High-throughput rapid screening system for high-luminous-efficiency rice materials based on visible light

Abstract

The invention relates to the technical field of image recognition, and provides a visible-light-based high-luminous-efficiency rice material high-throughput rapid screening system which specifically comprises the following steps: performing high-luminous-efficiency screening on test varieties through direct photographing of a camera; and extracting a new high-photosynthetic-efficiency index qw value created by the project through a Python code, taking the qw value of a known high-photosynthetic-efficiency variety as a CK control group, performing t inspection on other varieties and CK, and judging the high and low photosynthetic efficiency of the variety through a p value of the t inspection. The photosynthetic efficiency difference of different varieties is judged according to the specific value qw in the output results, and if the output results are all low in light efficiency, the variety with the higher qw value is high in light efficiency. Based on the system, an efficient screening model of high-photosynthetic-efficiency rice is constructed and verified, accurate prediction and selection of early-stage high-photosynthetic-efficiency individuals are achieved, the breeding process is accelerated, and rapid breeding of high-photosynthetic-efficiency rice varieties is achieved.

Description

Technical Field

[0001] This invention relates to the field of image recognition technology and discloses a high-throughput rapid screening system for high-efficiency rice materials based on visible light. Background Technology

[0002] Rice is one of my country's major food crops, and food security is essential for social stability and development. Light is an indispensable element for photosynthesis, affecting grain formation and quality, and is a significant limiting factor for crop yield. High photosynthetic efficiency crops can increase grain yield by 15-24%, which is key to ensuring high and stable crop yields. Rapid screening of high photosynthetic efficiency rice varieties is of great significance for increasing grain yield, ensuring food security, and achieving sustainable agricultural development. As rice progresses through its growth cycle, the light response indicators affecting rice growth change significantly, making it crucial to identify high photosynthetic efficiency indicators. Currently, no model has been reported for rapidly screening high photosynthetic efficiency rice varieties. Summary of the Invention

[0003] Based on the shortcomings of the above-mentioned technologies, the purpose of this invention is to construct an efficient screening model for high-light-efficiency rice by integrating high-light-efficiency varieties in the seedling stage, variant materials created by genomics, model algorithms and big data analysis methods, visible light sensors and other multidisciplinary approaches, so as to achieve accurate prediction and selection of high-light-efficiency individuals in the early stage, accelerate the breeding process and the rapid breeding of high-light-efficiency rice varieties.

[0004] The present invention achieves the above objectives through the following technical solutions:

[0005] S1 screened high photosynthetic efficiency indicators for plants treated with L0 light. A multi-functional plant measuring instrument was used to measure the physiological indicators of the third leaf at the 3-leaf stage of rice. Differences in the obtained physiological indicators of the third leaf were analyzed to screen for photosynthetic response indicators. Those containing chlorophyll fluorescence parameters were selected as high photosynthetic efficiency indicators.

[0006] S2 uses Photoshop to acquire RGB data by sampling points. The camera takes photos of rice at the three-leaf stage under different light intensities, backgrounds, and angles, capturing images of the rice at the three-leaf stage. The color sampling function in Photoshop is then used to sample the marked positions on the leaves, and the RGB values ​​are obtained for data acquisition.

[0007] S3 constructs an efficient screening model for high-light-efficiency rice. Correlation analysis is performed between the Kit (Kitaake) index under L0 illumination treatment (where all light efficiency indicators are high) and RGB data obtained from PS. The index with the strongest correlation to each RGB indicator, gH, is identified as the most relevant. + As an important indicator for evaluating the high photosynthetic efficiency of rice, this set of high photosynthetic efficiency indicators and RGB data obtained through PS were used for modeling and analysis. The high photosynthetic efficiency indicators were estimated using the RGB indicators, and the model was obtained.

[0008] S4 Model Validation. Two sets of images of plants of the same variety under light and dark conditions were taken, and the data were sequentially fed into the model for filtering. The filtering results for the same variety were consistent. Validation showed that the model can accurately distinguish between high and low light efficiency of varieties, and the high light efficiency varieties selected were consistent with the reports, indicating that the model is feasible.

[0009] Beneficial effects:

[0010] The tool is flexible and easy to operate: the visual model can be used as a mobile phone camera to perform high-efficiency screening of the current varieties by directly taking pictures.

[0011] A new high light efficiency index was created as the main evaluation indicator: three consecutive images of each variety were taken, and qw (high light efficiency index qw = gH) was extracted using Python code. + / Fo), using the qw value of known high photosynthetic efficiency varieties as the CK control group, and comparing the other varieties with the CK using a t-test, the high and low photosynthetic efficiency of the varieties was determined based on the P value.

[0012] Applicable to relative comparison between multiple varieties: The difference in light effect between two varieties is judged by the ratio qw in the output result. By comparing qw, the variety with the larger qw value has higher light effect. This method is also applicable to multiple varieties. When the output of two varieties is low light effect, the variety with the larger qw value has higher light effect. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the concept of a high-throughput rapid screening system for high-efficiency rice materials based on visible light, as described in this invention.

[0014] Figure 2 This is a site map of the high-efficiency rice site inversion model of a high-throughput rapid screening system for high-efficiency rice materials based on visible light, according to the present invention. Detailed Implementation

[0015] The technical solution of the present invention will now be clearly and comprehensively described in conjunction with the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. In the description of the present invention, it should be noted that the terms "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0016] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0017] Example 1

[0018] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 , Figure 1 This is a flowchart illustrating the conceptual design of a high-throughput rapid screening system for high-efficiency rice materials based on visible light, according to an embodiment of the present invention. Figure 1 The instructions include the following steps:

[0019] S1 screened high photosynthetic efficiency indicators for plants treated with L0 light. A multi-functional plant measuring instrument was used to measure the physiological indicators of the third leaf at the 3-leaf stage of rice. Differences in the obtained physiological indicators of the third leaf were analyzed to screen for photosynthetic response indicators. Those containing chlorophyll fluorescence parameters were selected as high photosynthetic efficiency indicators.

[0020] S2 uses Adobe Photoshop 2022 to acquire RGB data by sampling points. The camera takes photos of rice at the three-leaf stage under different light intensities, backgrounds, and angles, capturing images of the rice at the three-leaf stage. The color sampling function in Photoshop is used to sample the marked positions on the leaves, and the RGB values ​​are then collected for data acquisition.

[0021] S3 constructs an efficient screening model for high-light-efficiency rice. Correlation analysis is performed using the Kit index under L0 illumination treatment (where all light efficiency indicators are high) and RGB data obtained via PS. The gH+ index, which has the strongest correlation with each RGB index, is selected as a key indicator for evaluating high light efficiency in rice. This set of high-light-efficiency indicators is then used in conjunction with the RGB data obtained via PS for modeling analysis. The high-light-efficiency index is estimated using the RGB indicators, resulting in the model.

[0022] S4 Model Validation. Two sets of images of plants of the same variety under light and dark conditions were taken, and the data were sequentially fed into the model for filtering. The filtering results for the same variety were consistent. Validation showed that the model can accurately distinguish between high and low light efficiency of varieties, and the high light efficiency varieties selected were consistent with the reports, indicating that the model is feasible.

[0023] Specifically, the process of executing step S1 can include the following steps: Using a multi-functional plant measuring instrument MQ-V2.0, the widest position of a rice leaf is clamped and marked to obtain the physiological indicators of the third leaf at the three-leaf stage of rice. The Kit physiological indicators under L0 illumination are used as the CK control group. The Kit data for each group are compared with the CK data (L-L0) / L0, and a comparison line graph is generated using Excel 2017 to screen for physiological indicators with significant differences. Principal component analysis is performed on the obtained individual physiological indicators with significant differences to explain most of the variation with fewer variables. Then, scree plots are used to divide the above indicators into four principal components, and further screening is performed based on loading coefficients. The above indicators, including chlorophyll fluorescence parameters (FmPrime, FoPrime, Fs, FvP_over_FmP, NPQt, Phi2, gH), are further analyzed. + The photoresponse index was used as a high photoefficiency index to construct a physiological index model, as shown in the table.

[0024]

[0025] The process of executing step S2 can specifically include the following steps: Place the experimental materials of the modeling group under four growing lights with different light intensities (L0, L1, L2, L3), and place a Kit (wild-type material) under each growing light. Use a Sony IMX686 camera to acquire images of rice at the 3-leaf stage. Based on the leaf positions captured by the multi-functional plant measuring instrument, obtain the RGB values ​​using the color extraction function in Photoshop.

[0026] The process of executing step S4 can specifically include the following steps: processing the rice leaf image to convert the acquired RGB image into an HSV image, segmenting the green color blocks, extracting the rice image separately, obtaining the average RGB value of the extracted portion, and inputting it into the model gH. + Using FoPrime, we can determine the relative light efficacy among varieties.

[0027] Note that the above description is merely a preferred embodiment and application of the technical principles of the present invention. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the specific embodiments described herein, and may include many other effective embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A high-throughput rapid screening system for high-efficiency rice materials based on visible light, comprising an RGB data acquisition unit, high-efficiency index, a screening model for high-efficiency rice varieties, and model visualization code; characterized in that: The RGB data acquisition unit includes a high-efficiency point inversion model built based on independently improved Python code. This project assumes that the gH of each point in the leaf... + The average value best represents the physiological indicators of the entire leaf. Based on this, when the number of pixels of G in the image equals gH... + When the mean is taken, this point best represents the physiological indicators of the leaf and is marked with a blue dot. RGB data is obtained using the color picker function in Photoshop. The high light efficiency index is the chlorophyll fluorescence parameter gH. + The light response index is used as an indicator of high luminous efficiency.

2. The high-throughput rapid screening system for high-efficiency rice materials based on visible light according to claim 1, characterized in that... Correlation analysis was performed on the acquired rice indicators and RGB data, and a high-efficiency screening model for high-efficiency rice was constructed by combining the highly correlated high light efficiency indicators with RGB data.

3. The high-throughput rapid screening system for high-efficiency rice materials based on visible light according to claim 1, characterized in that... Rice is photographed using a camera to construct a rice dataset. The acquired RGB images are converted into HSV images, green color blocks are segmented, and the rice images are extracted separately. The parameter set is input into a preset high light efficiency index model for feature extraction to determine the relative high and low light efficiency among varieties.

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