Sugarcane variety drought resistance evaluation method based on remote sensing

Abstract

The invention discloses a sugarcane variety drought resistance evaluation method based on remote sensing, and belongs to the technical field of remote sensing, and the method comprises the following steps: S1, selecting a plurality of different varieties of sugarcane plants in a research area for marking and positioning; s2, constructing a sugarcane drought tolerance comprehensive index DTI according to the reflectivity value of the sensitive wave band; s3, calculating DTI indexes of different sugarcane varieties through the constructed sugarcane drought tolerance comprehensive index DTI, and comparing and verifying whether the constructed sugarcane drought tolerance comprehensive index DTI can effectively reflect the drought resistance of the sugarcane or not; and S4, calculating the drought tolerance of the sugarcane in the to-be-predicted region through the constructed drought tolerance comprehensive index DTI of the sugarcane, formulating drought tolerance evaluation standards of different sugarcane varieties, and comparing the calculated drought tolerance of the sugarcane with the real condition of the region for verification. The method provides basis and technical support for drought resistance identification and drought resistance germplasm resource screening of sugarcane, and fills the blank of research in the field at the present stage.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing, and in particular relates to a method for evaluating drought resistance of sugarcane varieties based on remote sensing. Background Art

[0002] Sugarcane is one of my country's primary raw materials for sugar production and a crucial raw material for light industry. Sugarcane production plays a crucial role in agricultural production. With global warming, high temperatures and droughts are becoming increasingly frequent. Drought has become a major factor hindering the sustainable and stable development of sugarcane production. Drought stress slows sugarcane growth, leaves dry up, and stems shrink, significantly reducing yield and sweetness.

[0003] Traditional crop drought stress research requires manual measurement of multiple indicators, which has many disadvantages such as being time-consuming, labor-intensive, and highly destructive. When it is necessary to measure the growth conditions of crops in large-scale cultivated fields or in complex environments such as mountains and hills, this method is difficult to implement and cannot meet the needs of modern agriculture. With the rapid development of modern technology, the use of satellite remote sensing technology to obtain large-scale crop remote sensing images and to judge crop drought conditions based on remote sensing images has been favored by relevant researchers. Satellite remote sensing technology, with its large-scale, continuous, all-weather dynamic monitoring characteristics, has greatly reduced the cost of manual surveys. At the same time, the non-contact nature of satellite remote sensing technology also avoids irreversible damage to crops during surveys and sampling. Existing research has mostly focused on food crops such as wheat and corn, while there are fewer studies on major economic crops such as sugarcane.

[0004] For example, Chinese patent CN 112183292 B proposes a method for extracting drought phenotypes and assessing drought resistance in field crops based on low-altitude remote sensing. Based on the physiological responses of crops under drought stress, the following specific phenotypes are extracted: (1) leaf curl index (LRS) estimated using a deep convolutional neural network to indicate the degree of response of crop leaves to drought stress; (2) aboveground volume (AGV) extracted based on the digital surface model (DSM) to indicate crop biomass, as well as fresh weight (FW), dry weight (DW), and water content (PWC) obtained using the empirical linear model; and (3) a comprehensive drought resistance assessment index (LWI) to indicate the relative water content of crop leaves. Furthermore, high-frequency drone image data is used to obtain the temporal changes of the above-mentioned phenotypic traits, revealing the dynamic response of crops under drought stress and assessing their drought resistance. Finally, genetic analysis is performed in combination with gene sequencing data of germplasm resources to locate potential drought-resistant genes for subsequent functional verification and genetic improvement. This method can be used for crops such as wheat, corn, and rice, but it is not suitable for sugarcane crops. It requires the extraction of multiple specific phenotypes, and the amount of data calculation is relatively large.

[0005] Therefore, the existing technology urgently needs a remote sensing-based sugarcane variety drought resistance evaluation method to quickly screen out drought-resistant sugarcane varieties suitable for future climate change by establishing a sugarcane drought resistance evaluation standard. Summary of the Invention

[0006] The present invention proposes a remote sensing-based method for evaluating the drought resistance of sugarcane varieties. By utilizing the band information of satellite images, the sugarcane drought resistance comprehensive index (DTI) is constructed, and a sugarcane drought resistance evaluation standard is established based on the DTI. This method provides a basis and technical support for the identification of drought resistance of sugarcane and the screening of drought-resistant germplasm resources. At the same time, it also fills the gap in the current research in this field.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A remote sensing-based method for evaluating drought resistance of sugarcane varieties comprises the following steps:

[0009] S1. Select several sugarcane plants of different varieties in the study area for marking and positioning, and simultaneously conduct ground measurements of agronomic and physiological and biochemical parameters, including sugarcane plant height and number of effective stems;

[0010] S2. Use drones to acquire hyperspectral images of sugarcane fields in the study area and perform preprocessing. Then, extract hyperspectral curves of different sugarcane varieties. Based on the characteristics of the hyperspectral curves, determine the sensitive bands of different sugarcane varieties to drought stress. Based on the reflectance values ​​of the sensitive bands, construct a comprehensive sugarcane drought tolerance index (DTI).

[0011] S3. Calculate the DTI index of different sugarcane varieties using the constructed sugarcane drought tolerance comprehensive index (DTI), and compare the DTI index with sugarcane plant height, number of effective stems, and yield data after harvest to verify whether the constructed sugarcane drought tolerance comprehensive index (DTI) can effectively reflect the drought resistance of sugarcane. If so, proceed to step S4; if not, reconstruct the index.

[0012] S4. Calculate the sugarcane drought tolerance of the predicted area through the constructed sugarcane drought tolerance comprehensive index DTI, formulate drought tolerance evaluation standards for different sugarcane varieties based on the sugarcane drought tolerance comprehensive index DTI, and compare and verify the calculated sugarcane drought tolerance with the actual situation in the area.

[0013] Furthermore, in step S1, different sugarcane varieties are planted more than twice in the experimental field of the study area and marked and located.

[0014] Furthermore, in step S1, the agronomic and physiological and biochemical parameters also include the number of stem nodes and chlorophyll content.

[0015] Furthermore, in step S2, the camera carried by the drone is a Laisen iSpecHyper-VM100 push-broom airborne hyperspectral imaging camera.

[0016] Furthermore, in step S2, the flight operation altitude of the UAV is 20-40m, and the spatial resolution of the acquired image data is 5-15cm.

[0017] Furthermore, in step S2, the calculation formula of the sugarcane drought tolerance comprehensive index DTI is as follows:

[0018] DTI=FVC×NDVI×Ref 730-810

[0019] FVC = (number of vegetation pixels / total number of pixels in the plot) × 100%

[0020] NDVI = (NIR - Red) / (NIR + Red)

[0021] Where FVC is the vegetation coverage, NDVI is the normalized vegetation index, NIR is the near infrared band reflectance, Red is the red light band reflectance, Ref 730-810 It is the average reflectivity of the hyperspectral curve in the wavelength range of 730-810nm.

[0022] Furthermore, in step S3, it is observed whether the changing trends of the DTI index of different sugarcane varieties are consistent with the changing trends of their corresponding sugarcane plant height, number of effective stems, and post-harvest yield data. If they are consistent, the constructed sugarcane drought resistance comprehensive index DTI can effectively reflect the drought resistance of sugarcane; otherwise, it cannot reflect the drought resistance of sugarcane.

[0023] Furthermore, in step S4, when calculating the drought resistance of sugarcane in the area to be predicted, the drone hyperspectral image of the sugarcane field in the area to be predicted is first obtained, and then the drone hyperspectral image of the area is preprocessed, and the hyperspectral curves of different sugarcane varieties are extracted. Finally, the sugarcane drought resistance of different sugarcane varieties is calculated based on the constructed sugarcane drought resistance comprehensive index DTI.

[0024] Furthermore, in step S4, the preprocessing of the UAV hyperspectral image includes: firstly performing radiometric calibration and correction on the hyperspectral image based on the photometer and the standard reflector, and then performing geometric correction and mosaicking on the hyperspectral image using the ground control points (GCP) and the polynomial method to obtain the correct UAV hyperspectral image of the area.

[0025] Due to the adoption of the above technical solution, the present invention has the following beneficial effects:

[0026] 1. This invention, by constructing a spectral index and criteria sensitive to sugarcane drought tolerance, can quickly and accurately determine the drought tolerance of sugarcane varieties. This addresses the high cost, limited timeliness, and lack of clarity associated with existing drought resistance evaluations of sugarcane varieties. By setting appropriate thresholds to categorize sugarcane drought resistance, this drought tolerance assessment standard allows growers and relevant departments to implement timely irrigation based on the varying drought conditions of sugarcane crops. This allows them to select high-quality drought-resistant sugarcane varieties for future plantings, thereby increasing sugarcane yields and reducing agricultural economic losses.

[0027] 2. The present invention provides a basis and technical support for the drought resistance identification and drought-resistant germplasm resource screening of sugarcane, and at the same time, fills the gap in the current research in this field.

[0028] 3. The present invention does not require high-frequency phenotypic monitoring of the dynamic process of crop response to drought stress, nor does it require the calculation of multiple monitoring models. The amount of data calculation is relatively low, which can effectively improve the efficiency of the calculation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart for realizing the method of the present invention;

[0030] Figure 2 This is the drone hyperspectral image of the experimental field where each sugarcane variety has been marked in the present invention;

[0031] Figure 3 It is a partial hyperspectral curve (680-900nm) of different sugarcane varieties of the present invention;

[0032] Figure 4 This is the drought tolerance distribution of different sugarcane varieties obtained based on the drought tolerance comprehensive index DTI of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] Example

[0035] like Figure 1 As shown, a method for evaluating drought resistance of sugarcane varieties based on remote sensing includes steps S1-S4.

[0036] S1. Select several sugarcane plants of different varieties in the study area for marking and positioning, and simultaneously conduct on-the-ground measurements of agronomic and physiological and biochemical parameters, including plant height, number of effective stems, number of stem nodes, and chlorophyll content.

[0037] To minimize the impact of experimental field soil conditions and other factors on different sugarcane varieties and reduce the uncertainty of experimental results, different sugarcane varieties were planted at least twice in the experimental fields within the study area and marked for location. In this example, the project team planted each sugarcane variety three times and marked them to differentiate them. They also conducted field measurements of plant height, number of nodes, number of effective stems, and chlorophyll content for each variety to facilitate subsequent comparison and verification.

[0038] S2. Use drones to obtain hyperspectral images of sugarcane fields in the study area and perform preprocessing. Then, extract hyperspectral curves of different sugarcane varieties. The sensitive bands of different sugarcane varieties to drought stress are determined based on the characteristics of the hyperspectral curves. The comprehensive sugarcane drought tolerance index (DTI) is constructed based on the reflectance values ​​of the sensitive bands.

[0039] S3. Calculate the DTI index of different sugarcane varieties using the constructed sugarcane drought tolerance comprehensive index (DTI), and compare the DTI index with sugarcane plant height, number of effective stems, and post-harvest yield data to verify whether the constructed sugarcane drought tolerance comprehensive index (DTI) can effectively reflect the drought resistance of sugarcane. If so, proceed to step S4; if not, reconstruct the index.

[0040] Specifically, we observed whether the changing trends of the DTI index of different sugarcane varieties were consistent with the changing trends of their corresponding sugarcane plant height, number of effective stems, and post-harvest yield data. If they were consistent, the constructed sugarcane drought resistance comprehensive index DTI could effectively reflect the drought resistance of sugarcane; otherwise, it could not.

[0041] S4. Calculate the sugarcane drought tolerance of the predicted area through the constructed sugarcane drought tolerance comprehensive index DTI, formulate drought tolerance evaluation standards for different sugarcane varieties based on the sugarcane drought tolerance comprehensive index DTI, and compare and verify the calculated sugarcane drought tolerance with the actual situation in the area.

[0042] To calculate the drought tolerance of sugarcane in the predicted area, we first acquire drone hyperspectral imagery of the sugarcane fields in the predicted area. We then preprocess the drone hyperspectral imagery and extract hyperspectral curves for different sugarcane varieties. Finally, we calculate the drought tolerance of each sugarcane variety based on the constructed sugarcane drought tolerance index (DTI). Preprocessing the drone hyperspectral imagery involves radiometric calibration and correction using a photometer and standard reflectors. Then, we geometrically correct and mosaic the hyperspectral imagery using ground control points (GCPs) and a polynomial method to obtain accurate drone hyperspectral imagery of the area.

[0043] Figure 2 UAV hyperspectral images of experimental fields with various sugarcane varieties marked. The present invention carried out hyperspectral UAV flights on the experimental fields, and simultaneously carried out ground measurements of agronomic and physiological and biochemical parameters such as sugarcane plant height, number of stem nodes, number of effective stems, and chlorophyll content. Among them, the camera carried by the UAV is the Laisen iSpecHyper-VM100 push-broom airborne hyperspectral imaging camera, which has a wavelength range of 400-1000nm, a spectral resolution of 3.5nm, 270 imaging bands, and a full-width pixel of 1394 (spatial dimension) × 1040 (spectral dimension). The UAV's flight operating altitude is 20-40m, and the spatial resolution of the image data obtained is 5-15cm. In this embodiment, the UAV's flight operating altitude is 30m, and the spatial resolution of the image data obtained is 10cm. After the flight data is processed by radiation correction and geometric correction, a hyperspectral orthophoto data product is obtained, and data analysis and modeling are performed on this basis. In addition, in order to effectively distinguish the various sugarcane varieties planted in the experimental fields, the hyperspectral images taken by drones were numbered one by one according to the corresponding sugarcane varieties. Each red box in the image represents a sugarcane variety, and each sugarcane variety was planted three times in the experimental fields.

[0044] Figure 3 Part of the hyperspectral curve (680-900nm) of different sugarcane varieties. Taking each red box (i.e., each replicated plot) as a unit, the soil background pixels in the red box are eliminated by the image segmentation algorithm, and then all the remaining sugarcane canopy pixels are averaged by band, and the average spectral curve is used as the hyperspectral curve of the replicated plot. After analyzing the hyperspectral curves of different replicated plots, it was found that under the same drought stress conditions, the reflectivity of different sugarcane varieties showed obvious changes in the wavelength range of 680-900nm. Therefore, this embodiment selected the 730-810nm band as the sensitive band, and combined with indicators such as vegetation coverage FVC and normalized vegetation index NDVI to construct the sugarcane drought tolerance comprehensive index DTI (Drought Tolerance Index). The calculation formula of the sugarcane drought tolerance comprehensive index DTI is as follows:

[0045] DTI=FVC×NDVI×Ref 730-810

[0046] FVC = (number of vegetation pixels / total number of pixels in the plot) × 100%

[0047] NDVI = (NIR - Red) / (NIR + Red)

[0048] Where FVC is the vegetation coverage, NDVI is the normalized vegetation index, NIR is the near infrared band reflectance, Red is the red light band reflectance, Ref 730-810 It is the average reflectivity of the hyperspectral curve in the wavelength range of 730-810nm.

[0049] The table below shows hyperspectral remote sensing phenotypic indicators for various sugarcane varieties. By calculating the comprehensive drought tolerance index (DTI) for different sugarcane varieties and analyzing it with field-measured plant height, effective stem, and post-harvest yield data, the results show that the changing trends of the DTI index values ​​are generally consistent with those of the agronomic traits comprising plant height, effective stem, and yield, demonstrating that the sugarcane drought tolerance comprehensive index model constructed in this paper can effectively reflect the drought resistance of sugarcane.

[0050] strain FVC <![CDATA[Ref 730-810 ]]> NDVI DTI Sorting Yield Sorting 19-286 0.8326 0.3486 0.5892 0.1710 1 6.6961 4 19-309 0.8236 0.3318 0.5911 0.1615 2 6.8430 3 18-566 0.7039 0.3369 0.5436 0.1289 3 6.3462 6 18-164 0.6805 0.3226 0.5258 0.1154 4 6.6373 5 18-600 0.6003 0.3267 0.5069 0.0994 5 5.2609 11 18-471 0.5933 0.3155 0.4868 0.0911 6 7.5758 1 18-117 0.5778 0.3062 0.4899 0.0867 7 5.6126 9 05-136 0.5603 0.3126 0.4881 0.0855 8 5.5324 10 18-837 0.5295 0.3403 0.4648 0.0838 9 5.6839 8 19-294 0.5327 0.3167 0.4637 0.0782 10 6.8512 2 18-590 0.4491 0.3059 0.4341 0.0596 11 4.0175 13 18-702 0.3647 0.3075 0.4074 0.0457 12 4.7571 12 17-553 0.3439 0.3126 0.4007 0.0431 13 5.7861 7 17-491 0.1841 0.2828 0.3260 0.0170 14 2.4953 14

[0051] Figure 4 This figure shows the drought tolerance distribution of different sugarcane varieties based on the drought tolerance index (DTI). A severe meteorological drought occurred in southern Guangxi from July to August 2022, significantly impacting sugarcane production. After the sugarcane crops had been exposed to high temperatures and drought for nearly 20 consecutive days, the project team conducted a hyperspectral drone flight over the experimental fields on August 3, 2022. The drone hyperspectral imagery of the area was preprocessed, including radiometric correction, geometric correction, and stitching, to obtain reflectance images of the region. The sugarcane drought tolerance index (DTI) was calculated using the 730-810 nm band of the experimental field hyperspectral imagery, as well as indicators such as vegetation cover fraction (FVC) and the normalized difference vegetation index (NDVI). Based on the calculated spectral index and actual conditions, thresholds were set to categorize sugarcane drought tolerance. For example, a DTI ≥ 0.35 indicates good drought resistance, while a DTI < 0.35 indicates poor drought resistance. As the chart shows, the higher the DTI index (and the greener the color), the better the sugarcane's drought resistance. By categorizing sugarcane drought resistance, growers and relevant departments can implement timely irrigation based on the varying drought conditions of sugarcane and select high-quality, drought-resistant sugarcane varieties for future plantings, thereby increasing sugarcane yields and reducing agricultural economic losses.

[0052] The above description is a detailed description of the preferred embodiments of the present invention, but the embodiments are not intended to limit the scope of the patent application of the present invention. Any equivalent changes or modifications completed under the technical spirit suggested by the present invention should fall within the patent scope covered by the present invention.

Claims

1. A method for evaluating drought resistance of sugarcane varieties based on remote sensing, characterized in that: The following steps are involved: S1. Select several sugarcane plants of different varieties in the study area for marking and positioning, and simultaneously carry out ground measurement of agronomic and physiological and biochemical parameters, including sugarcane plant height and number of effective stems; S2. Use UAVs to obtain hyperspectral images of sugarcane fields in the study area and perform preprocessing. Then extract hyperspectral curves of different sugarcane varieties. Determine the sensitive bands of different sugarcane varieties to drought stress through the characteristics of hyperspectral curves. Construct the sugarcane drought tolerance comprehensive index DTI based on the reflectance values ​​of sensitive bands. S3, calculating the DTI index of different sugarcane varieties through the constructed sugarcane drought resistance comprehensive index DTI, and comparing the DTI index with the sugarcane plant height, the number of effective stems and the yield data after harvesting, to verify whether the constructed sugarcane drought resistance comprehensive index DTI can effectively reflect the drought resistance of sugarcane; if it can, executing step S4, if not, reconstructing; S4. Calculate the sugarcane drought resistance of the predicted area through the constructed sugarcane drought resistance comprehensive index DTI, formulate drought resistance evaluation standards for different sugarcane varieties based on the sugarcane drought resistance comprehensive index DTI, and compare and verify the calculated sugarcane drought resistance with the actual situation in the area.

2. The method for evaluating drought resistance of sugarcane varieties based on remote sensing according to claim 1, characterized in that: In step S1, different sugarcane varieties are planted more than twice in the experimental field in the study area and marked and located.

3. The method for evaluating drought resistance of sugarcane varieties based on remote sensing according to claim 1, characterized in that: In step S1, the agronomic and physiological and biochemical parameters also include the number of stem nodes and the chlorophyll content.

4. The method for evaluating drought resistance of sugarcane varieties based on remote sensing according to claim 1, characterized in that: In step S2, the camera carried by the drone is the Laisen iSpecHyper-VM100 push-broom airborne hyperspectral imaging camera.

5. The method for evaluating drought resistance of sugarcane varieties based on remote sensing according to claim 1, characterized in that: In step S2, the flying operation altitude of the UAV is 20-40m, and the spatial resolution of the acquired image data is 5-15cm.

6. The method for evaluating drought resistance of sugarcane varieties based on remote sensing according to claim 1, characterized in that: In step S2, the calculation formula of the sugarcane drought tolerance comprehensive index DTI is as follows: DTI=FVC×NDVI×Ref 730-810 FVC = (number of vegetation pixels / total number of pixels in the sample plot) × 100% NDVI=(NIR-Red) / (NIR+Red) In the formula, FVC is the vegetation coverage, NDVI is the normalized vegetation index, NIR is the near-infrared band reflectance, Red is the red light band reflectance, Ref 730-810 It is the average reflectivity in the wavelength range of 730-810nm of the hyperspectral curve.

7. The method for evaluating drought resistance of sugarcane varieties based on remote sensing according to claim 1, characterized in that: In step S3, it is observed whether the change trend of the DTI index of different sugarcane varieties is consistent with the change trend of the corresponding sugarcane plant height, effective stem number and yield data after harvesting. If they are consistent, the constructed sugarcane drought resistance comprehensive index DTI can effectively reflect the drought resistance of sugarcane, otherwise, it cannot reflect it.

8. The method for evaluating drought resistance of sugarcane varieties based on remote sensing according to claim 1, characterized in that: In step S4, when calculating the drought resistance of sugarcane in the area to be predicted, first obtain the drone hyperspectral image of the sugarcane field in the area to be predicted, then preprocess the drone hyperspectral image of the area, and extract the hyperspectral curves of different sugarcane varieties, and finally calculate the drought resistance of sugarcane of different sugarcane varieties based on the constructed sugarcane drought resistance comprehensive index DTI.

9. The method for evaluating drought resistance of sugarcane varieties based on remote sensing according to claim 8, characterized in that: In step S4, the preprocessing of the UAV hyperspectral image includes: firstly, radiometric calibration and correction of the hyperspectral image based on the photometer and the standard reflector, and then geometric correction and mosaicking of the hyperspectral image using the ground control points GCP and the polynomial method to obtain the correct UAV hyperspectral image of the area.

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