Method and system for evaluating salt tolerance of rice based on sensor monitoring data
By using isogradient salt concentration experiments and infrared spectroscopy to monitor rice leaf and root parameters, a time-series dataset was constructed, which solved the problems of insufficient salt concentration coverage and inaccurate evaluation in existing technologies, and achieved the accuracy and comprehensiveness of rice salt tolerance evaluation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGDONG OCEAN UNIVERSITY
- Filing Date
- 2025-05-30
- Publication Date
- 2026-07-03
AI Technical Summary
Existing technologies for evaluating rice salt tolerance suffer from insufficient coverage of salt concentration gradients, making it impossible to accurately pinpoint the maximum salt tolerance threshold. Furthermore, they lack assessment of the synergistic mechanism between aboveground and underground components under salt stress, leading to inaccurate evaluations.
An experimental environment with equal-gradient salt concentration was used, and infrared spectroscopy was combined with monitoring of characteristic parameters of rice leaves and roots to construct time-series datasets of leaf growth index and root growth index. The salt tolerance response pattern of rice throughout its entire growth cycle was captured by monitoring at multiple time points, and leaf and root parameters were integrated for comprehensive evaluation.
It has achieved precise positioning of the maximum salt tolerance threshold of rice, reduced the evaluation misjudgment rate, improved the biological rationality and accuracy of the evaluation system, and can accurately identify rice varieties with strong salt tolerance under salt stress.
Smart Images
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Abstract
Description
Technical Field
[0001] This invention relates to the field of rice salt tolerance evaluation technology, specifically to a method and system for evaluating rice salt tolerance based on sensor monitoring data. Background Technology
[0002] Globally, the area of saline-alkali land is increasing year by year. As one of the world's major food crops, rice is sensitive to salt, making research on its salt tolerance particularly important. The problem of saline-alkali land is prominent and seriously affects agricultural production. Salt accumulation leads to the deterioration of soil structure, affecting crop growth and yield. In particular, important food crops such as rice are sensitive to salt, which makes salt tolerance research of great practical significance.
[0003] The prior art, disclosed in publication number CN118655096A, describes a method for evaluating rice salt tolerance based on reflectance spectra. Some techniques utilize spectral reflectance to construct the CRI1 index and predict seedling mortality by observing spectral changes before and after salt treatment. However, this method has the following problems: it uses a fixed salt concentration and lacks a gradient experimental design that covers the entire tolerance range, making it difficult to accurately determine the maximum salt tolerance threshold; moreover, it only performs short-term spectral measurements at the two-leaf-one-heart stage, resulting in short-term data that cannot accurately reflect the rice growth status and thus cannot accurately evaluate rice salt tolerance; and it does not incorporate root growth parameters, thus lacking an assessment of the aboveground and underground synergistic mechanisms under salt stress.
[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0005] The purpose of this invention is to provide a method and system for evaluating the salt tolerance of rice based on sensor monitoring data, so as to solve the problems mentioned in the background art.
[0006] To achieve the above objectives, the present invention provides the following technical solution:
[0007] A method for evaluating rice salt tolerance based on sensor monitoring data, comprising the following steps:
[0008] S1: Rice was cultivated in an environment with different salt concentrations at equal gradients as the experimental group, while rice was cultivated in an environment with zero salt concentration as the control group.
[0009] S2: At equal time intervals, infrared spectral images of rice leaves in each experimental group and the control group were acquired. Root characteristic parameters of each experimental group and control group were collected while acquiring the infrared spectra.
[0010] S3: Analyze the infrared spectral images, extract the leaf growth parameters in each spectral image, generate the leaf growth index corresponding to each spectral image, analyze the root feature parameters, and generate the root growth index at each acquisition time.
[0011] S4: Sort the leaf growth index according to the acquisition time of the corresponding spectral image to form a leaf growth time series dataset, and sort the root growth index according to the acquisition time of the corresponding root feature parameter to form a root growth time series dataset.
[0012] S5: Based on the leaf growth time series dataset and root growth time series dataset of each experimental group, determine the leaf growth and root growth of the rice in the corresponding experimental group, and select the experimental groups with normal leaf and root growth as pre-selected groups.
[0013] S6: Compare the leaf growth time series dataset and root growth time series dataset of the preselected group and the control group to determine the development status of each preselected group. The rice in the preselected group that meets the development requirements and has the highest salt concentration is taken as the rice with the highest salt tolerance, and its corresponding salt concentration is set as the maximum salt tolerance.
[0014] Furthermore, the experimental group consisted of at least 10 groups, with a salt concentration gradient of 20 mM NaCl between adjacent groups. The infrared spectral images of rice leaves in the experimental group and the control group were acquired at 7, 14, 21, 28 and 35 days after transplanting, respectively.
[0015] Furthermore, leaf growth parameters include chlorophyll retention rate, lignin content, and cell water content. The method for extracting leaf growth parameters from infrared spectral images is as follows:
[0016] Infrared spectral images of rice leaves from each experimental group and the control group were acquired at equal time intervals. The infrared spectral images were analyzed to extract leaf growth parameters from each image, generating a leaf growth index for each image. These parameters included chlorophyll retention rate, lignin content, and cell water content. The steps for acquiring the infrared spectral images included: infrared scanning of the surface of rice leaves from both groups; data preprocessing of the acquired infrared spectral images; extraction of the total absorbance in the visible light region; and extraction of the intensity of characteristic absorption peaks related to chlorophyll, lignin, and cell water content. The wavelength range of the visible light region was 400-3000 nm, and the wavelength ranges of the characteristic absorption peaks related to chlorophyll, lignin, and cell water content were 680-720 nm, 1630-1670 nm, and 1940-2000 nm, respectively.
[0017] Calculate chlorophyll retention rate (CR), lignin content (LC), and cell water content (WC):
[0018]
[0019] In the formula, A total A represents the total absorbance in the visible light region. green A represents the characteristic absorbance of chlorophyll. lignin A represents the characteristic absorbance of lignin. H2O The absorbance is a characteristic of cell water content.
[0020] Furthermore, leaf growth parameters are extracted from each spectral image to generate the Leaf Growth Index (LGI) for each spectral image. The formula used to calculate the leaf growth index is as follows:
[0021] LGI = w CR ·CR+w LC ·LC+w WC WC
[0022] In the formula, w CR w LC w WC These are weighting coefficients, all greater than 0, and satisfying w CR +w LC +w WC =1.
[0023] Furthermore, while acquiring infrared spectra, root characteristic parameters of each experimental and control group were collected. These parameters were analyzed to generate a root growth index for each acquisition time. The root characteristic parameters included root length, root area, and root volume. The formula used to calculate the root growth index was...
[0024] PRI = w RL ·RL+w RA ·RA+w RV ·RV
[0025] In the formula, w RL w RA w RV These are weighting coefficients, all greater than 0, and satisfying w RL +w RA +w RV =1.
[0026] Furthermore, a leaf growth time-series dataset is constructed by sorting the root growth index according to the acquisition time of the corresponding root feature parameters. The specific scheme is as follows: the leaf growth index (LGI) corresponding to each spectral image is sorted according to the acquisition time, generating the following dataset:
[0027]
[0028] In the formula, Let LGI represent the leaf growth time series dataset of the i-th experimental group. i (t j () represents the leaf growth index corresponding to the infrared spectral image collected at time point j of the i-th experimental group, where j = 1, 2, 3, 4, 5, t j These represent time points of 7 days, 14 days, 21 days, 28 days, and 35 days after transplanting, respectively.
[0029] The root growth index (PRI) at each time point is sorted according to the collection time, generating the following dataset:
[0030]
[0031] In the formula, PRI represents the root growth time series dataset of the i-th experimental group. i (t j ) represents the root growth index collected at the j-th time point of the i-th experimental group.
[0032] Furthermore, experimental groups with normal leaf and root growth were selected as pre-selection groups. Leaf growth index and root growth index were used to assess the growth status of rice in each experimental group.
[0033]
[0034] and This represents the leaf growth rate and root growth rate of the i-th experimental group. The experimental groups with normal leaf and root growth are selected. and If the condition is met, the group is judged to be growing normally, and the experimental groups that meet the criteria will be compared in the next step.
[0035] Furthermore, the leaf growth time series datasets and root growth time series datasets of the preselected group and the control group were screened. A second screening was conducted within the preselected group that met the requirements for normal rice growth, with the following screening criteria:
[0036]
[0037] In the formula, LGI(t) j ) represents the leaf growth index collected at the j-th time point of the control group, PRI(t) j ) represents the root growth index collected at the j-th time point of the control group. The rice in the pre-selected group that meets the screening requirements and has the highest salt concentration is taken as the rice with the highest salt tolerance, and its corresponding salt concentration is set as the maximum salt tolerance.
[0038] The present invention also provides a rice salt tolerance evaluation system based on sensor monitoring data. This system is used to execute the above-described rice salt tolerance evaluation method based on sensor monitoring data, and includes:
[0039] Environment setting module: used to set different salt concentrations in the environment at the same gradient to cultivate rice as the experimental group, and at the same time set an environment with zero salt concentration to cultivate rice as the control group.
[0040] Data acquisition module: used to acquire infrared spectral images of rice leaves in each experimental group and the control group at equal time intervals, and to acquire root characteristic parameters of each experimental group and the control group at the same time as acquiring infrared spectra.
[0041] Data processing module: used to analyze infrared spectral images, extract leaf growth parameters in each spectral image, generate leaf growth index corresponding to each spectral image, analyze root feature parameters, and generate root growth index at each acquisition time.
[0042] The time series generation module is used to sort the leaf growth index according to the acquisition time of the corresponding spectral image to form a leaf growth time series dataset, and sort the root growth index according to the acquisition time of the corresponding root feature parameters to form a root growth time series dataset.
[0043] The comparison and screening module is used to judge the leaf growth and root growth of rice in each experimental group based on the leaf growth time series dataset and root growth time series dataset of each experimental group, and to screen out the experimental groups with normal leaf and root growth as pre-selected groups.
[0044] Results output module: Used to compare the leaf growth time series dataset and root growth time series dataset of the preselected group and the control group, judge the development status of each preselected group, and take the rice of the preselected group with the highest salt concentration that meets the development requirements as the rice with the highest salt tolerance, and set the corresponding salt concentration as the maximum salt tolerance.
[0045] Compared with the prior art, the beneficial effects of the present invention are:
[0046] By setting up an experimental environment with equal-gradient salt concentrations to cover the full tolerance range of rice, the maximum salt tolerance threshold can be accurately located, avoiding threshold misjudgment caused by traditional single-concentration tests. Secondly, continuous monitoring of rice growth after transplanting, by constructing time-series datasets of leaf growth index and root growth index, can fully capture the salt tolerance response pattern of rice from vegetative growth to reproductive development, reducing the misjudgment rate compared to traditional single-detection methods. Finally, by integrating infrared spectral leaf and root parameter data, the synergistic mechanism of aboveground physiological processes such as chlorophyll metabolism, lignin deposition, and water transport under salt stress and root architecture reconstruction can be simultaneously analyzed, improving the biological rationality of the evaluation system and solving the major deficiencies of existing methods in terms of concentration coverage, monitoring continuity, and system comprehensiveness. Attached Figure Description
[0047] Figure 1 This is a schematic diagram of the overall method flow of the present invention;
[0048] Figure 2 This is a graph showing the leaf growth index data from the rice salt tolerance evaluation experiment of this invention.
[0049] Figure 3 This is a graph showing the root growth index data from the rice salt tolerance evaluation experiment in an embodiment of the present invention.
[0050] Figure 4 This is a screening diagram of growth rate and overall deviation in the rice salt tolerance evaluation experiment of this invention.
[0051] Figure 5 This is a schematic diagram of the overall system structure of the present invention. Detailed Implementation
[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.
[0053] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0054] Example:
[0055] Please see Figures 1 to 4 The present invention provides a technical solution:
[0056] A method for evaluating rice salt tolerance based on sensor monitoring data, comprising the following steps:
[0057] S1: Rice was cultivated in an environment with different salt concentrations at equal gradients as the experimental group, while rice was cultivated in an environment with zero salt concentration as the control group.
[0058] In this embodiment, the experimental group consists of at least 10 groups, with a salt concentration gradient of 20 mM NaCl between adjacent groups, namely 20 mM NaCl, 40 mM NaCl, 60 mM NaCl, 80 mM NaCl, 100 mM NaCl, 120 mM NaCl, 140 mM NaCl, 160 mM NaCl, 180 mM NaCl, and 200 mM NaCl. The 20 mM gradient interval is used as the gradient range, and the 20 mM-200 mM gradient range is used as the gradient range. The 20 mM interval can cover a sufficient range without being too dense, accurately capturing the nonlinear relationship between salt concentration and growth response, and avoiding threshold omissions due to excessively large intervals. The 20 mM-200 mM gradient range covers the extreme salinity conditions of 98% of my country's saline-alkali land, ensuring the practical application value of the screened varieties.
[0059] S2: At equal time intervals, infrared spectral images of rice leaves in each experimental group and the control group were acquired. Root characteristic parameters of each experimental group and control group were collected while acquiring the infrared spectra.
[0060] In this embodiment, rice seedlings that have grown normally to the two-leaf-one-heart stage were transplanted to gradient salt concentration experimental groups. The time points for obtaining the infrared spectral images of rice leaves and root characteristic parameters of the experimental group and the control group were 7 days, 14 days, 21 days, 28 days and 35 days after transplanting, respectively. The method for extracting the leaf growth parameters in the infrared spectral images was as follows: at the time interval, infrared spectral images of rice leaves in each experimental group and the control group were collected using an infrared spectrometer. At the same time, root characteristic parameters, including root length, root area and root volume, were obtained using a root parameter sensor. The images corresponded one-to-one with the root characteristic parameters.
[0061] S3: Analyze the infrared spectral images, extract the leaf growth parameters in each spectral image, generate the leaf growth index corresponding to each spectral image, analyze the root feature parameters, and generate the root growth index at each acquisition time.
[0062] In this embodiment, infrared spectral images are analyzed to extract leaf growth parameters from each spectral image, generating a leaf growth index corresponding to each spectral image. These leaf growth parameters include chlorophyll retention rate, lignin content, and cell water content. Chlorophyll, as the core pigment of photosynthesis, directly determines light energy conversion efficiency. Salt stress damages chloroplast structure, inhibits chlorophyll synthase activity, and leads to chlorophyll degradation. Under salt stress, reactive oxygen species (ROS) erupt, triggering upregulation of the phenylpropane metabolic pathway, promoting lignin deposition in the cell wall to enhance mechanical strength and reduce ion permeability. Cell water content also decreases under salt stress due to salt ion accumulation, leading to osmotic stress. Cells maintain turgor pressure by accumulating compatible solutes. Salt stress affects all three parameters, and all three parameters are clearly reflected in infrared spectral images.
[0063] The acquired infrared spectral images were preprocessed to extract the total absorbance in the visible light region and the intensity of characteristic absorption peaks related to chlorophyll, lignin, and cell water content. The wavelength range of the visible light region is 400-3000 nm. The wavelength range of the characteristic absorption peak of chlorophyll is 680-720 nm. This band can non-destructively quantify chlorophyll dynamics and is more sensitive than the traditional SPAD value. The wavelength range of the characteristic absorption peak of lignin is 1630-1670 nm. This band can specifically capture the characteristic peak of lignin and its vibration with C=O and CH bonds. The wavelength range of the characteristic absorption peak of water is 1940-2000 nm. This band is sensitive to water and can distinguish between free water and bound water, which is 10 times faster than the drying method.
[0064] Calculate chlorophyll retention rate (CR), lignin content (LC), and cell water content (WC):
[0065]
[0066] In the formula, A total A represents the total absorbance in the visible light region. green A represents the characteristic absorbance of chlorophyll. lignin A represents the characteristic absorbance of lignin. H2O The absorbance is a characteristic of cell water content.
[0067] After extracting the leaf growth parameters from each spectral image, the Leaf Growth Index (LGI) corresponding to each spectral image is generated. The formula used to calculate the leaf growth index is as follows:
[0068] LGI = w CR ·CR+w LC ·LC+w WC WC
[0069] In the formula, w CR w LC w WC These are weighting coefficients, all greater than 0, and satisfying w CR +w LC +w WC =1; w CR =0.42, w LC =0.31, w WC =0.27. Using principal component analysis, the weights were obtained. CR (chlorophyll content) had a high weight of 42%, because chlorophyll content is directly related to light energy conversion efficiency and determines the upper limit of biomass accumulation. LC (leaf lignin content) had a medium weight of 31%, because excessive lignin accumulation inhibits leaf expansion, and for every 1 mg / g increase, the specific leaf area decreases by 5.2%. WC (leaf octane content) had a low weight of 27%, because water parameters are easily affected by short-term environmental fluctuations, with daily variations reaching ±8%. LGI comprehensively quantifies the impact of salt stress on leaf photosynthetic efficiency, structural stability, and water balance. By assigning weights, key parameters are highlighted. The higher the LGI, the more likely the plant can maintain normal photosynthesis and transpiration under salt stress. The lower the LGI, the significantly increased risk of yield reduction. A persistently low LGI indicates a plant mortality probability >70%.
[0070] While acquiring infrared spectra, root characteristic parameters of each experimental and control group were collected. These parameters were analyzed to generate a root growth index for each acquisition time. The root characteristic parameters included root length, root area, and root volume. The formula used to calculate the root growth index was as follows:
[0071] PRI = w RL ·RL+w RA ·RA+w RV ·RV
[0072] In the formula, w RL w RA w RV These are weighting coefficients, all greater than 0, and satisfying w RL +w RA +w RV =1; w RL =0.52, w RA＝ 0.37, w RV=0.11. Using principal component analysis, the weights were obtained. RL had a high weight of 52% because leaf and root surface area is strongly positively correlated with ion selective absorption capacity; RA had a medium weight of 37% because root volume has a more significant energy buffering effect in the later stages of salt stress; RV had a low weight of 11% because root length measurement is limited by sensor depth, resulting in relatively low data confidence. PRI revealed the systemic damage of salt stress to root architecture and function, and the key parameters were highlighted through weight allocation. The higher the PRI, the higher the nitrogen absorption rate of the variety under salt stress, while the lower the PRI, the lower the effective tiller number of rice and the greater the yield reduction.
[0073] S4: Sort the leaf growth index according to the acquisition time of the corresponding spectral image to form a leaf growth time series dataset, and sort the root growth index according to the acquisition time of the corresponding root feature parameter to form a root growth time series dataset.
[0074] In this embodiment, a leaf growth time-series dataset is constructed by sorting the root growth index according to the acquisition time of the corresponding root feature parameters. The specific scheme is as follows: the leaf growth index (LGI) corresponding to each spectral image is sorted according to the acquisition time to generate the following dataset:
[0075]
[0076] In the formula, Let LGI represent the leaf growth time series dataset of the i-th experimental group. i (t j () represents the leaf growth index corresponding to the infrared spectral image collected at time point j of the i-th experimental group, where j = 1, 2, 3, 4, 5, t j These represent the time points of 7, 14, 21, 28 and 35 days after transplanting, respectively, because this time range can completely capture the salt tolerance response pattern of rice from the seedling stage to the maturity stage;
[0077] The root growth index (PRI) at each time point is sorted according to the collection time, generating the following dataset:
[0078]
[0079] In the formula, PRI represents the root growth time series dataset of the i-th experimental group. i (t j ) represents the root growth index collected at the j-th time point of the i-th experimental group.
[0080] In this embodiment, a rice variety was randomly selected and its salt tolerance was evaluated using this evaluation method. The experimental measurement data are shown in the table below:
[0081] Table 1
[0082]
[0083]
[0084] Reference Figure 2 , Figure 3 , Figure 2 , Figure 3 The data clearly show the calculated LGI and PRI values of the control group and the isosal concentration gradient experimental group at 5 time observation points. By collecting sensor data on chlorophyll retention rate, lignin content, cell water content, root length, root area, and root volume, the leaf growth index and root growth index are calculated for further evaluation and screening.
[0085] S5: Based on the leaf growth time series dataset and root growth time series dataset of each experimental group, determine the leaf growth and root growth of the rice in the corresponding experimental group, and select the experimental groups with normal leaf and root growth as pre-selected groups.
[0086] In this embodiment, experimental groups with normal leaf and root growth were selected as pre-selection groups. Leaf growth index and root growth index were used to determine the growth status of rice in each experimental group.
[0087]
[0088] and This represents the leaf growth rate and root growth rate of the i-th experimental group. The growth rate is defined as the average difference in growth indices between adjacent time points. Using data from multiple time points avoids misjudgments caused by environmental fluctuations, such as short-term salinity changes, resulting from a single measurement. Experimental groups with normal leaf and root growth are selected when… and If the growth rate is within a certain range, the group is considered to be growing normally. The logic for this judgment is that both the leaves and roots show positive growth throughout the entire experimental period. The leaf growth parameters and root growth parameters corresponding to the current time point and the previous time point should both show positive growth. That is, the leaves do not show signs such as slight curling of leaf margins, yellowing of leaf tips, or reddening of the entire leaf. The roots do not show signs such as delayed development of lateral roots, shortening of the taproot, loss of root hairs, or overall browning of the root system. The purpose of using leaf growth rate and root growth rate as screening conditions is to screen the experimental groups that meet the normal growth conditions as pre-selected groups, quickly screen out the experimental groups that can still maintain basic growth under salt stress, and quickly eliminate the experimental groups whose growth is severely inhibited under salt stress, thereby reducing the complexity of subsequent calculations and avoiding the processing of a large amount of invalid data in subsequent steps for the next comparison.
[0089] S6: Compare the leaf growth time series data and root growth time series data of the pre-selected groups and the control group to determine the development status of each pre-selected group. Rice varieties in the pre-selected group that meet the development requirements and have the highest salt concentration are designated as the rice varieties with the highest salt tolerance, and their corresponding salt concentration is set as the maximum salt tolerance. By analyzing the development status and salt concentration of the pre-selected groups, the growth performance of each rice variety under different saline-alkali conditions can be scientifically evaluated. This data-driven method reduces subjective judgment, improves the scientific rigor of selection, and ensures that rice varieties with the highest salt tolerance perform well in high-salt environments, thereby improving the overall... By comparing crop growth and yield with salt concentration, we can more clearly identify which rice varieties are not only salt-tolerant but also maintain good growth under specific environmental conditions. By combining data analysis of development and salt concentration, we can more accurately select the optimal rice varieties, thereby improving the effectiveness of salt tolerance evaluation. After selecting the most suitable varieties, we can reduce unnecessary experiments and resource inputs, lower agricultural production costs, optimize resource utilization efficiency, and enhance the adaptability of agricultural systems to saline-alkali environments by selecting salt-tolerant rice varieties, thereby improving crop stress resistance and ensuring food security.
[0090] In this embodiment, the leaf growth time series datasets and root growth time series datasets of the pre-selected group and the control group are screened. In the pre-selected group that meets the requirements for normal rice growth, a second screening is performed. The screening criteria are as follows:
[0091]
[0092] In the formula, LGI(t) j ) represents the leaf growth index collected at the j-th time point of the control group, PRI(t) j (j) represents the root growth index collected at the j-th time point in the control group. This method reflects the cumulative deviation at five measurement time points. Positive and negative deviations may cancel each other out, leading to an underestimation of the actual deviation. The cumulative deviation provides a more accurate reflection of the overall difference. It covers five time points from 7 to 35 days after transplanting to capture the salt tolerance response pattern of rice from seedling to maturity, avoiding the randomness of data from single time points. To dynamically bind the threshold to the actual growth level of the control group, the degree of growth deviation between the experimental group and the control group was quantified. The deviation of the rice growth status under salt stress from the salt-free control group was measured by the cumulative percentage difference. The threshold setting of 0.1 is equivalent to allowing salt-tolerant varieties to produce ≤10% physiological fluctuations under salt stress. An LGI deviation >10% may mean that the light energy conversion efficiency has decreased beyond the tolerance threshold, resulting in a significant limitation of photosynthesis. A PRI deviation >10% reflects damage to the root system architecture, affecting the ability to absorb water and nutrients. Experiments show that a fluctuation >15% will lead to a significant decrease in yield. Therefore, 10% was set as a safe threshold to ensure that the rice in the pre-selected group with the highest developmental status meets the screening requirements and is regarded as the rice with the highest salt concentration. The corresponding salt concentration was set as the maximum salt tolerance of the rice variety.
[0093] Based on the experimental data in Table 1, S5 and S6 were conducted to process the experimental data and complete the rice salt tolerance evaluation experiment, as shown in the table below.
[0094] Table 2
[0095]
[0096] Reference Figure 4 , Figure 4 The growth rate of the film-covered leaves in each group is clearly displayed. Root growth rate LGI and PRI comprehensive biases are determined by taking the experimental data of the experimental groups that meet the screening requirements for development and the rice of the pre-selected group with the highest salt concentration as the rice with the highest salt tolerance, and setting the corresponding salt concentration as the maximum salt tolerance of the rice variety.
[0097] Please see Figure 5 The present invention also provides a rice salt tolerance evaluation system based on sensor monitoring data. The system is used to execute the above-described rice salt tolerance evaluation method based on sensor monitoring data, comprising:
[0098] Environment setting module: used to set different salt concentrations in the environment at the same gradient to cultivate rice as the experimental group, and at the same time set an environment with zero salt concentration to cultivate rice as the control group.
[0099] Data acquisition module: used to acquire infrared spectral images of rice leaves in each experimental group and the control group at equal time intervals, and to acquire root characteristic parameters of each experimental group and the control group at the same time as acquiring infrared spectra.
[0100] Data processing module: used to analyze infrared spectral images, extract leaf growth parameters in each spectral image, generate leaf growth index corresponding to each spectral image, analyze root feature parameters, and generate root growth index at each acquisition time.
[0101] The time series generation module is used to sort the leaf growth index according to the acquisition time of the corresponding spectral image to form a leaf growth time series dataset, and sort the root growth index according to the acquisition time of the corresponding root feature parameters to form a root growth time series dataset.
[0102] The comparison and screening module is used to judge the leaf growth and root growth of rice in each experimental group based on the leaf growth time series dataset and root growth time series dataset of each experimental group, and to screen out the experimental groups with normal leaf and root growth as pre-selected groups.
[0103] Results output module: Used to compare the leaf growth time series dataset and root growth time series dataset of the preselected group and the control group, judge the development status of each preselected group, and take the rice of the preselected group with the highest salt concentration that meets the development requirements as the rice with the highest salt tolerance, and set the corresponding salt concentration as the maximum salt tolerance.
[0104] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.
[0105] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.
[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.
[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.
Claims
1. A method for evaluating the salt tolerance of rice based on sensor monitoring data, characterized in that, The specific steps include: Rice was cultivated in environments with different salt concentrations at equal gradients as the experimental group, while rice was cultivated in an environment with zero salt concentration as the control group. Infrared spectral images of rice leaves in each experimental group and the control group were acquired at equal time intervals. Root characteristic parameters of each experimental group and the control group were collected simultaneously with the acquisition of infrared spectra. Infrared spectral images are analyzed to extract leaf growth parameters from each image, generating a leaf growth index for each image. Root feature parameters are also analyzed to generate a root growth index for each acquisition time. The leaf growth parameters include chlorophyll retention rate, lignin content, and cell water content. The leaf growth index... The root system is obtained by weighted summation of chlorophyll retention rate, lignin content, and cell water content; the root system characteristic parameters include root length, root area, root volume, and root growth index. It is obtained by weighted summation of the root length, root area, and root volume; The leaf growth index was sorted according to the acquisition time of the corresponding spectral image to form a leaf growth time series dataset, and the root growth index was sorted according to the acquisition time of the corresponding root feature parameters to form a root growth time series dataset. Based on the leaf growth time series dataset and root growth time series dataset of each experimental group, the leaf growth rate and root growth rate were calculated respectively, and the experimental groups with both leaf growth rate and root growth rate greater than zero were selected as pre-selected groups. The leaf growth time series datasets and root growth time series datasets of the pre-selected group and the control group were compared. The cumulative relative deviation of the leaf growth index of each pre-selected group and the leaf growth index of the control group, as well as the cumulative relative deviation of the root growth index of each pre-selected group and the root growth index of the control group, were calculated. The rice in the pre-selected group with the highest cumulative relative deviation (not exceeding 10%) and the highest salt concentration was selected as the rice with the highest salt tolerance. The corresponding salt concentration was set as the maximum salt tolerance. The rice in the pre-selected group with the highest cumulative relative deviation (not exceeding 10%) and salt concentration is selected as the rice with the highest salt tolerance. Specifically, the leaf growth time series data and root growth time series data of the pre-selected group and the control group are screened. In the pre-selected group that meets the requirements for normal rice growth, a second screening is performed. The screening conditions are as follows: In the formula, For time point indexing, For the experimental group index, For the first The experimental group Leaf growth index collected at various time points For the first The experimental group Root growth index collected at various time points For the control group Leaf growth index collected at various time points For the control group The root growth index was collected at several time points. The rice in the pre-selected group that met the screening requirements and had the highest salt concentration was selected as the rice with the highest salt tolerance, and the corresponding salt concentration was set as the maximum salt tolerance.
2. The method for evaluating rice salt tolerance based on sensor monitoring data according to claim 1, characterized in that: The experimental group consisted of at least 10 groups, with a salt concentration gradient of 20 mM NaCl between adjacent groups. Infrared spectral images of rice leaves in the experimental and control groups were acquired at equal time intervals at 7, 14, 21, 28, and 35 days after transplanting.
3. The method for evaluating rice salt tolerance based on sensor monitoring data according to claim 1, characterized in that: Leaf growth parameters include chlorophyll retention, lignin content, and cell water content. The method for extracting leaf growth parameters from infrared spectral images is as follows: Infrared spectral images of rice leaves from each experimental group and the control group were acquired at equal time intervals. The infrared spectral images were analyzed to extract leaf growth parameters from each image, generating a leaf growth index for each image. These parameters included chlorophyll retention rate, lignin content, and cell water content. The steps for acquiring the infrared spectral images included: infrared scanning of the surface of rice leaves from both groups; data preprocessing of the acquired infrared spectral images; extraction of the total absorbance in the visible light region; and extraction of the intensity of characteristic absorption peaks related to chlorophyll, lignin, and cell water content. The wavelength range of the visible light region was 400-3000 nm, and the wavelength ranges of the characteristic absorption peaks related to chlorophyll, lignin, and cell water content were 680-720 nm, 1630-1670 nm, and 1940-2000 nm, respectively. Calculate chlorophyll retention rate Lignin content and cell water content : In the formula, The total absorbance in the visible light region. The characteristic absorbance of chlorophyll. The characteristic absorbance of lignin The absorbance is a characteristic of cell water content.
4. The method for evaluating rice salt tolerance based on sensor monitoring data according to claim 1, characterized in that: To construct a leaf growth time-series dataset, root growth indices are sorted according to the acquisition time of their corresponding root feature parameters. The specific scheme is as follows: The leaf growth index corresponding to each spectral image is... The datasets were sorted according to the collection time, resulting in the following datasets: In the formula, Indicates the first Leaf growth time series datasets from each experimental group. Indicates the first The experimental group Leaf growth index corresponding to infrared spectral images collected at each time point. hour, These represent time points of 7 days, 14 days, 21 days, 28 days, and 35 days after transplanting, respectively. Root growth index at each time point The datasets were sorted according to the collection time, resulting in the following datasets: In the formula, Indicates the first Root growth time series datasets for each experimental group Indicates the first The experimental group Root growth index collected at several time points.
5. The method for evaluating rice salt tolerance based on sensor monitoring data according to claim 4, characterized in that: Experimental groups with normal leaf and root growth were selected as pre-selection groups. Leaf growth index and root growth index were used to assess the growth of rice in each experimental group. and Indicates the first The leaf growth rate and root growth rate of each experimental group were compared, and the experimental group with normal leaf and root growth was selected. and If the condition is met, the group is judged to be growing normally, and the experimental groups that meet the criteria will be compared in the next step.
6. A rice salt tolerance evaluation system based on sensor monitoring data, characterized in that: The evaluation system is used to perform the rice salt tolerance evaluation method based on sensor monitoring data as described in any one of claims 1-5, including: Environment setting module: used to set different salt concentrations in the environment at the same gradient to cultivate rice as the experimental group, and at the same time set an environment with zero salt concentration to cultivate rice as the control group. Data acquisition module: used to acquire infrared spectral images of rice leaves in each experimental group and the control group at equal time intervals, and to acquire root characteristic parameters of each experimental group and the control group at the same time as acquiring infrared spectra. Data processing module: used to analyze infrared spectral images, extract leaf growth parameters in each spectral image, generate leaf growth index corresponding to each spectral image, analyze root feature parameters, and generate root growth index at each acquisition time. The time series generation module is used to sort the leaf growth index according to the acquisition time of the corresponding spectral image to form a leaf growth time series dataset, and sort the root growth index according to the acquisition time of the corresponding root feature parameters to form a root growth time series dataset. The comparison and screening module is used to judge the leaf growth and root growth of rice in each experimental group based on the leaf growth time series dataset and root growth time series dataset of each experimental group, and to screen out the experimental groups with normal leaf and root growth as pre-selected groups. Results output module: Used to compare the leaf growth time series dataset and root growth time series dataset of the preselected group and the control group, judge the development status of each preselected group, and take the rice of the preselected group with the highest salt concentration that meets the development requirements as the rice with the highest salt tolerance, and set the corresponding salt concentration as the maximum salt tolerance.