, and to determine the discrimination interval of the root zone nutrient absorption range in each growth stage based on the segment division, so as to form absorption discrimination interval data. S402, the absorption discrimination interval data and the nutrient reachability domain data are subjected to segment adaptation discrimination processing to obtain root region absorption adaptation data. The segment adaptation discrimination processing is based on the absorption discrimination interval data to determine the range of root region effect on nutrients in each growth period, and the nutrient reachability domain data is used to determine the spatial reach area of ​​nutrients in the corresponding period. Then, the spatial reach area and the range of effect are segmented to identify the nutrient effect area that can fall within the root region absorption range and form root region absorption adaptation data.

[0010] Furthermore, step S5 includes the following sub-steps: S501, Perform pose boundary analysis processing on the deep fertilization pose constraint data to obtain fertilization pose boundary data. The pose boundary analysis processing uses the deep fertilization pose constraint data to determine the allowable depth range of the fertilization point in the soil profile and the lateral interval limit relative to the root zone boundary, and determines the pose boundary of the fertilization point in space based on the allowable depth range and the lateral interval limit to form fertilization pose boundary data. S502, perform pose backtracking and extrapolation processing on the fertilization pose boundary data and the root zone absorption adaptation data to obtain a set of root zone matching fertilization points. The pose backtracking and extrapolation processing is based on the root zone absorption adaptation data to determine the location distribution of nutrient action areas at each growth stage, and performs reverse backtracking of fertilization positions along the nutrient release direction. The backtracking results are then limited to the spatial range represented by the fertilization pose boundary data to form a set of root zone matching fertilization points for pre-sowing deployment.

[0011] Furthermore, the root region time window decomposition process includes reproductive process segmentation and boundary time window extraction; the segment coupling process includes demand time window extraction and release segment coupling.

[0012] Furthermore, the migration path parsing process includes migration segment identification processing and path boundary extraction processing; the migration mapping process includes path orientation carrying processing and segment propagation domain generation processing.

[0013] Furthermore, the absorption segment parsing process includes absorption response segment division processing and absorption boundary interval extraction processing; the segment adaptation discrimination processing includes absorption range mapping processing and region adaptation discrimination processing.

[0014] Furthermore, the pose boundary resolution process includes depth interval resolution and lateral avoidance boundary generation; the pose backtracking and deduction process includes adaptation region backtracking and pose boundary constraint processing.

[0015] Furthermore, this application also discloses a deep fertilization location optimization system for root zone matching, characterized in that the system comprises: The acquisition unit is used to acquire data to be processed, which includes root zone evolution field data, nutrient release field data, water migration field data, staged root zone nutrient absorption threshold data, and deep fertilization pose constraint data. The temporal registration unit is used to perform temporal field registration processing on the root zone evolution field data and the nutrient release field data to establish a temporal coupling relationship between root zone action and nutrient supply, thereby obtaining root fertilizer temporal coupling data. The water and fertilizer mapping unit is used to perform water and fertilizer carrying capacity mapping processing on the root-fertilizer time-series coupling data and the water migration field data, so as to determine the water migration path in the soil profile according to the water migration field data, and combine the transfer relationship between nutrient release and water migration represented by the root-fertilizer time-series coupling data to form nutrient reachability domain data that represents the reachable range of nutrients in the soil profile. An absorption discrimination unit is used to perform absorption domain discrimination processing on the nutrient reachability domain data and the staged root zone nutrient absorption threshold data, so as to determine the range of root zone effect on nutrients in different growth stages based on the staged root zone nutrient absorption threshold data, and to perform adaptation discrimination on the nutrient reachability region in combination with the nutrient reachability domain data, forming root zone absorption adaptation data that characterizes the correspondence between the nutrient reachability region and the root zone absorption range. The pose inversion unit is used to perform fertilization pose inversion processing on the root zone absorption adaptation data and the deep fertilization pose constraint data, so as to determine the location distribution of the nutrient action area according to the root zone absorption adaptation data, and combine the depth range of the fertilization points in the soil profile and the lateral spacing relative to the root zone boundary defined by the deep fertilization pose constraint data to form a set of root zone matching fertilization points for pre-sowing layout.

[0016] The method and system proposed in this application for optimizing deep fertilization locations for root zone matching realizes the reverse deduction of fertilization locations based on root zone evolution, water and fertilizer migration relationship and root zone absorption capacity, thereby forming a set of deep fertilization points that match the absorption range of crop root zones and improving the matching degree between fertilization locations and root zone spatial distribution. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method flow for optimizing the location of deep fertilization for root zone matching proposed in this application; Figure 2 This is a schematic diagram of the coupling between the crop root zone action window and the nutrient release sequence in this embodiment; Figure 3 This is a schematic diagram of the reverse extrapolation space of the root zone matching fertilization points in this embodiment; Figure 4 A schematic diagram of a deep fertilization location optimization system for root zone matching is provided in an embodiment of this application; Detailed Implementation

[0018] The simulation technology route in 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.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] The features and performance of the present invention will be further described in detail below with reference to embodiments. Please refer to the appendix. Figure 1 As shown, a method for optimizing deep fertilization location for root zone matching includes the following steps: S1, acquire data to be processed, the data to be processed includes root zone evolution field data, nutrient release field data, water migration field data, staged root zone nutrient absorption threshold data, and deep fertilization posture constraint data. Furthermore, the root zone evolution field data characterizes the root zone expansion range, root zone stratum migration status, and root zone boundary evolution relationship of crops at different growth stages; the nutrient release field data characterizes the nutrient release process, release gradient, and release duration under different time conditions after the combined application of controlled-release fertilizer and urea; the water migration field data characterizes the migration path, retention zone, and carrying capacity transfer relationship of water along the depth and lateral directions in the soil profile; the staged root zone nutrient absorption threshold data characterizes the nutrient reach distance, nutrient action range, and absorption response limit of the root zone at different growth stages; the deep fertilization posture constraint data characterizes the allowable depth range of fertilization points in the soil profile and the lateral interval limit relative to the root zone boundary, and the deep fertilization posture constraint data includes effective fertilization depth window data and root zone lateral avoidance distance data. Furthermore, this step aims to construct a unified data input system to support the temporal extrapolation processing of fertilization sites in subsequent steps. Specifically, the data to be processed includes root zone evolution field data, nutrient release field data, water migration field data, stage-specific root zone nutrient absorption threshold data, and deep fertilization pose constraint data. These data form an interconnected data structure in spatial, temporal, and relational dimensions. It should be noted that this data structure is used to characterize the coupling relationship between root zone expansion behavior, nutrient release behavior, and water migration behavior during the crop growth cycle, and serves as the data input source for temporal field registration processing, water and fertilizer carrying capacity mapping processing, and absorption domain discrimination processing in subsequent steps. Preferably, all data is stored in structured data format and organized according to a unified timeline to ensure that various field data are analyzed at the same growth process scale.

[0021] In this embodiment, the root zone evolution field data is used to describe the evolution of the root zone spatial structure of crops during different growth stages. Specifically, the root zone evolution field data includes the root zone expansion range, root zone stratum migration status, and root zone boundary evolution relationship. The root zone expansion range represents the coverage area of ​​the root system in the soil profile; the root zone stratum migration status represents the extension process of the taproot and lateral roots in the soil depth direction; and the root zone boundary evolution relationship describes the spatial trajectory of the outer boundary of the root zone over time. In some embodiments, the root zone evolution field data is obtained by establishing a root growth model, which correlates the crop growth time t with the root zone spatial radius. The mapping relationship is established, and its expression is: ,in Indicates the initial root region radius. This represents the root spread rate coefficient. This represents the root growth index. Furthermore, this model can be used to obtain the spatial expansion curves of the root zone during different growth stages, and thereby generate root zone evolution field data. It should be noted that the methods for obtaining root zone growth parameters are well-known to those skilled in the art, and the calculation or processing methods are also well-known to those skilled in the art; therefore, they will not be elaborated upon further here.

[0022] In some embodiments, the nutrient release field data is used to characterize the nutrient release process of the compound fertilizer source in the soil environment. Specifically, the compound fertilizer source consists of controlled-release fertilizer and urea, with a preferred ratio of 1:2. The controlled-release fertilizer exhibits a slow-release characteristic in the soil, while urea exhibits a rapid release characteristic under the action of hydrolysis; the two are superimposed to form a composite nutrient release curve. Furthermore, the nutrient release field data is expressed by establishing a nutrient release function, the expression of which is as follows: ,in This indicates the total amount of nutrients released. This indicates the amount of nutrients released by the controlled-release fertilizer at time t. The nutrient release of urea at time t is shown. Furthermore, the release behavior of controlled-release fertilizers follows an exponential release model: ,in The initial nutrient content of the controlled-release fertilizer is represented by , and k represents the release rate coefficient. The urea release behavior is expressed using a hydrolysis reaction rate model, and its release rate satisfies the following relationship: ,in This indicates the initial nutrient content of urea. This represents the hydrolysis reaction rate coefficient. Through the above functional relationship, the nutrient release intensity and release zone distribution at different time points can be obtained, forming nutrient release field data.

[0023] In this embodiment, the water migration field data is used to describe the spatial migration behavior of water within a soil profile. Specifically, the migration process of water in the soil is influenced by gravitational potential, capillary potential, and soil pore structure, and its migration behavior is characterized by the soil water movement equation. In some embodiments, the water migration behavior is expressed by the Richards equation, which has the following form: ,in The vector represents soil moisture content, t represents time, and z represents soil depth coordinates. Let represent the soil hydraulic conductivity function, and h represent the matrix potential. By solving the above equation, the migration path and retention zone of water in the soil profile can be obtained, thereby constructing water migration field data. It should be noted that the numerical solution method of the Richards equation is a method well known to those skilled in the art, and the calculation or processing method is a method well known to those skilled in the art, so it will not be elaborated further here.

[0024] In some implementations, the staged root zone nutrient absorption threshold data is used to characterize the root zone's nutrient absorption capacity at different growth stages. Specifically, the root zone's nutrient absorption capacity is related not only to root density but also to root metabolic activity. Preferably, nutrient absorption capacity is described by establishing a root zone absorption function, the expression of which is as follows: ,in This represents the nutrient uptake capacity under the conditions of growth time t and root zone distance d. This represents the absorption activity coefficient of the root region at time t. This represents the nutrient absorption attenuation coefficient. This functional relationship allows us to obtain the nutrient absorption range and limits in the root zone during different growth stages, and thus generate staged root zone nutrient absorption threshold data.

[0025] In this embodiment, the deep fertilization pose constraint data is used to limit the layout range of fertilization points in the soil space. Specifically, the deep fertilization pose constraint data includes effective fertilization depth window data and root zone lateral avoidance distance data. Further, the effective fertilization depth window data is used to limit the depth range of fertilization points in the soil profile, and its expression is as follows: Where D represents the fertilization depth. This indicates the minimum depth at which fertilization is permitted. This indicates the maximum permissible fertilization depth. The lateral avoidance distance data for the root zone is used to define the minimum lateral interval between the fertilization point and the root zone boundary, and its expression is as follows: Where L represents the lateral distance between the fertilization point and the root zone boundary. This indicates the safe avoidance distance. These constraints create spatial constraints on fertilization sites, ensuring that their placement within the soil meets the growth needs of the crop root zone.

[0026] S2, perform time-series field registration processing on the root zone evolution field data and the nutrient release field data to establish a time-series coupling relationship between root zone action and nutrient supply, thereby obtaining root fertilizer time-series coupling data. Furthermore, this step includes the following sub-steps: S201, the root zone evolution field data is subjected to root zone time window decomposition processing to obtain root zone action time window data. The root zone time window decomposition processing is to use the root zone evolution field data to segmentally analyze the root zone expansion range, root zone stratum migration status and root zone boundary evolution relationship of crops in different growth stages, and determine the root zone action start interval, continuous coverage interval and boundary transfer interval in each growth period to form root zone action time window data that characterizes the stage action range of the root zone. In some implementations, the root region time window decomposition process includes reproductive history segmentation and boundary time window extraction. The reproductive history segmentation is based on the root region evolution field data to divide the spatial evolution behavior of the root region in the reproductive process into time domain segments, forming root region stage segments corresponding to the reproductive history. The boundary time window extraction is based on the root region stage segments to identify the entry, maintenance, and migration states of the root region boundaries, forming a time window representation of the root region's influence interval within each reproductive period. It should be noted that the root region evolution field data is no longer used as static distribution information in this step, but is transformed into time window structure data with time period boundaries, influence ranges, and migration relationships, for use in subsequent steps when segmenting and coupling nutrient supply time windows.

[0027] In this embodiment, the segmented processing of the reproductive process revolves around the evolutionary continuity of the root region along the time axis. Specifically, the root region expansion relationship established in step S1... To characterize the root zone extension scale of a crop at growth time t, and further, to ensure that the root zone expansion relationship has a registerable temporal structure with subsequent nutrient release segments, a root zone expansion rate function is used to segment the time axis. This root zone expansion rate function is expressed as... ,in This represents the root region expansion rate at reproductive time t. Based on... The changing characteristics are used to determine the segment boundaries where the root region transitions from germination and expansion to continuous coverage, and the segment boundaries where it transitions from continuous coverage to boundary migration. Preferably, a set of reproductive segments is constructed within the time interval [0,T]. Where T represents the end time of a crop's growth cycle. , m represents the number of stages after partitioning. This represents the i-th reproductive period. The dividing point... The determination is based on a joint assessment of the inflection point, rate plateau interval, and rate decay interval of the root region expansion rate curve, thereby ensuring that each reproductive period corresponds to a relatively stable set of root region expansion behaviors. The implementation methods for inflection point search, curve fitting, and segment determination are well known to those skilled in the art and will not be elaborated upon here.

[0028] In some implementations, the root zone evolution field data includes not only the root zone extension scale but also the root zone stratigraphic migration state, reflecting the root system's propagation relationship along the soil depth direction. Specifically, let the root zone center depth or the dominant root layer depth be... The The expression for the location of the root zone action layer in the soil profile at growth time t is: ,in Indicates the initial root layer depth. Indicates the root zone stratigraphic shift coefficient. This represents the stratigraphic migration index. Furthermore, the stratigraphic migration rate is defined as... ,in This indicates the migration velocity of the root zone's active layer along the soil depth direction. and This is jointly introduced into the segmentation of the reproductive history to distinguish between reproductive segments characterized primarily by lateral expansion and those characterized primarily by longitudinal downward shift. It should be noted that when... Dominant during the corresponding time period At a lower level, the fertility segment reflects the formation process of root zone cover; when Enhanced When the process slows down, the reproductive segment reflects the process of the root region's layer of action shifting to deeper layers. Therefore, the segmentation of the reproductive process is not merely a mechanical division based on time length, but rather a construction of stage segments based on the combined state of root region expansion behavior and layer migration behavior.

[0029] In this embodiment, the boundary time window extraction process is based on the root region stage segment and is used to transform the continuously changing root region boundary into a time window structure suitable for subsequent coupling processing calls. Specifically, let the root region boundary function be denoted as... The This represents the set of boundary locations of the root zone in the soil profile at a growth time t. To better express the spatial extent of the root zone boundary, a boundary reach function is defined. ,in The x-coordinate represents the spatial coordinates in the soil profile, and the z-coordinate represents the depth coordinate. This represents the root region coverage at reproductive time t. This indicates whether a specified location enters the root region's scope at time t. Based on... As the state changes over time, the starting interval, continuous coverage interval, and boundary transition interval of the root region at a specified spatial location are determined. Furthermore, if a minimum time exists... Make ,but This is denoted as the starting time of the root region action at that location; if it is within the interval... Internal continuous satisfaction If the boundary function is true, then the interval is denoted as the continuous coverage interval at that position; if the boundary function is true, then the interval is denoted as the continuous coverage interval at that position. The normal offset near this position satisfies And exceeding the preset migration threshold within a continuous time period Then, this continuous time period is identified as the boundary transition interval, where Indicates the time increment. Represents the boundary displacement norm. This represents the threshold for boundary migration determination. Through the above processing, the root region boundary evolves from a simple geometric boundary into a temporal window boundary structure that can describe the entry, maintenance, and migration states.

[0030] In some implementations, a root region action window function is constructed to uniformly organize the action start interval, continuous coverage interval, and boundary transition interval. Specifically, the root zone action window function is used to characterize the effect of the root zone on the corresponding soil region at each growth stage, and its expression is written as follows: ,in Indicates the function that identifies the starting interval of action. This indicates a function to identify the continuous coverage interval. The boundary transition interval identification function is used. Preferably, the three identification functions are written as follows: , , ,in Indicates the starting interval of the action. Indicates the continuous coverage area. This indicates the boundary transition interval. Furthermore, for any reproductive period... All of these methods can extract the root region's operational state within a given time period using the aforementioned set of functions, thereby forming root region operational window data with both temporal interval and spatial boundary attributes. It should be noted that the interval values, threshold tuning, and boundary closure methods of the identifier function are well-known to those skilled in the art, and will not be elaborated upon further here.

[0031] In this embodiment, to enhance the adaptability of the root zone interaction time window data to the coupling of subsequent nutrient supply segments, a root zone interaction intensity function is further introduced. The The expression used to describe the relative level of root zone activity during each reproductive stage is as follows: ,in This represents a measure of root zone boundary stability or root density at a reproductive time t. This represents the maximum root region radius within the reproductive cycle. Indicates the maximum root depth during the reproductive cycle. This represents the maximum value of the boundary stability or root density characterization quantity. This represents the corresponding weight coefficient, and they must add up to 1. This is achieved by introducing... The root zone action window data not only expresses when the root zone begins to act, when it continues to cover, and when boundary shifts occur, but also expresses the relative strength of the action state at different growth stages. This expression method, when used in subsequent step S202 for segmental matching with the nutrient release window, supports the differentiated expression of root zone temporal needs, thereby giving the root-fertilizer temporal coupling relationship a more explicit stage-specific constraint.

[0032] In some implementations, the root region action window data is denoted as ,in Indicates the i-th reproductive period. This represents the set of states within the effective time window during the i-th reproductive period. This represents the intensity of the root region effect during the i-th reproductive period. Let represent the root region coverage area within the i-th reproductive period, and m represent the number of stages after segmenting the reproductive process of the root region. Specifically, the... This refers to the root zone action window data formed in step S201. Structurally, this data simultaneously contains three types of information: time interval, spatial range, and action state. This provides a direct data foundation for the subsequent step S202, which determines the temporal demand of the root zone for nutrient action segments during each reproductive period based on the root zone action window data. Furthermore, after dividing the nutrient release field data into release initiation segments, continuous supply segments, and attenuation segments in subsequent step S202, it is possible to... The time intervals and states of action described herein are established in a one-to-one or multi-segment correspondence with the nutrient release window, thereby ensuring the continuity of data transfer between steps.

[0033] In this embodiment, the description of the root region time window decomposition process is not limited to the above-mentioned function form. Implementation paths that use growth curve fitting, boundary envelope extraction, time series segmentation, and state recognition to obtain root region action window data all fall within the scope of this step. It should be noted that the processing methods involving time series smoothing, boundary envelope calculation, parameter estimation, and threshold determination are well-known to those skilled in the art and will not be elaborated upon here. Through the continuous connection between the reproductive process segmentation processing and the boundary time window extraction processing, step S201 transforms the root region evolution field data obtained in step S1 into root region action window data that can directly participate in time-series coupling processing. This provides a temporal expression basis for segmented docking of root region evolution behavior with nutrient release behavior and establishes root region-side interval constraints for subsequent time-series back-calculation of deep fertilization locations.

[0034] S202, the root zone action window data and the nutrient release field data are subjected to segmental coupling processing to obtain root fertilizer time-series coupling data. The segmental coupling processing is based on the root zone action window data to determine the time-series demand of the root zone for nutrient action segments under each growth period, and uses the nutrient release field data to determine the nutrient release effective segment, continuous supply segment and attenuation segment under the corresponding time conditions after the application of controlled-release fertilizer and urea. Then, the time-series demand is segmentally matched with the nutrient release segments to form a coupling relationship between the root zone action window and the nutrient release window under each growth period.

[0035] In some implementations, the segment coupling process includes demand window extraction and release segment matching. The demand window extraction process, based on root zone action window data, temporally aggregates the root zone action state, intensity, and coverage changes during each growth period to form a stage-based demand expression for nutrient action segments from the root zone. The release segment matching process, based on the nutrient release field data, segments the nutrient release behavior of compound fertilizer sources under corresponding historical conditions and matches the segmentation results with the temporal demand expression to form root-fertilizer temporal coupling data with stage-based correspondences. It should be noted that this step inherits the root zone action window data, transforming it from a simple expression of root zone spatial action into a temporal demand expression oriented towards nutrient supply, thus providing a temporal structural basis for the stage-based analysis of nutrient migration behavior in subsequent steps.

[0036] In this embodiment, the demand window extraction process revolves around the root zone action window data. Specifically, step S201 has already obtained the root zone action interval, action intensity, and root zone coverage area for each growth stage. By comprehensively aggregating this information, the relationship between crop demand for nutrient supply during different growth stages can be obtained. Further, the demand window extraction process analyzes the trend of root zone action intensity over time and, combined with the changes in root zone coverage area in the soil profile, identifies the segments of root zone demand changes over time, thereby determining the initial segment, maintenance segment, and transition segment of root zone demand within each growth stage. It should be noted that, in the specific implementation process, the demand change relationship is obtained by analyzing the trend of the root zone action intensity versus time curve, and the demand segment is determined by identifying the interval boundaries of demand intensity changes. This calculation or processing method is well-known to those skilled in the art and will not be elaborated upon here. Through the above processing, the root region action window data is further organized into a time structure with demand interval attributes, so that the demand for nutrient supply in the root region at different growth stages can be expressed in segment form.

[0037] In some embodiments, the nutrient release field data is used to describe the release behavior of the compound fertilizer source in the soil environment. Specifically, the compound fertilizer source consists of controlled-release fertilizer and urea, wherein the controlled-release fertilizer exhibits continuous release behavior in the soil environment, and urea exhibits rapid release behavior under soil hydrolysis. By comprehensively analyzing the temporal variation relationship of the release behavior of the two fertilizer sources, the nutrient supply curve of the compound fertilizer source throughout the entire growth cycle can be obtained. Furthermore, by analyzing the changing trend of the nutrient supply curve on the time axis, the nutrient release process is divided into a release initiation phase, a continuous supply phase, and a release attenuation phase. The release initiation phase corresponds to the time interval during which nutrient release enters an effective supply state from a low level, the continuous supply phase corresponds to the time interval during which nutrient release maintains a stable supply level, and the release attenuation phase corresponds to the time interval during which nutrient supply gradually weakens. Preferably, the above phases are identified by comprehensively analyzing the changes in nutrient release intensity and cumulative release amount. This calculation or processing method is well known to those skilled in the art and will not be elaborated further here. Through the above processing, the nutrient release field data is organized into a time segment structure with release stage attributes.

[0038] In this embodiment, the release segment matching process establishes a stage correspondence between the root zone demand segment and the nutrient release segment. Specifically, by comparing the distribution of the root zone demand segment on the time axis with the distribution of the nutrient release segment on the time axis, segments where root zone demand and nutrient supply coexist within the same time interval are identified, thus forming a matching relationship between the demand segment and the release segment. Furthermore, within each growth period, by comparing the correspondence between the intensity of root zone demand and the intensity of nutrient supply, the supply contribution of controlled-release fertilizer and urea in the compound fertilizer source is distinguished within that growth period, thereby determining whether the nutrient supply structure within that growth period is dominated by rapid release or continuous release. It should be noted that the above-mentioned determination of supply contribution is obtained by analyzing the rate of change and cumulative release ratio of the nutrient release curve. This calculation or processing method is well known to those skilled in the art and will not be elaborated further here.

[0039] In some implementations, the continuous execution of the aforementioned demand window extraction and release segment matching processes enables the establishment of a correspondence between root zone demand and nutrient supply at each stage of the growth cycle. Specifically, within each growth period, the correspondence between the root zone demand segment and the nutrient release segment reflects not only the degree of overlap between the two in the temporal dimension but also the contribution of different fertilizer sources to the root zone demand during that stage. The resulting root-fertilizer time-series coupling data includes growth stage information, demand-supply correspondence, and fertilizer source contribution structure information. Furthermore, the root-fertilizer time-series coupling data provides temporal constraints for subsequent steps analyzing nutrient migration paths in the soil profile based on water migration field data, enabling the nutrient migration process to correspond with the crop root zone demand stages. This establishes a stage-based expression of the root-fertilizer relationship for the deduction of deep fertilization locations in the root zone.

[0040] Please see the appendix Figure 2 As shown, attached Figure 2 This illustrates the temporal coupling relationship between the root zone action window and the nutrient release behavior of compound fertilizer sources within the crop growth cycle, used to help explain the implementation process of the segmental coupling treatment in step S202. (Appendix) Figure 2 The horizontal axis represents the crop growth time process, the left vertical axis represents the intensity of nutrient absorption in the root zone, and the right vertical axis represents the intensity of nutrient release from the compound fertilizer source. The root zone intensity curve reflects the changing trend of nutrient absorption capacity in the root zone at different growth stages, while the nutrient release intensity curve reflects the phased release behavior of the compound fertilizer source in the soil environment. In this embodiment, as the crop growth progresses, the root zone expands and root activity gradually increases, causing the root zone's nutrient absorption capacity to show a trend of low to high and then gradually declining over time, thus forming the initial demand segment, the continuous demand segment, and the demand transfer segment of the root zone. Simultaneously, urea in the compound fertilizer source exhibits rapid release behavior in the initial application stage, while controlled-release fertilizer exhibits continuous release behavior over a longer period, thus forming the release onset segment, the continuous supply segment, and the release decay segment on the time axis. Through the analysis of the attached... Figure 2 By comparing the distribution of the two curves on the time axis, overlapping segments where root zone demand and nutrient supply coexist within the same time interval can be identified. These overlapping segments represent the time-series coupling segments formed by the root zone demand segment and the nutrient release segment. Therefore, a stage-specific correspondence between root zone demand and nutrient supply can be established at different growth stages, and root-fertilizer time-series coupling data can be generated accordingly. (See attached...) Figure 2 The temporal coupling relationship shown establishes a phased correspondence between the root zone action window and the nutrient release field data in the time dimension, thereby providing a temporal constraint for the analysis of nutrient migration behavior in the soil profile based on water migration field data in subsequent steps, and providing a phased expression basis for the deduction of deep fertilization location matched in the root zone.

[0041] S3, perform water and fertilizer carrying capacity mapping processing on the root-fertilizer time-series coupling data and the water migration field data to determine the water migration path in the soil profile based on the water migration field data, and combine the transfer relationship between nutrient release and water migration represented by the root-fertilizer time-series coupling data to form nutrient reachability domain data that represents the reachable range of nutrients in the soil profile. Furthermore, this step includes the following sub-steps: S301, perform migration path analysis processing on the water migration field data to obtain water carrying capacity path data. The migration path analysis processing involves using the water migration field data to identify the infiltration, lateral expansion, and retention sections of water along the depth and lateral directions in the soil profile, and determining the water transfer path and migration boundary in the profile structure based on the identification results, so as to form water carrying capacity path data that characterizes the water carrying capacity direction and its migration range. In some implementations, the migration path analysis process includes migration segment identification and path boundary extraction. The migration segment identification process, based on water migration field data, divides the soil profile into intervals representing the infiltration, lateral migration, and retention behaviors caused by the temporal changes in water distribution, forming a set of water migration segments with directional and persistent interval attributes. The path boundary extraction process, based on the set of water migration segments, analyzes the connectivity, extension direction, and boundary envelope of each segment in the soil profile, forming water carrying capacity path data corresponding to different migration behaviors. It should be noted that this step builds upon the water migration field data construction results in step S1. In this step, the water migration field data is no longer used solely as dynamic water distribution information but is transformed into path structure data with directional constraints, segment boundaries, and spatial envelope attributes to characterize the carrying capacity and transfer relationships of water in the soil profile.

[0042] In this embodiment, the migration segment identification process revolves around the soil moisture movement relationship established in step S1. Specifically, step S1 has already characterized the migration state of water in the soil profile by analyzing the relationship between soil moisture content and time and depth, the relationship between hydraulic conductivity and matrix potential. This step further extracts the migration components of water in the depth direction and the lateral direction, and uses these two migration components to characterize the local migration state of water in the soil profile. Furthermore, based on the differences in the direction and intensity of water migration at various spatial locations, the water migration state is segmented for identification: when the migration component in the depth direction is dominant and the lateral migration component is at a low level, the corresponding area is identified as an infiltration segment in the corresponding time period; when the migration component in the lateral direction is dominant and the migration component in the depth direction is at a low level, the corresponding area is identified as a lateral expansion segment in the corresponding time period; when the overall water migration intensity is at a low level and the moisture content remains high, the corresponding area is identified as a retention segment in the corresponding time period. Therefore, the water migration field data is decomposed into a set of migration segments with directional categories, spatial locations, and temporal duration attributes. The processing methods for flux calculation, direction determination, threshold setting, and segment identification are well known to those skilled in the art and will not be elaborated upon here.

[0043] In some implementations, the migration segment identification process does not only determine the moisture state at a single moment, but also organizes the moisture migration behavior into intervals based on temporal continuity. Specifically, moisture migration states that maintain the same migration direction and are located in adjacent spatial positions within adjacent time periods are grouped into the same migration segment; areas where the migration direction changes, the migration intensity shows a significant inflection point, or the moisture content distribution shows a stable stagnation state are identified as new migration segments. In this way, the moisture migration state in the soil profile is expressed as multiple continuous migration segments, each corresponding to different water transfer behaviors within the profile, thus providing a segment basis for subsequent path analysis.

[0044] In this embodiment, the path boundary extraction process is based on a set of migration segments and is used to organize discrete segments into continuous paths. Specifically, for each migration segment, its spatial extent, duration interval, and direction category in the soil profile are recorded. For spatially adjacent and temporally continuous migration segments, a transmission connection relationship is determined between them, and multiple migration segments are connected into the same water transfer link. Further, the spatial coverage areas of each migration segment are merged along the transfer link, and the outer boundary range of the transfer link in the soil profile is extracted to form the corresponding migration boundary. At the same time, the migration direction of each migration segment in the transfer link is comprehensively analyzed to determine the average extension direction of the transfer link, so that the path structure has both boundary and direction attributes. It should be noted that the processing methods involving spatial adjacency determination, temporal continuity determination, boundary envelope extraction, and direction synthesis are well known to those skilled in the art and will not be elaborated further here.

[0045] In some implementations, the water carrying capacity path data is organized as a set of path units. Specifically, each water carrying capacity path corresponds to a set of connected migration segments and simultaneously includes the spatial boundary of the area covered by the path, the carrying capacity direction of the path in the soil profile, and the time interval during which the path persists. Through this representation, the water carrying capacity path data no longer remains a local description of the water migration state, but forms a holistic path-based expression of the water carrying capacity relationship within the soil profile. Furthermore, the water carrying capacity path data can characterize where water forms downward transmission, where it forms lateral expansion, and where it forms local stagnation, thereby determining the effective carrying capacity range of water in the profile. This path-based expression provides a spatial constraint basis for subsequent processing based on the water transmission range.

[0046] In this embodiment, the implementation path for forming water carrying capacity path data using Richards equation discretization, flux field reconstruction, segment division, path connection, boundary extraction, and direction determination methods all fall within the scope of this step. It should be noted that the processing methods involving discretization, grid organization, adjacency calculation, and boundary determination are well-known to those skilled in the art and will not be elaborated upon here. Through the continuous organization of the migration segment identification and path boundary extraction processes, step S301 transforms the water migration field data obtained in step S1 into water carrying capacity path data with directionality, boundaries, and interval continuity. This gives the water transfer behavior within the soil profile a path-based expression structure and provides a spatial description basis for the subsequent expansion around the nutrient migration range.

[0047] S302, perform migration mapping processing on the water carrying path data and the root-fertilizer time-series coupling data to obtain nutrient reachability domain data. The migration mapping processing determines the migration direction of nutrients in the soil profile based on the water carrying path data, and uses the root-fertilizer time-series coupling data to determine the nutrient release segments in each growth period. Then, the nutrient release segments are propagated and mapped along the water carrying path to determine the spatial distribution range that nutrients can reach in the corresponding growth period, thereby forming nutrient reachability domain data.

[0048] In some implementations, the migration mapping process includes path-oriented carrying capacity processing and segment propagation domain generation processing. The path-oriented carrying capacity processing, based on water carrying capacity path data, analyzes the directional, boundary, and duration attributes of each water carrying capacity path within the soil profile to form a path constraint structure upon which nutrient propagation depends. The segment propagation domain generation processing, based on root-fertilizer temporal coupling data, calls upon nutrient release segments and corresponding coupling relationships within each growth stage, and performs spatial propagation mapping of nutrient release along the path constraint structure to form an expression of nutrient reachability domains within the corresponding growth stage. It should be noted that this step incorporates both water carrying capacity path data and root-fertilizer temporal coupling data. The former provides the migration boundaries and carrying directions of nutrients in the soil profile, while the latter provides the segmental structure and level of action of nutrient release within different growth stages. This ensures that the nutrient propagation process is simultaneously constrained by both the path structure and the time-series structure.

[0049] In this embodiment, the water carrying path data generated in step S301 is denoted as... ,in Let represent the set of migration segments included in the p-th water carrying capacity path. This indicates the migration boundary of the path in the soil profile. This represents the carrying direction vector of the path. The effective duration of the path is represented by , and u represents the number of carrying paths. In some embodiments, the path orientation carrying process constructs a path indication function by spatially organizing the carrying path direction vector and the path boundary. ,in Represents the spatial coordinates in the soil profile. This indicates the spatial position of the p-th carrying path at time t. The validity of the water carrying capacity is identified. Furthermore, a comprehensive carrying capacity direction function is constructed based on the direction vectors of each water carrying capacity path. ,in Indicates the spatial position at time t The migration direction field, formed by all water-carrying paths, is used to describe the main propagation direction of nutrients under the influence of water migration. Thus, the path-oriented carrying capacity processing transforms water-carrying path data into a continuous path direction field expression, thereby providing a spatial constraint basis for nutrient propagation calculations.

[0050] In some implementations, the root fertilizer timing coupling data formed in step S202 is denoted as ,in Indicates the i-th reproductive period. This represents the mating strength during the i-th reproductive period. This indicates the type of nutrient release segment corresponding to the given time period, and m represents the number of growth periods. In this embodiment, the segment propagation domain generation process determines the nutrient release segments within each growth period based on the root-fertilizer time-series coupling data and constructs the corresponding segment source function. ,in This represents the intensity of nutrient release sources during the i-th reproductive period. Indication and release segment type The corresponding segment action function is used to characterize the temporal action state of nutrients in the effective segment, continuous supply segment, and decay segment.

[0051] In this embodiment, the directional constraint function formed by the path orientation bearing processing Based on this, a propagation mapping is performed on the source function of the aforementioned segment, and a nutrient propagation field is constructed. ,in This indicates the spatial location of the soil profile during the i-th reproductive period. Nutrient concentration field or nutrient intensity function at location and time t. This represents the nutrient attenuation coefficient. Through this propagation relationship, the nutrient release zone propagates along the water-carrying path, and is subject to attenuation and path boundary constraints during propagation.

[0052] In some implementations, a nutrient reachability domain determination function is defined to determine the spatial distribution range that nutrients can reach during a corresponding reproductive period. ,in This represents the reachability threshold during the i-th reproductive period. Further, the nutrient reachability domain during the i-th reproductive period is denoted as... ,in This represents a soil profile region. Therefore, the nutrient transport results are transformed from a continuous transport field representation to a regional representation with spatial boundaries, thus forming nutrient reachability domain data.

[0053] In some implementations, the migration mapping process is not limited to the aforementioned expression of the propagation relationship. Implementation paths that use directional field propagation, path projection, region expansion, integral determination, and region boundary extraction to form nutrient reachability domain data are all within the scope of this step. It should be noted that processing methods involving discrete solutions of partial differential relationships, region boundary closure, time-period integration, area measurement, and spatial domain extraction are well-known to those skilled in the art and will not be elaborated upon here. Through the continuous organization of the path-oriented carrying capacity processing and the segment propagation domain generation processing, step S302 transforms the water carrying capacity path data formed in step S301 and the root-fertilizer temporal coupling data formed in step S202 into nutrient reachability domain data with time-period attributes, spatial range attributes, and level of action attributes. This allows the nutrient propagation results in the soil profile to obtain a spatial domain expression and provides a regional description basis for subsequent adaptation and discrimination around the root zone absorption range.

[0054] S4, perform absorption domain discrimination processing on the nutrient reachability domain data and the staged root zone nutrient absorption threshold data, so as to determine the range of root zone effect on nutrients in different growth stages based on the staged root zone nutrient absorption threshold data, and combine the nutrient reachability domain data to perform adaptation discrimination on the nutrient reachable area, forming root zone absorption adaptation data that characterizes the correspondence between the nutrient reachable area and the root zone absorption range; Furthermore, this step includes the following sub-steps: S401, the absorption segment analysis processing is performed on the staged root zone nutrient absorption threshold data to obtain absorption discrimination interval data. The absorption segment analysis processing is to use the staged root zone nutrient absorption threshold data to divide the root zone nutrient action distance, response range and absorption limit of the crop in different growth stages, and to determine the discrimination interval of the root zone nutrient absorption range in each growth stage based on the segment division, so as to form absorption discrimination interval data. In some embodiments, the absorption segment analysis process includes absorption response segment division and absorption limit interval extraction. The absorption response segment division process, based on the staged root zone nutrient absorption threshold data, divides the root zone into segments based on the changes in the distance of nutrient absorption and the changes in absorption activity during different growth stages, forming a staged segment structure of the root zone's absorption capacity at each growth stage. The absorption limit interval extraction process, based on the staged segment structure, analyzes the effective absorption range and absorption attenuation boundary of the root zone during each growth stage to determine the discrimination interval for the root zone's nutrient absorption range at the corresponding growth stage.

[0055] In this embodiment, the root zone's nutrient absorption capacity is determined by two factors: the root zone's absorption activity level corresponding to the growth stage, and the spatial distance between the nutrient application site and the root zone center. As the nutrient distance from the root zone center increases, the absorption capacity gradually decreases with increasing distance, exhibiting an exponential decay trend. When the growth stage changes, changes in root zone metabolic activity lead to phased changes in the overall absorption capacity level. Therefore, the phased root zone nutrient absorption threshold data can characterize the distribution of absorption capacity in the root zone at different growth stages. The methods for function fitting, parameter determination, and absorption activity curve construction are well-known to those skilled in the art and will not be elaborated upon here.

[0056] In some embodiments, the absorption response segmentation process is based on the relationship of absorption capacity change, dividing the stage structure formed by the change of root zone absorption capacity over time throughout the entire reproductive cycle. Specifically, according to the trend of root zone absorption activity changing with reproductive time, the entire reproductive cycle is divided into several consecutive reproductive periods, so that the root zone absorption activity in each reproductive period is in a relatively stable range, and a corresponding stage segment structure is formed accordingly. Through this process, the relationship of root zone absorption capacity change throughout the entire reproductive cycle is transformed into multiple absorption stage segments with time interval attributes, so that the root zone's nutrient absorption capacity at different reproductive stages can be expressed in the form of a stage structure. The processing methods for reproductive stage division, absorption activity curve analysis, and stage segment determination are well known to those skilled in the art and will not be elaborated on here.

[0057] In this embodiment, after completing the absorption response segmentation process, the effective range of the root region's effect on nutrients under each growth stage is determined through absorption boundary interval extraction. Specifically, within each growth stage, based on the relationship between the root region's absorption capacity and distance attenuation, a corresponding absorption capacity judgment threshold is set. When the absorption capacity of nutrients at a certain spatial location is higher than the threshold, the nutrients at that location are considered to be within the effective absorption range of the root region; when the absorption capacity is lower than the threshold, the nutrients at that location are considered to be outside the absorption attenuation region. This allows the determination of the maximum effective distance of the root region's effect on nutrients within the corresponding growth stage, and uses this maximum distance as the boundary of the root region's absorption range. Furthermore, by statistically processing the absorption boundaries corresponding to different time points within the growth stage, the average absorption boundary distance within that stage is obtained to form a more stable expression of the absorption range. The implementation methods of threshold setting, distance calculation, and statistical processing are well known to those skilled in the art and will not be elaborated upon here.

[0058] In this embodiment, through the continuous organization of the above-mentioned absorption response segmentation processing and absorption boundary interval extraction processing, a staged root region nutrient absorption discrimination structure is constructed, forming an absorption discrimination interval data set. This absorption discrimination interval data set records the time intervals corresponding to each reproductive stage and the effective range of nutrient absorption in the root region within those time intervals, thus forming a data structure with reproductive stage attributes and absorption distance attributes. Specifically, the absorption discrimination interval data can characterize the spatial distance range within which the root region can effectively absorb nutrients in different reproductive stages, and provide a discrimination basis for subsequent steps to discriminate the absorption adaptation of nutrient spatial distribution range.

[0059] In some implementations, the absorption segment analysis process is not limited to the above-described absorption function expression. Implementation paths that use absorption capacity curve fitting, stage threshold determination, interval boundary extraction, and time segment statistics to form absorption discrimination interval data all fall within the scope of this step. It should be noted that the processing methods involving function fitting, interval division, threshold determination, and integral calculation are well-known to those skilled in the art and will not be elaborated upon here. Through the continuous organization of the absorption response segment division process and the absorption boundary interval extraction process, this step transforms the staged root region nutrient absorption threshold data into absorption discrimination interval data with reproductive period attributes and absorption distance attributes, thereby providing a root region-side interval discrimination structure for absorption adaptation discrimination around the nutrient reachability domain in subsequent steps.

[0060] S402, the absorption discrimination interval data and the nutrient reachability domain data are subjected to segment adaptation discrimination processing to obtain root region absorption adaptation data. The segment adaptation discrimination processing is based on the absorption discrimination interval data to determine the range of root region effect on nutrients in each growth period, and the nutrient reachability domain data is used to determine the spatial reach area of ​​nutrients in the corresponding period. Then, the spatial reach area and the range of effect are segmented to identify the nutrient effect area that can fall within the root region absorption range and form root region absorption adaptation data.

[0061] In some implementations, the segment adaptation discrimination process includes absorption range mapping processing and region adaptation discrimination processing. The absorption range mapping processing maps the nutrient absorption distance intervals of the root region within each growth stage to corresponding spatial absorption regions based on absorption discrimination interval data, forming an absorption discrimination domain for the root region in different growth stages. The region adaptation discrimination processing, based on the absorption discrimination domain, performs region matching discrimination between the nutrient reachability region data (represented by nutrient reachability region data) and the absorption discrimination domain to identify nutrient action regions entering the root region's absorption range and form root region absorption adaptation data. It should be noted that the absorption range mapping processing transforms the root region's absorption capacity from a distance interval expression to a spatial region expression, while the region adaptation discrimination processing determines whether the nutrient propagation result can be effectively absorbed by the root region by comparing the overlap between the nutrient reachability region and the spatial absorption region, thereby forming a root region absorption adaptation structure with spatial adaptation significance.

[0062] In this embodiment, the absorption discrimination interval data is briefly referred to as... , Let m represent the i-th reproductive period, and m represent the number of reproductive periods. This represents the average reach of nutrient absorption in the root zone during this growth period. The nutrient reachability data is briefly denoted as... ,in This represents the spatial distribution area that nutrients can reach during the i-th reproductive period.

[0063] In some implementations, the absorption range mapping process spatially expresses the relationship between the spatial location of the root region and the absorption distance interval, forming the root region absorption discrimination domain within the i-th reproductive period. Specifically, let the root region coverage area within the i-th reproductive period be... (Should Consistent with the root zone action window data formed above, this is used to represent the spatial coverage area of ​​the root zone in the soil profile during the corresponding growth period, which will not be elaborated further here. The minimum distance from a spatial location (x,z) to the root zone coverage area is defined as the distance between the spatial location (x,z) and any spatial point within the root zone coverage area. Minimum value of Euclidean distance Furthermore, according to the aforementioned absorption distance limit... Construct the root region absorption discriminant domain during the i-th reproductive period. ,in This represents a soil profile region. Through the above processing, the absorption distance range is transformed into an absorption area with a clearly defined spatial boundary.

[0064] In this embodiment, the region adaptation discrimination processing is based on the absorption discrimination domain. With nutrient reach The spatial relationships between them are determined. Specifically, the overlapping regions within the i-th reproductive period are defined as... ,in This represents the spatial intersection between the nutrient dispersal region and the root absorption region. Further, this is achieved by calculating the overlap ratio of these regions. ,in Indicates the area of ​​the overlapping region. This indicates the area of ​​the nutrient-accessible region. This represents the proportion of nutrient-accessible areas that enter the root zone's absorption range. When this proportion exceeds a preset threshold, it is determined that the nutrient propagation area and the root zone's absorption range form an effective fit during that growth period. The calculation of the area, the determination of spatial intersection, and the threshold determination methods are well-known to those skilled in the art and will not be elaborated upon here.

[0065] In this embodiment, through the continuous organization of the absorption range mapping processing and the region adaptation discrimination processing, the absorption discrimination interval data and the nutrient reachability domain data are jointly analyzed to form root region absorption adaptation data. ,in This represents the nutrient absorption adaptation region during the i-th reproductive period. This indicates the degree of adaptation within the corresponding time period. The root zone absorption adaptation data is used to describe the spatial matching relationship between the nutrient propagation area and the root zone absorption range, thereby providing regional criteria for the reverse inference of fertilization points based on fertilization pose constraints in subsequent steps. It should be noted that the segment adaptation discrimination processing is not limited to the aforementioned expression form of joint discrimination of regional overlap and level of action. Implementation paths that use distance discrimination, regional overlap discrimination, stage threshold comparison, integral statistics, and regional boundary extraction to form root zone absorption adaptation data all fall within the scope of this step. Furthermore, the processing methods involving regional intersection calculation, minimum distance calculation, time period integration, area measurement calculation, and threshold determination are well-known to those skilled in the art and will not be elaborated upon here.

[0066] S5, perform fertilization pose back-calculation processing on the root zone absorption adaptation data and the deep fertilization pose constraint data to determine the location distribution of nutrient action areas based on the root zone absorption adaptation data, and combine the depth range of fertilization points in the soil profile and the lateral spacing relative to the root zone boundary defined by the deep fertilization pose constraint data to form a set of root zone matching fertilization points for pre-sowing layout.

[0067] Furthermore, this step includes the following sub-steps: S501, Perform pose boundary analysis processing on the deep fertilization pose constraint data to obtain fertilization pose boundary data. The pose boundary analysis processing uses the deep fertilization pose constraint data to determine the allowable depth range of the fertilization point in the soil profile and the lateral interval limit relative to the root zone boundary, and determines the pose boundary of the fertilization point in space based on the allowable depth range and the lateral interval limit to form fertilization pose boundary data. In some implementations, the pose boundary resolution process includes depth interval resolution and lateral avoidance boundary generation. The depth interval resolution process is used to determine the vertical distribution range of fertilization points in the soil profile, and the lateral avoidance boundary generation process is used to determine the lateral safety interval of fertilization points relative to the root zone boundary. This ensures that the spatial distribution of fertilization points is simultaneously constrained by both vertical depth constraints and lateral avoidance constraints, thereby forming the spatial pose boundary structure of the fertilization points.

[0068] In this embodiment, the depth interval analysis process is based on effective fertilization depth window data. Specifically, the effective fertilization depth window data is used to define the allowable depth interval of fertilization points in the soil profile. By analyzing the depth interval, the vertical distribution range of fertilization points in the soil profile can be determined. Furthermore, by extracting the allowable fertilization depth interval, the depth constraint range of fertilization points in the soil profile can be obtained, ensuring that the vertical distribution of fertilization points is always within the allowable depth window. This avoids nutrient loss due to excessively shallow fertilization points or nutrient absorption difficulties by the root zone due to excessively deep fertilization points.

[0069] In some implementations, after analyzing the fertilization depth range, a lateral avoidance boundary generation process is used to further determine the spatial constraints of fertilization points in the lateral direction. Specifically, the lateral avoidance boundary generation process analyzes the minimum lateral interval between fertilization points and the root zone boundary based on root zone lateral avoidance distance data. The root zone lateral avoidance distance is used to limit the safe interval between fertilization points and the root zone boundary. This interval constraint prevents fertilization points from directly entering the internal area of ​​the root zone boundary, thereby reducing the risk of interference with the root structure during fertilization. Furthermore, in the soil profile space, using the root zone boundary as a reference, by spatially expanding the outer side of the root zone boundary according to the lateral avoidance distance, a laterally permissible area for fertilization points can be formed, ensuring a safe interval between the fertilization points and the root zone boundary in the lateral direction.

[0070] In this embodiment, by spatially combining the vertical deployment range formed by the depth interval analysis process and the lateral allowable region formed by the lateral avoidance boundary generation process, the overall spatial pose boundary of the fertilization point in the soil profile can be determined. Specifically, fertilization points are only allowed to be deployed within a spatial region that simultaneously satisfies both the depth interval constraint and the lateral avoidance constraint, thereby forming the allowable deployment range of fertilization points in the soil profile. Through this spatial constraint structure, the fertilization points can be deployed in space while satisfying both the fertilization requirements in the soil profile depth direction and the safety interval requirements in the root zone boundary direction, thus forming complete fertilization pose boundary data.

[0071] In some implementations, the pose boundary resolution process is not limited to the aforementioned implementation methods of depth interval resolution and lateral avoidance boundary generation. Processing paths that determine the spatial layout range of fertilization points through spatial boundary resolution, distance constraint calculation, and region range extraction are all within the scope of this step. It should be noted that the processing methods involving spatial distance calculation, region boundary extraction, and spatial range determination are well-known to those skilled in the art and will not be elaborated upon here. Through the continuous organization of the aforementioned depth interval resolution and lateral avoidance boundary generation processes, the deep fertilization pose constraint data is transformed into fertilization pose boundary data with spatial boundary attributes, thereby providing spatial pose constraint conditions for the subsequent back-calculation of fertilization points based on the root region absorption adaptation area.

[0072] S502, perform pose backtracking and extrapolation processing on the fertilization pose boundary data and the root zone absorption adaptation data to obtain a set of root zone matching fertilization points. The pose backtracking and extrapolation processing is based on the root zone absorption adaptation data to determine the location distribution of nutrient action areas at each growth stage, and performs reverse backtracking of fertilization positions along the nutrient release direction. The backtracking results are then limited to the spatial range represented by the fertilization pose boundary data to form a set of root zone matching fertilization points for pre-sowing deployment.

[0073] In some implementations, the pose backtracking and extrapolation process includes adaptation region backtracking and pose boundary constraint processing. The adaptation region backtracking process, based on root zone absorption adaptation data, analyzes the location of spatial adaptation regions capable of effective nutrient absorption during each growth stage, and performs reverse extrapolation of fertilization source locations based on the direction of nutrient propagation in the soil profile to determine the range of fertilization source locations that can form the adaptation region. The pose boundary constraint processing, based on the fertilization source location range obtained through reverse extrapolation, combines the fertilization pose boundary data to spatially constrain and filter the fertilization source location range, limiting the fertilization source locations to the allowable fertilization depth range and lateral avoidance range, thereby forming a set of fertilization points that meet the pose constraints. Through the continuous organization of the above two processing steps, the determination process of fertilization points can satisfy both the root zone nutrient absorption adaptation relationship and the spatial pose constraints required for fertilization deployment.

[0074] In this embodiment, the root zone absorption adaptation data is used to characterize the spatial adaptation regions formed between the nutrient propagation area and the root zone absorption range during different growth stages. Specifically, the location distribution of corresponding adaptation regions can be obtained for each growth stage, and this region represents the range of locations where nutrients reach during propagation and can be effectively absorbed by the root zone. Furthermore, by analyzing the location distribution of the adaptation regions, the main functional areas of nutrients in the soil profile can be obtained.

[0075] In some implementations, to determine the fertilization source location that generates the suitable area, it is necessary to perform a reverse deduction of the nutrient propagation process. Specifically, in step S302, the propagation direction of nutrients in the soil profile has been determined. This propagation direction is determined by the water migration path and represents the main direction of nutrient diffusion outward from the fertilization point in the soil. Therefore, given the known location of the nutrient action area, the fertilization source location can be traced back along the reverse direction of the propagation. By performing a reverse back-tracing analysis on multiple spatial locations within the suitable area, a set of fertilization source locations that may form the nutrient action area can be obtained, thus forming candidate fertilization source areas. Through the above processing, the determination of the fertilization location no longer relies on empirical settings, but is obtained based on the reverse deduction of nutrient propagation results.

[0076] In this embodiment, after obtaining the candidate fertilization source region, it is also necessary to perform fertilization pose constraint screening. Specifically, in S501, the spatial pose boundary of the fertilization point in the soil profile has been determined. This pose boundary is jointly defined by the allowable fertilization depth range and the lateral avoidance distance of the root zone. Further, the candidate fertilization source region and the fertilization pose boundary region are subjected to spatial constraint screening, retaining only the spatial locations that simultaneously meet the fertilization depth condition and the lateral avoidance condition, thereby obtaining the fertilization location region that meets the fertilization pose constraint conditions. Through the above processing, the fertilization location obtained by back-derived calculation can meet the spatial safety requirements of actual fertilization deployment.

[0077] In some implementations, after determining the fertilization location area, several representative spatial locations within the area can be selected as fertilization points. Specifically, the fertilization locations can be discretely selected based on the principle of uniform spatial distribution, the coverage of the suitable area, or the operating spacing of the fertilization equipment, to form multiple fertilization point coordinates. The methods for determining the number of fertilization points, uniform spatial distribution, and calculating the operating spacing are well known to those skilled in the art and will not be elaborated upon here.

[0078] In this embodiment, through the continuous organization of the aforementioned adaptation region backtracking process and pose boundary constraint process, a set of fertilization points matching the root zone can be obtained. This set of fertilization points represents fertilization locations in the soil profile space that can form a root zone absorption adaptation region and satisfy the fertilization pose constraints. Furthermore, this set of fertilization points can serve as a reference location for deep fertilization before sowing, thereby establishing a spatial matching relationship between the fertilization location and the nutrient absorption range of the crop root zone, increasing the probability of effective nutrient absorption within the root zone, and reducing ineffective nutrient diffusion in the soil. Through the aforementioned pose backtracking and deduction process, the determination of deep fertilization points can be reversed based on the root zone absorption adaptation relationship, thus forming a fertilization layout scheme that matches the needs of the crop root zone.

[0079] In some implementations, the reverse tracing distance is determined based on the nutrient propagation range determined by the nutrient reachability data formed in step S302. Furthermore, after obtaining candidate fertilization source areas, these areas can be discretized according to the principle of uniform spatial distribution or the operating spacing of fertilization equipment, thereby forming multiple fertilization points. For candidate fertilization source areas formed at different growth stages, they can be comprehensively processed through spatial overlay, and locations covering suitable areas at multiple growth stages can be selected as fertilization points.

[0080] Please see the appendix Figure 3 As shown, attached Figure 3This diagram illustrates the spatial structure for the reverse inference of fertilization points in the root zone, serving to help explain the process. The diagram uses a soil profile as a background to illustrate the distribution area of ​​the crop root zone, the root zone absorption adaptation area, the boundary of the fertilization position, and the spatial relationships between fertilization points. In this embodiment, the irregular area represents the distribution range of the crop root zone, and the strip-shaped area formed around the outer edge of the root zone distribution area represents the root zone absorption adaptation area, characterizing the spatial location where nutrients can enter the root zone absorption range after propagation. The arrows pointing from the outside to the adaptation area indicate the direction of nutrient propagation in the soil, which is determined by the water migration path. Furthermore, given the known nutrient propagation direction and the location of the root zone absorption adaptation area, the fertilization source location can be retrospectively deduced along the reverse direction of the propagation direction to obtain the range of candidate fertilization source locations forming the adaptation area. The dashed arrows extending outward from the adaptation area in the diagram represent the reverse retrospective path of the fertilization source location. In this embodiment, the candidate fertilization source regions obtained through back-engineering also need to undergo spatial constraint screening with the fertilization pose boundary. The regular boundary region in the figure represents the fertilization pose boundary, which is jointly defined by the allowable fertilization depth range and the lateral avoidance distance of the root zone. By performing constraint screening on the candidate regions, only spatial locations within the fertilization pose boundary are retained, thereby obtaining fertilization location regions that meet the fertilization deployment conditions. Further, several discrete spatial locations are selected as fertilization points within the fertilization location region. The set of fertilization points is indicated by dots in the figure, representing deep fertilization locations that can be deployed in the soil profile. Through the above back-engineering and pose constraint screening process, the fertilization points can both form root zone absorption adaptation areas and meet the spatial constraints of fertilization deployment, thereby achieving a spatial matching relationship between the fertilization location and the crop root zone absorption range.

[0081] Based on the description of the above embodiments of the deep fertilization location optimization method for root zone matching, this application also discloses a deep fertilization location optimization system for root zone matching. This system can be a computer program (including program code) that runs the aforementioned deep fertilization location optimization method for root zone matching. Please see the appendix. Figure 4 As shown, the deep fertilization location optimization system for root zone matching can operate the following units: The acquisition unit 110 is used to acquire data to be processed, including root zone evolution field data, nutrient release field data, water migration field data, staged root zone nutrient absorption threshold data, and deep fertilization posture constraint data. The time registration unit 120 is used to perform time field registration processing on the root zone evolution field data and the nutrient release field data to establish a time coupling relationship between root zone action and nutrient supply, thereby obtaining root fertilizer time coupling data. The water and fertilizer mapping unit 130 is used to perform water and fertilizer carrying capacity mapping processing on the root fertilizer time-series coupling data and the water migration field data, so as to determine the water migration path in the soil profile according to the water migration field data, and combine the transfer relationship between nutrient release and water migration represented by the root fertilizer time-series coupling data to form nutrient reachability domain data that represents the reachable range of nutrients in the soil profile. The absorption discrimination unit 140 is used to perform absorption domain discrimination processing on the nutrient reachability domain data and the staged root zone nutrient absorption threshold data, so as to determine the range of root zone effect on nutrients in different growth stages according to the staged root zone nutrient absorption threshold data, and combine the nutrient reachability domain data to perform adaptation discrimination on the nutrient reachable area, forming root zone absorption adaptation data that characterizes the correspondence between the nutrient reachable area and the root zone absorption range; The pose inversion unit 150 is used to perform fertilization pose inversion processing on the root zone absorption adaptation data and the deep fertilization pose constraint data, so as to determine the location distribution of the nutrient action area according to the root zone absorption adaptation data, and combine the depth range of the fertilization points in the soil profile and the lateral spacing relative to the root zone boundary defined by the deep fertilization pose constraint data to form a set of root zone matching fertilization points for pre-sowing layout.

[0082] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for optimizing deep fertilization location for root zone matching, characterized in that, The method includes the following steps: S1, acquire data to be processed, the data to be processed includes root zone evolution field data, nutrient release field data, water migration field data, staged root zone nutrient absorption threshold data, and deep fertilization posture constraint data. S2, perform time-series field registration processing on the root zone evolution field data and the nutrient release field data to establish a time-series coupling relationship between root zone action and nutrient supply, thereby obtaining root fertilizer time-series coupling data. S3, perform water and fertilizer carrying capacity mapping processing on the root-fertilizer time-series coupling data and the water migration field data to determine the water migration path in the soil profile based on the water migration field data, and combine the transfer relationship between nutrient release and water migration represented by the root-fertilizer time-series coupling data to form nutrient reachability domain data that represents the reachable range of nutrients in the soil profile. S4, perform absorption domain discrimination processing on the nutrient reachability domain data and the staged root zone nutrient absorption threshold data, so as to determine the range of root zone effect on nutrients in different growth stages based on the staged root zone nutrient absorption threshold data, and combine the nutrient reachability domain data to perform adaptation discrimination on the nutrient reachable area, forming root zone absorption adaptation data that characterizes the correspondence between the nutrient reachable area and the root zone absorption range; S5, perform fertilization pose back-calculation processing on the root zone absorption adaptation data and the deep fertilization pose constraint data to determine the location distribution of nutrient action areas based on the root zone absorption adaptation data, and combine the depth range of fertilization points in the soil profile and the lateral spacing relative to the root zone boundary defined by the deep fertilization pose constraint data to form a set of root zone matching fertilization points for pre-sowing layout.

2. The method for optimizing deep fertilization location for root zone matching according to claim 1, characterized in that, Step S2 includes the following sub-steps: S201, the root zone evolution field data is subjected to root zone time window decomposition processing to obtain root zone action time window data. The root zone time window decomposition processing is to use the root zone evolution field data to segmentally analyze the root zone expansion range, root zone stratum migration status and root zone boundary evolution relationship of crops in different growth stages, and determine the root zone action start interval, continuous coverage interval and boundary transfer interval in each growth period to form root zone action time window data that characterizes the stage action range of the root zone. S202, the root zone action window data and the nutrient release field data are subjected to segmental coupling processing to obtain root fertilizer time-series coupling data. The segmental coupling processing is based on the root zone action window data to determine the time-series demand of the root zone for nutrient action segments under each growth period, and uses the nutrient release field data to determine the nutrient release effective segment, continuous supply segment and attenuation segment under the corresponding time conditions after the application of controlled-release fertilizer and urea. Then, the time-series demand is segmentally matched with the nutrient release segments to form a coupling relationship between the root zone action window and the nutrient release window under each growth period.

3. The method for optimizing deep fertilization location for root zone matching according to claim 1, characterized in that, Step S3 includes the following sub-steps: S301, perform migration path analysis processing on the water migration field data to obtain water carrying capacity path data. The migration path analysis processing involves using the water migration field data to identify the infiltration, lateral expansion, and retention sections of water along the depth and lateral directions in the soil profile, and determining the water transfer path and migration boundary in the profile structure based on the identification results, so as to form water carrying capacity path data that characterizes the water carrying capacity direction and its migration range. S302, perform migration mapping processing on the water carrying path data and the root-fertilizer time-series coupling data to obtain nutrient reachability domain data. The migration mapping processing determines the migration direction of nutrients in the soil profile based on the water carrying path data, and uses the root-fertilizer time-series coupling data to determine the nutrient release segments in each growth period. Then, the nutrient release segments are propagated and mapped along the water carrying path to determine the spatial distribution range that nutrients can reach in the corresponding growth period, thereby forming nutrient reachability domain data.

4. A method for optimizing deep fertilization location for root zone matching according to any one of claims 1-3, characterized in that, Step S4 includes the following sub-steps: S401, the absorption segment analysis processing is performed on the staged root zone nutrient absorption threshold data to obtain absorption discrimination interval data. The absorption segment analysis processing is to use the staged root zone nutrient absorption threshold data to divide the root zone nutrient action distance, response range and absorption limit of the crop in different growth stages, and to determine the discrimination interval of the root zone nutrient absorption range in each growth stage based on the segment division, so as to form absorption discrimination interval data. S402, the absorption discrimination interval data and the nutrient reachability domain data are subjected to segment adaptation discrimination processing to obtain root region absorption adaptation data. The segment adaptation discrimination processing is based on the absorption discrimination interval data to determine the range of root region effect on nutrients in each growth period, and the nutrient reachability domain data is used to determine the spatial reach area of ​​nutrients in the corresponding period. Then, the spatial reach area and the range of effect are segmented to identify the nutrient effect area that can fall within the root region absorption range and form root region absorption adaptation data.

5. The method for optimizing deep fertilization location for root zone matching according to claim 4, characterized in that, Step S5 includes the following sub-steps: S501, Perform pose boundary analysis processing on the deep fertilization pose constraint data to obtain fertilization pose boundary data. The pose boundary analysis processing uses the deep fertilization pose constraint data to determine the allowable depth range of the fertilization point in the soil profile and the lateral interval limit relative to the root zone boundary, and determines the pose boundary of the fertilization point in space based on the allowable depth range and the lateral interval limit to form fertilization pose boundary data. S502, perform pose backtracking and extrapolation processing on the fertilization pose boundary data and the root zone absorption adaptation data to obtain a set of root zone matching fertilization points. The pose backtracking and extrapolation processing is based on the root zone absorption adaptation data to determine the location distribution of nutrient action areas at each growth stage, and performs reverse backtracking of fertilization positions along the nutrient release direction. The backtracking results are then limited to the spatial range represented by the fertilization pose boundary data to form a set of root zone matching fertilization points for pre-sowing deployment.

6. The method for optimizing deep fertilization location for root zone matching according to claim 2, characterized in that, The root region time window decomposition process includes reproductive process segmentation and boundary time window extraction; the segment coupling process includes demand time window extraction and release segment coupling.

7. The method for optimizing deep fertilization location for root zone matching according to claim 3, characterized in that, The migration path parsing process includes migration segment identification and path boundary extraction; the migration mapping process includes path orientation carrying and segment propagation domain generation.

8. The method for optimizing deep fertilization location for root zone matching according to claim 4, characterized in that, The absorption segment parsing process includes absorption response segment division processing and absorption boundary interval extraction processing; the segment adaptation discrimination processing includes absorption range mapping processing and region adaptation discrimination processing.

9. The method for optimizing deep fertilization location for root zone matching according to claim 5, characterized in that, The pose boundary resolution process includes depth interval resolution and lateral avoidance boundary generation; the pose backtracking and deduction process includes adaptation region backtracking and pose boundary constraint processing.

10. A deep fertilization location optimization system for root zone matching, characterized in that, The system includes: The acquisition unit is used to acquire data to be processed, which includes root zone evolution field data, nutrient release field data, water migration field data, staged root zone nutrient absorption threshold data, and deep fertilization pose constraint data. The temporal registration unit is used to perform temporal field registration processing on the root zone evolution field data and the nutrient release field data to establish a temporal coupling relationship between root zone action and nutrient supply, thereby obtaining root fertilizer temporal coupling data. The water and fertilizer mapping unit is used to perform water and fertilizer carrying capacity mapping processing on the root-fertilizer time-series coupling data and the water migration field data, so as to determine the water migration path in the soil profile according to the water migration field data, and combine the transfer relationship between nutrient release and water migration represented by the root-fertilizer time-series coupling data to form nutrient reachability domain data that represents the reachable range of nutrients in the soil profile. An absorption discrimination unit is used to perform absorption domain discrimination processing on the nutrient reachability domain data and the staged root zone nutrient absorption threshold data, so as to determine the range of root zone effect on nutrients in different growth stages based on the staged root zone nutrient absorption threshold data, and to perform adaptation discrimination on the nutrient reachability region in combination with the nutrient reachability domain data, forming root zone absorption adaptation data that characterizes the correspondence between the nutrient reachability region and the root zone absorption range. The pose inversion unit is used to perform fertilization pose inversion processing on the root zone absorption adaptation data and the deep fertilization pose constraint data, so as to determine the location distribution of the nutrient action area according to the root zone absorption adaptation data, and combine the depth range of the fertilization points in the soil profile and the lateral spacing relative to the root zone boundary defined by the deep fertilization pose constraint data to form a set of root zone matching fertilization points for pre-sowing layout.

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