CN115310680A - Tomato seedling model modeling and growth prediction method - Google Patents
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Abstract
The invention relates to a modeling and growth prediction method for a tomato seedling model. The method comprehensively considers the influence of temperature, illumination and light-temperature coupling on the growth of the tomato seedlings, and establishes a growth and development prediction model of tomato morphological indexes and strong seedling indexes based on photosynthetically active accumulated temperature and relative light effect. The method eliminates the dimension of illumination data by processing and calculating photosynthetically active radiation, defines a relative light effect which is a completely new parameter to describe the light energy absorbed by crops, reduces the magnitude difference between the effective accumulated temperature and the photosynthetically active radiation, and considers the night growth and development process in the model, thereby optimizing the prediction accuracy of the model. The trend of the tomato seedling model established by the method accords with the growth rule of the seedlings, the fitting degree of the model is high, and the shape indexes and the change process of the strong seedling index of the tomato seedlings can be accurately predicted.
Description
Technical Field
The invention belongs to the field of agricultural informatization and automation, and particularly relates to a tomato seedling model modeling and growth prediction method.
Background
The research of greenhouse crop growth and development models is started with the large-scale planting of greenhouse crops from the late 70 s to the early 80 s of the last century. With the improvement of greenhouse management technology, the development of the internet of things sensor technology and the continuous deep research on the growth and development mechanism of horticultural crops, the research on greenhouse crop growth models is more and more at present. With the rapid advancement of agricultural information technology, crop growth models are considered as scientific basis and core technology of "digital agriculture" and bridges leading to agricultural informatization, and become popular fields of research at home and abroad.
In the industrial seedling raising process, if the environmental conditions are improperly controlled, the seedlings are extremely easy to overgrow, and further the growth, development, yield and quality of the tomatoes are influenced. The temperature and the illumination in the greenhouse are important environmental factors influencing the growth of seedlings, and the accurate control of environmental conditions such as the temperature and the illumination in the greenhouse can be realized by researching a crop growth model, so that the method has important significance for strong seedling cultivation.
Although the growth models of various horticultural crops are researched in China at present, the models are mostly plant full-growth-period models, the research on the growth models focusing on the seedling period of the crops is less aiming at the seedling production, and the research on the growth models relating to the seedling period of the tomatoes is less. The morphological index and the strong seedling index of the seedling are the most intuitive parameters for evaluating the robustness of the seedling, but the research on a tomato strong seedling index simulation model is rarely reported. The existing growth models mainly comprise a morphological index model, a dry matter model, a growth and development days model and the like. Although some growth models have certain reference values for the growth tendency of crops, the environmental factors included in the reference are relatively single.
In order to build a more accurate growth model, researchers have begun to focus on models involving a variety of environmental factors. Although some strong seedling index models comprehensively consider the influence of various environmental factors on the growth of crops and model by taking the product of the environmental factors as an independent variable, the nighttime growth of seedlings is ignored by the strong seedling index models because the nighttime photosynthetically active radiation value is zero. Research shows that only the nighttime growth temperature of the seedling is changed, and the growth and development difference of the seedling is obvious under different nighttime conditions, although the daytime temperature and the daytime illumination intensity are kept consistent. This further illustrates that the seedling also grows at night with photosynthetically active radiation of 0, whereas the existing radiology-thermolysis model ignores the growth of the seedling at night.
Disclosure of Invention
Technical problem to be solved
In order to overcome the defects of the prior art, in particular to a method for modeling and predicting the growth of tomato seedlings, which solves the problems that environmental factors related to a growth model are single, and the night growth of plants is neglected by a model established based on the radiation heat product, avoids too few environmental parameters and model errors caused by the process of omitting the night growth and development of the plants, and improves the accuracy and the prediction capability of the model.
(II) technical scheme
In order to solve the problems, the invention provides a tomato seedling model modeling and growth prediction method, and the technical scheme is as follows.
A modeling and growth prediction method for a tomato seedling model is characterized in that a tomato seedling growth prediction model is established based on effective product temperature and relative light effect, and the growth and development of seedlings are predicted, and the method specifically comprises the following steps:
s1, obtaining environmental parameters in a greenhouse in a seedling raising process, including temperature and photosynthetic effective radiation;
s2, covering the physiological seedling age of the seedlings, and sampling for 8 times to obtain the phenotypic parameters of the tomato plug seedlings, wherein the phenotypic parameters specifically comprise plant height, stem thickness, leaf area, dry weight and strong seedling index;
s3, processing the environmental parameters, eliminating dimensions, considering the growth and development characteristics of plants, and calculating to obtain an effective product and a relative light effect;
s4, fitting the effective temperature and the relative light effect obtained by calculation with the acquired seedling phenotype parameters, and comparing R 2 And RMSE, determining a model equation;
and S5, adopting the illumination and temperature data of weather forecast for several days in the future as input, and accurately predicting the growth and development of the tomato seedlings based on the model equation determined in the step S4.
Preferably, the environmental parameters in the seedling raising process are acquired in the step S1, and specifically, the environmental parameters in the whole growth period of the seedlings are automatically acquired through a sensor; the collected data comprises the air temperature and photosynthetic total radiation above the seedling canopy; the acquisition frequency was 1 data acquisition every 10 min.
Preferably, the tomato plug seedling phenotype parameters are obtained in the step S2, specifically, the tomato seedling period is divided into 8 times according to the physiological seedling age of seedling growth and development, and sampling is carried out, wherein the sampling is respectively carried out at a cotyledon flattening period, a 1-leaf 1 heart period, a 2-leaf 1 heart period, 7 days after 2-leaf 1 heart, a 3-leaf 1 heart period, a 4-leaf 1 heart period, 2 days after 4-leaf 1 heart and a 5-leaf 1 heart period; randomly taking 30 seedlings for each sampling, and measuring morphological indexes; the measurement indexes comprise plant height, stem thickness, leaf area, whole plant dry weight and strong seedling index; the strong seedling index is calculated according to the formula (1):
strong seedling index = stem thickness/plant height × dry weight of the whole plant (1).
Preferably, when the environmental parameters are processed in step S3, the effective accumulated temperature is calculated by using the formulas (2-1), (2-2) and (2-3),
GD day ＝T mean -T b (2-1)
GDD day ＝GDD day-1 +GD day (2-2)
in the formula, GD day Effective temperature for a certain purpose, in ° c.d; GDD (gas diffusion device) day-1 The effective temperature of the previous day is in the unit of DEG C.d; GDD (gas diffusion device) day From the last growth periodThe effective accumulated temperature up to now is given by the unit of DEG C.d, T mean Is the actual measured daily average air temperature, T b 、T m Respectively the biological lower limit temperature and the biological upper limit temperature of the tomato in a certain development stage.
Preferably, when the effective accumulated temperature is calculated, the lower limit temperature and the upper limit temperature of the development of the tomato seedlings in the germination period and the seedling period are respectively set differently; specifically, the lower growth limit temperature in the germination period was set to 12 ℃, the upper growth limit temperature was set to 35 ℃, the lower growth limit temperature in the seedling period was set to 10 ℃, and the upper growth limit temperature was set to 35 ℃.
Preferably, when the environmental parameters are processed in step S3, the relative light effect is calculated according to the formulas (3-1) and (3-2),
RLE＝∑DRLE (3-1)
in the formula, DRLE (k) Represents the relative light effect at day k, L min Represents the optical compensation point of tomato seedling with the unit of mu mol.m -2 ·s -1 I.e. when the photosynthetic rate and respiratory rate of the tomato seedlings are equal and no dry matter is accumulated, L max Represents the light saturation point of tomato seedlings and has the unit of mu mol.m -2 ·s -1 That is, the photosynthetic rate of the tomato seedlings can not increase with the increase of the illumination intensity, and L is the average photosynthetic effective radiation per hour measured actually and has the unit of mu mol.m -2 ·s -1 。
Preferably, when calculating the relative light effect, the optical compensation point of the tomato seedling is set to 37.05 [ mu ] mol-m -2 ·s -1 The light saturation point of the tomato seedling is set to be 1361.49 mu mol.m -2 ·s -1 。
Preferably, in step S4, the environmental parameters and seedling phenotype parameters are fitted to determine a model equation, specifically, a binary nonlinear curve fitting is performed on the data using an lsqcurvefit function of Matlab, and R is compared 2 And RMSE to determine model parameters. The formula (4-1) and the formula (4-2) are as follows:
SP in the formula (4-1) is a product sum; SS x Is the mean sum of the mean deviations of x; SS y Is the mean sum of the deviations of y.
In the formula (4-2), OBSi is an observed value, SIMi is an analog value, i is a sample serial number, and n is a sample capacity.
Preferably, the step S5 accurately predicts the growth and development of the tomato seedlings, specifically, calculates the effective heat accumulation and the relative light effect for several days in the future according to the weather forecast for several days in the future, calculates the phenotypic parameters of the tomato seedlings for several days in the future according to the model equation determined in the step S4, and accurately predicts the nursery time of the mature seedlings.
(III) advantageous effects
Compared with the prior art, the tomato seedling model modeling and growth prediction method provided by the invention has obvious and positive technical effects, and is specifically represented in the following aspects:
(1) The method considers the influence of temperature and illumination on the seedlings respectively, also considers the effect of light-temperature coupling, sets a plurality of parameters to fit the binary curve model, is closer to the actual growth of the seedlings in the aspect of physical significance representation, is obviously superior to the prior art, and is beneficial to obtaining a prediction model with higher precision.
(2) The method eliminates the dimension of illumination data through a mathematical calculation processing mode, originally defines the parameter of relative light effect, is used for describing the light energy absorbed by crops, effectively reduces the magnitude difference between the effective accumulated temperature and the photosynthetically active radiation, and further optimizes the accuracy of model fitting and prediction.
(3) The invention performs fitting on each phenotype and strong seedling index of tomato seedlings, and the result shows that R of each phenotype fitting equation 2 Are all greater than 0.99, sayObviously, the fitting effect is excellent and is obviously superior to the prior art. The three-dimensional simulated combination of each phenotype established by the model pattern conforms to the growth rule of the seedling.
Drawings
FIG. 1 is a technical flow chart of the present invention;
FIG. 2 is a graph showing the temperature of the incubation environment in examples 1 to 2;
FIG. 3 is a graph showing the environmental illumination of the incubation in examples 1 to 2;
FIG. 4 is a model diagram of the plant height, stem thickness and leaf area fitting of example 1;
FIG. 5 is a plot of the aerial dry weight, underground dry weight, strong seedling index fit model of example 1;
FIG. 6 is a graph of observed values versus phenotypic analogs of example 2.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and examples, which are provided for illustration only and are not intended to limit the scope of the invention.
The technical flow chart shown in fig. 1 is adopted in the embodiments 1 and 2 of the invention.
Examples 1 and 2 tomato seeds were used for zidali (ex zhengda seeds, ltd) and jinggu No. 8 (ex west ann jinggu germchit, ltd), respectively. The matrix consists of turf, vermiculite and perlite in a volume ratio of = 3: 1. The tomato seedlings were cultivated in a plug culture system of XQA72 plug (from Taizhou plastics Co., ltd.) with a volume of 36ml per plug. Two fertilizers are alternately used for top dressing during the cultivation period of tomato seedlings, namely No. 1 and No. 2 (purchased from Glan Keel company) special fertilizers for seedling raising. The sensor used for collecting the environmental data of the greenhouse is a ZENTRA sensor.
Example 1 the seeds from tomato were zidary and example 2 the seeds from tomato were golden shed No. 8. The incubation ambient temperatures of example 1 and example 2 are shown in fig. 2, and the incubation ambient light is shown in fig. 3. Two examples predict the growth and development of seedlings, and each comprises the following steps:
s1, obtaining environmental parameters in a greenhouse in a seedling raising process, including temperature and photosynthetic effective radiation;
s2, covering the physiological seedling age of the seedlings, and sampling for 8 times to obtain the phenotypic parameters of the tomato plug seedlings, wherein the phenotypic parameters specifically comprise plant height, stem thickness, leaf area, dry weight and strong seedling index;
s3, processing the environmental parameters, eliminating dimensions, considering plant growth and development characteristics, and calculating to obtain an effective product and a relative light effect;
s4, fitting the effective temperature and the relative light effect obtained by calculation with the acquired seedling phenotype parameters, and comparing R 2 And RMSE, determining a model equation;
and S5, adopting the illumination and temperature data of weather forecast for several days in the future as input, and accurately predicting the growth and development of the tomato seedlings based on the model equation determined in the step S4.
S1, acquiring environmental parameters in a seedling raising process, specifically, automatically acquiring the environmental parameters of the seedling in the whole growth period through a sensor; the collected data comprises the air temperature and photosynthetic total radiation above the seedling canopy; the acquisition frequency was 1 data acquisition every 10 min.
S2, acquiring phenotype parameters of the tomato plug seedlings, specifically dividing a tomato seedling culture period into 8 times according to the physiological seedling age of seedling growth and development, and sampling the tomato seedling culture period respectively to obtain a cotyledon flattening period, a 1-leaf 1 heart period, a 2-leaf 1 heart period, 7 days after 2-leaf 1 heart, a 3-leaf 1 heart period, a 4-leaf 1 heart period, 2 days after 4-leaf 1 heart and a 5-leaf 1 heart period; randomly taking 30 seedlings for each sampling, and measuring morphological indexes; the measurement indexes comprise plant height, stem thickness, leaf area, whole plant dry weight and strong seedling index; the strong seedling index is calculated according to the formula (1):
strong shoot index = stem thickness/plant height × dry weight of whole plant (1).
When the environmental parameters are processed in the step S3, the effective accumulated temperature is calculated by adopting the formulas (2-1), (2-2) and (2-3),
GD day ＝T mean -T b (2-1)
GDD day ＝GDD day -1+GD day (2-2)
in the formula, GD day Is the effective temperature of a certain day, and the unit is ℃. D; GDD (gas diffusion device) day-1 The effective temperature of the previous day is in the unit of DEG C.d; GDD (gas diffusion device) day The effective accumulated temperature from the last growth period to the present is given by the unit of ℃. D, T mean Is the actual measured daily average temperature, T b 、T m The biological lower limit temperature and the biological upper limit temperature of the tomato in a certain development stage are respectively.
When the effective accumulated temperature is calculated, the lower limit temperature and the upper limit temperature of the development of the tomato seedlings in the germination period and the seedling period are respectively set differently; specifically, the lower growth limit temperature in the germination period was set to 12 ℃, the upper growth limit temperature was set to 35 ℃, the lower growth limit temperature in the seedling period was set to 10 ℃, and the upper growth limit temperature was set to 35 ℃.
When the environmental parameters are processed in step S3, the relative light effect is calculated according to the formulas (3-1) and (3-2),
RLE＝∑DRLE (3-1)
in the formula, DRLE (k) Represents the relative light effect at day k, L min Represents the optical compensation point of tomato seedling with the unit of mu mol.m -2 ·s -1 That is, the photosynthetic rate and the respiratory rate of the tomato seedlings are equal at the moment, dry matter is not accumulated, L max Represents the light saturation point of tomato seedlings and has the unit of mu mol.m -2 ·s -1 That is, the photosynthetic rate of tomato seedlings will not increase with the increase of illumination intensity, and L is the average photosynthetic effective radiation per hour measured actually and has the unit of μmol · m -2 ·s -1 。
When calculating the relative light effect, the light compensation point of the tomato seedling is set as 37.05 mu mol.m -2 ·s -1 The light saturation point of the tomato seedling is set to be 1361.49 mu mol.m -2 ·s -1 。
And S4, fitting the environmental parameters and seedling phenotype parameters to determine a model equation, specifically, performing binary nonlinear curve fitting on the data by using an lsqcurvefit function of Matlab, and determining the model parameters by comparing R2 with RMSE. The formula (4-1) and the formula (4-2) are as follows:
SP in the formula (4-1) is a product sum; SS x Is the mean sum of the mean deviations of x; SS y Is the mean sum of the mean deviations of y.
In the formula (4-2), OBSi is an observed value, SIMi is an analog value, i is a sample serial number, and n is a sample capacity.
And S5, accurately predicting the growth and development of the tomato seedlings, specifically calculating effective heat accumulation and relative light effect of the tomato seedlings for several days in the future through weather forecast for several days in the future, calculating phenotypic parameters of the tomato seedlings for several days in the future according to the model equation determined in S4, and accurately predicting the outplanting time of the mature seedlings.
Example 1 phenotypic indicators and environmental parameter statistics are shown in table 1.
TABLE 1 example 1 statistics of phenotypic indicators and environmental parameters
It should be added that each index value in the table is the average number of 30 samples; '- -' indicates that the seedling data was too small to measure, as follows.
Example 1 use of the form
The model style of the tomato seedlings is fitted to the phenotypes and the strong seedling indexes of the tomato seedlings, and the fitting results of the plant height, the stem thickness and the leaf area are shown in the figure4, the results of fitting the above-ground dry weight, the below-ground dry weight, and the strong seedling index are shown in FIG. 5, and the fitting model is shown in Table 2. Determining the coefficient R 2 Preferably, the value approaches 1; the smaller the RMSE value, the better the consistency between the simulated value and the measured value, and the smaller the deviation between the simulated value and the measured value, i.e. the more accurate and reliable the simulation result of the model, which is generally considered to be within 15. R is calculated according to the formula (4-1) and the formula (4-2) 2 And RMSE. The results of the calculations are shown in Table 2, for each phenotype-fitted model R 2 The RMSE values are all larger than 0.99 and are very small, so that the model fitting effect is good.
Table 2 example 1 phenotypical fitting model parameters and effects
The simulation value was calculated using the model (Table 2) according to the environmental data (FIG. 2) collected during the seedling raising in example 2, and the determination coefficient R was used 2 And the mean root variance RMSE is used for carrying out statistical analysis on the coincidence degree between the model value SIM and the observed value OBS. The simulation values and observation values calculated from the model in example 2 are shown in table 3, and the environmental parameters are shown in table 4.
Table 3 example 2 index simulation values and observed values
Table 4 example 2 statistical results of environmental parameters
It was statistically analyzed using the RMSE model test statistical method (formula (4-2)). The RMSE of the plant height, stem thickness, leaf area, overground part dry weight, underground part dry weight and strong seedling index model is calculated to be 0.2776, 0.0452, 2.3947, 0.0089, 0.0012 and 0.0024 respectively. The calculation results show that each phenotype model is well predictable.
The simulated and observed values of example 2 were plotted in a 1: 1 relationship and subjected to regression analysis. The results of regression analysis of each phenotypic model are shown in table 4 and fig. 6. The results show that each equation R is obtained by analyzing each phenotype analog value and each observed value equation 2 All are larger than 0.9, which shows that the relation between each analog value and the observed value is close.
TABLE 4 tomato seedling phenotype fitting model
The specific examples described in the application are only illustrative of the spirit of the invention. Various modifications, additions and substitutions of types may be made by those skilled in the art without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (9)
1. A modeling and growth prediction method for a tomato seedling model is characterized in that a tomato seedling growth model is established based on effective heat accumulation and relative light effect, and the growth and development of seedlings are predicted, and the method specifically comprises the following steps:
s1, obtaining indoor environmental parameters in a seedling raising process, including temperature and photosynthetic effective radiation;
s2, covering the physiological seedling age of the seedlings, and sampling for 8 times to obtain the phenotypic parameters of the tomato plug seedlings, wherein the phenotypic parameters specifically comprise plant height, stem thickness, leaf area, dry weight and strong seedling index;
s3, processing the environmental parameters, eliminating dimensions, considering the growth and development characteristics of plants, and calculating to obtain an effective product and a relative light effect;
s4, fitting the effective heat accumulation and the relative light effect obtained by calculation with seedling phenotype parameters obtained by sampling, and comparing R 2 And the RMSE is used for determining the maximum ratio of the total weight of the rubber,determining a model equation;
and S5, adopting the illumination and temperature data of weather forecast for several days in the future as input, and accurately predicting the growth and development of the tomato seedlings based on the model equation determined in the step S4.
2. The tomato seedling model modeling and growth prediction method as claimed in claim 1, wherein the environmental parameters in the seedling process are obtained in step S1, specifically, the environmental parameters of the seedling in the whole growth period are automatically collected by a sensor; the collected data comprises the air temperature and photosynthetic total radiation above the seedling canopy; the acquisition frequency was 1 data acquisition every 10 min.
3. The tomato seedling model modeling and growth prediction method as claimed in claim 1, characterized in that the tomato plug seedling phenotype parameters are obtained in step S2, specifically, the tomato seedling period is divided into 8 times according to the physiological seedling age of seedling growth and development, and the sampling is performed respectively in cotyledon flattening period, 1-leaf 1 heart period, 2-leaf 1 heart period, 7 days after 2-leaf 1 heart period, 3-leaf 1 heart period, 4-leaf 1 heart period, 2 days after 4-leaf 1 heart period and 5-leaf 1 heart period; randomly taking 30 seedlings for each sampling, and measuring morphological indexes; the measurement indexes comprise plant height, stem thickness, leaf area, whole plant dry weight and strong seedling index; the strong seedling index is calculated according to the formula (1):
strong seedling index = stem thickness/plant height × dry weight of the whole plant (1).
4. The tomato seedling model modeling and growth prediction method as claimed in claim 1, wherein the effective accumulated temperature is calculated by using the formulas (2-1), (2-2) and (2-3) when the environmental parameters are processed in step S3,
GD day ＝T mean -T b (2-1)
GDD day ＝GDD day-1 +GD day (2-2)
in the formula, GD day Is the effective temperature of a certain day, and the unit is ℃. D; GDD (gas diffusion device) day-1 The effective temperature of the previous day is in the unit of DEG C.d; GDD (gas diffusion device) day The effective accumulated temperature from the last growth period to the present is given by the unit of ℃. D, T mean Is the actual measured daily average air temperature, T b 、T m Respectively the biological lower limit temperature and the biological upper limit temperature of the tomato in a certain development stage.
5. The tomato seedling model modeling and growth prediction method as claimed in claim 4, wherein the lower limit temperature and the upper limit temperature for the development of the tomato seedling in the germination stage and the seedling stage are set differently when calculating the effective accumulated temperature; specifically, the lower growth limit temperature in the germination period was set to 12 ℃, the upper growth limit temperature was set to 35 ℃, the lower growth limit temperature in the seedling period was set to 10 ℃, and the upper growth limit temperature was set to 35 ℃.
6. The tomato seedling model modeling and growth prediction method as claimed in claim 1, wherein the relative light effect is calculated according to the formulas (3-1) and (3-2) when the environmental parameters are processed in step S3,
RLE＝∑DRLE (3-1)
in the formula, DRLE (k) Represents the relative light effect at day k, L min Represents the optical compensation point of tomato seedling with the unit of μmol · m -2 ·s -1 I.e. when the photosynthetic rate and respiratory rate of the tomato seedlings are equal and no dry matter is accumulated, L max Represents the light saturation point of tomato seedlings in units of mu mol.m -2 ·s -1 That is, the photosynthetic rate of the tomato seedlings can not increase with the increase of the illumination intensity, and L is the average photosynthetic effective radiation per hour measured actually and has the unit of mu mol.m -2 ·s -1 。
7. The method of claim 6, wherein the optical compensation point of the tomato seedling is set to 37.05 μmol m when calculating the relative optical effect -2 ·s -1 The light saturation point of the tomato seedling is set to be 1361.49 mu mol.m -2 ·s -1 。
8. The tomato seedling model modeling and growth prediction method of claim 1, characterized in that in step S4, the environmental parameters and seedling phenotype parameters are fitted to determine a model equation, specifically, a binary nonlinear curve fitting is performed on the data using the lsqcurvefit function of Matlab, and R is compared 2 And RMSE to determine model parameters; the formula (4-1) and the formula (4-2) are as follows:
SP in the formula (4-1) is a product sum; SS x Is the mean sum of the mean deviations of x; SS y Is the mean sum of the mean deviations of y,
in the formula (4-2), OBSi is an observed value, SIMi is an analog value, i is a sample number, and n is a sample capacity.
9. The tomato seedling model modeling and growth prediction method as claimed in claim 1, wherein step S5 is used for accurately predicting the growth and development of tomato seedlings, specifically, calculating the effective heat accumulation and relative light effect of several days in the future according to the weather forecast of several days in the future, and calculating the phenotypic parameters of several days in the future of the tomato seedlings according to the model equation determined in step S4, so as to accurately predict the time for adult seedlings to leave the nursery.
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| CN115836639A (en) * | 2022-11-11 | 2023-03-24 | 四川省农业科学院园艺研究所 | Water and fertilizer supply method and device for tomato protected soilless substrate cultivation and storage medium |
| CN117391472A (en) * | 2023-10-26 | 2024-01-12 | 北京麦麦趣耕科技有限公司 | Device and method for predicting growth period of wheat and application of device and method |
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| CN115836639A (en) * | 2022-11-11 | 2023-03-24 | 四川省农业科学院园艺研究所 | Water and fertilizer supply method and device for tomato protected soilless substrate cultivation and storage medium |
| CN117391472A (en) * | 2023-10-26 | 2024-01-12 | 北京麦麦趣耕科技有限公司 | Device and method for predicting growth period of wheat and application of device and method |
| CN117391472B (en) * | 2023-10-26 | 2024-02-13 | 北京麦麦趣耕科技有限公司 | Device and method for predicting growth period of wheat and application of device and method |
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