WO2018103137A1 - 一种预测温室番茄果实生长的方法 - Google Patents

一种预测温室番茄果实生长的方法 Download PDF

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WO2018103137A1
WO2018103137A1 PCT/CN2016/110906 CN2016110906W WO2018103137A1 WO 2018103137 A1 WO2018103137 A1 WO 2018103137A1 CN 2016110906 W CN2016110906 W CN 2016110906W WO 2018103137 A1 WO2018103137 A1 WO 2018103137A1
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leaf
blade
fruit
inter
resistance
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倪纪恒
刘勇
周靖宇
毛罕平
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Jiangsu University
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    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining

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  • the present technology uses a crop simulation model method to predict the growth of greenhouse tomato fruit, specifically a method for predicting the growth of greenhouse tomato fruit, and belongs to the field of facility cultivation technology.
  • the greenhouse crop growth simulation model is a powerful tool for greenhouse environmental regulation and water and fertilizer management.
  • the dry matter distribution sub-model of greenhouse crops is an important part of the greenhouse crop growth simulation model; the dry matter distribution of greenhouse crops directly determines the yield and quality of crop products.
  • the simulation of dry matter distribution in greenhouse crops has always been a hotspot and a difficult point in research.
  • the main methods for simulating the dry matter distribution of greenhouse crops are the rate-of-speed growth and source-bank theory.
  • the model based on the rate-increasing growth is practical, and the established model has poor universality.
  • the model based on the source-bank theory is highly mechanistic, but because the parameters required to construct the model are many and difficult to measure, the application of the model is limited.
  • the leaves absorb light and carbon dioxide, synthesize organic matter and release oxygen.
  • the organic matter synthesized by the leaves is transported through stems to organs such as fruits.
  • Assimilation products form a gradient difference between organs such as leaves and fruits. This is consistent with the transport of current in Ohm's law (although there are two-way transport phenomena in the transport of assimilation products, the overall assimilation product flow is from the blade to the fruit and other organs).
  • the present invention uses a blade as a battery, a stem as a wire, a fruit as an electrical appliance, and an assimilation product flow as a current, and constructs a greenhouse tomato growth model based on Ohm's law, which provides a theoretical basis for improving the level of greenhouse production and environmental regulation in China.
  • the object of the present invention is to provide a method for predicting the growth of greenhouse tomato fruit, to improve the environmental control of the greenhouse and the level of water and fertilizer management, and to improve the yield and quality of the greenhouse crop.
  • the specific technical solutions are as follows:
  • a method for predicting the growth of greenhouse tomato fruit comprising the following steps:
  • Step 1 First, the leaves of the tomato plants are used as the source and the reservoir, and the stems and fruits are used as the reservoirs, which are transformed into the source-sink relationship; then, the source is used as the power source, and the reservoir is the resistance, and the tomato plants are converted into circuits according to the source/library relationship;
  • Step 2 determining the leaf voltage, leaf resistance, internode resistance and fruit resistance of the greenhouse tomato plant by using the relevant conditions in the greenhouse and the circuit obtained in the step 1;
  • Step 3 Combine the circuit obtained in step 1, and then calculate the voltage and current at both ends of the fruit according to the determined blade voltage, blade resistance, inter-segment resistance and fruit resistance, and obtain the electric power of the fruit, and the electric power value of the fruit is The amount of assimilation product obtained in a greenhouse tomato fruit within one day;
  • Step 4. Convert the obtained assimilation product amount of the greenhouse tomato fruit into the diameter of the greenhouse tomato fruit.
  • Each leaf branch of the m-leaf branch is transformed into a blade resistance RL connected in series with a blade battery E, a total of m blades and m blade batteries, and m blade resistances are respectively recorded as RL1, RL2, ... RLm
  • the voltages of the m vane batteries are respectively recorded as E1, E2, ... Em;
  • Each fruit branch is transformed into a fruit resistance Rf, a total of n fruit resistances, n fruit resistances are recorded as Rf1, Rf2 ... Rfn;
  • the stem of the plant of the m-leaf branch is composed of m+n internodes, and each internode is converted into an inter-segment resistance Rs, and the inter-segment resistance Rs from the root is sequentially recorded as Rs1, Rs2... Rsm+n, then connect the Rs1, Rs2...Rsm+n node resistances in series;
  • each blade resistor RL is connected in series with one vane battery E, and the other end of each vane resistor RL is connected between different two inter-node resistors Rs, and the other end of each vane battery E is grounded;
  • each fruit resistance Rf is grounded, and the other end is connected between different two inter-node resistances Rs, so that the other end of each fruit resistance Rf is connected in series with the other end of each blade resistance RL.
  • step 2 the blade voltage satisfies the following formula:
  • E is the blade voltage
  • P max is the maximum photosynthesis rate of the blade (unit: kgCO 2 ⁇ ha -1 ⁇ h -1 )
  • ⁇ is the light conversion factor, ie the initial light energy utilization efficiency of the absorbed light (unit: kg CO 2 ⁇ ha -1 ⁇ h -1 /J ⁇ m -2 ⁇ s -1 )
  • the value of ⁇ is 0.40 (unit: kg CO 2 ⁇ ha -1 ⁇ h -1 /J ⁇ m - 2 ⁇ s -1 )
  • P max is 37 (unit: kg CO 2 ⁇ ha -1 ⁇ h -1 )
  • PAR (T) is the photosynthetically active radiation PAR absorbed by the leaf at time T (unit: J ⁇ m - 2 ⁇ s -1), a leaf area (blade unit: m 2);
  • d is the density of the plants, strains units / m2, T is the time in hours.
  • the method for determining the leaf area of the blade is:
  • Step a1 determining the maximum leaf length of the blade and the average growth rate of the blade according to the leaf order, the maximum leaf length satisfying the following formula:
  • the average growth rate of the blades satisfies the following formula:
  • Lmax is the maximum leaf length of the leaf, the maximum leaf length is the maximum length when the leaf grows into mature leaves;
  • V is the average growth rate of the leaves;
  • i is the leaf order, that is, the number of leaves i obtained from the root number, this When the greenhouse tomato grows from vegetative growth to vegetative growth and reproductive growth;
  • Step a2 determining the cumulative effective accumulated temperature of the blade:
  • the cumulative effective accumulated temperature of the first true leaf is expanded to the nth day after the expansion of any leaf:
  • G is the cumulative effective accumulated temperature required for the first true leaf to the nth day after expansion of any leaf
  • Tmean is the daily average temperature of the day
  • Tb is the boundary temperature, which is common knowledge
  • ⁇ Grj is the first true leaf unfolded until Cumulative effective accumulated temperature from day r to day j after a leaf unfolding
  • Gr is the cumulative effective accumulated temperature required for the first true leaf to the r-day after any leaf unfolding
  • Gj is the first true leaf unfolded to any leaf The cumulative effective accumulated temperature required on the jth day after deployment;
  • Step a3 determining the leaf length of the blade according to the maximum leaf length of the blade obtained by the steps a1 and a2, the average growth rate of the blade, and the cumulative effective accumulated temperature, which satisfy the following formula:
  • L is the leaf length of the blade
  • Lmax is the maximum leaf length of the blade
  • V is the average growth rate of the blade
  • ⁇ Grj is the cumulative effective accumulated temperature from the rth day to the jth day after the blade is deployed
  • Step a4 determining the leaf area of the blade according to the leaf length of the blade obtained in the step a3, and satisfying the following formula:
  • A is the leaf area of the blade and L is the leaf length of the blade.
  • the photosynthetically active radiation PAR absorbed by the blade satisfies the following formula:
  • PARO is the photosynthetically active radiation value above the blade, which is common knowledge
  • k is the extinction coefficient, and the value is 0.8
  • AI is the leaf area index above the blade, that is, the leaf area above the blade is multiplied by the blade density.
  • step 2 the blade resistance satisfies the following formula:
  • RL is the blade resistance
  • Rm(T 25 ) is the maintenance breathing coefficient of tomato at 25 °C
  • Rm(T 25 ) is 0.015 (kg CH 2 O ⁇ kg -1 DM ⁇ d -1 )
  • W is leaf stem Heavy (kgDM ⁇ ha -1 )
  • T is the daily average temperature (°C).
  • W is the dry weight of the leaves
  • A is the leaf area of the leaves
  • S A is the specific leaf area, which is 40m 2 kg -1 .
  • step 2 the inter-segment resistance satisfies the following formula:
  • Rs is the inter-segment resistance
  • C is the inter-blocking resistivity
  • Ln is the inter-segment length
  • Dn is the inter-segment diameter
  • the method for determining the length of the internode is:
  • Step b1 determining an average growth rate between the nodes, the average growth rate of the internodes satisfying the following formula:
  • Vn is the average growth rate between the nodes, and i is the leaf order
  • Step b2 Determine the inter-segment length according to the cumulative effective accumulated temperature of the blade obtained in step b1 and the inter-node average growth rate, wherein the inter-segment length satisfies the following formula:
  • Ln is the internode length
  • Vn is the internode average growth rate
  • ⁇ Grj is the cumulative effective accumulated temperature from the rth day to the jth day after the blade is deployed.
  • the method for determining the internode diameter is:
  • Step c1 determining an average growth rate of the internode diameter according to the leaf order, and the average growth rate of the internode diameter satisfies the following formula:
  • Vdn is the average growth rate of the internode diameter and i is the leaf order
  • Step c2 determining an internode diameter according to an accumulated effective accumulated temperature of the blade and an average growth rate of the internode diameter, wherein the internode diameter satisfies the following formula:
  • Dn is the internode diameter
  • Vdn is the average growth rate of the internode diameter
  • ⁇ Grj is the cumulative effective accumulated temperature from the rth day to the jth day after the blade is deployed.
  • the method for determining the inter-blocking resistivity is:
  • the inter-sector resistivity is determined according to the inter-segment diameter and the inter-segment length, and the inter-segment resistivity satisfies the following formula:
  • C is the inter-blocking resistivity
  • Dn is the inter-node diameter
  • Ln is the inter-segment length
  • Step d1 Conversion of tomato plants: For tomato plants having m-leaf branches and n-fruit branches, tomato leaves start from the roots, and are referred to as leaves 1, leaves 2, leaves m, then leaves 1 and 2 ... leaf m is a battery and a resistor, respectively, the voltage is E1, E2 ... Em, the blade resistance is R1, R2 ... Rm; a total of m+1 Between the nodes, the resistance is called Rs1, Rs2, ... Rsm, Rsm+1; there are n fruit branches, respectively called Rf1, Rf2, ... Rfn;
  • Step d2 predicting each blade according to the maximum leaf length of the blade, the average growth rate of the blade, the effective accumulated temperature on the nth day after the blade is deployed, the cumulative effective accumulated temperature from the rth day to the jth day after the blade is deployed, and the leaf length of the blade
  • the leaf length, the internode length and the internode diameter of each stem are obtained according to the inter-segment resistance, the internode average growth rate, the internode length, the average growth rate of the internode diameter, and the internode diameter;
  • Step d3 according to the photosynthetically active radiation PAR absorbed by the blade, the collected photosynthetically effective radiation in the greenhouse
  • the value of photosynthetically active radiation received by each blade is calculated by the injection value, and the voltage of the blade is calculated by Equation 1.
  • the cumulative effective accumulated temperature from the r-day to the j-th day, the leaf length of the leaves, the leaf area of the leaves, and the leaf resistance calculate the dry weight of each leaf, ie the resistance of each blade; the simulated internode length and internode diameter of the stem
  • the inter-segment resistivity obtained in step 2 obtains the inter-block resistance;
  • Step d4 using the superposition theorem to divide the circuit into m sub-circuits, respectively calculate the currents on the fruit resistances of the m sub-circuits and sum them, that is, obtain the actual current on the fruit resistance, and use the Thevenin theorem on the current in each sub-circuit Solve; find the current of Rf on m sub-circuits, and then add to get the total current, then the total power on the fruit branch is multiplied by the square of the total current to get the total electric power of the fruit in one day, that is, one day The amount of assimilation product.
  • step 4 the diameter of the greenhouse tomato fruit satisfies the following formula:
  • P is the electric power P of the fruit.
  • this model Compared with the traditional fruit prediction model, this model has the following advantages:
  • the current greenhouse tomato growth model mostly uses the source-sink theory to simulate the growth of greenhouse tomato fruit.
  • the strength of the reservoir, especially the arsenic of the vegetative organs is difficult to quantify.
  • the present invention proposes a prediction model for greenhouse tomato fruit growth based on Ohm's law, which overcomes the insufficiency of vegetative organ pooling, and regulates environmental management and nutrient solution in greenhouse tomato production in China. Agronomic measures provide a theoretical basis.
  • the traditional greenhouse tomato growth model takes crop production as the core, while the preparation of greenhouse environmental regulation and nutrient solution management is mostly based on electricity, which is caused by different ways of thinking.
  • the growth model brings a lot of inconvenience in application promotion.
  • the distribution of greenhouse tomato assimilation products in plants is embodied in the form of electricity, which is more conducive to the preparation of greenhouse environment regulation and nutrient solution management.
  • the traditional greenhouse tomato fruit prediction model is based on the growth process of tomato.
  • the invention patent is based on the intersection of agronomy and electricity. Very innovative.
  • FIG. A is a schematic diagram of a greenhouse tomato plant with 9 leaves and 1 leaf
  • FIG. B is a source-sink relationship diagram of a greenhouse tomato plant of FIG. A
  • FIG. Converted from Figure B The circuit diagram is formed; RL1 to RL9 are the first to ninth blade resistances; Rs1 to Rs10 are the first to tenth inter-segment resistances; E1 to E9 are the first to ninth blade batteries; and Rf1 is the fruit resistance of the first fruit.
  • Example 2 is a schematic view showing a double-bar pruned tomato plant in Example 1;
  • Fig. A is an equivalent circuit diagram of a double-bar pruned tomato plant in the first embodiment
  • Fig. B is a simplified circuit diagram of Fig. A
  • Fig. C is a current flow diagram
  • Fig. A is a schematic view of a tomato plant in Example 2
  • Fig. B is an equivalent circuit diagram of Fig. A.
  • the number of tomato fruits on the fruit branches may be more than one, and when the number of fruits is more than one, it is assumed that all the fruits have the same diameter.
  • Step 1 Converting the tomato plants; firstly, converting the tomato plants according to the source/library relationship, that is, the leaves are the source and the reservoir (the leaves in the greenhouse tomato production are used for photosynthesis production, and the partial photosynthetic products are used to maintain the leaf consumption, ie The leaves maintain the breathing), the stems and fruits are the reservoirs, which are transformed into source-pool relationships. Then, using the source as the power source and the library as the resistor, the tomato plants are converted into circuits.
  • the greenhouse tomato plants with 9 leaves and 1 fruit (9 leaf branches and 1 fruit branch) are taken as an example to illustrate the transformation method. First, the greenhouse tomato plants with 9 leaves and 1 fruit are selected, and then the transformation is carried out according to the source-sink relationship.
  • Source/library, stem and fruit are libraries. Then, the source is the battery, and the library is used as a resistor to convert the greenhouse tomato plants. After conversion, the leaves on the greenhouse tomato plants were transformed into a resistance plus a battery, and the resistance was sequentially referred to as RL1, RL2--RL9 from the root, and the voltages were sequentially referred to as E1, E2--E9. 9 leaves 1
  • the stem of the greenhouse tomato consists of 10 internodes, each of which is a resistor, which is called Rs1--Rs10 from the root.
  • One fruit branch is a resistor called Rf1.
  • the present invention selects a greenhouse tomato with 9 leaves and 1 fruit, and other forms of tomato can be converted.
  • Each leaf branch of the m-leaf branch is transformed into a blade resistance RL connected in series with a blade battery E, a total of m blades and m blade batteries, and m blade resistances are respectively recorded as RL1, RL2, ... RLm
  • the voltages of the m vane batteries are respectively recorded as E1, E2, ... Em;
  • Each fruit branch is transformed into a fruit resistance Rf, a total of n fruit resistances, n fruit resistances are recorded as Rf1, Rf2 ... Rfn;
  • the stem of the plant of the m-leaf n-fruit branch consists of m+n internodes, transforming each internode into an inter-segment resistance Rs,
  • the inter-segment resistance Rs from the root is sequentially referred to as Rs1, Rs2, ..., Rsm+n, and then the Rs1, Rs2, ..., Rsm+n node resistances are sequentially connected in series;
  • each blade resistor RL is connected in series with one vane battery E, and the other end of each vane resistor RL is connected between different two inter-node resistors Rs, and the other end of each vane battery E is grounded;
  • each fruit resistance Rf is grounded, and the other end is connected between different two inter-node resistances Rs, so that the other end of each fruit resistance Rf is connected in series with the other end of each blade resistance RL.
  • a schematic view as shown in Fig. 1 is formed.
  • Step 2 determining the leaf voltage, leaf resistance, internode resistance and fruit resistance of the greenhouse tomato plant by using the relevant conditions in the greenhouse and the circuit obtained in the step 1;
  • E is the blade voltage
  • P max is the maximum photosynthesis rate of the blade (unit: kg CO 2 ⁇ ha -1 ⁇ h -1 )
  • ⁇ is the light conversion factor, ie the initial light energy utilization efficiency of the absorbed light (unit: kg CO 2 ⁇ ha -1 ⁇ h -1 /J ⁇ m -2 ⁇ s -1 )
  • ⁇ is 0.40 (unit: kg CO 2 ⁇ ha -1 ⁇ h -1 /J ⁇ m -2 ⁇ s -1)
  • P max value of 37 unit: kg CO 2 ⁇ ha -1 ⁇ h -1
  • A is the leaf area of the leaf (unit: m 2 );
  • d is the plant density, the unit is plant / square meter, T is the time, the unit is hour.
  • the method for determining the leaf area of the blade is:
  • Step a1 determining a maximum leaf length of the blade and an average growth rate of the blade according to the leaf order, the maximum leaf length satisfying the formula (2), and the average growth rate of the blade satisfies the formula (3):
  • Lmax is the maximum leaf length of the leaf, the maximum leaf length is the maximum length when the leaf grows into mature leaves;
  • V is the average growth rate of the leaves;
  • i is the leaf order, that is, the number of leaves i obtained from the root number, this When the greenhouse tomato grows from vegetative growth to vegetative growth and reproductive growth;
  • Step a2 determining the cumulative effective accumulated temperature of the blade:
  • the cumulative effective accumulated temperature of the first true leaf is expanded to the nth day after the expansion of any leaf:
  • G is the cumulative effective accumulated temperature required for the first true leaf to the nth day after expansion of any leaf
  • Tmean is the daily average temperature of the day
  • Tb is the boundary temperature, which is common knowledge
  • ⁇ Grj is the first true leaf unfolded until Cumulative effective accumulated temperature from day r to day j after a leaf unfolding
  • Gr is the cumulative effective accumulated temperature required for the first true leaf to the r-day after any leaf unfolding
  • Gj is the first true leaf unfolded to any leaf The cumulative effective accumulated temperature required on the jth day after deployment;
  • Step a3 determining the leaf length of the blade according to the maximum leaf length of the blade obtained by the steps a1 and a2, the average growth rate of the blade, and the cumulative effective accumulated temperature, which satisfies the formula (6):
  • L is the leaf length of the blade
  • Lmax is the maximum leaf length of the blade
  • V is the average growth rate of the blade
  • ⁇ Grj is the cumulative effective accumulated temperature from the rth day to the jth day after the blade is deployed
  • Step a4 determining the leaf area of the blade according to the leaf length of the blade obtained in the step a3, and satisfying the formula (7):
  • the photosynthetically active radiation PAR absorbed by the blade satisfies the formula (8):
  • PARO is the photosynthetically active radiation value above the blade, which is common knowledge
  • k is the extinction coefficient, and the value is 0.8
  • AI is the leaf area index above the blade, that is, the leaf area above the blade is multiplied by the blade density.
  • RL is the blade resistance
  • Rm(T 25 ) is the maintenance breathing coefficient of tomato at 25 °C
  • Rm(T 25 ) is 0.015 (kg CH 2 O ⁇ kg -1 DM ⁇ d -1 )
  • W is leaf stem Heavy (kg DM ⁇ ha -1 )
  • T is the daily average temperature (°C).
  • W is the dry weight of the leaves
  • A is the leaf area of the leaves
  • S A is the specific leaf area, which is 40m 2 kg -1 .
  • Rs is the inter-segment resistance
  • C is the inter-blocking resistivity
  • Ln is the inter-segment length
  • Dn is the inter-segment diameter
  • the method for determining the length of the internode is:
  • Step b1 determining an average growth rate between the nodes, wherein the average internode growth rate satisfies the formula (12):
  • Vn is the average growth rate between the nodes, and i is the leaf order
  • Step b2 determining the inter-segment length according to the cumulative effective accumulated temperature of the blade obtained in step b1 and the inter-node average growth rate, wherein the inter-segment length satisfies the formula (13):
  • Ln is the internode length
  • Vn is the average internode growth rate
  • ⁇ Grj is the rth to jth after the blade is unfolded The cumulative effective accumulated temperature of the day.
  • the method for determining the internode diameter is:
  • Step c1 determining an average growth rate of the internode diameter according to the leaf order, and the average growth rate of the internode diameter satisfies the formula (14):
  • Vdn is the average growth rate of the internode diameter and i is the leaf order
  • Step c2 determining an internode diameter according to an accumulated effective accumulated temperature of the blade and an average growth rate of the internode diameter, wherein the internode diameter satisfies the formula (15):
  • Dn is the internode diameter
  • Vdn is the average growth rate of the internode diameter
  • ⁇ Grj is the cumulative effective accumulated temperature from the rth day to the jth day after the blade is deployed.
  • the inter-blocking resistivity C and the fruit resistance Rf were determined by experiments.
  • test greenhouse crop was tomato, which was cultivated in a cultivation tank, and the cultivation substrate was perlite. Irrigation with Hoagland nutrient solution. Tomatoes use double-rod pruning, which are A and B respectively. Three representative plants were selected for measurement. details as follows:
  • the internode length and internode diameter in the A and B rods were measured daily; the leaf length and the fruit diameter.
  • Environmental factors The required greenhouse and radiation data are automatically collected by the greenhouse control system.
  • Inter-node dry weight inter-section length ⁇ 0.11 (16)
  • Internode length and fruit diameter can be obtained by direct measurement.
  • the measured leaf lengths of greenhouse tomatoes were 25, 29, 35, and 33 cm, respectively.
  • the photosynthetically active radiation values and temperature values above the leaves are shown in Table 2.
  • the daily assimilation product amount ie, voltage
  • the photosynthetic product amounts of the leaves of tomato in one day were 7.31, 7.43, 8.01, 5.19 Kg CO 2 ha -1 d -1 , respectively .
  • the leaf-retaining spirometry was calculated to be 0.010, 0.0122, 0.018, 0.016 Kg CO 2 ha -1 d -1 (ie, blade resistance).
  • the voltages of the first, second, third and fourth leaves were 7.31, 7.43, 8.01, 5.19, respectively, and the resistances were 0.010, 0.0122, 0.018, and 0.016, respectively.
  • the daily growth of stem, leaf and fruit were 6.25, 7.25 and 10.75Kg, respectively.
  • DM ha -1 the stems, leaves electric power, 6.25,7.25 and 10.75 for the fruit.
  • the growth of the four blades was 2.01, 2.3, 1.94 and 1 (measured values), respectively, and the electric power of the four blades was 2.01, 2.3, 1.94 and 1.
  • R' (Rs5+Rf)//(Rs6+Rs7+Rs8)+Rs1;
  • U1, U2, U3, U4, RL1, RL2, RL3, RL4, P1, P2, P3, P4, Pf, Ps are known; solution, Rs1, Rs2, Rs3, Rs4, Rs5, Rs6, Rs7, Rs8, and Rf; wherein RL1 to RL4 are blade resistances of four blades, respectively, P1, P2, P3, and P4 are electric powers of four blades, respectively, Pf is fruit electric power, and Ps is inter-segment electric power.
  • C is the inter-blocking resistivity
  • Dn is the inter-node diameter
  • Ln is the inter-segment length
  • the internode resistance of the internodes 1, 5, 6, 7, and 8 is calculated according to the formula (11). . Then, the value of R' is calculated, and the fruit resistance Rf is finally calculated.
  • Table 3 shows the greenhouse tomato electrical parameters calculated based on environmental factors:
  • Step 3 Using the determined blade voltage, blade resistance, internode resistance, and fruit resistance, the voltage and current at both ends of the fruit are calculated using Ohm's law to obtain the fruit power.
  • the blade voltage and the blade resistance were calculated by temperature and radiation, and then the length and diameter of each section were calculated by the effective accumulated temperature.
  • the inter-node resistance and the fruit resistance were calculated by the inter-block resistivity obtained in step 2.
  • the current of each fruit branch is obtained, and the electric power of each fruit branch current is calculated.
  • the electric power of each fruit current is the amount of daily assimilation product obtained by the fruit branch.
  • the daily growth of the fresh weight of the fruit is derived, and finally the diameter of the fruit and the daily increase of the fresh weight are obtained.
  • the fresh weight of the fruit 0.5 ⁇ the cube of the diameter of the fruit.
  • tomato leaves start from the root and are called leaf 1 , leaf 2 ... leaf m in order, then leaf 1 , leaf 2 ... leaf m respectively
  • the voltages are E1, E2, ... Em
  • the blade resistances are R1, R2, ... Rm, respectively.
  • Rs1, Rs2, ... Rsm, Rsm+1 There are n fruit branches, which are called Rf1, Rf2, ... Rfn.
  • each leaf length of each leaf is predicted by the formulas (2) to (6), and the internode length and the internode diameter of each stem are obtained by the formulas (11) to (15).
  • the photosynthetically active radiation value received by each blade is calculated by using the formula (8) to calculate the photosynthetically active radiation value per hour of the collected greenhouse, and the voltage of the blade is calculated by Equation 1. Then, the dry weight of each blade, that is, the resistance of each blade, is calculated using equations (2) to (7) and (9).
  • the inter-node resistance was obtained by the formula (11) by the inter-segment length and the inter-segment diameter of the simulated stem and the inter-block resistivity obtained in the step 2.
  • the superposition theorem is used to divide the circuit into m sub-circuits, and the currents on the fruit resistances of the m sub-circuits are respectively obtained and summed to obtain the actual current on the fruit resistance.
  • the current on the fruit branches in each sub-circuit is solved by the Thevenin theorem. Find the current of Rf on m sub-circuits, and then add them to get the total current. Then the total power on the fruit branches is multiplied by the square of the total current to obtain the total electric power of the fruit in one day, that is, the assimilation obtained in one day. The amount of product.
  • Step 4 The electric power P of the fruit obtained by the step 3, that is, the amount of assimilation product obtained in one day of the greenhouse tomato fruit.
  • the diameter of the fruit is then determined based on the amount of assimilated product obtained from the fruit. details as follows:
  • Fresh fruit weight 0.5 ⁇ fruit diameter cube
  • P is the electric power P of the fruit.
  • the diameter of the fruit on the first day was 1.5 cm.
  • Step 3 in Embodiment 1 is exemplified, as shown in FIG. 4:
  • the greenhouse tomato plants with 3 leaves and 1 fruit (3 leaf branches and 1 fruit branch) were selected, and the tomato leaves were leaf 1, leaf 2 and leaf 3 from the root.
  • leaf 1, leaf 2, and leaf 3 are respectively a battery and a resistor, and the voltages thereof are E1, E2, and E3, respectively, and the blade resistances are RL1, RL2, and RL3, respectively.
  • the resistance is called Rs1, Rs2, Rs3 and Rs4, respectively, and the fruit branch is a resistor called Rf.
  • the photosynthetically active radiation and temperature are still based on the environmental data in Table 2, combined with the data in Table 4, according to the model predictions (25, 29, 35 cm).
  • Leaf voltage, blade resistance, internode resistance, and fruit resistance Rf were obtained.

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Abstract

一种预测温室番茄果实生长的方法，包括以下步骤：1、以番茄植株的叶片为源和库，茎和果实为库，转化为源库关系；以源为电源，库为电阻，将番茄植株依据源/库关系转化为电路；2、利用温室内相关条件及步骤1中的电路，确定温室番茄植株的叶片电压、叶片电阻、节间电阻和果实电阻；3、结合步骤1的电路，根据确定的叶片电压、叶片电阻、节间电阻和果实电阻，利用欧姆定律，计算果实两端的电压和电流，得到果实的电功率，果实的电功率值即为温室番茄果实一天内获得的同化产物量值；4、将得到的果实的同化产物量转化为温室番茄果实直径。该方法将温室番茄同化产物在植株间的分配以电的形式体现，更有利于温室环境调控和营养液管理。

Description

一种预测温室番茄果实生长的方法 技术领域
本技术是采用作物模拟模型方法对来预测温室番茄的果实生长，具体为一种预测温室番茄果实生长的方法，属于设施栽培技术领域。
背景技术
温室作物生长模拟模型是温室环境调控和水肥管理的有力工具。温室作物干物质分配子模型是温室作物生长模拟模型的重要组成部分；温室作物的干物质分配直接决定作物产品的产量和品质。但由于对温室作物干物质分配的机理尚不清楚，因此温室作物干物质分配的模拟一直是研究的热点和难点。
目前关于温室作物干物质分配的模拟方法中，占据主要地位的分别是比速生长和源库理论。基于比速生长建立的模型实用性强，所建立的模型普适性差。而基于源库理论建立的模型机理性强，但由于构建模型所需参数多且难以测定，因此限制了模型的应用。
在温室作物生长过程中，叶片吸收光和二氧化碳后，合成有机物并释放出氧气。叶片合成的有机物经茎运输至果实等器官中。同化产物在叶片和果实等器官之间形成梯度差。这与欧姆定律中的电流的运输过程较为一致(虽然同化产物的运输中存在双向运输现象，但总体上同化产物的流向是从叶片到果实等器官)。鉴于此，本发明以叶片为电池，茎为导线，果实为用电器，同化产物流为电流，构建基于欧姆定律的温室番茄生长模型，为提高中国温室生产和环境调控水平提供理论依据。
发明内容
本发明的目的是提供一种预测温室番茄果实生长的方法，提高温室环境控制和水肥管理水平，提高温室作物产量和品质。具体技术方案如下：
一种预测温室番茄果实生长的方法，包括以下步骤：
步骤1、首先，以番茄植株的叶片为源和库，茎和果实为库，转化为源库关系；然后，以源为电源，库为电阻，将番茄植株依据源/库关系转化为电路；
步骤2、利用温室内的相关条件以及步骤1中得到的电路，确定温室番茄植株的叶片电压、叶片电阻、节间电阻和果实电阻；
步骤3、结合步骤1得到的电路，再根据确定的叶片电压、叶片电阻、节间电阻和果实电阻，利用欧姆定律，计算果实两端的电压和电流，得到果实的电功率，果实的电功率值即为温室番茄果实一天内获得的同化产物量值；
步骤4、将得到的温室番茄果实的同化产物量转化为温室番茄果实直径。
所述将番茄植株转化为电路的具体过程为：
当番茄植株是一个m叶枝n果枝的植株时，m是≥1的自然数，n＝0或是≥1的自然数，其转化的电路是：
将m叶枝中的每个叶枝均转变为一个叶片电阻RL串接一个叶片电池E，共m个叶片和m个叶片电池，m个叶片电阻分别记为RL1、RL2、......RLm，m个叶片电池的电压分别记为E1、E2......Em；
将每个果枝转变为一个果实电阻Rf，共有n个果实电阻，n个果实电阻分别记为Rf1、Rf2......Rfn；
m叶枝n果枝的植株的茎由m+n个节间组成，将每个节间转变为一个节间电阻Rs，从根部向上的节间电阻Rs依次记为Rs1、Rs2......Rsm+n，再将这Rs1、Rs2......Rsm+n个节电阻依次串联；
每个叶片电阻RL的一端串接一个叶片电池E，将每个叶片电阻RL的另一端均连接于不同的每两个节间电阻Rs之间，将每个叶片电池E的另一端均接地；
将每个果实电阻Rf的一端接地，另一端均连接于不同的每两个节间电阻Rs之间，使每个果实电阻Rf的另一端与每个叶片电阻RL另一端之间串接一个节间电阻Rs；
因此，共m个叶片电阻的另一端、共n个果实电阻的另一端连接于不同的每两个节间电阻Rs1、Rs2......Rsm+n之间。
步骤2中，所述叶片电压满足下述公式：
Figure PCTCN2016110906-appb-000001
其中，E为叶片电压，Pmax为该叶片最大光合作用速率(单位：kgCO2·ha-1·h-1)，ε为光转换因子即吸收光的初始光能利用效率(单位：kg CO2·ha-1·h-1/J·m-2·s-1)，依据试验观测数据，ε取值为0.40(单位：kg CO2·ha-1·h-1/J·m-2·s-1)，Pmax取值为37(单位：kg CO2·ha-1·h-1)；PAR(T)为T时刻叶片吸收的光合有效辐射PAR(单位：J·m-2·s-1)，A为叶片的叶面积(单位：m2)；d为植株密度，单位为株/平方米，T为时刻，单位为小时。
所述叶片的叶面积的确定方法为：
步骤a1、根据叶序确定叶片的最大叶长和叶片的平均生长速率，所述最大叶长满足下述公式：
Figure PCTCN2016110906-appb-000002
所述叶片的平均生长速率满足下述公式：
Figure PCTCN2016110906-appb-000003
其中，Lmax为叶片的最大叶长，最大叶长为叶片长成成熟叶时的最大长度；V为叶片的平均生长速率；i为叶序，即从根部开始数所得到的叶片数i，此时温室番茄从营养生长进入营养生长与生殖生长并重时期；
步骤a2、确定叶片的累积有效积温：
首先根据下述公式求得第一真叶展开至任一叶展开后第n天所需累积有效积温：
G＝∑(Tmean-Tb)，
再根据下述公式求得第一真叶展开至任一叶展开后从第r天至第j天的累积有效积温：
ΔGrj＝Gj-Gr，
其中，G为第一真叶展开至任一叶展开后第n天所需累积有效积温；Tmean为一天的日平均温度；Tb为界限温度，为公知常识；ΔGrj为第一真叶展开至任一叶展开后从第r天至第j天的累积有效积温；Gr为第一真叶展开至任一叶展开后第r天所需累积有效积温，Gj为第一真叶展开至任一叶展开后第j天所需累积有效积温；
步骤a3、根据步骤a1、a2得到的叶片的最大叶长、叶片的平均生长速率和累积有效积温确定叶片的叶长，满足下述公式下述：
Figure PCTCN2016110906-appb-000004
其中，L为叶片的叶长，Lmax为叶片的最大叶长，V为叶片的平均生长速率，ΔGrj为叶片展开后从第r天至第j天的累积有效积温；
步骤a4、根据步骤a3所得到的叶片的叶长确定叶片的叶面积，满足下述公式：
A＝K×L2/10000，
其中，A为叶片的叶面积，L为叶片的叶长。
所述叶片吸收的光合有效辐射PAR满足下述公式：
PAR＝PARo×e-kAI，
其中，PARo为叶片上方的光合有效辐射值，属于公知常识；k为消光系数，取值0.8；AI为叶片上方的叶面积指数，即以叶片上方叶面积乘以叶片密度。
步骤2中，所述叶片电阻满足下述公式：
Figure PCTCN2016110906-appb-000005
其中，RL为叶片电阻，Rm(T25)为25℃时番茄的维持呼吸系数，Rm(T25)取值0.015(kg CH2O·kg-1DM·d-1)，W为叶干重(kgDM·ha-1)，T为每日平均温度(℃)。
所述叶干重满足公式下述：
W＝A×SA，
其中，W为叶干重，A为叶片的叶面积，SA为比叶面积，取值40m2kg-1。
步骤2中，所述节间电阻满足下述公式：
Figure PCTCN2016110906-appb-000006
其中，Rs为节间电阻，C为节间电阻率，Ln为节间长度，Dn为节间直径。
所述节间长度的确定方法为：
步骤b1、确定节间平均生长速率，所述节间平均生长速率满足下述公式：
Figure PCTCN2016110906-appb-000007
其中，Vn为节间平均生长速率，i为叶序；
步骤b2、根据步骤b1得到的叶片的累积有效积温和节间平均生长速率，确定节间长度，所述节间长度满足下述公式：
Figure PCTCN2016110906-appb-000008
其中，Ln为节间长度，Vn为节间平均生长速率，ΔGrj为叶片展开后从第r天至第j天的累积有效积温。
所述节间直径的确定方法为：
步骤c1、根据叶序确定节间直径的平均生长速率，所述节间直径的平均生长速率满足下述公式：
Figure PCTCN2016110906-appb-000009
其中，Vdn为节间直径的平均生长速率，i为叶序；
步骤c2、根据叶片的累积有效积温和节间直径的平均生长速率确定节间直径，所述节间直径满足下述公式：
Figure PCTCN2016110906-appb-000010
其中，Dn为节间直径，Vdn为节间直径的平均生长速率，ΔGrj为叶片展开后从第r天至第j天的累积有效积温。
所述节间电阻率的确定方法为：
根据节间直径和节间长度确定节间电阻率，所述节间电阻率满足下述公式：
Figure PCTCN2016110906-appb-000011
其中，C为节间电阻率，Dn为节间直径，Ln为节间长度。
所述果实电功率的具体推导过程为：
步骤d1、对番茄植株进行转换：对于拥有m叶枝和n果枝的番茄植株，番茄叶片从根部开始，向上依次称为叶1、叶2......叶m，则叶1、叶2......叶m分别为一个一个电池和电阻，其电压分别为E1、E2......Em，叶片电阻分别为R1、R2......Rm；共有m+1个节间，其电阻分别称为Rs1、Rs2......Rsm、Rsm+1；有n个果枝，分别称为Rf1、Rf2......Rfn；
步骤d2、根据叶片的最大叶长、叶片的平均生长速率、叶片展开后第n天的有效积温、叶片展开后从第r天至第j天的累积有效积温、叶片的叶长来预测各叶片的叶长，根据节间电阻、节间平均生长速率、节间长度、节间直径的平均生长速率、节间直径求得各茎的节间长度和节间直径；
步骤d3、根据叶片吸收的光合有效辐射PAR将采集到的温室内每小时的光合有效辐 射值计算出各叶片接受的光合有效辐射值，利用公式1计算叶片的电压；然后根据叶片的最大叶长、叶片的平均生长速率、叶片展开后第n天的有效积温、叶片展开后从第r天至第j天的累积有效积温、叶片的叶长、叶片的叶面积和叶片电阻计算各叶片的干重，即各叶片的电阻；以模拟出的茎的节间长度和节间直径和步骤2中获得的节间电阻率得到各节间电阻；
步骤d4、利用叠加定理将电路分为m个子电路，分别求出m个子电路中果实电阻上的电流并求和，即得到可果实电阻上的实际电流，每个子电路中果枝上电流用戴维南定理求解；求出m个子电路上Rf的电流，然后相加，得到总电流，则果枝上的总功率采用总电流的平方乘以电阻，即可得到果枝在一天中的总电功率，即一天中获得的同化产物量。
步骤4中，所述温室番茄果实直径满足下述公式：
Figure PCTCN2016110906-appb-000012
Figure PCTCN2016110906-appb-000013
其中，P为果实的电功率P。
有益效果：
与传统的果实预测模型相比，本模型具有以下几个方面的优势：
第一、目前的温室番茄生长模型大多以源库理论来模拟温室番茄果实的生长，源库理论中库强，尤其是营养器官的库强很难以量化。本发明在压力-阻抗理论的基础上，提出了基于欧姆定律的温室番茄果实生长的预测模型，克服了营养器官库强难以量化的不足，为中国温室番茄生产中的环境调控、营养液管理和农艺措施提供理论依据。
第二、传统的温室番茄生长模型以作物生产为核心，而温室环境调控和营养液管理等程序的编写等大多是以电学为基础的来编写，这二者之间由于思维方式不同，造成作物生长模型在应用推广时带来诸多不便。而本发明是将温室番茄同化产物在植株间的分配以电的形式来体现，更有利于温室环境调控和营养液管理等程序编写。
第三、传统的温室番茄果实预测模型是基于番茄的生长过程建立的。而本发明专利是基于农学与电学的交叉的基础上建立的。具有很强的创新性。
附图说明
图1为步骤实施例1步骤1中的示意图，其中，图A为9叶1果的温室番茄植株示意图，图B为图A的温室番茄植株示意图转化而成的源库关系图，图C为由图B转化而 成的电路图；RL1～RL9为第1～9叶片电阻；Rs1～Rs10分别为第1～10节间电阻；E1～E9为第1～9叶片电池；Rf1为第1果枝上的果实电阻。
图2为实施例1中，双杆整枝的番茄植株示意图；
图3中，图A为实施例1中双杆整枝的番茄植株等效电路图，图B为图A的简化电路图，图C为电流流向图；
图4中，图A为实施例2中番茄植株的示意图，图B为图A的等效电路图。
具体实施方式
下面结合附图和实施例对本发明作进一步描述：
实施例1：
本发明中，果枝上的番茄果实的数量可以不只1个，当果实的数量大于1个时，假定所有果实的直径相等。
步骤1、将番茄植株进行转换；首先将番茄植株依据源/库关系进行转换，即叶片为源和库(温室番茄生产中叶片用于光合作用生产，同时部分光合产物用于维持叶片消耗，即叶片的维持呼吸)，茎和果实为库，转化为源库关系图。然后，以源为电源，库为电阻，将番茄植株转化为电路。下面以9叶1果(9个叶枝，1个果枝)温室番茄植株为例，对转化方式进行具体说明；首先选取9叶1果的温室番茄植株，然后依据源库关系进行转换，叶片均为源/库，茎和果实为库。然后以源为电池，库为电阻对温室番茄植株进行转换。转换后温室番茄植株上的叶片均转变为一个电阻加上一个电池，从根部向上，电阻依次被分别称为RL1、RL2--RL9，电压依次被称为E1、E2--E9。9叶1果的温室番茄的茎由10个节间组成，则每个节间为一个电阻，从根部向上以此称为Rs1--Rs10。1个果枝为一个电阻，称为Rf1。本发明为了将这个转换过程做清晰的说明，选取了9叶1果的温室番茄，其他形式的番茄均可以以此进行转换。
也就是说：当番茄植株是一个m叶枝n果枝的植株时(m是≥1的自然数，n＝0或是≥1的自然数)，其转化的电路是：
将m叶枝中的每个叶枝均转变为一个叶片电阻RL串接一个叶片电池E，共m个叶片和m个叶片电池，m个叶片电阻分别记为RL1、RL2、......RLm，m个叶片电池的电压分别记为E1、E2......Em；
将每个果枝转变为一个果实电阻Rf，共有n个果实电阻，n个果实电阻分别记为Rf1、Rf2......Rfn；
m叶枝n果枝的植株的茎由m+n个节间组成，将每个节间转变为一个节间电阻Rs， 从根部向上的节间电阻Rs依次记为Rs1、Rs2......Rsm+n，再将这Rs1、Rs2......Rsm+n个节电阻依次串联；
每个叶片电阻RL的一端串接一个叶片电池E，将每个叶片电阻RL的另一端均连接于不同的每两个节间电阻Rs之间，将每个叶片电池E的另一端均接地；
将每个果实电阻Rf的一端接地，另一端均连接于不同的每两个节间电阻Rs之间，使每个果实电阻Rf的另一端与每个叶片电阻RL另一端之间串接一个节间电阻Rs；
因此，共m个叶片电阻的另一端、共n个果实电阻的另一端连接于不同的每两个节间电阻Rs1、Rs2......Rsm+n之间；
如上所述，便形成如图1所示的示意图。
步骤2、利用温室内的相关条件以及步骤1中得到的电路，确定温室番茄植株的叶片电压、叶片电阻、节间电阻和果实电阻；
2.1、所述叶片电压满足公式(1)：
Figure PCTCN2016110906-appb-000014
其中，E为叶片电压，Pmax为该叶片最大光合作用速率(单位：kg CO2·ha-1·h-1)，ε为光转换因子即吸收光的初始光能利用效率(单位：kg CO2·ha-1·h-1/J·m-2·s-1)，依据试验观测数据，ε取值为0.40(单位：kg CO2·ha-1·h-1/J·m-2·s-1)，Pmax取值为37(单位：kg CO2·ha-1·h-1)；PAR(T)为T时刻叶片吸收的光合有效辐射PAR(单位：J·m-2·s-1)，A为叶片的叶面积(单位：m2)；d为植株密度，单位为株/平方米，T为时刻，单位为小时。
所述叶片的叶面积的确定方法为：
步骤a1、根据叶序确定叶片的最大叶长和叶片的平均生长速率，所述最大叶长满足公式(2)，所述叶片的平均生长速率满足公式(3)：
Figure PCTCN2016110906-appb-000015
Figure PCTCN2016110906-appb-000016
其中，Lmax为叶片的最大叶长，最大叶长为叶片长成成熟叶时的最大长度；V为叶片的平均生长速率；i为叶序，即从根部开始数所得到的叶片数i，此时温室番茄从营养生长进入营养生长与生殖生长并重时期；
步骤a2、确定叶片的累积有效积温：
首先根据下述公式求得第一真叶展开至任一叶展开后第n天所需累积有效积温：
G＝∑(Tmean-Tb)    (4)，
再根据下述公式求得第一真叶展开至任一叶展开后从第r天至第j天的累积有效积温：
ΔGrj＝Gj-Gr    (5)，
其中，G为第一真叶展开至任一叶展开后第n天所需累积有效积温；Tmean为一天的日平均温度；Tb为界限温度，为公知常识；ΔGrj为第一真叶展开至任一叶展开后从第r天至第j天的累积有效积温；Gr为第一真叶展开至任一叶展开后第r天所需累积有效积温，Gj为第一真叶展开至任一叶展开后第j天所需累积有效积温；
番茄各生育时期界限温度Tb的取值参见表1：
表1番茄各生育时期界限温度Tb的取值
Figure PCTCN2016110906-appb-000017
步骤a3、根据步骤a1、a2得到的叶片的最大叶长、叶片的平均生长速率和累积有效积温确定叶片的叶长，满足公式(6)：
Figure PCTCN2016110906-appb-000018
其中，L为叶片的叶长，Lmax为叶片的最大叶长，V为叶片的平均生长速率，ΔGrj为叶片展开后从第r天至第j天的累积有效积温；
步骤a4、根据步骤a3所得到的叶片的叶长确定叶片的叶面积，满足公式(7)：
A＝K×L2/10000    (7)，
其中，A为叶片的叶面积；K为比例系数，无量纲；L为叶片的叶长。
所述叶片吸收的光合有效辐射PAR满足公式(8)：
PAR＝PARo×e-kAI    (8)，
其中，PARo为叶片上方的光合有效辐射值，属于公知常识；k为消光系数，取值0.8；AI为叶片上方的叶面积指数，即以叶片上方叶面积乘以叶片密度。
2.2、叶片电阻RL的计算：
所述叶片电阻满足公式(9)：
Figure PCTCN2016110906-appb-000019
其中，RL为叶片电阻，Rm(T25)为25℃时番茄的维持呼吸系数，Rm(T25)取值0.015(kg CH2O·kg-1DM·d-1)，W为叶干重(kg DM·ha-1)，T为每日平均温度(℃)。
所述叶干重满足公式(10)：
W＝A×SA    (10)，
其中，W为叶干重，A为叶片的叶面积，SA为比叶面积，取值40m2kg-1。
2.3、节间电阻Rs的计算：
所述节间电阻满足公式(11)：
Figure PCTCN2016110906-appb-000020
其中，Rs为节间电阻，C为节间电阻率，Ln为节间长度，Dn为节间直径。
所述节间长度的确定方法为：
步骤b1、确定节间平均生长速率，所述节间平均生长速率满足公式(12)：
Figure PCTCN2016110906-appb-000021
其中，Vn为节间平均生长速率，i为叶序；
步骤b2、根据步骤b1得到的叶片的累积有效积温和节间平均生长速率，确定节间长度，所述节间长度满足公式(13)：
Figure PCTCN2016110906-appb-000022
其中，Ln为节间长度，Vn为节间平均生长速率，ΔGrj为叶片展开后从第r天至第j 天的累积有效积温。
所述节间直径的确定方法为：
步骤c1、根据叶序确定节间直径的平均生长速率，所述节间直径的平均生长速率满足公式(14)：
Figure PCTCN2016110906-appb-000023
其中，Vdn为节间直径的平均生长速率，i为叶序；
步骤c2、根据叶片的累积有效积温和节间直径的平均生长速率确定节间直径，所述节间直径满足公式(15)：
Figure PCTCN2016110906-appb-000024
其中，Dn为节间直径，Vdn为节间直径的平均生长速率，ΔGrj为叶片展开后从第r天至第j天的累积有效积温。
2.4、节间电阻率C和果实电阻Rf的确定：
节间电阻率C和果实电阻Rf采用试验来确定。
2.4.1试验设计
试验在温室中进行，供试温室作物为番茄，采用栽培桶栽培，栽培基质为珍珠岩。采用霍格兰营养液灌溉。番茄采用双杆整枝，分别为A杆和B杆。选取具代表性的植株3株进行测定。具体如下：
A杆上留4片叶，在B杆第二节间处留果枝1个，果枝上留果实2个；B杆不留叶片，如图2所示。
2.4.2测定项目和方法
处理后第三天开始每天测定A、B杆中各节间长度、节间直径；叶片长度，果实直径。环境因子：所需要的温室和辐射资料由温室控制系统自动采集。
2.4.3数据分析
(a)首先利用测定的番茄双杆上的叶片长度，利用公式(2)：A＝K×L2/10000，计算温室番茄的单叶面积A，再利用公式(11)：W＝A×SA，计算出叶干重W，利用式(1)：
Figure PCTCN2016110906-appb-000025
计算出各叶片电压E，利用公式(10)：RL＝Rm(T25)×W×2(T-25)/10，计算出各叶片电阻RL。
(b)将试验中各处理进行转换，将各处理转化为电路图模式。
(c)依据测定的节间长、叶长和果实直径，计算出节间干重、叶干重和果实干重。
节间干重＝节间长度×0.11    (16)，
果实干重＝0.025×果实直径的立方    (17)，
节间长度和果实直径可以通过直接测定获得。
(d)依据公式(7)、(16)、(17)得到茎、叶、果干重减去前一天的茎、叶、果干重，即可得到茎、叶、果的日增长量，节间干重即茎的节间干重。
(e)利用茎、叶、果日增长量为茎叶果的电功率，求解果实电阻Rf和茎上各节间的电阻。
2.4.4具体计算过程
测定的温室番茄叶长分别为25、29、35、33cm，叶片上方的光合有效辐射值和温度值见表2，利用公式(1)，计算出每日的同化产物量(即电压)，则在一天中的番茄各叶片的光合产物量分别为7.31，7.43，8.01，5.19Kg CO2ha-1d-1。利用公式(9)计算出叶片维持呼吸量分别为0.010，0.0122，0.018，0.016Kg CO2ha-1d-1(即叶片电阻)。则第1、2、3、4片叶的电压分别为7.31，7.43，8.01，5.19，电阻分别为0.010，0.0122，0.018，0.016，茎、叶、果日增长量分别为6.25、7.25和10.75Kg DM ha-1，则茎、叶、果的电功率为6.25、7.25和10.75。其中四片叶片的增长量分别为2.01、2.3、1.94和1(实测值)，则四片叶片的电功率分别为2.01、2.3、1.94和1。
表2温室番茄光合有效辐射和温度
Figure PCTCN2016110906-appb-000026
Figure PCTCN2016110906-appb-000027
根据图3中的图A所示的等效电路图以及图B得到的简化电路图，得到：
R′＝(Rs5+Rf)//(Rs6+Rs7+Rs8)+Rs1；
对于图3所示的电路图，已知有：U1、U2、U3、U4、RL1、RL2、RL3、RL4、P1、P2、P3、P4、Pf、Ps；求解，Rs1、Rs2、Rs3、Rs4、Rs5、Rs6、Rs7、Rs8和Rf；其中，RL1～RL4分别为4个叶片的叶片电阻，P1、P2、P3、P4分别为4个叶片的电功率，Pf为果实电功率，Ps节间电功率。
解：
Figure PCTCN2016110906-appb-000028
Figure PCTCN2016110906-appb-000029
Figure PCTCN2016110906-appb-000030
将已知的U1、U2、U3、U4、RL1、RL2、RL3、RL4、P1、P2、P3、P4的值代入上述公式中，即可得到Rs2、Rs3、Rs4，分别为0.092、0.052、0.096。利用测定得到的第2、3、4节间的节间直径和节间长度，利用下面公式(18)计算第2、3、4节间的电阻率。
Figure PCTCN2016110906-appb-000031
其中，C为节间电阻率，Dn为节间直径，Ln为节间长度。
然后取平均值，得到节间的平均电阻率。依据得到的平均电阻率和节间1、5、6、7、8的节间长度和节间直径，再根据公式(11)计算出节间1、5、6、7、8的节间电阻。然后计算出的R′的值，最终计算出果实电阻Rf。
表3为依据环境因子计算出的温室番茄电参数：
表3依据环境因子计算出的温室番茄电参数
Figure PCTCN2016110906-appb-000032
步骤3、利用确定的叶片电压、叶片电阻、节间电阻和果实电阻，利用欧姆定律，计算果实两端的电压和电流，得到果实的功率。
对于m叶枝、n果枝的番茄植株，依据转化后的电路图。首先利用温度和辐射计算出叶片电压和叶片电阻，然后利用有效积温计算出各节间的长度和直径，利用步骤2中得到的节间电阻率，计算出各节间电阻值和果实电阻。然后求出各果枝的电流，计算各果枝电流的电功率。各果枝电流的电功率即为果枝获得的日同化产物量。进而推导出果实的鲜重的日增长量，最终得到果实的直径和鲜重的日增长量。
其中，果实鲜重＝0.5×果实直径的立方。
具体如下：
对番茄植株进行转换。对于拥有m叶枝和n果枝的番茄植株，番茄叶片从根部开始，向上依次称为叶1、叶2......叶m，则叶1、叶2......叶m分别为一个一个电池和电阻，其电压分别为E1、E2......Em，叶片电阻分别为R1、R2......Rm。共有m+1个节间，其电阻分别称为Rs1、Rs2......Rsm、Rsm+1。有n个果枝，分别称为Rf1、Rf2......Rfn。
利用公式(2)～(6)预测各叶片的叶长，利用公式(11)～(15)求得各茎的节间长度和节间直径。
利用公式(8)将采集到的温室内每小时的光合有效辐射值计算出各叶片接受的光合有效辐射值，利用公式1计算叶片的电压。然后利用公式(2)～(7)和(9)计算各叶片的干重，即各叶片的电阻。以模拟出的茎的节间长度和节间直径和步骤2中获得的节间电阻率利用公式(11)得到各节间电阻。
利用叠加定理将电路分为m个子电路，分别求出m个子电路中果实电阻上的电流并求和，即得到可果实电阻上的实际电流，每个子电路中果枝上电流用戴维南定理求解。求出m个子电路上Rf的电流，然后相加，得到总电流，则果枝上的总功率采用总电流的平方乘以电阻，即可得到果枝在一天中的总电功率，即一天中获得的同化产物量。
步骤4、利用步骤3得到的果实的电功率P，即温室番茄果实一天中所得到的同化产物量。然后依据果实得到的同化产物量求得果实直径。具体如下：
果实鲜重＝0.5×果实直径立方，
Figure PCTCN2016110906-appb-000033
Figure PCTCN2016110906-appb-000034
其中，P为果实的电功率P。
结果第一天的果实直径取值为1.5厘米。
实施例2：
对实施例1中的步骤3进行举例，如图4：
对番茄植株进行转换。为简化起见，选取3叶1果(3个叶枝和1个果枝)的温室番茄植株，番茄叶片从根部向上分别为叶1、叶2、叶3。则叶1、叶2、叶3分别为一个电池和电阻，其电压分别为E1、E2、E3，叶片电阻分别为RL1、RL2、RL3。共有4个节间，其电阻分别称为Rs1、Rs2、Rs3和Rs4，果枝为一个电阻，称为Rf。
依据模型预测得到的叶片长度分别为25、29、35cm，依据公式(1)、(3)和(8)，光合有效辐射和温度依然采用表2中的环境数据，再结合表4中的数据得到叶片电压、叶片电阻、节间电阻和果实电阻Rf。
表4温室番茄植株电参数
Figure PCTCN2016110906-appb-000035

Claims (10)

  1. 一种预测温室番茄果实生长的方法，其特征在于，包括以下步骤：
    步骤1、首先，以番茄植株的叶片为源和库，茎和果实为库，转化为源库关系；然后，以源为电源，库为电阻，将番茄植株依据源/库关系转化为电路；
    步骤2、利用温室内的相关条件以及步骤1中得到的电路，确定温室番茄植株的叶片电压、叶片电阻、节间电阻和果实电阻；
    步骤3、结合步骤1得到的电路，再根据确定的叶片电压、叶片电阻、节间电阻和果实电阻，利用欧姆定律，计算果实两端的电压和电流，得到果实的电功率，果实的电功率值即为温室番茄果实一天内获得的同化产物量值；
    步骤4、将得到的温室番茄果实的同化产物量转化为温室番茄果实直径。
  2. 根据权利要求1所述的一种预测温室番茄果实生长的方法，其特征在于，所述将番茄植株依据源/库关系转化为电路的具体过程为：
    当番茄植株是一个m叶枝n果枝的植株时，m是≥1的自然数，n＝0或是≥1的自然数，其转化的电路是：
    将m叶枝中的每个叶枝均转变为一个叶片电阻RL串接一个叶片电池E，共m个叶片和m个叶片电池，m个叶片电阻分别记为RL1、RL2、……RLm，m个叶片电池的电压分别记为E1、E2……Em；
    将每个果枝转变为一个果实电阻Rf，共有n个果实电阻，n个果实电阻分别记为Rf1、Rf2……Rfn；
    m叶枝n果枝的植株的茎由m+n个节间组成，将每个节间转变为一个节间电阻Rs，从根部向上的节间电阻Rs依次记为Rs1、Rs2……Rsm+n，再将这Rs1、Rs2……Rsm+n个节电阻依次串联；
    每个叶片电阻RL的一端串接一个叶片电池E，将每个叶片电阻RL的另一端均连接于不同的每两个节间电阻Rs之间，将每个叶片电池E的另一端均接地；
    将每个果实电阻Rf的一端接地，另一端均连接于不同的每两个节间电阻Rs之间，使每个果实电阻Rf的另一端与每个叶片电阻RL另一端之间串接一个节间电阻Rs；
    因此，共m个叶片电阻的另一端、共n个果实电阻的另一端连接于不同的每两个节间电阻Rs1、Rs2……Rsm+n之间。
  3. 根据权利要求1所述的一种预测温室番茄果实生长的方法，其特征在于，步骤2中，所述叶片电压满足下述公式：
    Figure PCTCN2016110906-appb-100001
    其中，E为叶片电压，Pmax为该叶片最大光合作用速率；ε为光转换因子；PAR(T)为T时刻叶片吸收的光合有效辐射PAR；A为叶片的叶面积；d为植株密度；T为时刻。
  4. 根据权利要求3所述的一种预测温室番茄果实生长的方法，其特征在于，所述叶片的叶面积的确定方法为：
    步骤a1、根据叶序确定叶片的最大叶长和叶片的平均生长速率，所述最大叶长满足下述公式：
    Figure PCTCN2016110906-appb-100002
    所述叶片的平均生长速率满足下述公式：
    Figure PCTCN2016110906-appb-100003
    其中，Lmax为叶片的最大叶长；V为叶片的平均生长速率；i为叶序；
    步骤a2、确定叶片的累积有效积温：
    首先根据下述公式求得第一真叶展开至任一叶展开后第n天所需累积有效积温：
    G＝∑(Tmean－Tb)，
    再根据下述公式求得第一真叶展开至任一叶展开后从第r天至第j天的累积有效积温：
    ΔGrj＝Gj-Gr，
    其中，G为第一真叶展开至任一叶展开后第n天所需累积有效积温；Tmean为一天的日平均温度；Tb为界限温度，为公知常识；ΔGrj为第一真叶展开至任一叶展开后从第r天至第j天的累积有效积温；Gr为第一真叶展开至任一叶展开后第r天所需累积有效积温，Gj为第一真叶展开至任一叶展开后第j天所需累积有效积温；
    步骤a3、根据步骤a1、a2得到的叶片的最大叶长、叶片的平均生长速率和累积有效积温确定叶片的叶长，满足下述公式下述：
    Figure PCTCN2016110906-appb-100004
    其中，L为叶片的叶长；
    步骤a4、根据步骤a3所得到的叶片的叶长确定叶片的叶面积，满足下述公式：
    A＝K×L2/10000，
    其中，K为比例系数。
  5. 根据权利要求3所述的一种预测温室番茄果实生长的方法，其特征在于，所述叶片吸收的光合有效辐射PAR满足下述公式：
    PAR＝PARo×e-kAI，
    其中，PARo为叶片上方的光合有效辐射值；k为消光系数；AI为叶片上方的叶面积指数。
  6. 根据权利要求1所述的一种预测温室番茄果实生长的方法，其特征在于，步骤2中，所述叶片电阻满足下述公式：
    Figure PCTCN2016110906-appb-100005
    其中，RL为叶片电阻，Rm(T25)为25℃时番茄的维持呼吸系数；W为叶干重；T为每日平均温度；
    所述叶干重满足公式下述：
    W＝A×SA，
    其中，A为叶片的叶面积；SA为比叶面积。
  7. 根据权利要求1所述的一种预测温室番茄果实生长的方法，其特征在于，步骤2中，所述节间电阻满足下述公式：
    Figure PCTCN2016110906-appb-100006
    其中，Rs为节间电阻；C为节间电阻率；Ln为节间长度；Dn为节间直径。
  8. 根据权利要求7所述的一种预测温室番茄果实生长的方法，其特征在于，所述节间长度的确定方法为：
    步骤b1、确定节间平均生长速率，所述节间平均生长速率满足下述公式：
    Figure PCTCN2016110906-appb-100007
    其中，Vn为节间平均生长速率；i为叶序；
    步骤b2、根据步骤b1得到的叶片的累积有效积温和节间平均生长速率，确定节间长度，所述节间长度满足下述公式：
    Figure PCTCN2016110906-appb-100008
    其中，Ln为节间长度；Vn为节间平均生长速率；ΔGrj为叶片展开后从第r天至第j天的累积有效积温；
    所述节间直径的确定方法为：
    步骤c1、根据叶序确定节间直径的平均生长速率，所述节间直径的平均生长速率满足下述公式：
    Figure PCTCN2016110906-appb-100009
    其中，Vdn为节间直径的平均生长速率，i为叶序；
    步骤c2、根据叶片的累积有效积温和节间直径的平均生长速率确定节间直径，所述节间直径满足下述公式：
    Figure PCTCN2016110906-appb-100010
    其中，Dn为节间直径；Vdn为节间直径的平均生长速率；ΔGrj为叶片展开后从第r天至第j天的累积有效积温；
    所述节间电阻率的确定方法为：
    根据节间直径和节间长度确定节间电阻率，所述节间电阻率满足下述公式：
    Figure PCTCN2016110906-appb-100011
    其中，C为节间电阻率；Dn为节间直径；Ln为节间长度。
  9. 根据权利要求1所述的一种预测温室番茄果实生长的方法，其特征在于，步骤3中，所述果实电功率的具体推导过程为：
    步骤d1、对番茄植株进行转换：对于拥有m叶枝和n果枝的番茄植株，番茄叶片从根部开始，向上依次称为叶1、叶2……叶m，则叶1、叶2……叶m分别为一个一个电池和电阻，其电压分别为E1、E2……Em，叶片电阻分别为R1、R2……Rm；共有m+1个节间，其电阻分别称为Rs1、Rs2……Rsm、Rsm+1；有n个果枝，分别称为Rf1、 Rf2……Rfn；
    步骤d2、根据叶片的最大叶长、叶片的平均生长速率、叶片展开后第n天的有效积温、叶片展开后从第r天至第j天的累积有效积温、叶片的叶长来预测各叶片的叶长，根据节间电阻、节间平均生长速率、节间长度、节间直径的平均生长速率、节间直径求得各茎的节间长度和节间直径；
    步骤d3、根据叶片吸收的光合有效辐射PAR将采集到的温室内每小时的光合有效辐射值计算出各叶片接受的光合有效辐射值，利用公式1计算叶片的电压；然后根据叶片的最大叶长、叶片的平均生长速率、叶片展开后第n天的有效积温、叶片展开后从第r天至第j天的累积有效积温、叶片的叶长、叶片的叶面积和叶片电阻计算各叶片的干重，即各叶片的电阻；以模拟出的茎的节间长度和节间直径和步骤2中获得的节间电阻率得到各节间电阻；
    步骤d4、利用叠加定理将电路分为m个子电路，分别求出m个子电路中果实电阻上的电流并求和，即得到可果实电阻上的实际电流，每个子电路中果枝上电流用戴维南定理求解；求出m个子电路上Rf的电流，然后相加，得到总电流，则果枝上的总功率采用总电流的平方乘以电阻，即可得到果枝在一天中的总电功率，即一天中获得的同化产物量。
  10. 根据权利要求1所述的一种预测温室番茄果实生长的方法，其特征在于，步骤4中，所述温室番茄果实直径满足下述公式：
    Figure PCTCN2016110906-appb-100012
    Figure PCTCN2016110906-appb-100013
    其中，P为果实的电功率P。
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114155526A (zh) * 2021-11-09 2022-03-08 中国农业大学 一种番茄果实生长预测方法、装置、设备及产品
CN116642529A (zh) * 2023-04-21 2023-08-25 中国农业科学院都市农业研究所 数字化植株冠层水分蒸散量数据挖掘系统

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108062602B (zh) * 2017-12-28 2019-08-02 江苏大学 一种预测温室茄果类作物同化产物产量的方法
CN108184564B (zh) * 2017-12-28 2020-03-31 江苏大学 一种构建温室茄果类作物同化产物分配模型的方法
CN109479481B (zh) * 2019-01-07 2020-11-24 中国农业科学院茶叶研究所 一种预测名优绿茶春茶对当季肥料氮吸收的方法
CN112070241A (zh) * 2020-09-11 2020-12-11 腾讯科技（深圳）有限公司 基于机器学习模型的植物生长预测方法、装置和设备
CN112904920B (zh) * 2021-01-15 2022-05-10 康子秋 一种预测温室作物光合作用干物质产量的方法
CN114793785B (zh) * 2022-03-31 2023-11-10 河源弘稼农业科技有限公司 番茄植株种植方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101950321A (zh) * 2010-07-30 2011-01-19 沈阳农业大学 一种建立日光温室长季节栽培番茄植株叶片数模型用于控制番茄长势的方法
CN104268387A (zh) * 2014-09-18 2015-01-07 南京信息工程大学 一种基于出叶和展叶的设施作物叶面积模拟方法
CN105912884A (zh) * 2016-06-13 2016-08-31 江苏大学 一种基于生理效应的作物茎直径生长速率预测方法

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104657593B (zh) * 2015-01-21 2018-01-26 中国科学院自动化研究所 一种作物性状动态形成过程评估品种的方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101950321A (zh) * 2010-07-30 2011-01-19 沈阳农业大学 一种建立日光温室长季节栽培番茄植株叶片数模型用于控制番茄长势的方法
CN104268387A (zh) * 2014-09-18 2015-01-07 南京信息工程大学 一种基于出叶和展叶的设施作物叶面积模拟方法
CN105912884A (zh) * 2016-06-13 2016-08-31 江苏大学 一种基于生理效应的作物茎直径生长速率预测方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
E. HEUVELINK: "Evaluation of A Dynamic Simulation Model for Tomato Crop Growth and Development", ANNALS OF BOTANY, vol. 83, no. 4, 1 April 1999 (1999-04-01), XP055491421, ISSN: 0305-7364 *
ZHU, JINYU ET AL.: "Simulation of dry matter production and partitioning based on source-sink growth unit in greenhouse tomato", ACTA ECOLOGICA SINICA, vol. 29, no. 12, 31 December 2009 (2009-12-31), pages 6527 - 6533, ISSN: 1000-0933 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114155526A (zh) * 2021-11-09 2022-03-08 中国农业大学 一种番茄果实生长预测方法、装置、设备及产品
CN114155526B (zh) * 2021-11-09 2024-04-16 中国农业大学 一种番茄果实生长预测方法、装置、设备及产品
CN116642529A (zh) * 2023-04-21 2023-08-25 中国农业科学院都市农业研究所 数字化植株冠层水分蒸散量数据挖掘系统

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