Method, device, medium and equipment for dynamic monitoring of cotton phenotype

Abstract

The present invention discloses a method, device, medium and equipment for dynamically monitoring cotton phenotypes, which relate to the field of crop growth monitoring. The method comprises the following steps: performing correlation analysis on cotton phenotypic characteristics, determining characterization information of daily plant height increment and length of top five internodes as phenotypic characteristics sensitive to jointing annulus; extracting daily plant height increment based on acquired multispectral images of cotton in a test area, constructing an inversion model of daily plant height increment by performing univariate linear regression on the daily plant height increment; constructing a machine learning model; determining characterization information of the length of top five internodes by measuring the total length of five internodes below the top of the main stem of cotton in the test area, training the machine learning model in combination with the cotton multispectral images to obtain a prediction model for the length of top five internodes; and determining a prediction result of phenotypic characteristics sensitive to jointing annulus according to the prediction model for the length of top five internodes and the inversion model for daily plant height increment by acquiring multispectral images of cotton in real time, thereby realizing monitoring of the phenotype sensitive to jointing annulus.

Description

Technical Field

[0001] The present invention relates to the technical field of crop growth monitoring, and in particular to a method, device, medium and equipment for dynamically monitoring cotton phenotypes. Background Art

[0002] With the rapid development of the cotton cultivation industry, full mechanization, simplification, and precision are becoming increasingly important in cotton field operations. The quantitative relationship between sensor data within cotton fields and plant growth and development, as well as pesticide application, is the foundation for precision operations. Previous studies have used vehicle-mounted sensors to obtain phenotypic data such as biomass, plant height, and SPAD values ​​in cotton fields, and combined these with the Normalized Difference Vegetation Index (NDVI) distribution to conduct regional-scale variable application of MC (Foote et al., 2016; Motomiya et al., 2014; Trevisan et al., 2018; Vellidis et al., 2009). However, using a single vegetation index (VI) such as NDVI to guide MC application has limitations. Spectral saturation occurs in late cotton fields (Pabuayon et al., 2019), and MC application is easily affected by environmental factors, making it difficult to accurately evaluate its effectiveness.

[0003] Mepiquat Chloride (MC) is widely used as a growth regulator in cotton cultivation (Feng et al., 2024). MC monitors cotton growth and development, controlling plant height and colony structure by adjusting cotton hormone levels (Wang et al., 2014), and coordinating vegetative and reproductive growth.

[0004] In recent years, the integration of drone technology and multispectral imaging systems has opened up new avenues for inferring crop phenotypic traits. This has expanded the spatial scope and temporal frequency of data collection and significantly improved the accuracy and precision of crop growth assessments by comprehensively analyzing multidimensional information (Maes and Steppe, 2019). Drone-mounted multispectral cameras can capture subtle differences in the light reflected from crop leaves across different wavelengths. Spectral signatures, including specific band values ​​within multispectral imagery and constructed spectral indices, are directly correlated with crop leaf area index (LAI), chlorophyll content, water status, nutrient levels, and early signs of pests and diseases (Chen et al., 2021; Liu et al., 2023a; Risal et al., 2024; B. Zhang et al., 2024; C. Zhang et al., 2024), providing key indicators for understanding crop vigor and stress responses. In addition to spectral characteristics, high-resolution imagery acquired by drones also allows for the extraction of rich texture features, which have been widely used to estimate crop physiological parameters such as AGB, LAI, and chlorophyll content (Liu et al., 2019; Wang et al., 2022; Yuan et al., 2023). These features reflect crop growth heterogeneity and potential responses to environmental stresses. Furthermore, point cloud data generated by drone photogrammetry provides information on the three-dimensional geometric structure of crop canopies. By analyzing structural and geometric features such as crop height, volume, canopy density, point cloud curvature, and planarity (Dhakal et al., 2023; Meacham-Hensold et al., 2019; Tsoulias et al., 2020), we can further refine our understanding of crop plant architecture, biomass, and population competition, which is of great value for optimizing planting patterns and precision pesticide application.

[0005] Although the above studies involve the inversion of many crop phenotypes, no targeted research has been conducted on MC-sensitive phenotypes in cotton cultivation, which has certain limitations in the decision-making of MC application, thereby affecting the accuracy of monitoring cotton phenotypes sensitive to chloramphenicol. Summary of the Invention

[0006] The present invention provides a method, device, medium and equipment for dynamically monitoring cotton phenotypes, which are used to solve the above-mentioned problem existing in the prior art, namely, how to accurately and dynamically monitor the phenotype of cotton that is sensitive to truncation. The present invention provides a method for dynamically monitoring cotton phenotypes, which comprises:

[0007] The Spearman correlation coefficient method was used to analyze the correlation of cotton phenotypic characteristics, and the characterization information of daily plant height increment and the length of the top five internodes were determined as the sensitive phenotypic characteristics of the joint reduction anhydrase.

[0008] Acquire multispectral images of cotton in the experimental area;

[0009] Based on the multispectral images of cotton in the experimental area, the daily increment of plant height was extracted, and an inversion model of the daily increment of plant height was constructed by performing a univariate linear regression on the daily increment of plant height.

[0010] Construct a machine learning model; by measuring the total length of the five internodes below the top of the cotton main stem in the experimental area, determine the characterization information of the length of the top five internodes, and combine it with multispectral images of cotton at the corresponding period to train the machine learning model to obtain a prediction model for the length of the top five internodes;

[0011] By acquiring cotton multispectral images in real time, the prediction results of the phenotypic characteristics sensitive to taeniain are determined based on the prediction model of the top 5 internode length and the inversion model of the daily increase in plant height, and the phenotypic characteristics sensitive to taeniain are monitored.

[0012] Also includes:

[0013] The multispectral images of cotton in the experimental area were subjected to radiometric calibration, image alignment, dense point cloud construction and orthomosaic processing.

[0014] Optionally, extracting daily increments of plant height based on multispectral images of cotton in the test area specifically includes:

[0015] Based on the multispectral image of cotton in the experimental area, the experimental area image was obtained. According to the experimental area image, the daily increase in plant height was calculated using the following formula:

[0016]

[0017]

[0018] Where n is the number of image data points in the experimental area, p is the percentile, k is the index number of the sorted dataset, DSM1 is the image of the early experimental area, DSM2 is the image of the later experimental area, DAYS is the number of days between the two images, and PHI is the daily increase in plant height.

[0019] Optionally, extracting daily increments of plant height based on multispectral images of cotton in the test area specifically includes:

[0020] Based on the multispectral images of cotton in the experimental area, the quantiles of the experimental area images were obtained, the best estimated plant height quantiles of the corresponding growth period were obtained, the difference in plant height between adjacent periods was determined, and the daily increase in plant height was calculated using the following formula:

[0021]

[0022]

[0023] Where n is the number of image data points in the experimental area, p is the percentile, k is the index number of the sorted dataset, DSM1 is the image of the early experimental area, DSM2 is the image of the later experimental area, DAYS is the number of days between the two images, and PHI is the daily increase in plant height.

[0024] The present invention provides a cotton phenotype dynamic monitoring device, comprising:

[0025] An extraction module is used to perform correlation analysis on cotton phenotypic characteristics using the Spearman correlation coefficient method, and to determine the characterization information of the daily increment of plant height and the length of the top five internodes as the phenotypic characteristics of the sensitivity to stunting;

[0026] Acquisition module, used to obtain multispectral images of cotton in the experimental area;

[0027] The plant height daily increment inversion model construction module is used to extract the plant height daily increment based on the multispectral image of cotton in the experimental area, and to construct an inversion model of the plant height daily increment by performing a univariate linear regression on the plant height daily increment;

[0028] The module for building a prediction model for the length of the top five internodes is used to build a machine learning model. This module measures the total length of the five internodes below the top of the cotton main stem in the experimental area, determines the characterization information of the length of the top five internodes, and trains the machine learning model based on the multispectral images of cotton during the corresponding period to obtain a prediction model for the length of the top five internodes.

[0029] The monitoring module is used to obtain cotton multispectral images in real time, determine the prediction results of the phenotypic characteristics sensitive to coleus based on the prediction model of the top 5 internode length and the inversion model of the daily increase in plant height, and monitor the phenotypic characteristics sensitive to coleus.

[0030] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method for dynamically monitoring cotton phenotypes is implemented.

[0031] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for dynamically monitoring cotton phenotypes is implemented.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a method for dynamically monitoring cotton phenotypes, which selects characterization information of daily plant height increment and length of top 5 internodes as the joint-shortening sensitive phenotype by performing correlation analysis on cotton phenotypes; extracts daily plant height increment based on cotton multispectral images, constructs an inversion model of daily plant height increment, and adopts a machine learning model to predict the length of top 5 internodes based on the characterization information of the length of top 5 internodes. By real-time input of multispectral images, the method can obtain the prediction results of two types of joint-shortening sensitive phenotypic characteristics, namely, daily plant height increment and length of top 5 internodes, thereby realizing accurate monitoring of the joint-shortening sensitive phenotypic characteristics in cotton phenotypes. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0034] Figure 1 A flowchart of a method for dynamically monitoring cotton phenotypes provided by an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of a computer device for a method for dynamically monitoring cotton phenotypes provided by an embodiment of the present invention.

[0036] The above drawings illustrate specific embodiments of the present invention, which will be described in more detail below. These drawings and the accompanying description are not intended to limit the scope of the present invention in any way, but rather to illustrate the concept of the present invention to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0037] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0038] The following describes in detail the technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems using specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following embodiments of the present invention are described in conjunction with the accompanying drawings.

[0039] Figure 1 Flowchart of a method for dynamically monitoring cotton phenotypes provided by an embodiment of the present invention. Figure 1As shown, this embodiment shows a method for dynamically monitoring cotton phenotypes, comprising:

[0040] S1: Spearman correlation coefficient method was used to conduct correlation analysis on cotton phenotypic characteristics, and the characterization information of daily plant height increment and length of top 5 internodes were determined as the phenotypic characteristics of sensitivity to truncation angiogenesis.

[0041] Exemplarily, cotton phenotypic characteristics may include plant height (PH), length of top 5 internodes (TOP5), daily increment of plant height (PHI), node above white flower (NAWF), leaf area index (LAI), aggregation index (ACF), canopy openness (DIFN), mean leaf inclination angle (MTA), porosity (GAPS) and SPAD value.

[0042] S2: Acquire multispectral images of cotton in the experimental area.

[0043] For example, 10 representative cotton plants can be selected from the test area for marking, and the cotton phenotypic data can be measured 0d, 9d, 17d, 26d, and 31d after the application of cyclopentane from the bud stage to the full flowering stage. The drone platform can be the DJI Phantom 4 Multispectral Edition to collect multispectral images of the cotton canopy.

[0044] Optionally, perform radiometric calibration, image alignment, dense point cloud construction, and orthomosaic processing on the multispectral image.

[0045] S3: Based on the multispectral images of cotton in the experimental area, the daily increment of plant height was extracted, and an inversion model of the daily increment of plant height was constructed by performing a univariate linear regression on the daily increment of plant height.

[0046] For example, the test area image DSM generated by the acquired drone multispectral image is used as the digital terrain model DTM and as the surface reference plane for the subsequent plant height data extraction. The calibrated test area image is obtained by calculating the difference between the test area image DSM and the digital terrain model DTM at each cotton period.

[0047] Optionally, an image of the test area is obtained based on a multispectral image of cotton in the test area. Based on the image of the test area, the daily increment of plant height is calculated using the following formula:

[0048]

[0049]

[0050] Where n is the number of image data points in the experimental area, p is the percentile, k is the index number of the sorted dataset, DSM1 is the image of the early experimental area, DSM2 is the image of the later experimental area, DAYS is the number of days between the two images, and PHI is the daily increase in plant height.

[0051] In addition, the quantiles of the experimental area images can be obtained through multispectral images of cotton in the experimental area, the best estimated plant height quantiles of the corresponding growth period can be obtained, the difference in plant heights in adjacent periods can be determined, and the daily increase in plant height can be obtained using the following formula:

[0052]

[0053]

[0054] Where n is the number of image data points in the experimental area, p is the percentile, k is the index number of the sorted dataset, DSM1 is the image of the early experimental area, DSM2 is the image of the later experimental area, DAYS is the number of days between the two images, and PHI is the daily increase in plant height.

[0055] S4: Construct a machine learning model; by measuring the total length of the five internodes below the top of the cotton main stem in the experimental area, determine the characterization information of the length of the top five internodes, and combine it with the multispectral images of cotton in the corresponding period to train the machine learning model to obtain a prediction model for the length of the top five internodes.

[0056] Optionally, six machine learning algorithms, including partial least squares regression, random forest regression and support vector regression, Gaussian process regression, gradient boosted tree, or cross-validated ridge regression, were used to obtain the top five internode length prediction model.

[0057] S5: By acquiring cotton multispectral images in real time, the prediction results of the phenotypic characteristics sensitive to thiamethoxam are determined based on the prediction model of the top 5 internode length and the inversion model of the daily increase in plant height, and the phenotypic characteristics sensitive to thiamethoxam are monitored.

[0058] The above is a method for dynamically monitoring cotton phenotypes provided in one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding device for dynamically monitoring cotton phenotypes, including:

[0059] An extraction module is used to perform correlation analysis on cotton phenotypic characteristics using the Spearman correlation coefficient method, and to determine the characterization information of the daily increment of plant height and the length of the top five internodes as the phenotypic characteristics of the sensitivity to stunting;

[0060] Acquisition module, used to obtain multispectral images of cotton in the experimental area;

[0061] The plant height daily increment inversion model construction module is used to extract the plant height daily increment based on the multispectral image of cotton in the experimental area, and to construct an inversion model of the plant height daily increment by performing a univariate linear regression on the plant height daily increment;

[0062] The module for building a prediction model for the length of the top five internodes is used to build a machine learning model. This module measures the total length of the five internodes below the top of the cotton main stem in the experimental area, determines the characterization information of the length of the top five internodes, and trains the machine learning model based on the multispectral images of cotton during the corresponding period to obtain a prediction model for the length of the top five internodes.

[0063] The monitoring module is used to obtain cotton multispectral images in real time, determine the prediction results of the phenotypic characteristics sensitive to coleus based on the prediction model of the top 5 internode length and the inversion model of the daily increase in plant height, and monitor the phenotypic characteristics sensitive to coleus.

[0064] The specific definitions of the cotton phenotypic dynamic monitoring device can be found in the aforementioned definitions of the cotton phenotypic dynamic monitoring method and will not be further elaborated here. Each module in the aforementioned cotton phenotypic dynamic monitoring device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0065] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program can be used to execute the cotton phenotype dynamic monitoring method provided above.

[0066] The present invention also provides Figure 2 The structural diagram of the computer equipment shown in FIG. Figure 2 As shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile storage, and may also include other hardware required for operations. The processor reads the corresponding computer program from the non-volatile storage into the internal memory and then runs it to implement the method for dynamically monitoring cotton phenotypes provided in the above embodiment.

[0067] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0068] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A method for dynamically monitoring cotton phenotypes, characterized in that: include: The Spearman correlation coefficient method was used to analyze the correlation of cotton phenotypic characteristics, and the characterization information of daily plant height increment and the length of the top five internodes were determined as the sensitive phenotypic characteristics of the joint reduction anhydrase. Acquire multispectral images of cotton in the experimental area; Based on the multispectral images of cotton in the experimental area, the daily increment of plant height was extracted, and an inversion model of the daily increment of plant height was constructed by performing a univariate linear regression on the daily increment of plant height. Construct a machine learning model; by measuring the total length of the five internodes below the top of the cotton main stem in the experimental area, determine the characterization information of the length of the top five internodes, and combine it with multispectral images of cotton at the corresponding period to train the machine learning model to obtain a prediction model for the length of the top five internodes; By acquiring cotton multispectral images in real time, the prediction results of the phenotypic characteristics sensitive to taeniain are determined based on the prediction model of the top 5 internode length and the inversion model of the daily increase in plant height, and the phenotypic characteristics sensitive to taeniain are monitored.

2. The method for dynamic monitoring of cotton phenotypes according to claim 1, wherein: The method further comprises: The multispectral images of cotton in the experimental area were subjected to radiometric calibration, image alignment, dense point cloud construction and orthomosaic processing.

3. The method for dynamic monitoring of cotton phenotypes according to claim 1, wherein: The method of extracting daily plant height increment based on the multispectral image of cotton in the test area specifically includes: Based on the multispectral image of cotton in the experimental area, the experimental area image was obtained. According to the experimental area image, the daily increase in plant height was calculated using the following formula: Where n is the number of image data points in the experimental area, p is the percentile, k is the index number of the sorted dataset, DSM1 is the image of the early experimental area, DSM2 is the image of the later experimental area, DAYS is the number of days between the two images, and PHI is the daily increase in plant height.

4. The method for dynamic monitoring of cotton phenotypes according to claim 1, wherein: The method of extracting daily plant height increment based on the multispectral image of cotton in the test area specifically includes: Based on the multispectral images of cotton in the experimental area, the quantiles of the experimental area images were obtained, the best estimated plant height quantiles of the corresponding growth period were obtained, the difference in plant height between adjacent periods was determined, and the daily increase in plant height was calculated using the following formula: Where n is the number of image data points in the experimental area, p is the percentile, k is the index number of the sorted dataset, DSM1 is the image of the early experimental area, DSM2 is the image of the later experimental area, DAYS is the number of days between the two images, and PHI is the daily increase in plant height.

5. A cotton phenotype dynamic monitoring device, characterized in that: include: Acquisition module, used to obtain multispectral images of cotton in the experimental area; An extraction module is used to perform correlation analysis on cotton phenotypic characteristics using the Spearman correlation coefficient method, and to determine the characterization information of the daily increment of plant height and the length of the top five internodes as the phenotypic characteristics of the sensitivity to stunting; The plant height daily increment inversion model construction module is used to extract the plant height daily increment based on the multispectral image of cotton in the experimental area, and to construct an inversion model of the plant height daily increment by performing a univariate linear regression on the plant height daily increment; The module for constructing a prediction model for the length of the top five internodes is used to obtain the total length of the five internodes below the top of the cotton main stem, determine the characterization information of the length of the top five internodes, and use a machine learning model to construct a prediction model for the length of the top five internodes; The monitoring module is used to obtain cotton multispectral images in real time, determine the prediction results of the phenotypic characteristics sensitive to thiamin through the top 5 internode length prediction model and the inversion model of the daily increase in plant height, and monitor the phenotypic characteristics sensitive to thiamin. An extraction module is used to perform correlation analysis on cotton phenotypic characteristics using the Spearman correlation coefficient method, and to determine the characterization information of the daily increment of plant height and the length of the top five internodes as the phenotypic characteristics of the sensitivity to stunting; Acquisition module, used to obtain multispectral images of cotton in the experimental area; The plant height daily increment inversion model construction module is used to extract the plant height daily increment based on the multispectral image of cotton in the experimental area, and to construct an inversion model of the plant height daily increment by performing a univariate linear regression on the plant height daily increment; The module for building a prediction model for the length of the top five internodes is used to build a machine learning model. This module measures the total length of the five internodes below the top of the cotton main stem in the experimental area, determines the characterization information of the length of the top five internodes, and trains the machine learning model based on the multispectral images of cotton during the corresponding period to obtain a prediction model for the length of the top five internodes. The monitoring module is used to obtain cotton multispectral images in real time, determine the prediction results of the phenotypic characteristics sensitive to coleus based on the prediction model of the top 5 internode length and the inversion model of the daily increase in plant height, and monitor the phenotypic characteristics sensitive to coleus.

6. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method for dynamically monitoring cotton phenotypes according to any one of claims 1 to 4 is implemented.

7. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the method for dynamically monitoring cotton phenotypes according to any one of claims 1 to 4 is realized.

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