Method and device for predicting plant fruit weight growth rate and mature weight

Abstract

The present invention relates to a method and apparatus for predicting the weight growth rate and mature weight of plant fruits. Specifically, the present invention provides a method for constructing a prediction model for the weight growth rate or mature weight of plant ears, seeds, or fruits. This method can quickly and accurately determine the weight growth rate and mature weight of plant ears, seeds, or fruits by using respiration rate without removing or damaging the plant.

Description

Technical Field

[0001] The present invention belongs to the field of plant yield analysis and specifically relates to a technique for measuring gas exchange rates in plant organs and mathematical modeling, and more specifically, to a method and apparatus for non-invasively predicting the weight growth rate and mature weight of plant ears, seeds, or fruits. Background Art

[0002] Plants, especially crop ears, seeds, or fruits, are the primary nutrient storage and flow destinations for many economic and food crops. These organs are harvested and directly correlated with grain yield and the economic value of crops. The weight gain rate of crop ears, seeds, or fruits during growth (also known as the grain filling rate in cereal crops) is an important indicator of the activity and coordination between the source and sink in the crop. Therefore, the dynamic weight gain rate and final weight of crop ears, seeds, or fruits are of vital importance to crop breeding and crop physiology. Current methods for measuring dynamic weight gain rate and harvest weight rely on destructive sampling (severing the crop ear, seed, or fruit from the plant). This method is time-consuming, labor-intensive, and inefficient due to poor repeatability and the need for averaging numerous replicates. Furthermore, these measurements are destructive, impairing the normal growth of the sampled plants. This approach is impractical when planting a small number of plants or when the plants are relatively rare. In addition, the current harvest weight measurement method requires waiting until the crop is fully mature. If the harvest weight of crop ears, seeds or fruits can be predicted before the crop is fully mature, it will help breeders and cultivators improve the efficiency of yield testing and shorten the line screening and variety evaluation cycle. Summary of the Invention

[0003] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a non-destructive prediction method for the weight growth rate and mature weight of crop ears, seeds or fruits.

[0004] A first aspect of the present invention provides a method for constructing a prediction model for the weight growth rate of ear, seed or fruit of a plant, comprising:

[0005] (1) detecting the weight of the ear, seed or fruit of the plant at one or more times after the plant begins to flower, and detecting the respiration rate of the ear, seed or fruit at one or more times after the plant begins to flower,

[0006] (2) Using the weight values ​​at different times obtained in step (1), a weight increase rate curve is fitted.

[0007] (3) A regression model is established using the respiratory rate in step (1) and the weight gain rate in step (2).

[0008] In one or more embodiments, the plant is a crop, including an oil crop, a food crop, or a cash crop.

[0009] In one or more embodiments, the plant is rice or wheat.

[0010] In one or more embodiments, the ears, seeds, or fruits of the plants are similar in size when fully emerged.

[0011] In one or more embodiments, the weight detection and / or respiration rate detection in step (1) is performed after the plant ear, seed or fruit is fully exposed.

[0012] In one or more embodiments, in step (1), the weight of ears, seeds or fruits of one or more plants at one or more times is measured. Preferably, the plurality of plants is at least 5.

[0013] In one or more embodiments, the weight is dry weight.

[0014] In one or more embodiments, in step (1), the respiration rate of ears, seeds or fruits of one or more plants at one or more times is detected. Preferably, the plurality of plants is at least 5.

[0015] In one or more embodiments, the weight detection and the respiration rate detection in step (1) are continued until the weight of the ear, seed or fruit no longer increases.

[0016] In one or more embodiments, the one or more times at which weight is measured is every 0-10 days, every 1-8 days, or every 2-5 days.

[0017] In one or more embodiments, the one or more times at which respiratory rate is detected is every 0-10 days, every 1-8 days, or every 2-5 days.

[0018] In one or more embodiments, the one or more times at which weight is detected are the same as or different from the one or more times at which respiratory rate is detected.

[0019] In one or more embodiments, the plants tested for weight are different from the plants tested for respiration rate.

[0020] In one or more embodiments, the method further comprises the step of normalizing the measured respiratory rate; for example, in units of nmol s -1 Standardize.

[0021] In one or more embodiments, the fitting in step (2) is performed using a beta growth formula.

[0022] In one or more embodiments, the weight gain rate is fitted according to the following formula:

[0023]

[0024] 0≤t m1 ≤t e1 ,0≤t m1 <t m2 ≤t e2

[0025] GFR(t)=W(t+1)-W(t)

[0026] Where t is the number of days after the plant starts to flower, W(t) is the weight of the plant ear, seed or fruit after t days, GFR(t) is the rate of increase of the weight of the plant ear, seed or fruit after t days; W max1 , t m1 and t e1 W are the final weight of the first crop ears, seeds or fruits, the time to reach the maximum growth rate and the time to reach the maximum weight, respectively; max2 , t m2 and t e2 are the final weight, time to reach maximum growth rate and time to reach maximum weight of the second batch of plant ears, seeds or fruits respectively. If there is only one seed, one fruit, or multiple seeds growing synchronously, then W max2 =0.

[0027] In one or more embodiments, a regression model is established according to the following formula:

[0028] GFR(t)=k·R d (t)+b

[0029] Where t is the number of days after the plant starts flowering, GFR(t) is the increase rate of ear dry weight t days after the first flowering, Rd(t) is the respiratory rate of the ear t days after the first flowering, and k and b are parameters.

[0030] In one or more embodiments, the plant is rice, k=0.013, b=0.

[0031] In one or more embodiments, the plant is wheat, k = 0.018, b = -0.095.

[0032] The present invention also provides a method for predicting the weight growth rate of ears, seeds or fruits of a plant, comprising the steps of:

[0033] (1) constructing a prediction model using the method described in the first aspect of this article, and

[0034] (2) Measure the respiration rate of the plant ear, seed or fruit t days after the first flowering, and predict the weight growth rate based on the model in step (1).

[0035] In one or more embodiments, the model is as follows:

[0036] GFR(t)=k·R d (t)+b

[0037] Where t is the number of days after flowering, GFR(t) is the weight gain rate on the tth day, R d (t) is the respiration rate of crop ears, seeds or fruits on the tth day, and k and b are parameters.

[0038] The present invention also provides a method for predicting the weight growth rate of rice or wheat ears, comprising the steps of measuring the respiration rate of the rice or wheat ear t days after the onset of flowering, and predicting the weight growth rate according to the following formula:

[0039] GFR(t)=k·R d (t)+b

[0040] Where t is the number of days after anthesis, GFR(t) is the rate of increase of ear dry weight t days after anthesis, Rd(t) is the respiration rate of ear t days after anthesis, and k and b are parameters: for rice, k = 0.013, b = 0; for wheat, k = 0.018, b = -0.095.

[0041] The present invention also provides a medium having recorded thereon the method for constructing a predictive model for the growth rate of plant ear, seed or fruit weight as described herein, and / or the method for predicting the growth rate of plant ear, seed or fruit weight as described herein and / or the model therein, and / or the method for predicting the growth rate of rice or wheat ear weight as described herein and / or the model therein.

[0042] In one or more embodiments, the medium is a carrier printed with the method or model, including a card, such as a paper, plastic, metal, or glass card.

[0043] In one or more embodiments, the medium is a computer-readable medium storing the method or model and a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0044] (I)

[0045] (1) obtaining the weight of the ear, seed or fruit at one or more times after the plant begins to flower and the respiration rate of the ear, seed or fruit at one or more times after the plant begins to flower,

[0046] (2) Using the weight values ​​at different times obtained in step (1), fit the weight increase rate curve, and

[0047] (3) establishing a regression model using the respiratory rate in step (1) and the weight gain rate in step (2); and

[0048] Optionally (4) obtaining the respiration rate of the ear, seed or fruit of the plant t days after the beginning of flowering, and predicting the weight growth rate according to the model of step (3); preferably, the model is as follows:

[0049] GFR(t)=k·R d (t)+b

[0050] Where t is the number of days after the plant starts flowering, GFR(t) is the weight gain rate t days after the plant starts flowering, and R d (t) is the respiration rate of crop ears, seeds or fruits after t days, k and b are parameters;

[0051] or,

[0052] (II)

[0053] The steps for obtaining the respiration rate of rice or wheat spikelets t days after the first flowering and predicting the weight growth rate according to the following formula:

[0054] GFR(t)=k·R d (t)+b

[0055] Where t is the number of days after anthesis, GFR(t) is the rate of increase of ear dry weight t days after anthesis, Rd(t) is the respiration rate of ear t days after anthesis, and k and b are parameters: for rice, k = 0.013, b = 0; for wheat, k = 0.018, b = -0.095.

[0056] Another aspect of the present invention provides a device for predicting the weight growth rate of ears, seeds, or fruits of plants, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the following steps are implemented:

[0057] (I)

[0058] (1) obtaining the weight of the ear, seed or fruit at one or more times after the plant begins to flower and the respiratory rate of the ear, seed or fruit at one or more times;

[0059] (2) Using the weight values ​​at different times obtained in step (1), fit the weight increase rate curve, and

[0060] (3) establishing a regression model using the respiratory rate in step (1) and the weight gain rate in step (2); and

[0061] (4) Obtaining the respiration rate of the ear, seed, or fruit of the plant t days after the beginning of flowering, and predicting the weight growth rate based on the model of step (3); preferably, the model is as follows:

[0062] GFR(t)=k·Rd (t)+b

[0063] Where t is the number of days after the plant starts flowering, GFR(t) is the weight gain rate t days after the plant starts flowering, and R d (t) is the respiration rate of crop ears, seeds or fruits after t days, k and b are parameters;

[0064] or,

[0065] (II)

[0066] The steps for obtaining the respiration rate of rice or wheat spikelets t days after the first flowering and predicting the weight growth rate according to the following formula:

[0067] GFR(t)=k·R d (t)+b

[0068] Where t is the number of days after anthesis, GFR(t) is the rate of increase of ear dry weight t days after anthesis, Rd(t) is the respiration rate of ear t days after anthesis, and k and b are parameters: for rice, k = 0.013, b = 0; for wheat, k = 0.018, b = -0.095.

[0069] A second aspect of the present invention provides a method for constructing a prediction model for the mature weight of a plant ear, seed, or fruit, comprising:

[0070] (1) measuring the respiration rate of ears, seeds or fruits of one or more plants at one or more times after the onset of flowering,

[0071] (2) measuring the mature weight of the plant ear, seed or fruit, preferably the dry weight;

[0072] (3) Constructing a regression model using one, two or more of the respiratory rates in step (1) and the mature weights in step (2).

[0073] In one or more embodiments, the detection in step (1) is performed after the plant ear, seed or fruit is fully exposed.

[0074] In one or more embodiments, step (3) includes: performing a correlation analysis on one, two or more combinations of the respiratory rates in step (1) and the mature weight in step (2) to obtain correlation parameters, and selecting the respiratory rate or respiratory rate combination and the mature weight according to the correlation parameters to construct a regression model.

[0075] In one or more embodiments, the correlation parameter is selected from the group consisting of: correlation coefficient r, RMSE, MAE, and MAPE. A respiratory rate or respiratory rate combination with a high correlation coefficient r and / or a low MAPE is selected to construct a regression model with mature weight.

[0076] In one or more embodiments, one, two or more respiration rates or the sum of respiration rates are used in a correlation analysis with mature weight.

[0077] In one or more embodiments, the correlation analysis includes: performing a correlation analysis using one, two or more respiratory rates or the sum of respiratory rates and mature weight to obtain a correlation coefficient r that measures the degree of linear correlation between two random variables x and y:

[0078]

[0079] Where n is the number of samples, x i ,y i is the i-th observation value of two variables x and y, and is the mean of two variables x and y.

[0080] In one or more embodiments, the regression model is as follows:

[0081] Y=c1·(R d (t1)+R d (t2))+c2

[0082] Where t1 and t2 are days, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, and c1 and c2 are fitting parameters.

[0083] In one or more embodiments, the plant is a crop, including an oil crop, a food crop, or a cash crop.

[0084] In one or more embodiments, the plant is rice or wheat.

[0085] In one or more embodiments, the ears, seeds, or fruits of the plants are of similar size after emergence.

[0086] In one or more embodiments, in step (1), the plurality of plants is at least 5.

[0087] In one or more embodiments, the respiration rate in step (1) is monitored until the weight of the ear, seed, or fruit no longer increases.

[0088] In one or more embodiments, the one or more times is every 0-10 days, every 1-8 days, or every 2-5 days.

[0089] In one or more embodiments, the method further comprises the step of normalizing the measured respiratory rate; for example, in units of nmol s -1 Standardize.

[0090] The present invention also provides a method for predicting the mature weight of an ear, seed or fruit of a plant, comprising the steps of:

[0091] (1) constructing a prediction model using the method described in the second aspect of this invention, and

[0092] (2) Measuring the respiration rate of the ear, seed or fruit of the plant and predicting the mature weight based on the model in step (1).

[0093] In one or more embodiments, the respiration rate is the respiration rate of the ear, seed or fruit of the plant at t1 and t2 days after the beginning of flowering, and the model is as follows:

[0094] Y=c1·(R d (t1)+R d (t2))+c2

[0095] Where t1 and t2 are the days after flowering, Y is the mature weight, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, and c1 and c2 are parameters.

[0096] The present invention also provides a method for predicting the mature weight of a rice or wheat ear, comprising the steps of measuring the respiration rate of the rice or wheat ear at t1 and t2 days after initial flowering, and predicting the mature weight according to the following formula:

[0097] Y=c1·(R d (t1)+R d (t2))+c2

[0098] Y is the mature weight, R d (t) is the respiratory rate of the ear t days after the start of flowering, c1 and c2 are parameters, and t1 and t2 are each independently selected from: 5±2, 16±2, 36±2.

[0099] In one or more embodiments, for rice, t1 = 16 ± 2, t2 = 36 ± 2, c1 = 0.1373, c2 = 2.04.

[0100] The present invention also provides a medium having recorded thereon the method for constructing a prediction model for the mature weight of plant ears, seeds or fruits as described herein, and / or the method for predicting the mature weight of plant ears, seeds or fruits as described herein and / or the model therein, and / or the method for predicting the mature weight of rice or wheat ears as described herein and / or the model therein.

[0101] In one or more embodiments, the medium is a carrier printed with the method or model, including a card, such as a paper, plastic, metal, or glass card.

[0102] In one or more embodiments, the medium is a computer-readable medium storing the method or model and a computer program, and when the computer program is executed by a processor, the method of constructing a prediction model for the mature weight of a plant ear, seed or fruit, the method of predicting the mature weight of a plant ear, seed or fruit, and / or the method of predicting the mature weight of a rice or wheat ear as described herein is implemented.

[0103] In one or more embodiments, when the computer program is executed by a processor, the following steps are implemented:

[0104] (I)

[0105] (1) obtaining the respiratory rate of ears, seeds or fruits of one or more plants at one or more times after the onset of flowering,

[0106] (2) obtaining the mature weight, preferably dry weight, of the plant ear, seed or fruit,

[0107] (3) constructing a regression model using one, two or more of the respiratory rates in step (1) and the mature weights in step (2),

[0108] Optionally (4) measuring the respiration rate of the ear, seed or fruit of the plant and predicting the mature weight according to the model of step (3); preferably, the model is as follows:

[0109] Y=c1·(R d (t1)+R d (t2))+c2

[0110] Where t1 and t2 are days, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, c1 and c2 are fitting parameters;

[0111] or,

[0112] (II)

[0113] Obtain the respiration rate of rice or wheat spikelets at t1 and t2 days after anthesis, and predict the mature weight according to the following formula:

[0114] Y=c1·(R d (t1)+R d (t2))+c2

[0115] Y is the mature weight, R d (t) is the respiratory rate of the panicle t days after the onset of flowering, c1 and c2 are parameters, t1 and t2 are independently selected from: 5±2, 16±2, 36±2; for rice, t1=16±2, t2=36±2, c1=0.1373, c2=2.04.

[0116] On the other hand, the present invention also provides a device for predicting the weight growth rate of plant ears, seeds or fruits, which includes a memory, a processor and a computer program stored in the memory and runnable on the processor, and is characterized in that when the processor executes the program, it implements the method described in this article for constructing a prediction model for the mature weight of plant ears, seeds or fruits, the method for predicting the mature weight of plant ears, seeds or fruits, and / or the method for predicting the mature weight of rice or wheat ears.

[0117] In one or more embodiments, the processor implements the following steps when executing the program:

[0118] (I)

[0119] (1) obtaining the respiratory rate of ears, seeds or fruits of one or more plants at one or more times after the onset of flowering,

[0120] (2) obtaining the mature weight, preferably dry weight, of the plant ear, seed or fruit,

[0121] (3) constructing a regression model using one, two or more of the respiratory rates in step (1) and the mature weights in step (2),

[0122] Optionally (4) measuring the respiration rate of the ear, seed or fruit of the plant and predicting the mature weight according to the model of step (3); preferably, the model is as follows:

[0123] Y=c1·(R d (t1)+R d (t2))+c2

[0124] Where t1 and t2 are days, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, c1 and c2 are fitting parameters;

[0125] or,

[0126] (II)

[0127] Obtain the respiration rate of rice or wheat spikelets at t1 and t2 days after anthesis, and predict the mature weight according to the following formula:

[0128] Y=c1·(R d (t1)+R d (t2))+c2

[0129] Y is the mature weight, R d(t) is the respiratory rate of the panicle t days after the onset of flowering, c1 and c2 are parameters, t1 and t2 are independently selected from: 5±2, 16±2, 36±2; for rice, t1=16±2, t2=36±2, c1=0.1373, c2=2.04. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] Figure 1 , a flow chart of the method of the present invention used in rice.

[0131] Figure 2 , the fitting graph of the predicted final dry weight of rice panicles at harvest and the actual final dry weight of rice panicles at harvest.

[0132] Figure 3 , the fitting diagram of the predicted rice ear weight increase rate and the actual rice ear weight increase rate.

[0133] Figure 4 , the fitting diagram of the predicted wheat ear weight increase rate and the actual ear weight increase rate.

[0134] Figure 5 ,The structural diagram of the respiratory measurement chamber in this paper. ,Including, 2: Cavity; 3: Gas mixing fan; 4: Rubber sealing gasket; 5: Hinge; 6: Lock; 7: Air temperature sensor inside the measurement chamber; 8: Bracket connector. DETAILED DESCRIPTION

[0135] The inventors have found that by detecting the respiration rate of cereal ears at different stages, the weight increase rate of ears, seeds or fruits and the final mature dry weight can be accurately predicted.

[0136] The plant described herein may be any non-crop plant or crop plant, such as oil crops, food crops, cash crops, wherein food crops include cereal crops, bean crops and root crops.

[0137] Herein, the "flowering time" of a plant refers to the time when the first flower of the plant opens. For cereal crops (such as rice and wheat), the "flowering time" is the time when the first spikelet on the tiller opens.

[0138] The modeling and prediction in this paper involve measuring the weight and respiration rate of ears, seeds, or fruits after anthesis. For cereal crops, weight and respiration rate are usually measured after the ears are fully exposed to reduce measurement errors.

[0139] Ear, seed or fruit weight growth rate

[0140] The present invention first provides a method for constructing a prediction model for the weight growth rate of a plant's ear, seed, or fruit using the weight and respiration rate of the plant's ear, seed, or fruit at different stages, comprising: (1) detecting the weight of the ear, seed, or fruit at one or more times after the plant begins to flower, and detecting the respiration rate of the ear, seed, or fruit at one or more times; (2) fitting a weight increase rate curve using the weight values ​​at different times obtained in step (1); and (3) performing a correlation analysis between the respiration rate in step (1) and the weight increase rate in step (2) to establish a regression model. The weight is preferably dry weight. The ears, seeds, or fruits of the plant are of similar size, for example, the volume difference is within 30%, within 20%, or within 10%.

[0141] The method for detecting the respiration rate of ears, seeds or fruits can be any method known in the art. Alternatively, the respiration measurement chamber described herein can be used for measurement.

[0142] The structure of the respiratory measurement room is shown in the figure below. Figure 5 As shown. The measuring chamber comprises an openable or closed cavity, a gas output device and a detection device, the cavity comprises an air inlet and an air outlet, and the air inlet and the air outlet are gas-connected to the gas output device and the detection device through a conduit, respectively. The gas output device provides gas to the measuring chamber, and the detection device is used to analyze the concentration of CO2 or O2 in the sample gas flowing out of the cavity. The detection device can also be connected to the gas output device to receive and analyze the concentration of CO2 or O2 in the reference gas that has not passed through the measuring chamber. Specifically, the measuring chamber is an openable and sealable measuring chamber for measuring the gas exchange rate of plants, comprising: a cavity, which can be opened or closed, an air flow mixing device provided in the cavity, an air inlet and an air outlet connected to the inside and outside of the cavity on the cavity wall, and connected to a gas analyzer. The size of the measuring chamber needs to be sufficient to wrap the entire crop seed or fruit, for example, 20 to 50 cm long, 4 to 10 cm wide, and 3 to 10 cm high. The gas analyzer includes: a main unit, which is connected to the air inlet of the cavity through a ventilation conduit to provide gas to the measurement chamber; an analyzer measuring head, which is connected to the air outlet of the cavity through a ventilation conduit to analyze the CO2 or O2 concentration of the sample gas flowing out of the cavity. The analyzer measuring head is also connected to the main unit through the ventilation conduit to receive and analyze the CO2 or O2 concentration of the reference gas provided by the main unit and not passing through the measurement chamber.

[0143] The gas exchange rate can be detected by wrapping the crop ear, seed or fruit to be tested in the measuring chamber cavity and closing the measuring chamber cavity. Specifically, the measuring chamber cavity is fixed on a bracket, and the two ventilation tubes on the measuring chamber cavity are respectively connected to the gas analyzer air inlet and the gas analyzer detector. The bracket is adjusted so that the posture of the measuring chamber cavity is consistent with the posture of the crop ear, seed or fruit to be tested. The measuring chamber cavity is opened, and the crop ear, seed or fruit to be tested is wrapped in the measuring chamber cavity. The measuring chamber cavity is closed to measure the CO2 production rate or O2 consumption rate of the crop ear, seed or fruit. The measured value is displayed on the gas analyzer host. The measured respiration rate (CO2 production rate or O2 consumption rate) can be standardized, for example, in units of nmol s -1 Standardize.

[0144] In the method, the one or more times for measuring weight or respiration rate are independently every 0-10 days, every 1-8 days, or every 2-5 days. This time can be determined by a person skilled in the art based on the growth cycle and growth conditions of the plant. Furthermore, the one or more times for measuring weight and the one or more times for measuring respiration rate can be the same or different; the plants for measuring weight and the plants for measuring respiration rate can also be the same or different, preferably different plants.

[0145] The fitting of step (2) can be performed by any method known in the art. For example, the fitting of the weight gain rate GFR can be calculated as the weight gain of several days before and after divided by the number of days:

[0146] GFR(t)=[W(t+T1)-W(t-T2)] / (T1+T2),

[0147] Among them, T1>=0, T2>=0.

[0148] For example, the fitting method can be Richard curve fitting:

[0149] GFR(t)=A(1-B*e -k*t ) 1 / (1-m)

[0150] Where A, B, k, and m are parameters.

[0151] For example, in a specific embodiment, the weight increase rate curve can be fitted using the following beta growth formula:

[0152]

[0153] 0≤t m1 ≤t e1 ,0≤t m1 <t m2 ≤t e2

[0154] GFR(t)=W(t+1)-W(t)

[0155] Where t is the number of days after the plant starts to flower, W(t) is the weight of the plant ear, seed or fruit after t days, GFR(t) is the rate of increase of the weight of the plant ear, seed or fruit after t days; W max1 , t m1 and t e1 W are the final weight of the first crop ears, seeds or fruits, the time to reach the maximum growth rate and the time to reach the maximum weight, respectively; max2 , t m2 and t e2 are the final weight, time to reach maximum growth rate and time to reach maximum weight of the second batch of plant ears, seeds or fruits respectively. If there is only one seed, one fruit, or multiple seeds growing synchronously, then W max2 =0. It should be understood that the method for fitting the weight gain rate curve is not limited to the specific formula used herein. Those skilled in the art can select an appropriate fitting method based on specific data.

[0156] Next, a regression model for accurately predicting the rate of weight gain can be established using the respiratory rate versus weight gain rate curve. Those skilled in the art can select any mathematical regression method known in the art (e.g., linear regression and logistic regression) to establish the model. An exemplary regression model is shown below:

[0157] GFR(t)=k·R d (t)+b

[0158] Where t is the number of days after the plant starts flowering, GFR(t) is the increase rate of ear dry weight t days after the first flowering, Rd(t) is the respiratory rate of the ear t days after the first flowering, and k and b are parameters.

[0159] After constructing a prediction model for weight gain rate using the method described in this article, the weight growth rate of the ear, seed or fruit can be predicted based on the model by measuring the respiration rate of the plant ear, seed or fruit t days after flowering.

[0160] The inventor has calculated that for the regression method exemplified in this article, in the prediction model of weight gain rate

[0161] GFR(t)=k·R d (t)+b

[0162] For rice, k = 0.013, b = 0; for wheat, k = 0.018, b = -0.095. Therefore, once the respiration rate of a rice or wheat ear t days after anthesis is obtained, the weight growth rate of the rice or wheat ear can be measured.

[0163] In a specific embodiment, the method for predicting the growth rate of plant ear, seed or fruit weight of the present invention comprises the following steps:

[0164] (1.1) Mark N (e.g., "200") similar ears, seeds, or fruits to be measured: After the ears, seeds, or fruits to be measured are fully exposed, select N ears, seeds, or fruits of similar size and mark them with tags. Randomly select M (e.g., "5") from the N tags and mark them with different colors. These M ears, seeds, or fruits will be used to measure respiration rate in the following step (1.4);

[0165] (1.2) Measuring the weight gain pattern of ears, seeds, or fruits: On the day of listing and at fixed intervals thereafter (e.g., "5 days"), randomly sample N1 ears, seeds, or fruits (e.g., "15") from N listed ears, seeds, or fruits (excluding M marked with different colors) until they are fully mature (no longer increase in weight). Each time N1 sample is taken, it is immediately dried and weighed;

[0166] (1.3) Fitting the weight increase rate curve of ear, seed or fruit: Using the weight values ​​on different dates obtained in step (1.2), fit the weight increase rate curve using the beta growth formula;

[0167] (1.4) Measure the respiration rate: On the day of listing and at fixed intervals thereafter (e.g., "5 days"), measure the respiration rate of crop ears, seeds, or fruits at fixed times (e.g., "16:00");

[0168] (1.5) Convert the measured respiration rate of crop ears, seeds, or fruits into standard units: Convert the measured respiration rate values ​​to nmol s -1 ;

[0169] (1.6) Correlation analysis is performed on the respiration rate on the corresponding date in step (1.5) and the weight gain rate in step (1.3) to obtain a linear regression model, which is used to predict the weight growth rate of crop ears, seeds or fruits on different dates.

[0170] The formula for fitting the weight increase rate in step (1.4) is:

[0171]

[0172] 0≤t m1 ≤t e1 ,0≤t m1 <t m2 ≤t e2

[0173] GFR(t)=W(t+1)-W(t)

[0174] Where t is the number of days after the crop starts to flower, W(t) is the weight of the crop ear, seed or fruit after t days, GFR(t) is the rate of increase of the weight of the crop ear, seed or fruit after t days; W max1 , t m1 and t e1 W are the final dry weight of the first crop ears, seeds or fruits, the time to reach the maximum growth rate and the time to reach the maximum weight, respectively; max2 , t m2 and t e2 The final dry weight of the second crop ear, seed or fruit, the time to reach the maximum growth rate and the time to reach the maximum weight respectively. If there is only one seed, one fruit, or multiple seeds growing simultaneously, specify W max2 =0.

[0175] Furthermore, the specific process of step (1.6) is: performing a correlation analysis on the respiratory rate on the corresponding day in step (1.5) and the weight gain rate in step (1.3) to obtain a regression analysis model:

[0176] GFR(t)=k·R d (t)+b

[0177] Where t is the number of days after the crop begins to flower, GFR(t) is the weight gain rate after t days, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, k and b are fitting parameters.

[0178] Mature weight of ear, seed or fruit

[0179] The present invention also provides a method for constructing a prediction model for the mature weight of a plant ear, seed, or fruit using the respiration rate and mature weight of the ear, seed, or fruit of the plant, comprising: (1) detecting the respiration rate of the ear, seed, or fruit of one or more plants at one or more times after the plant begins to flower; (2) measuring the mature weight, preferably dry weight, of the ear, seed, or fruit of the plant; and (3) constructing a regression model using one, two, or more of the respiration rates in step (1) and the mature weight in step (2). Similarly, the ears, seeds, or fruits of the plants are of similar size, for example, the volume difference is within 30%, within 20%, or within 10%.

[0180] The method for measuring the respiration rate of the ear, seed or fruit can be any method known in the art. Alternatively, the respiration rate can be measured using a respiration measurement chamber as described herein, as described elsewhere herein. The measured respiration rate (the rate of CO2 production or O2 consumption by respiration) can be standardized, for example, in units of nmol s -1 Standardize.

[0181] In some embodiments of the method for constructing a mature weight prediction model, the one or more times are every 0-10 days, every 1-8 days, or every 2-5 days, and the time can be specifically determined by those skilled in the art based on the growth cycle and growth conditions of the plant.

[0182] In some embodiments of the method for constructing a mature weight prediction model, step (3) includes: performing a correlation analysis on one, two or more combinations of the measured respiratory rates (for example, summing up the respiratory rates of exhaustively enumerated "two-by-two" combinations) and the mature weight in step (2) to obtain correlation parameters, and selecting respiratory rates or respiratory rate combinations (for example, "two-by-two" combinations with higher correlation coefficients and / or lower MAPE) and mature weight according to the correlation parameters to construct a regression model.

[0183] The correlation analysis can be performed using any method known to those skilled in the art. Exemplary correlation analysis includes performing a correlation analysis using one, two, or more respiratory rates or the sum of respiratory rates with mature weight to obtain a correlation coefficient r that measures the degree of linear correlation between two random variables x and y:

[0184]

[0185] Where n is the number of samples, x i ,y i is the i-th observation value of two variables x and y, and is the mean of two variables x and y.

[0186] Next, a combination of respiratory rates with a high correlation coefficient and / or a low MAPE (e.g., respiratory rates on day t1 and day t2) is selected, and together with the measured mature weight, an accurate regression model for predicting mature weight can be established. Those skilled in the art can select any mathematical regression known in the art (e.g., linear regression and logistic regression) to establish a model. An exemplary regression model is shown below:

[0187] Y=c1·(R d (t1)+R d (t2))+c2

[0188] Where t1 and t2 are days, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, and c1 and c2 are fitting parameters.

[0189] After constructing a mature weight prediction model using the methods described herein, the mature weight of a plant's panicle, seed, or fruit can be predicted based on the model by measuring its respiration rate t1 and t2 days after anthesis. The inventors have calculated that for rice, t1 and t2 are each independently selected from the following: 5±2, 16±2, and 36±2. In the embodiment where t1 = 16±2 and t2 = 36±2, c1 = 0.1373 and c2 = 2.04. Therefore, by obtaining the respiration rate of a rice panicle at 5, 16, and 36 days after anthesis, the mature weight of the rice panicle can be predicted.

[0190] In a specific embodiment, the method for predicting the mature weight of a plant ear, seed or fruit of the present invention comprises the following steps:

[0191] (2.1) Recording the initial time of crop ear, seed, or fruit: For example, for cereal crops such as rice or wheat, observe the ear emergence every day when the crop is close to heading. For the tillers of interest (such as the main tiller or the tiller at the middle of the height), mark them when the first spikelet on the ear opens, and record the date of the day;

[0192] (2.2) Measuring respiration rate: Measure the respiration rate of crop ears, seeds, or fruits at different times (e.g., 5 days, 16 days, and 36 days) after anthesis.

[0193] (2.3) Convert the measured respiratory rate to standard units: Convert the measured respiratory rate value to the standard unit of nmol s -1 ;

[0194] (2.4) Measuring the final weight of crop ears, seeds or fruits: When each marked crop ear, seed or fruit is fully mature, sample them separately, dry them and weigh them separately;

[0195] (2.5) exhaustively enumerate all the combinations of respiration rates on different dates in step (2.3), add them up, and perform correlation analysis with the weight of the corresponding crop ears, seeds, or fruits in step (2.4) to obtain the correlation coefficient r; sort the correlation coefficients r obtained from all different "two-by-two" combinations from large to small, select the combination with the largest r value, and use the corresponding linear regression model to predict the final weight of the crop ears, seeds, or fruits.

[0196] Furthermore, the specific process of step (2.5) is: exhaustively enumerate the combinations of respiration rates of crop ears, seeds, or fruits on different dates in step (2.3), add them together, and perform correlation analysis with the corresponding dry weight in step (2.4) to obtain the correlation coefficient r that measures the degree of linear correlation between the two random variables x and y:

[0197]

[0198] Where n is the number of samples, x i ,y i is the i-th observation value of two variables x and y, and is the mean of the two variables x and y. Then sort the correlation coefficients r obtained from all different "two-by-two" combinations from large to small, select the combination with the largest r value, and construct the corresponding linear regression model to predict the crop ear, seed or fruit weight Y:

[0199] Y=c1·(R d (t1)+R d (t2))+c2

[0200] Where t1 and t2 are the days after flowering, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, c1 and c2 are fitting parameters.

[0201] Media and devices

[0202] The present invention also provides a medium having recorded thereon (a) the method for constructing a prediction model for the growth rate of plant ear, seed, or fruit weight, and / or the method for predicting the growth rate of plant ear, seed, or fruit weight, or the model described therein, and / or the method for predicting the growth rate of rice or wheat ear weight, or the model described therein; and / or (b) the method for constructing a prediction model for the mature weight of plant ear, seed, or fruit, and / or the method for predicting the mature weight of plant ear, seed, or fruit, or the model described therein, and / or the method for predicting the mature weight of rice or wheat ear, or the model described therein. The medium can be a carrier printed with the method or model, including a card, such as a paper, plastic, metal, or glass card.

[0203] The medium may also be a computer-readable storage medium storing a computer program, wherein the computer program stored on the storage medium executes the method described herein after execution. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. If implemented in software as a computer program product, each function may be stored on or transmitted via a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one location to another. The storage medium may be any available medium that can be accessed by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative embodiment, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0204] The present invention provides a device for predicting the weight growth rate or mature weight of plant ears, seeds or fruits. The device includes a memory, a processor and a computer program stored in the memory and executable on the processor. The device is characterized in that when the processor executes the program, a method for predicting the weight growth rate or mature weight of plant ears, seeds or fruits is implemented.

[0205] The present invention will be further described below in conjunction with specific examples. It should be understood that these examples are intended to illustrate the present invention only and are not intended to limit the scope of the invention. The experimental methods in the following examples, for which specific conditions are not specified, are generally carried out under conventional conditions or under conditions recommended by the manufacturer.

[0206] Example

[0207] Example 1: Prediction of Rice Head Weight at Harvest

[0208] In this embodiment, the ear weight of the rice ears at harvest is predicted by measuring the ear respiration rate 16 days and 36 days after the first flowering of the rice ears.

[0209] (1) Plant different rice strains in different years, record the flowering date of the main panicle, and mark 30 panicles of each strain with tags. After the panicles are fully drawn out, select 15-20 panicles of uniform size from the 30 marked panicles, and remove the tags from the remaining panicles.

[0210] (2) Five ears were randomly selected from the marked ears at 5, 16, and 36 days after the first flowering and their respiration rate R was measured. d ;

[0211] (3) After the ears are fully mature, 5 ears are randomly sampled, dried at 70°C for 72 h, and then the dry weight W is weighed;

[0212] (4) R d (5),R d (16),R d (36),R d (5)+R d (16),R d (5)+R d (36),R d (16)+R d (36) is the independent variable, W is the dependent variable, and a linear regression model is constructed.

[0213] W=c1·(R d (t1)+R d (t2))+c2

[0214] Where t1 and t2 are the days after flowering, R d (t) is the respiratory rate of the panicle t days after the beginning of flowering, c1 and c2 are fitting parameters.

[0215] Table 1 below lists the respiration rate and harvested ear weight measurements for rice from different years and strains. Table 2 shows the actual ear weights compared to the weights predicted by different linear regression models, along with various metrics for evaluating model accuracy. Each metric is explained below.

[0216] R 2 , the square of the linear correlation coefficient r reflects the degree of correlation between the actual ear weight and the predicted ear weight, and the formula is:

[0217]

[0218] Where n is the number of samples, x i is the actual weight of the ith ear, y i is the predicted ear weight of the ith ear, is the actual average ear weight, is the predicted mean ear weight.

[0219] RMSE, the square root of the mean of the sum of squares of the errors between the predicted and actual values, reflects the degree of dispersion of the errors between the model-predicted ear weight and the actual ear weight. The formula is:

[0220]

[0221] Where n is the number of samples, x i is the actual weight of the ith ear, y i is the predicted ear weight of the i-th ear.

[0222] MAE, the mean of the absolute errors between the predicted and actual values, reflects the actual error of the model in predicting ear weight. The formula is:

[0223]

[0224] Where n is the number of samples, x i is the actual weight of the ith ear, y i is the predicted ear weight of the i-th ear.

[0225] MAPE, the mean relative error between the predicted value and the true value, reflects the prediction accuracy of the model. The formula is:

[0226]

[0227] Where n is the number of samples, x i is the actual weight of the ith ear, y i is the predicted ear weight of the i-th ear.

[0228] Table 1. Panicle respiration rate R of rice panicles in different years and different lines d (nmol s -1 ) and ear weight at harvest (g).

[0229] years Rice varieties <![CDATA[R d (5)]]> <![CDATA[R d (16)]]> <![CDATA[R d (36)]]> Harvest ear weight 2016 Y Liangyou 900 40.20 29.96 13.86 7.01 2016 Super 1000 37.40 27.53 16.18 8.05 2016 Shanyou 63 33.60 18.32 7.74 6.44 2016 93-11 45.50 26.96 7.76 6.29 2016 Xiushui 134 20.30 14.69 6.69 4.65 2016 Yongyou 538 26.20 20.79 8.02 6.89 2016 Yongyou 17 26.80 20.18 12.29 7.41 2020 Nipponbare (normal nitrogen) 15.18 8.92 1.83 2.92 2020 Japan Sunny (Low Nitrogen) 15.27 7.81 2.12 2.72 2020 Xiushui 134 (normal nitrogen) 22.71 13.79 6.00 4.81 2020 Xiushui 134 (low nitrogen) 20.24 16.39 6.00 5.02 2020 93-11 40.60 23.12 5.41 6.01

[0230] Table 2. Prediction results and accuracy of different linear regression models.

[0231]

[0232]

[0233] It can be seen that R d (16)+R d The linear regression model with (36) as the independent variable and W as the dependent variable has the best prediction for ear weight. The R 2 The value reaches 0.87, and the average relative error MAPE is only 8.19%. The specific fitting formula is:

[0234] W=0.1373·[R d (16)+R d (36)]+2.04

[0235] Therefore, this example shows that for rice, the respiration rate R of the rice ear can be measured at 16±2 days and 36±2 days after the first flowering.d (16) with R d (36) is substituted into the above formula to predict the final ear weight. Furthermore, if the number of ears per unit land area is known, the rice yield can be estimated about 36 days after the first flowering by multiplying the ear weight by the number of ears.

[0236] Figure 2 A fitting graph showing the predicted final dry weight of rice panicles at harvest and the actual final dry weight of rice panicles at harvest.

[0237] Example 2: Prediction of the Rate of Increase in Ear Weight During Rice and Wheat Ear Filling

[0238] In this embodiment, the ear weight increase rate is predicted by measuring the ear respiration rate of rice and wheat ears at different dates after the onset of flowering.

[0239] (1) Plant different strains of rice and wheat in different years, record the first flowering date of the main ear, and mark 300 ears of each strain. After the ears are completely drawn out, select 200 ears of uniform size from the 300 marked ears, and remove the tags from the remaining ears.

[0240] (2) 16 ears were randomly selected from the listed ears at 6, 9, 12, 15, 18, 21, 24, 27, 30, and 33 days after anthesis, dried at 70°C for 72 h, and then the dry weight was measured;

[0241] (3) For the rice variety Nipponbare planted in Shanghai in 2020, 5 ears were randomly selected from the listed ears at 6, 8, 10, 13, 18, 22, 26, and 31 days after the first flowering to measure their respiration rate; for rice and wheat varieties from other years, different dates after the first flowering were randomly selected, and 5 ears were randomly selected from the listed ears to measure their respiration rate;

[0242] (4) According to the formula:

[0243]

[0244] 0≤t m1 ≤t e1 ,0≤t m1 <t m2 ≤t e2

[0245] GFR(t)=W(t+1)-W(t)

[0246] Fitting the rate of increase of ear dry weight. Where t is the number of days after the crop starts flowering, W(t) is the weight of the crop ear, seed or fruit after t days, GFR(t) is the rate of increase of the weight of the crop ear, seed or fruit after t days; W max1 , tm1 and t e1 W are the final dry weight of the first crop ears, seeds or fruits, the time to reach the maximum growth rate and the time to reach the maximum weight, respectively; max2 , t m2 and t e2 The final dry weight of the second crop ear, seed or fruit, the time to reach the maximum growth rate and the time to reach the maximum weight respectively. If there is only one seed, one fruit, or multiple seeds growing simultaneously, specify W max2 =0.

[0247] (5) Correlation analysis was performed on the respiratory rate of the ear on the corresponding date in step (3) and the increase rate of the dry weight of the ear in step (4) to obtain a regression analysis model:

[0248] GFR(t)=k·R d (t)+b

[0249] Where t is the number of days after the first flowering, GFR(t) is the rate of increase of ear dry weight t days after the first flowering, R d (t) is the respiratory rate of the panicle t days after the beginning of flowering, k and b are fitting parameters.

[0250] Table 3 below lists the measurement results of ear respiration rate and ear dry weight increase rate of rice and wheat in different years and different strains, as well as the linear regression model and fitting R of ear respiration rate and ear dry weight increase rate. 2 value.

[0251] It can be seen that among different rice varieties, the linear relationship between the rate of increase of ear weight and the rate of ear respiration is:

[0252] GFR(t)=0.013·R d (t)

[0253] That is, the slope k = 0.013, and the intercept b = 0. The linear relationship between the rate of increase of ear weight and the ear respiration rate among different wheat varieties is:

[0254] GFR(t)=0.018·R d (t)-0.095

[0255] That is, the slope k = 0.018 and the intercept b = -0.095. Considering that the unit of spike respiration rate is nmol CO2 s -1 , and the unit of ear weight increase rate is gd -1 If the spike respiration rate unit is converted to g CH2O d -1 ,but

[0256]

[0257] That is, in rice and wheat, approximately 5.0 and 6.9 g of dry matter accumulate for every 1 g of CH2O consumed, respectively. Therefore, in practical applications, the values ​​of k and b can be determined in advance for different crops, allowing for non-destructive measurement of the dynamic changes in the growth rate of crop ears, seeds, or fruits.

[0258] Table 3. Ear respiration rate R of rice and wheat in different years and different lines d (nmol s -1 ), measured and predicted ear dry weight increase rate GFR (gd -1 ), and the linear regression model used for prediction and the fitting R 2 value.

[0259]

[0260]

[0261] Figure 3 and Figure 4 The fitting graphs of the predicted rice and wheat ear weight increase rates and the actual ear weight increase rates are shown respectively.

[0262] In summary, the present invention provides a non-invasive method for predicting the weight growth rate and final weight of crop ears, seeds, or fruits, including steps such as measuring the respiration rate of the crop on a specific date after the crop begins to flower, and then performing linear regression modeling. Because the method utilizes the high correlation between the respiration rate and storage metabolic rate in plant storage organs, it means that this method is not only applicable to the crop organs such as rice ears and wheat ears described in the examples, but also to the prediction of the weight growth rate and final weight of other crop ears, fruits, or seed organs (such as cereals, beans, fruits, tomatoes, sesame, rapeseed, cotton, etc.).

[0263] It should be understood that after reading the above teachings of the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. A method for constructing a prediction model for the weight growth rate of a plant ear, seed, or fruit, comprising: (1) Detecting the weight of the ear, seed or fruit at multiple times after the plant begins to flower, and detecting the respiration rate of the ear, seed or fruit at multiple times after the plant begins to flower, (2) Using the weight values ​​at different times obtained in step (1), fit the weight increase rate curve. (3) Using the respiratory rate in step (1) and the weight gain rate in step (2) to establish a regression model, The fitting in step (2) is performed using the beta growth formula, and the weight gain rate is fitted according to the following formula: Where t is the number of days after the plant starts flowering, W(t) is the weight of the plant ear, seed or fruit after t days, GFR(t) is the rate of increase of the weight of the plant ear, seed or fruit after t days; W max1 , t m1 and t e1 W are the final weight of the first crop ears, seeds or fruits, the time to reach the maximum growth rate and the time to reach the maximum weight, respectively; max2 , t m2 and t e2 are the final weight, time to reach maximum growth rate and time to reach maximum weight of the second batch of plant ears, seeds or fruits, respectively. The regression model described in step (3) is: Where t is the number of days after the plant starts flowering, GFR(t) is the rate of increase of ear dry weight t days after the first flowering, R d (t) is the respiratory rate of the panicle t days after the beginning of flowering, and k and b are parameters.

2. The method according to claim 1, wherein For rice, k=0.013, b=0; for wheat, k=0.018, b=-0.

095.

3. The method according to claim 1, wherein The multiple times for measuring weight are every 0-10 days; and / or the multiple times for measuring respiratory rate are every 0-10 days.

4. The method according to claim 1, wherein The multiple times for measuring weight are every 1-8 days; and / or the multiple times for measuring respiratory rate are every 1-8 days.

5. The method according to claim 1, wherein The multiple times of measuring weight are every 2-5 days; and / or the multiple times of measuring respiratory rate are every 2-5 days.

6. A method for predicting the weight growth rate of ears, seeds or fruits of a plant, comprising the steps of: (1) constructing a prediction model using the method described in any one of claims 1 to 5, and (2) Measure the respiration rate of the plant's ear, seed, or fruit t days after flowering and predict the weight growth rate based on the model in step (1).

7. The method according to claim 6, wherein The model is as follows: Where t is the number of days after the first flowering, GFR(t) is the weight gain rate on the tth day, R d (t) is the respiration rate of crop ears, seeds or fruits on the tth day, and k and b are parameters.

8. A method for predicting the weight growth rate of rice or wheat ears, comprising the steps of measuring the respiration rate of the rice or wheat ears t days after the beginning of flowering, and predicting the weight growth rate according to the following formula: Where t is the number of days after anthesis, GFR(t) is the rate of increase of ear dry weight t days after anthesis, Rd(t) is the respiratory rate of ear t days after anthesis, k and b are parameters, in, For rice, k=0.013, b=0; for wheat, k=0.018, b=-0.

095.

9. A medium having recorded thereon one or more items selected from the following: (a) The method for constructing a prediction model for the growth rate of ear, seed or fruit weight of a plant according to any one of claims 1 to 5, (b) The method for predicting the growth rate of ear, seed or fruit weight of a plant according to claim 6 or 7 and / or the model therein, (c) The method for predicting the growth rate of ear weight of rice or wheat according to claim 8 and / or the model therein.

10. The medium according to claim 9, wherein The medium is a carrier printed with the method or model; and / or The medium is a computer-readable medium storing the method or model and a computer program. When the computer program is executed by a processor, the following steps are implemented: （I） (1) Obtain the weight of the ear, seed or fruit at multiple times after the plant begins to flower and the respiratory rate of the ear, seed or fruit at multiple times after the plant begins to flower, (2) Using the weight values ​​at different times obtained in step (1), fit the weight increase rate curve, and (3) establishing a regression model using the respiratory rate in step (1) and the weight gain rate in step (2); or, (II) The steps for obtaining the respiration rate of rice or wheat spikelets t days after the first flowering and predicting the weight growth rate according to the following formula: where t is the number of days after anthesis, GFR(t) is the rate of increase of ear dry weight t days after anthesis, Rd(t) is the respiration rate of the ear t days after anthesis, and k and b are parameters: for rice, k = 0.013, b = 0; for wheat, k = 0.018, b = -0.

095.

11. The medium according to claim 10, wherein (I) also includes step (4): obtaining the respiration rate of the ear, seed or fruit of the plant t days after the beginning of flowering, and predicting the weight growth rate based on the model of step (3).

12. A device for predicting the weight growth rate of ears, seeds or fruits of a plant, the device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the following steps are implemented: （I） (1) Obtain the weight of the ear, seed or fruit at multiple times after the plant begins to bloom and the respiratory rate of the ear, seed or fruit at multiple times, (2) Using the weight values ​​at different times obtained in step (1), fit the weight increase rate curve, and (3) constructing a regression model using the respiratory rate in step (1) and the weight gain rate in step (2); and (4) Obtain the respiration rate of the plant ear, seed or fruit t days after the first flowering, and predict the weight growth rate based on the model in step (3); The fitting in step (2) is performed using the beta growth formula, and the weight gain rate is fitted according to the following formula: Where t is the number of days after the plant starts flowering, W(t) is the weight of the plant ear, seed or fruit after t days, GFR(t) is the rate of increase of the weight of the plant ear, seed or fruit after t days; W max1 , t m1 and t e1 W are the final weight of the first crop ears, seeds or fruits, the time to reach the maximum growth rate and the time to reach the maximum weight, respectively; max2 , t m2 and t e2 are the final weight, time to reach maximum growth rate and time to reach maximum weight of the second batch of plant ears, seeds or fruits, respectively. The regression model described in step (3) is: Where t is the number of days after the plant starts flowering, GFR(t) is the rate of increase of ear dry weight t days after the first flowering, R d (t) is the respiratory rate of the spike t days after the beginning of flowering, k and b are parameters, or, (II) The steps for obtaining the respiration rate of rice or wheat spikelets t days after the first flowering and predicting the weight growth rate according to the following formula: where t is the number of days after anthesis, GFR(t) is the rate of increase of ear dry weight t days after anthesis, Rd(t) is the respiration rate of the ear t days after anthesis, and k and b are parameters: for rice, k = 0.013, b = 0; for wheat, k = 0.018, b = -0.

095.

13. A method for constructing a prediction model for the mature weight of a plant ear, seed or fruit, comprising: (1) Detect the respiration rate of ears, seeds or fruits of one or more plants at multiple times after flowering. (2) Measuring the mature weight of plant ears, seeds or fruits; (3) Perform correlation analysis on one, two or more of the respiratory rates in step (1) and the mature weight in step (2) to obtain correlation parameters, and select the respiratory rate or respiratory rate combination and mature weight according to the correlation parameters to construct a regression model. Wherein, the regression model is: Where t1 and t2 are days, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, and c1 and c2 are fitting parameters.

14. The method according to claim 13, wherein The correlation analysis includes: performing a correlation analysis on one, two or more respiratory rates or the sum of respiratory rates and mature weight to obtain a correlation coefficient r that measures the degree of linear correlation between two random variables x and y: Where n is the number of samples, x i ,y i is the i-th observation value of two variables x and y, and is the mean of two variables x and y.

15. The method according to claim 13, wherein The multiple times are every 0-10 days.

16. The method according to claim 13, wherein The multiple times are every 1-8 days.

17. The method according to claim 13, wherein The multiple times are every 2-5 days.

18. A method for predicting the mature weight of an ear, seed or fruit of a plant, comprising the steps of: (1) constructing a prediction model using the method described in any one of claims 13 to 17, and (2) Measure the respiration rate of the plant's ear, seed, or fruit and predict the mature weight based on the model in step (1).

19. The method according to claim 18, wherein The respiration rate is the respiration rate of the ear, seed or fruit of the plant at t1 and t2 days after the beginning of flowering. The model is as follows: Where t1 and t2 are days after flowering, Y is the mature weight, R d (t) is the respiration rate of crop ears, seeds or fruits after t days, and c1 and c2 are parameters.

20. A method for predicting the mature weight of a rice or wheat ear, comprising the steps of measuring the respiration rate of the rice or wheat ear at t1 and t2 days after anthesis, and predicting the mature weight according to the following formula: Y is the mature weight, R d (t) is the respiratory rate of the panicle t days after the onset of anthesis, c1 and c2 are parameters, t1 and t2 are independently selected from: 5±2, 16±2, 36±2; for rice, t1=16±2, t2=36±2, c1=0.1373, c2=2.

04.

21. A medium having recorded thereon one or more items selected from the following: (a) The method for constructing a prediction model for the mature weight of a plant ear, seed or fruit according to any one of claims 13 to 17, (b) The method for predicting the mature weight of ears, seeds or fruits of a plant according to claim 18 or 19, or the model wherein said method, (c) The method for predicting mature ear weight of rice or wheat according to claim 20, or the model therein.

22. The medium according to claim 21, wherein The medium is a carrier printed with the method or model; and / or The medium is a computer-readable medium storing the method or model and a computer program. When the computer program is executed by a processor, the following steps are implemented: （I） (1) Obtain the respiration rate of ears, seeds or fruits of one or more plants at multiple times after flowering. (2) Obtaining the mature weight of plant ears, seeds or fruits, (3) constructing a regression model using one, two, or more of the respiratory rates in step (1) and the mature weights in step (2); or, (II) Obtain the respiration rate of rice or wheat spikelets at t1 and t2 days after anthesis, and predict the mature weight according to the following formula: Y is the mature weight, R d (t) is the respiratory rate of the panicle t days after the onset of anthesis, c1 and c2 are parameters, t1 and t2 are independently selected from: 5±2, 16±2, 36±2; for rice, t1=16±2, t2=36±2, c1=0.1373, c2=2.

04.

23. The medium according to claim 22, wherein (I) also includes step (4): measuring the respiration rate of the ear, seed or fruit of the plant and predicting the mature weight based on the model of step (3).

24. A device for predicting the mature weight of ears, seeds or fruits of plants, the device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the following steps are implemented: （I） (1) Obtain the respiration rate of ears, seeds or fruits of one or more plants at multiple times after flowering. (2) Obtaining the mature weight of plant ears, seeds or fruits, (3) constructing a regression model using one, two, or more of the respiratory rates in step (1) and the mature weights in step (2); or, (II) Obtain the respiration rate of rice or wheat spikelets at t1 and t2 days after anthesis, and predict the mature weight according to the following formula: Y is the mature weight, R d (t) is the respiratory rate of the panicle t days after the onset of anthesis, c1 and c2 are parameters, t1 and t2 are independently selected from: 5±2, 16±2, 36±2; for rice, t1=16±2, t2=36±2, c1=0.1373, c2=2.

04.

25. The device according to claim 24, wherein (I) also includes step (4): measuring the respiration rate of the ear, seed or fruit of the plant and predicting the mature weight based on the model of step (3).

Images