A method for analyzing corn lodging resistance synergism based on conservation tillage conditions

Abstract

The present application relates to the field of agricultural data processing, and more particularly to a corn anti-lodging synergistic analysis method based on conservation tillage conditions, which comprises: collecting corn plant growth form parameters, multi-depth soil layer root biomass data, and environmental and macro stress parameters to obtain anti-lodging risk assessment basic data; obtaining root shallow layer distribution gradient attenuation factors through vertical distribution gradient attenuation analysis, and combining with growth form parameters to obtain form imbalance factors; obtaining a comprehensive anti-lodging risk index based on the basic anti-lodging risk index and the form imbalance factor, and then generating an anti-lodging synergistic analysis result and a field management strategy, so as to identify hidden lodging risks caused by root shallow layer aggregation and aboveground and underground form imbalance, and improve the accuracy of corn anti-lodging evaluation under conservation tillage conditions.

Description

Technical Field

[0001] This invention relates to the field of agricultural data processing technology, and in particular to a method for analyzing the lodging resistance and efficiency enhancement of maize based on conservation tillage conditions. Background Technology

[0002] Conservation tillage techniques, through methods such as no-till, reduced tillage, and surface straw mulching, improve the water and heat conditions and nutrient retention capacity of topsoil in farmland, and have been widely applied in maize production for moisture conservation, fertilization, and yield enhancement. To assess the lodging resistance of maize plants under conservation tillage conditions, current techniques typically collect agronomic parameters such as plant height, stem morphology, canopy area, and underground root biomass. These parameters are then combined with static analysis models or crop growth models to calculate the wind load on the plants and the anchoring capacity of the underground root system, thereby obtaining a lodging risk assessment result.

[0003] Current methods for assessing lodging risk typically sum the root biomass at different soil depths when processing root data, using the total root biomass to characterize the plant's underground anchoring capacity. However, in the early stages of conservation tillage or when deep soils are relatively compacted, maize roots tend to concentrate in the shallow soil due to the enrichment of surface water and nutrients, while the development of medium- and deep roots is insufficient. At this time, the shallow root biomass is large, and the total root biomass may still reach a high level, but this root distribution structure is unlikely to form a stable deep anchoring effect. Assessing solely based on total root biomass makes it difficult to distinguish between shallow, aggregated roots and uniformly rooted roots, easily underestimating the risk of lodging under strong winds and rainfall.

[0004] Furthermore, the risk of lodging in maize plants is influenced by both the anchoring status of the underground root system and the stress morphology of the aboveground plant. When shallow root aggregation is combined with morphological characteristics such as excessive plant height and thinner internodes in the base and lower middle parts, the lever arm generated by wind load increases, while the bending resistance of the stem and the underground anchoring capacity are relatively insufficient, easily leading to an imbalance between aboveground and underground morphology. Existing assessment methods lack sufficient analysis of the correlation between the spatial distribution characteristics of the root system and the stress characteristics of the aboveground stem, making it difficult to accurately identify the aforementioned combined lodging risks. Therefore, how to combine the distribution changes of root biomass in the vertical soil profile and the aboveground morphological stress characteristics of maize plants to improve the accuracy of lodging risk assessment and lodging resistance enhancement analysis under conservation tillage conditions has become an urgent problem to be solved in this field. Summary of the Invention

[0005] In view of this, the present invention aims to propose a method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions, in order to solve the problem that existing methods rely solely on total root biomass for lodging resistance assessment, making it difficult to identify shallow root aggregation and imbalance between aboveground and belowground morphology, resulting in inaccurate lodging risk assessment.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0007] A method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions, the method comprising:

[0008] Step S1: Obtain basic data for lodging risk assessment by collecting growth morphology parameters of maize plants in the target field, root biomass data of soil layers at multiple depths, and environmental and macroscopic stress parameters;

[0009] Step S2: Obtain the shallow root distribution gradient decay factor by performing vertical distribution gradient decay analysis on root biomass data from multiple soil depths.

[0010] Step S3: Obtain the morphological imbalance factor by performing a coupled analysis of the shallow root distribution gradient decay factor and growth morphology parameters above and below ground.

[0011] Step S4: Calculate the basic lodging risk index based on the basic data of the lodging risk assessment, and obtain the comprehensive lodging risk index by jointly correcting the basic lodging risk index and the morphological imbalance factor.

[0012] Step S5: By conducting risk classification analysis on the comprehensive lodging resistance risk index, obtain the results of lodging resistance enhancement analysis and field management strategies under conservation tillage conditions.

[0013] Furthermore, the process involves collecting growth morphology parameters of maize plants in the target field, root biomass data from multiple soil depths, and environmental and macroscopic stress parameters to obtain basic data for lodging risk assessment, including:

[0014] During the corn grain-filling to maturity stage, multiple target corn plants were selected from the target fields where conservation tillage was implemented. Three-dimensional morphological data of the aboveground plants of each target corn plant were collected using a three-dimensional laser scanner. The total plant height of the target corn plant was extracted from the aboveground plant three-dimensional morphological data. The aboveground stem of the target corn plant was divided into multiple internodes from the base of the ground to the ear node. The height coordinates of each internode from the ground surface were obtained. The stem cross-sectional diameter of each internode was measured using a vernier caliper. The total plant height, the height coordinates of each internode, and the stem cross-sectional diameter were used as the growth morphological parameters of the target corn plant.

[0015] The underground root system samples of each target maize plant were obtained by excavation sampling. The underground soil profile corresponding to the target maize plant was continuously divided into multiple soil depth layers from the surface downwards, and the center depth of each soil depth layer was obtained. The underground root system samples in each soil depth layer were separated into layers, and the root biomass density corresponding to each soil depth layer was obtained by root image analysis software. The center depth and root biomass density of each soil depth layer were used as the multi-depth soil layer root biomass data of the target maize plant.

[0016] The canopy image of the windward side of the target maize plant was acquired using a canopy image analysis system, and the canopy projection area of ​​the target maize plant on the windward side was obtained based on the canopy image. The effective soil volume of the root system of the target maize plant was obtained based on the horizontal distribution range and vertical distribution depth of the underground root system. The extreme gust wind speed and standard wind pressure drag coefficient were obtained for lodging risk assessment. The canopy projection area, effective soil volume of the root system, extreme gust wind speed, and standard wind pressure drag coefficient were used as environmental and macroscopic stress parameters of the target maize plant.

[0017] The growth morphology parameters of the target maize plant, root biomass data from multiple soil depths, and environmental and macroscopic stress parameters are correlated and stored to obtain basic data for lodging risk assessment of the target maize plant.

[0018] Furthermore, the step of obtaining the shallow root distribution gradient attenuation factor by performing vertical distribution gradient attenuation analysis on root biomass data from multiple soil depths includes:

[0019] By performing differential and unidirectional attenuation screening on root biomass data from multiple soil layers, vertical attenuation gradient data of root biomass was obtained.

[0020] By performing depth-weighted cumulative processing on the vertical decay gradient data of root biomass and the corresponding soil depth data, the shallow root distribution gradient decay factor is obtained.

[0021] Furthermore, the step of obtaining root biomass vertical decay gradient data by performing differential and unidirectional decay screening on root biomass data from multiple soil depths includes:

[0022] For any target maize plant, the center depth and root biomass density of each soil layer corresponding to the target maize plant are extracted from the root biomass data of the multi-depth soil layers, and the soil layers are arranged in order of increasing center depth.

[0023] For any two adjacent soil depth layers, the soil depth layer with the smaller center depth is designated as the target shallow soil depth layer, and the soil depth layer with the larger center depth is designated as the target deep soil depth layer. The difference between the root biomass density of the target shallow soil depth layer and the root biomass density of the target deep soil depth layer is used as an assessment of the difference in root biomass density between adjacent soil layers, and the difference between the center depth of the target deep soil depth layer and the center depth of the target shallow soil depth layer is used as an assessment of the depth interval between adjacent soil layers.

[0024] The difference in root biomass density between adjacent soil layers is used as the numerator, and the depth interval between adjacent soil layers is used as the denominator. The resulting fraction is used as the initial vertical gradient of root biomass between adjacent soil depth layers. When the initial vertical gradient of root biomass is greater than a constant 0, it is used as the vertical decay gradient of root biomass between adjacent soil depth layers. When the initial vertical gradient of root biomass is less than or equal to a constant 0, the vertical decay gradient of root biomass between adjacent soil depth layers is set to a constant 0. The vertical decay gradient of root biomass between all adjacent soil depth layers corresponding to the target maize plant is used as the vertical decay gradient data of root biomass.

[0025] Furthermore, the step of obtaining the root shallow layer distribution gradient decay factor by performing depth-weighted cumulative processing on the root biomass vertical decay gradient data and the corresponding soil depth data includes:

[0026] For any target maize plant, obtain the vertical decay gradient data of root biomass corresponding to the target maize plant, and extract the center depth of the target shallow soil layer corresponding to each root biomass vertical decay gradient from the root biomass data of multiple soil layers, and use the center depth of the target shallow soil layer as the corresponding soil depth data.

[0027] For any target root biomass vertical decay gradient, the soil depth data corresponding to the target root biomass vertical decay gradient is added to constant 1, and the square root operation is performed on the corresponding addition result. Constant 1 is used as the numerator, the square root operation result is used as the denominator, and the corresponding fraction is used as the soil depth decay weight corresponding to the target root biomass vertical decay gradient.

[0028] The result of multiplying the target root biomass vertical decay gradient by the corresponding soil depth decay weight is used as the target weighted root biomass vertical decay gradient; the result of summing all the target weighted root biomass vertical decay gradients corresponding to the target maize plant is used as the root shallow distribution gradient decay factor of the target maize plant.

[0029] Furthermore, the method of obtaining morphological imbalance factors by coupling the aboveground and underground morphological parameters of the shallow root distribution gradient decay factor and growth morphology parameters includes:

[0030] By performing height difference processing on the total plant height of maize and the height coordinates of each internode in the growth morphology parameters, the wind load arm characteristic data corresponding to each internode is obtained.

[0031] By coupling the wind load arm characteristic data of each internode with the stalk cross-sectional diameter for bending strength processing, the structural fragility of the above-ground stalk can be obtained.

[0032] The morphological imbalance factor was obtained by nonlinearly coupling the shallow root distribution gradient attenuation factor with the structural vulnerability of the aboveground stem.

[0033] Furthermore, the method involves performing height difference processing on the total plant height of the maize plant and the height coordinates of each internode in the growth morphology parameters to obtain the wind load arm characteristic data corresponding to each internode, including:

[0034] For any target maize plant, extract the total plant height and the height coordinates of each internode from the base of the ground to the ear node from the growth morphology parameters.

[0035] For any target internode of the target maize plant, the difference between the total height of the target maize plant and the height coordinate corresponding to the target internode is taken as the wind load arm characteristic value corresponding to the target internode; the wind load arm characteristic values ​​corresponding to all internodes of the target maize plant are taken as the wind load arm characteristic data of the target maize plant.

[0036] Furthermore, the method of obtaining the aboveground stem structural vulnerability by coupling the wind load arm characteristic data corresponding to each internode with the stem cross-sectional diameter for bending strength processing includes:

[0037] For any target maize plant, obtain the wind load arm characteristic data corresponding to the target maize plant, and extract the stem cross section diameter corresponding to each internode of the target maize plant from the growth morphology parameters.

[0038] For any target internode of the target maize plant, the diameter of the stem section corresponding to the target internode is calculated to the cube, and the result of the cube calculation is added to a minimum constant to prevent the denominator from being zero to obtain the bending section strength assessment corresponding to the target internode; the wind load arm characteristic value corresponding to the target internode is used as the numerator, the bending section strength assessment corresponding to the target internode is used as the denominator, and the resulting fraction is used as the internode structural fragility assessment corresponding to the target internode.

[0039] The internode structural vulnerability assessments of all target internodes of the target maize plant are summed to obtain the cumulative internode structural vulnerability assessment of the target maize plant. The cumulative internode structural vulnerability assessment is added to the natural constant, and the corresponding sum is processed by natural logarithmic mapping. The corresponding natural logarithmic mapping result is used as the aboveground stem structural vulnerability of the target maize plant.

[0040] Furthermore, the method of obtaining the morphological imbalance factor by nonlinearly coupling the shallow root distribution gradient attenuation factor with the aboveground stem structural vulnerability includes:

[0041] For any target maize plant, obtain the root shallow distribution gradient decay factor and the aboveground stem structure vulnerability of the target maize plant.

[0042] The root shallow distribution gradient decay factor corresponding to the target maize plant is multiplied by the aboveground stem structural vulnerability, and the resulting product is used as the morphological imbalance factor of the target maize plant.

[0043] Furthermore, the basic lodging risk index is calculated based on the basic data of the lodging risk assessment, and a comprehensive lodging risk index is obtained by jointly correcting the basic lodging risk index and the morphological imbalance factor, including:

[0044] For any target maize plant, extract the canopy projection area, effective soil volume of roots, extreme gust wind speed, standard wind pressure drag coefficient, and root biomass density of each soil depth layer from the basic data of lodging risk assessment.

[0045] The extreme gust wind speed corresponding to the target corn plant is squared. The squared result is multiplied by the standard wind pressure drag coefficient and the canopy projection area in sequence. The product is used as the theoretical wind load level assessment of the target corn plant.

[0046] The root biomass density of the target maize plant is summed up across all soil depth layers to obtain the cumulative assessment of the root biomass density of the target maize plant. The gravitational acceleration constant, the cumulative assessment of the root biomass density of the target maize plant, and the effective soil volume of the root system are multiplied sequentially, and the corresponding product is used as the theoretical anti-overturning anchoring force assessment of the target maize plant.

[0047] The theoretical wind load level assessment of the target corn plant is used as the numerator, the theoretical overturning anchorage force assessment of the target corn plant is used as the denominator, and the corresponding fraction is used as the basic lodging risk index of the target corn plant.

[0048] Obtain the morphological imbalance factor corresponding to the target maize plant, multiply the basic lodging risk index of the target maize plant by the morphological imbalance factor, and use the resulting product as the comprehensive lodging risk index of the target maize plant.

[0049] Compared with the prior art, the present invention has the following advantages:

[0050] This invention discloses a method for analyzing the lodging resistance enhancement of maize under conservation tillage conditions. By extracting the vertical distribution gradient of root biomass at multiple soil depths and combining it with differentiated weighting of root attenuation characteristics at different soil depths, it can effectively distinguish between shallow aggregated roots and uniformly downward-growing roots when the total root biomass is similar. This avoids the masking effect of high shallow root biomass on insufficient anchoring capacity in the middle and deep layers, thereby improving the accuracy of identifying the underground anchoring status of maize under conservation tillage conditions. Furthermore, this invention couples the shallow root distribution characteristics with the aboveground stress patterns reflected by plant height, internode position, and stem diameter to comprehensively characterize the combined imbalance risk between insufficient underground anchoring and excessively long aboveground wind load arm and weak stem bending resistance. This allows for more accurate identification of potential root lodging or stem breakage risks under extreme wind and rain conditions, providing a reliable basis for formulating field management measures such as deep tillage, deep application of fertilizer and seeds, and chemical regulation, thus enhancing the scientific rigor and relevance of maize lodging resistance enhancement analysis under conservation tillage. Attached Figure Description

[0051] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0052] Figure 1 This is a flowchart illustrating a method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions, as described in an embodiment of the present invention. Detailed Implementation

[0053] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0054] See Figure 1 This is a flowchart of a method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions, as provided in Embodiment 1 of the present invention. Figure 1 As shown, a method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions may include:

[0055] Step S1: By collecting growth morphology parameters of maize plants in the target field, root biomass data of soil layers at multiple depths, and environmental and macroscopic stress parameters, basic data for lodging risk assessment are obtained.

[0056] First, during the grain-filling to maturity stage of maize, multiple target maize plants were selected from the target fields where conservation tillage was implemented. Three-dimensional morphological data of the aboveground plants of each target maize plant were collected using a 3D laser scanner, and the total plant height was extracted from this data. The aboveground stems of the target maize plants were then divided into multiple internodes from the base of the soil surface to the ear node. The height coordinates of each internode from the ground surface were obtained, and the stem cross-sectional diameter of each internode was measured using calipers. The total plant height, the height coordinates of each internode, and the stem cross-sectional diameter were used as the growth morphological parameters of the target maize plants.

[0057] The underground root system samples of each target maize plant were obtained using an excavation sampling method. The underground soil profile corresponding to the target maize plant was continuously divided into multiple soil depth layers from the surface downwards, and the center depth of each soil depth layer was obtained. The underground root system samples within each soil depth layer were stratified and separated, and the root biomass density corresponding to each soil depth layer was obtained using root image analysis software. The center depth and root biomass density of each soil depth layer were used as the multi-depth soil layer root biomass data of the target maize plant.

[0058] Canopy images of the windward side of the target maize plant were acquired using a canopy image analysis system, and the canopy projection area of ​​the target maize plant on the windward side was obtained from the canopy images. The effective soil volume of the root system of the target maize plant was obtained based on the horizontal distribution range and vertical depth of the underground root system. Extreme gust wind speeds and standard wind pressure drag coefficients were obtained for lodging risk assessment. The canopy projection area, effective soil volume of the root system, extreme gust wind speeds, and standard wind pressure drag coefficients were used as environmental and macroscopic stress parameters of the target maize plant.

[0059] The growth morphology parameters of the target maize plant, root biomass data from multiple soil depths, and environmental and macroscopic stress parameters are correlated and stored to obtain basic data for lodging risk assessment of the target maize plant.

[0060] Thus, the basic data for lodging risk assessment was obtained by collecting growth morphology parameters of maize plants in the target field, root biomass data of soil layers at multiple depths, and environmental and macroscopic stress parameters.

[0061] Step S2: By performing vertical distribution gradient attenuation analysis on root biomass data from multiple soil depths, the shallow root distribution gradient attenuation factor is obtained.

[0062] Under conservation tillage conditions, the actual underground anchoring capacity of maize plants depends not only on the total root biomass but also on the spatial distribution structure of biomass at different soil depths. During specific stages of no-till farming and straw mulching, the superior water and fertilizer conditions of the topsoil easily lead to a large accumulation of maize roots in the shallow soil, resulting in insufficient development of deeper roots. Existing lodging resistance assessment algorithms only measure anchoring strength by accumulating the total root biomass, failing to identify structural distortions in biomass distribution along depth. When roots accumulate in the shallow layer, their total biomass remains large, but at the boundary between the shallow and medium-deep soil layers, a spatial gradient of decreasing biomass density appears. This shallow, disc-shaped structure, lacking deep root support, is highly susceptible to uprooting and lodging under lateral wind loads. To address the problem of existing algorithms failing to identify shallow root distribution defects due to their reliance on absolute biomass values, this step extracts root biomass density data from multiple continuous soil depths and constructs a feature analysis mechanism capable of quantifying the rate of biomass density decay along the vertical profile. By identifying the spatial gradient characteristics of biomass density decreasing with increasing soil depth, and assigning different mathematical weights to the specific depths where the gradient decreases, the algorithm accurately reconstructs the actual impact of root distribution structure on the underground anchoring strength of plants from the bottom layer of data, providing a reliable basis for subsequent correction of lodging resistance assessment results.

[0063] In summary, this invention first obtains vertical decay gradient data of root biomass by performing differential analysis and unidirectional attenuation screening on root biomass data from multiple soil depths. Specifically, for any target maize plant, the center depth and root biomass density of each soil depth corresponding to the target maize plant are extracted from the multi-depth soil root biomass data, and the soil depths are arranged in ascending order of center depth. For any two adjacent soil depths, the soil depth with the smaller center depth is designated as the target shallow soil depth, and the soil depth with the larger center depth is designated as the target deep soil depth. The difference between the root biomass density of the target shallow soil depth and the target deep soil depth is used as an assessment of the difference in root biomass density between adjacent soil layers, and the difference between the center depth of the target deep soil depth and the center depth of the target shallow soil depth is used as an assessment of the depth interval between adjacent soil layers. The difference in root biomass density between adjacent soil layers is used as the numerator, and the depth interval between adjacent soil layers is used as the denominator. The resulting fraction is used as the initial vertical gradient of root biomass between adjacent soil depth layers. When the initial vertical gradient of root biomass is greater than a constant 0, it is used as the vertical decay gradient of root biomass between adjacent soil depth layers. When the initial vertical gradient of root biomass is less than or equal to a constant 0, the vertical decay gradient of root biomass between adjacent soil depth layers is set to a constant 0. The vertical decay gradients of root biomass in all adjacent soil depth layers corresponding to the target maize plant are used as the root biomass vertical decay gradient data.

[0064] After obtaining the root biomass vertical decay gradient data, the root shallow distribution gradient decay factor is obtained by performing depth-weighted accumulation processing on the root biomass vertical decay gradient data and the corresponding soil depth data. Specifically, for any target maize plant, the root biomass vertical decay gradient data corresponding to the target maize plant is obtained, and the center depth of the target shallow soil layer corresponding to each root biomass vertical decay gradient is extracted from the root biomass data of multiple soil depths. The center depth of the target shallow soil layer is used as the corresponding soil depth data. For any target root biomass vertical decay gradient, the soil depth data corresponding to the target root biomass vertical decay gradient is added to a constant 1, and the square root operation is performed on the result. The constant 1 is used as the numerator, the square root operation result is used as the denominator, and the resulting fraction is used as the soil depth decay weight corresponding to the target root biomass vertical decay gradient. The result of multiplying the target root biomass vertical decay gradient by the corresponding soil depth decay weight is used as the target weighted root biomass vertical decay gradient. The calculation result of summing the vertical attenuation gradients of all target weighted root biomass corresponding to the target maize plant is used as the root shallow distribution gradient attenuation factor of the target maize plant.

[0065] In one implementation, assume the first The root biomass density of the shallow soil depth layer is ;No. The root biomass density at each deep soil depth layer is ;No. The center depth of the target deep soil layer is: ;No. The center depth of each shallow soil layer is The total number of soil depth layers is The formula for calculating the shallow root gradient attenuation factor is:

[0066]

[0067] in, This represents the gradient decay factor in the shallow root system. Indicates the total number of soil depth layers; Represents the maximum value function; Indicates the first Root biomass density in the shallow soil depth layer; Indicates the first Root biomass density in the deepest soil layers; Indicates the first The center depth of the target deep soil layer; Indicates the first The center depth of the shallow soil layer.

[0068] It should be noted that in this embodiment, to ensure the accuracy of the algorithm processing matches real agronomic data, the total number of continuous soil depth layers is set to 5, with each layer having a thickness of 10 cm. This means acquiring root data within a depth range of 0 to 50 cm. The core anchoring function of maize roots is mainly concentrated in the 0 to 50 cm soil layer; roots beyond this depth primarily function as absorbers rather than mechanical supports. The root shallow distribution gradient decay factor formula constructed in this step aims to quantify and extract the shallow aggregation state of maize roots caused by specific tillage conditions. Whether a plant possesses the ability to resist overturning depends on whether its biomass can be evenly and deeply distributed in the middle and lower soil layers to form a stable mechanical lever, rather than merely accumulating on the surface. To map this physical structure in the calculation, the formula first calculates the rate of change of root biomass density between two adjacent upper and lower depth layers, i.e. The calculation results in this section characterize the vertical spatial distribution gradient of biomass. Subsequently, the formula uses a maximum value function to perform unidirectional numerical filtering on this gradient. When biomass density decreases with increasing depth, the gradient is positive, and the maximum value function retains it accurately; when there is a slight increase in biomass in the deep root system due to local soil conditions, the gradient is negative, and the maximum value function forces it to zero. This design eliminates computational interference caused by local fluctuations in deep data, allowing the algorithm to accurately pinpoint the spatial range where root biomass rapidly declines downwards. After extracting the positive biomass decline gradient, the formula introduces a weighted penalty term related to soil depth. The value of this weighting term decreases non-linearly with increasing soil depth. When the assessed object experiences shallow root aggregation due to the initial effects of conservation tillage, a significant and sharp drop in biomass occurs in the shallower soil layers. Smaller values ​​correspond to depth-weighted terms that approach their maximum value. The rapid loss of biomass in the shallow soil layer directly leads to the loss of deep anchoring force; therefore, this attenuation gradient caused by shallow enrichment is given a very high weight. Conversely, if the plant root system is healthy and deeply rooted, a significant decrease in biomass occurs in deeper soil layers. The large numerical value and the significant decrease in the weighted term value indicate that the reduction in deep, normally terminated root systems does not pose a significant threat to the overall lodging resistance of the plant, and its impact in the calculation is naturally attenuated. Finally, the formula sums up all the weighted gradient values ​​to obtain the final root shallow distribution gradient attenuation factor. The magnitude of the root shallow distribution gradient attenuation factor directly and independently reflects the severity of root shallow distribution defects, completely eliminating the dependence of existing assessment systems on the total root biomass value, and endowing the algorithm with the autonomous diagnostic ability to identify spatial distortions in the vertical distribution of biomass.

[0069] Thus, the vertical distribution gradient decay analysis of root biomass data from multiple soil depths was completed, and the shallow root distribution gradient decay factor was obtained.

[0070] Step S3: Obtain the morphological imbalance factor by performing a coupled analysis of the shallow root distribution gradient decay factor and growth morphology parameters.

[0071] After determining the degree of lack of deep underground anchoring force of the plant through the shallow root distribution gradient attenuation factor in step S2, it is necessary to further evaluate the response characteristics of the aboveground structural morphology of the plant to wind load moment. The actual lodging resistance of maize plants is the result of the combined effect of the underground root anchoring strength and the stress state of the aboveground stem. Under conservation tillage conditions, superior surface water and fertilizer conditions usually promote the growth of taller and more vigorous maize plants (stems and canopy). Existing algorithms generally extract root or stem data in isolation when performing static evaluation, without establishing a comprehensive calculation of aboveground morphological parameters and abnormal underground root distribution. When there is a shallow distribution defect in the underground root system, the interface between the plant base and the soil becomes fragile, and the ability to resist wind load bending moment is substantially reduced. At this time, if the height of the aboveground stem is too large and the internodes in the middle and lower parts are too thin, the lever arm generated under wind load is longer, the bending cross-sectional strength of the stem itself is weaker, and the risk of stem breakage or overall overturning of the plant will increase nonlinearly. Existing algorithms, with their single data extraction model, cannot fully quantify the combined imbalance risk resulting from insufficient underground anchoring and a high center of gravity and weak structure above ground. To accurately assess the actual lodging resistance under complex tillage conditions, this step, based on the extraction of underground root attenuation factors, obtains the height and cross-sectional geometric parameters of the above-ground stems, constructing a coupling factor for above-ground and underground morphological imbalances to quantify the superimposed imbalance effect of above-ground mechanical morphological defects and the risk of shallow underground roots.

[0072] In summary, this invention first obtains wind load arm characteristic data for each internode by performing height difference processing on the total plant height and the height coordinates of each internode in the growth morphology parameters of maize. Specifically, for any target maize plant, the total plant height and the height coordinates of each internode, divided from the base of the ground to the ear node, are extracted from the growth morphology parameters. For any target internode of the target maize plant, the difference between the total plant height and the height coordinates of the target internode is used as the wind load arm characteristic value of the target internode. The wind load arm characteristic values ​​corresponding to all internodes of the target maize plant are used as the wind load arm characteristic data of the target maize plant.

[0073] After obtaining the wind load arm characteristic data for each internode, the bending strength of the aboveground stem structure is obtained by coupling the wind load arm characteristic data of each internode with the stem cross-sectional diameter. Specifically, for any target maize plant, the wind load arm characteristic data of the target maize plant is obtained, and the stem cross-sectional diameter of each internode of the target maize plant is extracted from the growth morphology parameters. For any target internode of the target maize plant, the stem cross-sectional diameter of the target internode is cubed, and the result of the cubed operation is added to a minimum constant to prevent the denominator from being zero, thus obtaining the bending section strength assessment of the target internode. The wind load arm characteristic value of the target internode is used as the numerator, and the bending section strength assessment of the target internode is used as the denominator, and the resulting fraction is used as the internode structural vulnerability assessment of the target internode. The internode structural vulnerability assessments of all target internodes of the target maize plant are added together to obtain the cumulative internode structural vulnerability assessment of the target maize plant. The cumulative assessment of internode structural vulnerability is added to the natural constant, and the result is processed by natural logarithmic mapping. The corresponding natural logarithmic mapping result is used as the aboveground stem structural vulnerability of the target maize plant.

[0074] After obtaining the aboveground stem structural vulnerability, the morphological imbalance factor is obtained by nonlinearly coupling the root shallow distribution gradient attenuation factor with the aboveground stem structural vulnerability. Specifically, for any target maize plant, the root shallow distribution gradient attenuation factor and the aboveground stem structural vulnerability of the target maize plant are obtained. The root shallow distribution gradient attenuation factor and the aboveground stem structural vulnerability of the target maize plant are multiplied, and the resulting product is used as the morphological imbalance factor of the target maize plant.

[0075] In one implementation, assume the first The height coordinates of each segment are ;No. The diameter of the stem cross section between each node is The total height of the corn plants is The total number of internodes from the base of the corn stalk to the ear is: The formula for calculating the morphological imbalance factor is:

[0076]

[0077] in, Indicates the morphological imbalance factor; This represents the gradient decay factor in the shallow root system. This represents the logarithmic function with the natural constant e as the base. This indicates the total number of internodes from the base of the corn stalk to the ear. Indicates the total height of the corn plant; Indicates the first The height coordinates of each segment; Indicates the first The diameter of the stem cross section between each node; This represents a minimal constant used to prevent the denominator from being zero, and is set in the embodiments of the present invention. .

[0078] It should be noted that the formula constructed in this step aims to establish a composite assessment of the geometric structure of the above-ground stem and the distribution characteristics of the underground root system. Whether corn plants lodging under severe weather conditions such as strong winds and heavy rains depends on whether the bending moment generated by wind pressure on the above-ground parts exceeds the ultimate bending strength of the stem itself and the ultimate anchoring bearing capacity of the underground root system. To quantify the mechanical response characteristics of the above-ground stem in the calculation, the formula first extracts the difference between the total plant height and the height of each internode. This difference characterizes the wind load acting on the canopy during the [first] phase. The formula then extracts the cross-sectional diameter of the segment and calculates its cube. In structural mechanics, the section modulus of a circular cross-section is proportional to the cube of its diameter; therefore, the denominator... The intrinsic strength characteristics of the corresponding internodes resisting bending deformation were accurately characterized. Through... The calculation formula yields an index of stress concentration in a single internode under wind load. Next, the formula applies this index to all internodes from the base to the ear position. The fragility of the aboveground stem is quantified by summing the values ​​of each internode. This summation increases significantly with increasing plant height and decreasing internode diameter, objectively reflecting the high risk of lodging in top-heavy plants with weak stems. After quantifying the aboveground morphological characteristics, the formula smooths the summation using the natural logarithm function to prevent numerical overflow due to isolated anomalies in extremely small internodes, ensuring the stability of the evaluation data. Finally, the formula multiplies the processed aboveground structural fragility index with the root shallow distribution gradient attenuation factor obtained in step S2. This combination establishes a clear evaluation guideline: only when the plant simultaneously possesses two conditions—significant shallow root distribution (i.e., high root shallow distribution gradient attenuation factor value, lack of deep anchorage) and aboveground stem morphology susceptible to wind load moment (i.e., high summation value)—is the morphological imbalance factor considered. Only then will it output high numerical results characterizing a high risk of imbalance. Through the nonlinear combination of the above steps, this scheme fills the technical blind spot of existing algorithms that do not consider the above-ground stress pattern and the abnormal phase of the underground structure, providing a basis for subsequent numerical correction of the foundation collapse resistance score.

[0079] Thus, the morphological imbalance factor was obtained by coupling the aboveground and underground morphological analysis of the root shallow distribution gradient decay factor and growth morphological parameters.

[0080] Step S4: Calculate the basic lodging risk index based on the basic data of the lodging risk assessment, and obtain the comprehensive lodging risk index by jointly correcting the basic lodging risk index and the morphological imbalance factor.

[0081] In existing maize lodging resistance assessments, basic lodging risk indicators are typically calculated based on the total root biomass of the entire plant and a basic wind speed model. While this method reflects the plant's overall resilience under ideal conditions, it neglects to consider distortions in root spatial distribution, particularly shallow root aggregation and imbalances in aboveground and belowground morphological moments. When assessing maize under conservation tillage conditions, the highly developed shallow root system leads to an inflated total biomass input to the model, resulting in an incorrectly lower lodging risk score. This step directly applies the aboveground and belowground morphological imbalance factors, derived from the aforementioned deductions, to the existing basic lodging risk indicators. This transforms the static indicators, which previously masked spatial distribution deficiencies, into a comprehensive dynamic indicator incorporating the first-order root depth difference and the aboveground bending section modulus. When the target plant exhibits significant shallow root aggregation due to conservation tillage and the above-ground stem structure is unable to withstand the corresponding wind load moment, this correction method can directly and significantly amplify its basic lodging risk value at the numerical level. This accurately corrects the blind spots in the evaluation of existing algorithms under complex agronomic conditions, providing precise data support for the final lodging resistance enhancement analysis.

[0082] Specifically, for any target maize plant, the canopy projection area, effective root soil volume, extreme gust wind speed, standard wind pressure drag coefficient, and root biomass density at each soil depth are extracted from the basic data for lodging risk assessment. The extreme gust wind speed corresponding to the target maize plant is squared, and the squared result is multiplied sequentially by the standard wind pressure drag coefficient and the canopy projection area. The resulting product is used as the theoretical wind load assessment for the target maize plant. The root biomass densities at all soil depths corresponding to the target maize plant are summed to obtain the cumulative root biomass density assessment. The gravitational acceleration constant, the cumulative root biomass density assessment, and the effective root soil volume are multiplied sequentially, and the resulting product is used as the theoretical overturning anchoring force assessment for the target maize plant. The theoretical wind load assessment is used as the numerator, and the theoretical overturning anchoring force assessment is used as the denominator. The resulting fraction is used as the basic lodging risk index for the target maize plant. Obtain the morphological imbalance factor corresponding to the target maize plant, multiply the basic lodging risk index of the target maize plant by the morphological imbalance factor, and use the resulting product as the comprehensive lodging risk index of the target maize plant.

[0083] It should be noted that when the target plant has a deep root system and thick above-ground stems, the morphological imbalance factor is relatively small, and the comprehensive lodging risk index calculated by the formula remains within a low, safe range, indicating that the plant has good lodging resistance. However, when the target plant appears to have sufficient total biomass, but actually exhibits severe shallow root aggregation (i.e., top-heavy), and the above-ground stems cannot provide sufficient bending strength, the morphological imbalance factor will be significantly greater than 1. The formula artificially amplifies the plant's baseline risk value. This correction mechanism eliminates the blind spot in the original assessment model for plants with large total biomass but lacking deep roots, ensuring that the final comprehensive lodging risk index accurately reflects the true probability of lodging under extreme weather conditions.

[0084] Thus, the calculation of the basic lodging risk index based on the basic data of the lodging risk assessment was completed, and the comprehensive lodging risk index was obtained by jointly correcting the basic lodging risk index and the morphological imbalance factor.

[0085] Step S5 involves conducting risk grading analysis on the comprehensive lodging resistance risk index to obtain the results of lodging resistance enhancement analysis and field management strategies under conservation tillage conditions.

[0086] After obtaining the comprehensive lodging resistance risk index for each target maize plant, a lodging resistance risk grading threshold is preset based on the historical lodging records of the target field, local extreme wind and rain conditions, and the lodging resistance characteristics of maize varieties. The comprehensive lodging resistance risk index of each target maize plant is then compared with the lodging resistance risk grading threshold to determine the lodging resistance risk level corresponding to each target maize plant. Further statistical analysis is performed on the proportion and spatial distribution of target maize plants with different risk levels within the target field to obtain the overall lodging resistance risk status of the target field under conservation tillage conditions, and corresponding lodging resistance enhancement analysis results are generated.

[0087] When the comprehensive lodging resistance risk index of the target maize plant is at a low risk level, it indicates that the root system of the target maize plant has a good degree of extension to the middle and deep layers, the above-ground stem morphology and the underground root anchoring capacity are relatively coordinated, the current conservation tillage measures have not caused obvious risk of shallow root aggregation, output the analysis results of good lodging resistance enhancement effect, and generate field management strategies to maintain the current conservation tillage model.

[0088] When the comprehensive lodging risk index of the target corn plant is at the medium risk level, it indicates that the target corn plant has shown a certain degree of shallow root aggregation or weak above-ground stem structure. The lodging risk warning is output, and field management strategies such as appropriate deep loosening, adjusting the depth of seed and fertilizer application, or strengthening water and fertilizer management are generated in combination with the soil compaction, fertilization method and plant growth status of the target field.

[0089] When the comprehensive lodging risk index of the target maize plant is at a high risk level, it indicates that the target maize plant has obvious defects in shallow root distribution and an imbalance between above-ground and below-ground morphology. A high lodging risk warning is issued, and field management strategies are generated, including deep plowing and deep tillage in the next planting season to break up the plow pan, deep application of seed fertilizer to guide the roots to grow deeper, and chemical regulation in the current growing season to shorten the length of basal internodes and increase the stem thickness. This provides a decision-making basis for evaluating the lodging resistance and efficiency of maize under conservation tillage conditions and for disaster prevention and mitigation management.

[0090] Thus, the analysis results of lodging resistance enhancement under conservation tillage conditions and field management strategies were obtained by conducting risk classification analysis on the comprehensive lodging resistance risk index.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions, characterized in that, The method includes: Step S1: Obtain basic data for lodging risk assessment by collecting growth morphology parameters of maize plants in the target field, root biomass data of soil layers at multiple depths, and environmental and macroscopic stress parameters; Step S2: Obtain the shallow root distribution gradient decay factor by performing vertical distribution gradient decay analysis on root biomass data from multiple soil depths. Step S3: Obtain the morphological imbalance factor by performing a coupled analysis of the shallow root distribution gradient decay factor and growth morphology parameters above and below ground. Step S4: Calculate the basic lodging risk index based on the basic data of the lodging risk assessment, and obtain the comprehensive lodging risk index by jointly correcting the basic lodging risk index and the morphological imbalance factor. Step S5: By conducting risk classification analysis on the comprehensive lodging resistance risk index, obtain the results of lodging resistance enhancement analysis and field management strategies under conservation tillage conditions.

2. The method for analyzing the lodging resistance and efficiency enhancement of maize based on conservation tillage conditions according to claim 1, characterized in that, The process involves collecting growth morphology parameters of maize plants in the target field, root biomass data from multiple soil depths, and environmental and macroscopic stress parameters to obtain basic data for lodging resistance risk assessment, including: During the corn grain-filling to maturity stage, multiple target corn plants were selected from the target fields where conservation tillage was implemented. Three-dimensional morphological data of the aboveground plants of each target corn plant were collected using a three-dimensional laser scanner. The total plant height of the target corn plant was extracted from the aboveground plant three-dimensional morphological data. The aboveground stem of the target corn plant was divided into multiple internodes from the base of the ground to the ear node. The height coordinates of each internode from the ground surface were obtained. The stem cross-sectional diameter of each internode was measured using a vernier caliper. The total plant height, the height coordinates of each internode, and the stem cross-sectional diameter were used as the growth morphological parameters of the target corn plant. The underground root system samples of each target maize plant were obtained by excavation sampling. The underground soil profile corresponding to the target maize plant was continuously divided into multiple soil depth layers from the surface downwards, and the center depth of each soil depth layer was obtained. The underground root system samples in each soil depth layer were separated into layers, and the root biomass density corresponding to each soil depth layer was obtained by root image analysis software. The center depth and root biomass density of each soil depth layer were used as the multi-depth soil layer root biomass data of the target maize plant. The canopy image of the windward side of the target maize plant was acquired using a canopy image analysis system, and the canopy projection area of ​​the target maize plant on the windward side was obtained based on the canopy image. The effective soil volume of the root system of the target maize plant was obtained based on the horizontal distribution range and vertical distribution depth of the underground root system. The extreme gust wind speed and standard wind pressure drag coefficient were obtained for lodging risk assessment. The canopy projection area, effective soil volume of the root system, extreme gust wind speed, and standard wind pressure drag coefficient were used as environmental and macroscopic stress parameters of the target maize plant. The growth morphology parameters of the target maize plant, root biomass data from multiple soil depths, and environmental and macroscopic stress parameters are correlated and stored to obtain basic data for lodging risk assessment of the target maize plant.

3. The method for analyzing the lodging resistance and efficiency enhancement of maize based on conservation tillage conditions according to claim 1, characterized in that, The method involves performing vertical distribution gradient decay analysis on root biomass data from multiple soil depths to obtain the shallow root distribution gradient decay factor, including: By performing differential and unidirectional attenuation screening on root biomass data from multiple soil layers, vertical attenuation gradient data of root biomass was obtained. By performing depth-weighted cumulative processing on the vertical decay gradient data of root biomass and the corresponding soil depth data, the shallow root distribution gradient decay factor is obtained.

4. The method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions according to claim 3, characterized in that, The process involves performing differential and unidirectional attenuation screening on root biomass data from multiple soil depths to obtain vertical attenuation gradient data of root biomass, including: For any target maize plant, the center depth and root biomass density of each soil layer corresponding to the target maize plant are extracted from the root biomass data of the multi-depth soil layers, and the soil layers are arranged in order of increasing center depth. For any two adjacent soil depth layers, the soil depth layer with the smaller center depth is designated as the target shallow soil depth layer, and the soil depth layer with the larger center depth is designated as the target deep soil depth layer. The difference between the root biomass density of the target shallow soil depth layer and the root biomass density of the target deep soil depth layer is used as an assessment of the difference in root biomass density between adjacent soil layers, and the difference between the center depth of the target deep soil depth layer and the center depth of the target shallow soil depth layer is used as an assessment of the depth interval between adjacent soil layers. The difference in root biomass density between adjacent soil layers is used as the numerator, and the depth interval between adjacent soil layers is used as the denominator. The resulting fraction is used as the initial vertical gradient of root biomass between adjacent soil depth layers. When the initial vertical gradient of root biomass is greater than a constant 0, it is used as the vertical decay gradient of root biomass between adjacent soil depth layers. When the initial vertical gradient of root biomass is less than or equal to a constant 0, the vertical decay gradient of root biomass between adjacent soil depth layers is set to a constant 0. The vertical decay gradient of root biomass between all adjacent soil depth layers corresponding to the target maize plant is used as the vertical decay gradient data of root biomass.

5. The method for analyzing the lodging resistance and efficiency enhancement of maize based on conservation tillage conditions according to claim 3, characterized in that, The method involves performing depth-weighted cumulative processing on the vertical decay gradient data of root biomass and the corresponding soil depth data to obtain the shallow root distribution gradient decay factor, including: For any target maize plant, obtain the vertical decay gradient data of root biomass corresponding to the target maize plant, and extract the center depth of the target shallow soil layer corresponding to each root biomass vertical decay gradient from the root biomass data of multiple soil layers, and use the center depth of the target shallow soil layer as the corresponding soil depth data. For any target root biomass vertical decay gradient, the soil depth data corresponding to the target root biomass vertical decay gradient is added to constant 1, and the square root operation is performed on the corresponding addition result. Constant 1 is used as the numerator, the square root operation result is used as the denominator, and the corresponding fraction is used as the soil depth decay weight corresponding to the target root biomass vertical decay gradient. The result of multiplying the target root biomass vertical decay gradient by the corresponding soil depth decay weight is used as the target weighted root biomass vertical decay gradient; the result of summing all the target weighted root biomass vertical decay gradients corresponding to the target maize plant is used as the root shallow distribution gradient decay factor of the target maize plant.

6. The method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions according to claim 1, characterized in that, The method involves coupling analysis of aboveground and belowground morphology based on the root shallow distribution gradient decay factor and growth morphology parameters to obtain morphological imbalance factors, including: By performing height difference processing on the total plant height of maize and the height coordinates of each internode in the growth morphology parameters, the wind load arm characteristic data corresponding to each internode is obtained. By coupling the wind load arm characteristic data of each internode with the stalk cross-sectional diameter for bending strength processing, the structural fragility of the above-ground stalk can be obtained. The morphological imbalance factor was obtained by nonlinearly coupling the shallow root distribution gradient attenuation factor with the structural vulnerability of the aboveground stem.

7. The method for analyzing the lodging resistance and efficiency enhancement of maize based on conservation tillage conditions according to claim 6, characterized in that, The method involves performing height difference processing on the total plant height and internode height coordinates of the maize plant in the growth morphology parameters to obtain the wind load arm characteristic data corresponding to each internode, including: For any target maize plant, extract the total plant height and the height coordinates of each internode from the base of the ground to the ear node from the growth morphology parameters. For any target internode of the target maize plant, the difference between the total height of the target maize plant and the height coordinate corresponding to the target internode is taken as the wind load arm characteristic value corresponding to the target internode; the wind load arm characteristic values ​​corresponding to all internodes of the target maize plant are taken as the wind load arm characteristic data of the target maize plant.

8. The method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions according to claim 6, characterized in that, The method involves coupling the wind load arm characteristic data of each internode with the stalk cross-sectional diameter to obtain the structural vulnerability of the aboveground stalk, including: For any target maize plant, obtain the wind load arm characteristic data corresponding to the target maize plant, and extract the stem cross section diameter corresponding to each internode of the target maize plant from the growth morphology parameters. For any target internode of the target maize plant, the diameter of the stem section corresponding to the target internode is calculated to the cube, and the result of the cube calculation is added to a minimum constant to prevent the denominator from being zero to obtain the bending section strength assessment corresponding to the target internode; the wind load arm characteristic value corresponding to the target internode is used as the numerator, the bending section strength assessment corresponding to the target internode is used as the denominator, and the resulting fraction is used as the internode structural fragility assessment corresponding to the target internode. The internode structural vulnerability assessments of all target internodes of the target maize plant are summed to obtain the cumulative internode structural vulnerability assessment of the target maize plant. The cumulative internode structural vulnerability assessment is added to the natural constant, and the corresponding sum is processed by natural logarithmic mapping. The corresponding natural logarithmic mapping result is used as the aboveground stem structural vulnerability of the target maize plant.

9. The method for analyzing the lodging resistance enhancement of maize based on conservation tillage conditions according to claim 6, characterized in that, The method involves nonlinearly coupling the shallow root distribution gradient attenuation factor with the aboveground stem structural vulnerability to obtain the morphological imbalance factor, including: For any target maize plant, obtain the root shallow distribution gradient decay factor and the aboveground stem structure vulnerability of the target maize plant. The root shallow distribution gradient decay factor corresponding to the target maize plant is multiplied by the aboveground stem structural vulnerability, and the resulting product is used as the morphological imbalance factor of the target maize plant.

10. The method for analyzing the lodging resistance and efficiency enhancement of maize based on conservation tillage conditions according to claim 1, characterized in that, The basic lodging risk index is calculated based on the basic data of the lodging risk assessment, and a comprehensive lodging risk index is obtained by jointly correcting the basic lodging risk index and the morphological imbalance factor, including: For any target maize plant, extract the canopy projection area, effective soil volume of roots, extreme gust wind speed, standard wind pressure drag coefficient, and root biomass density of each soil depth layer from the basic data of lodging risk assessment. The extreme gust wind speed corresponding to the target corn plant is squared. The squared result is multiplied by the standard wind pressure drag coefficient and the canopy projection area in sequence. The product is used as the theoretical wind load level assessment of the target corn plant. The root biomass density of the target maize plant is summed up across all soil depth layers to obtain the cumulative assessment of the root biomass density of the target maize plant. The gravitational acceleration constant, the cumulative assessment of the root biomass density of the target maize plant, and the effective soil volume of the root system are multiplied sequentially, and the corresponding product is used as the theoretical anti-overturning anchoring force assessment of the target maize plant. The theoretical wind load level assessment of the target corn plant is used as the numerator, the theoretical overturning anchorage force assessment of the target corn plant is used as the denominator, and the corresponding fraction is used as the basic lodging risk index of the target corn plant. Obtain the morphological imbalance factor corresponding to the target maize plant, multiply the basic lodging risk index of the target maize plant by the morphological imbalance factor, and use the resulting product as the comprehensive lodging risk index of the target maize plant.

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