CN113141941A - Fruit frost prevention method and device - Google Patents

Fruit frost prevention method and device Download PDF

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CN113141941A
CN113141941A CN202110491861.4A CN202110491861A CN113141941A CN 113141941 A CN113141941 A CN 113141941A CN 202110491861 A CN202110491861 A CN 202110491861A CN 113141941 A CN113141941 A CN 113141941A
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growth
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frost
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CN113141941B (en
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古丽米拉·克孜尔别克
孙伟
王蕾
曹姗姗
李全胜
马妍
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Xinjiang Academy Of Forestry Sciences Modern Forestry Research Institute
Xinjiang Agricultural University
Agricultural Information Institute of CAAS
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Xinjiang Agricultural University
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G13/00—Protecting plants
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G13/00—Protecting plants
    • A01G13/06—Devices for generating heat, smoke or fog in gardens, orchards or forests, e.g. to prevent damage by frost
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Abstract

The invention provides a fruit frost prevention method, which comprises the following steps: the method comprises the steps of obtaining growth condition parameter data, identifying the current growth state of fruits, obtaining fruit growth environment temperature parameter data, judging temperature thresholds borne by frost damage in different time periods, deciding a frost prevention target scheme, regulating and controlling a frost prevention mode, and selecting the end time of a frost prevention control instruction. The invention also provides a prevention device based on the method, which comprises a fruit growth state parameter acquisition module, a growth environment temperature acquisition module and an environment temperature regulation and control module. The fruit frost prevention method can know the temperature requirement of the fruit on frost prevention in real time, assist in carrying out environment temperature optimization regulation, improve the efficiency of frost prevention, solve the problem that fruit growers are difficult to control in the existing fruit frost prevention method, improve the fruit tree fruiting rate and save manpower.

Description

Fruit frost prevention method and device
Technical Field
The invention belongs to the technical field of fruit tree planting, and particularly relates to a fruit frost prevention method and a device thereof.
Background
With the modernization of agriculture, frost prevention systems are widely applied in ecological agriculture, facility agriculture and other aspects. The time for preventing frost is generally determined by fruit growers according to experience in the process of planting fruits, so that the current situation of low fruit ripening rate is often caused, and the problems of increased cost and reduced income of the fruit growers are caused. The method optimizes the fruit frost prevention mode, determines the optimal period for preventing frost, and has important significance for improving fruit growers to scientifically prevent frost damage.
Nowadays, the research on fruit frost prevention systems is numerous, but the research is mainly focused on the frost prevention research of the germination period, the bud period and the flowering period of fruits, most of which are based on the experience model of plants, monitor the growth state of the plants, and give the minimum adaptive temperature of the plants in different periods according to the experience to prevent frost damage to the plants. The above studies do not actually analyze the influence of temperature changes in plant growth environments at different periods on fruit growth, and cannot meet the frost prevention requirements of real plants. With the development of the internet of things technology, the research on frost prevention of plants is carried out more according to the temperature threshold values of frost prevention of different periods of time judged according to the growth states of the plants in the current frost prevention technology, but most models do not monitor the growth information of the fruits, the closed-loop control research of the regulation and control setting of the frost prevention is not carried out according to the growth information of the plants, the fruits of the plants are caused to grow inefficiently, the yield of the fruits is reduced, and the economic benefit maximization cannot be realized.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a fruit frost prevention method and a device thereof, aiming at the defects of the prior art, for solving the problems that the fruit yield of the plant is low and the yield is low due to the fact that adaptive frost prevention and control cannot be performed according to the growth condition and the growth environment temperature of the plant fruit in the prior art.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for preventing fruit frost is characterized by comprising the following steps:
s1, acquiring parameter data of the real-time growth period and the growth condition of the fruit: identifying the current growth state of the fruit based on an XTION sensor, acquiring temperature parameter data of the fruit growth environment based on an intelligent sensor, and carrying out comparative analysis on the growth period and growth condition parameter data of the fruit under the optimal frost prevention growth environment temperature parameter data; the growth period sequentially comprises a germination period, a bud period, a flowering period and a fruiting period;
s2, judging temperature thresholds of the fruits for frost damage in different time periods according to the influence degree of the growth environment temperature parameters of the fruits on growth period and growth condition parameter data, deciding a frost prevention target scheme, and regulating and controlling a frost prevention mode through a control instruction;
and S3, obtaining a growth environment parameter predicted value based on a Logistic regression frost prediction model, obtaining comparative analysis through the growth environment temperature predicted value and the growth environment temperature parameter standard value, and selecting the end time of the frost prevention control instruction.
Preferably, identifying the current fruit growth state based on XTION sensors comprises in particular the steps of:
s201, acquiring and three-dimensionally reconstructing a plant fruit point cloud, acquiring a multi-view plant fruit point cloud by using an optical sensor, and realizing the reconstruction of a geometric model of plant fruits with irregular shape characteristics to obtain a three-dimensional visual model reflecting the geometric shapes of the plant fruits at different periods;
s202, acquiring plant leaf point cloud data and three-dimensional reconstruction thereof: XTION is adopted as point cloud data acquisition equipment, a Euclidean clustering segmentation method is utilized to segment the blade, Euclidean distance is used to judge and extract target blade point cloud, a voxelized grid is adopted to sample, and a greedy triangle method is adopted to carry out gridding treatment, so that the original natural growth form of the blade is reflected;
s203, extracting plant fruit morphological parameters: and (3) performing parameter extraction on different characteristics of the plant fruit at different periods by using three-dimensional related software such as solidworks and the like on the obtained three-dimensional visual model of the plant fruit.
Preferably, the Logistic regression frost prediction model is a probabilistic nonlinear regression model, and the obtaining of the growth environment parameter prediction value by using the Logistic regression frost prediction model specifically comprises the following steps:
s301, acquiring a data set by surface temperature data acquisition and preprocessing;
s302, taking out a third group in the surface temperature data as a test set for detecting the prediction quality of the sample; taking residual data samples in the surface temperature data as a training set for constructing a Logistic regression model;
s303, training the Logistic regression model by using a training set;
s304, predicting the surface temperature of the growing environment by using the trained Logistic regression model.
Preferably, the preprocessing described in S301 specifically includes the following steps:
s401, eliminating data from the data set or missing data;
s402, normalizing all data in the removed data set by using MinMaxScaler, wherein the formula is as follows:
Z*＝(Z-Zmax)/(Zmax-Zmin) Formula (1)
Wherein Z is*Representing normalized data, ZmaxRepresents the maximum value of data, ZminRepresenting the minimum value of the data.
Preferably, the training of the Logistic regression model by using the training set specifically includes the following steps:
s501, calculating an output value of the earth surface temperature through an equation (1);
s502, calculating an error term of each earth surface temperature; wherein: l (m-L) is the total number of samples trained; t isiIs a predicted value; y isiIs the true value;
s503, calculating each softwords (T) according to the corresponding error termsi)；
S504, selecting an optimization algorithm based on gradient, and calculating each software word (T)i) And updating the trained model parameters according to a reverse calculation mode to minimize network loss.
A frost prevention device applied to the fruit frost prevention method is characterized by comprising a growth state parameter acquisition module, a growth environment temperature acquisition module and an environment temperature regulation module;
the growth state parameter acquisition module is an XTION sensor;
the growth environment temperature acquisition module is an intelligent sensor and is used for detecting surface temperature parameters;
the environment temperature regulation and control module comprises a main control box, a wind power vortex machine and a warm air machine, wherein the main control box is electrically connected with the wind power vortex machine and the warm air machine; the wind power vortex machine and the warm air blower are installed through a support, the support is of a rectangular frame structure and comprises vertical beams and cross beams, the cross beams are fixedly arranged between the four equal-height vertical beams, a sliding platform is fixedly installed on the cross beams, and the wind power vortex machine and the warm air blower are installed on the sliding platform in a sliding mode through pulleys. One wind power vortex machine and one warm air blower are in a group, and the two groups of wind power vortex machines and the two groups of warm air blowers are respectively positioned at the diagonal ends of the sliding platform. Because fruit frost is advection frost basically, after frost occurs, countercurrent flow occurs in the temperature of the growing environment, the temperature of the upper layer is high, and the temperature of the lower layer is low; the lower layer is blown to the upper layer at low temperature through the wind-force vortex machine, so that the temperature regulation and control effect is achieved, and meanwhile, the wind-force vortex machine blows and mixes warm air blown out by the warm air blower in the air to improve the heating effect. In order to improve the surface temperature of the ground and carry out frost protection measures on the orchard in time, the two wind turbines are respectively arranged at the height of 30cm to 50cm from the ground surface and at the height of 350cm to 400cm from the ground surface, so that the rapid improvement of the environmental temperature is facilitated.
Preferably, a processor and a memory connected with the processor are arranged in the main control box, a control signal output end of the processor is connected with the wind power vortex machine and the warm air blower, and a signal input end of the processor is connected with the XTION sensor and the intelligent sensor.
The plant fruits have different characteristics under different growth conditions, and the different characteristics can reflect the cold resistance of the fruits under different growth states, so that after the growth conditions of the fruits are determined, the cold resistance of the fruits under the conditions is selected to be compared with the growth environment temperature monitored in real time, whether the fruits are at the frost critical value of the fruits is judged, when the fruits are at the frost critical state, the frost state is further analyzed, a wind power vortex machine is turned on according to the analysis result, and the frost damage prevention of the fruits is realized by adjusting the air temperature. And (3) carrying out and predicting the environmental temperature parameters by using the growth environmental temperature parameters acquired historically and the growth environmental temperature parameters acquired by monitoring the current growth environment in real time, comparing the predicted values with the reference values of the environmental temperature parameters under the optimal growth condition of the acquired environmental temperature, and when the predicted values are consistent with the reference values, closing the wind power vortex machine, stopping blowing and ending the frost prevention operation.
Compared with the prior art, the invention has the following advantages:
1. according to the invention, by detecting the growth period characteristics of fruits and combining the temperature data change of the growth environment, the requirements of plants on temperature are known in real time, and the environment temperature is assisted to be optimally regulated and controlled, so that the time lag of a frost prevention model is improved to a certain extent, the flowering rate and the fruiting rate are effectively improved, the problem that fruit growers are difficult to master is solved, the fruit yield of the fruit growers is improved, and the manpower is saved.
2. The invention has the advantages of scientific and reasonable design, good temperature-adjusting and anti-freezing effects, low cost, high intelligent degree, convenient use and maintenance and popularization and application.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
FIG. 1 is a flowchart of the overall operation of the method for preventing frost in fruits according to the present invention.
FIG. 2 is a flow chart of a method for identifying the current fruit growth state based on XTION sensors in the present invention.
Fig. 3 is a schematic view of an installation structure of the ambient temperature control module according to the present invention.
Description of reference numerals:
601-lower layer beam; 602-lower vertical beam; 603, a slide rail;
604-a pulley; 605-wind vortex machine; 606-warm air blower;
607-Stent. 608-upper beam; 609-upper vertical beam;
Detailed Description
As shown in fig. 1, the present invention comprises a method for preventing frost on a fruit, comprising the steps of:
s1, acquiring parameter data of the real-time growth period and the growth condition of the fruit: identifying the current growth state of the fruit based on an XTION sensor, acquiring temperature parameter data of the fruit growth environment based on an intelligent sensor, and carrying out comparative analysis on the growth period and growth condition parameter data of the fruit under the optimal frost prevention growth environment temperature parameter data; the growth period sequentially comprises a germination period, a bud period, a flowering period and a fruiting period;
s2, judging temperature thresholds of the fruits for frost damage in different time periods according to the influence degree of the growth environment temperature parameters of the fruits on growth period and growth condition parameter data, deciding a frost prevention target scheme, and regulating and controlling a frost prevention mode through a control instruction;
and S3, obtaining a growth environment parameter predicted value based on a Logistic regression frost prediction model, obtaining comparative analysis through the growth environment temperature predicted value and the growth environment temperature parameter standard value, and selecting the end time of the frost prevention control instruction.
As shown in fig. 2, in this embodiment, identifying the current growth state of the fruit based on the XTION sensor specifically includes the following steps:
s201, acquiring and three-dimensionally reconstructing a plant fruit point cloud, acquiring a multi-view plant fruit point cloud by using an optical sensor, and realizing the reconstruction of a geometric model of plant fruits with irregular shape characteristics to obtain a three-dimensional visual model reflecting the geometric shapes of the plant fruits at different periods;
s202, acquiring plant leaf point cloud data and three-dimensional reconstruction thereof: XTION is adopted as point cloud data acquisition equipment, a Euclidean clustering segmentation method is utilized to segment the blade, Euclidean distance is used to judge and extract target blade point cloud, a voxelized grid is adopted to sample, and a greedy triangle method is adopted to carry out gridding treatment, so that the original natural growth form of the blade is reflected;
s203, extracting plant fruit morphological parameters: and (3) performing parameter extraction on different characteristics of the plant fruit at different periods by using three-dimensional related software such as solidworks and the like on the obtained three-dimensional visual model of the plant fruit.
In this embodiment, the Logistic regression frost prediction model is a probabilistic nonlinear regression model, and obtaining the growth environment parameter prediction value by using the Logistic regression frost prediction model specifically includes the following steps:
s301, acquiring a data set by surface temperature data acquisition and preprocessing;
s302, taking out a third group in the surface temperature data as a test set for detecting the prediction quality of the sample; taking residual data samples in the surface temperature data as a training set for constructing a Logistic regression model;
s303, training the Logistic regression model by using a training set;
s304, predicting the surface temperature of the growing environment by using the trained Logistic regression model.
In this embodiment, the preprocessing described in S301 specifically includes the following steps:
s401, eliminating data from the data set or missing data;
s402, normalizing all data in the removed data set by using MinMaxScaler, wherein the formula is as follows:
Z*＝(Z-Zmax)/(Zmax-Zmin) Formula (1)
Wherein Z is*Representing normalized data, ZmaxRepresents the maximum value of data, ZminRepresenting the minimum value of the data.
In this embodiment, training the Logistic regression model using the training set specifically includes the following steps:
s501, calculating an output value of the earth surface temperature through an equation (1);
s502, calculating an error term of each earth surface temperature; wherein: l (m-L) is the total number of samples trained; t isiIs a predicted value; y isiIs the true value;
s503, calculating each softwords (T) according to the corresponding error termsi)；
S504, selecting an optimization algorithm based on gradient, and calculating each software word (T)i) And updating the trained model parameters according to a reverse calculation mode to minimize network loss.
A frost prevention device applied to the fruit frost prevention method is characterized by comprising a growth state parameter acquisition module, a growth environment temperature acquisition module and an environment temperature regulation module;
the growth state parameter acquisition module is an XTION sensor;
the growth environment temperature acquisition module is an intelligent sensor and is used for detecting surface temperature parameters;
as shown in fig. 3, the ambient temperature control module includes a main control box, a wind turbine, and a heater unit, wherein the main control box is electrically connected to the wind turbine and the heater unit; the wind power vortex machine and the warm air blower are installed through a support, the support is of a rectangular frame structure, the support 607 comprises vertical beams 602 and cross beams 601, the cross beams 601 are fixedly arranged between the four vertical beams 602 with the same height, rectangular communicating slide rails 603 are fixedly installed on the cross beams 601, the rectangular communicating slide rails 603 form a sliding platform, the wind power vortex machine 605 and the warm air blower 606 are installed on the slide rails 603 in a sliding mode through pulleys 604, and the pulleys 603 are driven by electric power and controlled through a main control box. One wind vortex machine 605 and one heater 606 are in a group, and the two groups of wind vortex machines 605 and the two groups of heater 606 are respectively positioned at the farthest diagonal ends of the upper layer and the lower layer of the sliding platform. Because fruit frost is advection frost basically, after frost occurs, countercurrent flow occurs in the temperature of the growing environment, the temperature of the upper layer is high, and the temperature of the lower layer is low; the lower layer is blown to the upper layer by the wind vortex machine 605, and the warm air of the warm air blower 606 is blown away to achieve the temperature regulation effect. The wind power vortex machines 605 on the lower layer of the support 607 are arranged on the lower layer beam 601 and are 30cm to 50cm away from the ground, the wind power vortex machines 605 on the upper layer of the support 607 are arranged on the upper layer beam 608 and are 350cm to 400cm away from the ground, and the two groups are positioned at the diagonal positions in the space, so that the efficient convection temperature control effect is realized; the intelligent sensor is a temperature sensor, the temperature in the environment can change along with seasonal changes, the changing temperature needs to be monitored at any time in real time, and the sensitivity coefficient of the temperature sensor is required to be higher, so that the temperature sensor in the C8051F sheet is selected, and the temperature sensor is high in measurement accuracy, low in price and convenient to use.
In this embodiment, a processor and a memory connected to the processor are disposed in the main control box, a control signal output end of the processor is connected to the wind turbine 605 and the heater 606, and a signal input end of the processor is connected to the XTION sensor and the intelligent sensor.
In this embodiment, when the wind vortex machine 605 and the warm air blower 606 receive the regulation and control instruction of the main control box, the wind vortex machine 606 blows the warm air released by the warm air blower 606 to the lower layer of the fruit tree in the moving process of the wind vortex machine 605 and the warm air blower 606, and meanwhile, the planting area can be partitioned, and a preset position is set for each block area.
Generally speaking, the method judges the criticality of fruit frost prevention by acquiring the growth state of the fruit, and decides a corresponding frost prevention scheme through a fruit growth environment temperature regulation module, sends a control instruction to turn on a wind scroll machine 605 and a warm air fan 606, judges whether the critical requirement of the frost prevention is met or not by combining with fruit growth environment prediction, and further controls the electromagnetic valve switch of the fruit growth environment temperature control module, so that the control of the fruit frost prevention is realized, and the flowering rate and the fruiting rate of the fruit are improved.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way. Any simple modification, change and equivalent changes of the above embodiments according to the technical essence of the invention are still within the protection scope of the technical solution of the invention.

Claims (7)

1. A method for preventing fruit frost is characterized by comprising the following steps:
s1, acquiring parameter data of the real-time growth period and the growth condition of the fruit: identifying the current growth state of the fruit based on an XTION sensor, acquiring temperature parameter data of the fruit growth environment based on an intelligent sensor, and carrying out comparative analysis on the growth period and growth condition parameter data of the fruit under the optimal frost prevention growth environment temperature parameter data; the growth period sequentially comprises a germination period, a bud period, a flowering period and a fruiting period;
s2, judging temperature thresholds of the fruits for frost damage in different time periods according to the influence degree of the growth environment temperature parameters of the fruits on growth period and growth condition parameter data, deciding a frost prevention target scheme, and regulating and controlling a frost prevention mode through a control instruction;
and S3, obtaining a growth environment parameter predicted value based on a Logistic regression frost prediction model, obtaining comparative analysis through the growth environment temperature predicted value and the growth environment temperature parameter standard value, and selecting the end time of the frost prevention control instruction.
2. Fruit frost prevention method according to claim 1, wherein identifying the current fruit growth status based on XTION sensor comprises in particular the steps of:
s201, acquiring and three-dimensionally reconstructing a plant fruit point cloud, acquiring a multi-view plant fruit point cloud by using an optical sensor, and realizing the reconstruction of a geometric model of plant fruits with irregular shape characteristics to obtain a three-dimensional visual model reflecting the geometric shapes of the plant fruits at different periods;
s202, acquiring plant leaf point cloud data and three-dimensional reconstruction thereof: XTION is adopted as point cloud data acquisition equipment, a Euclidean clustering segmentation method is utilized to segment the blade, Euclidean distance is used to judge and extract target blade point cloud, a voxelized grid is adopted to sample, and a greedy triangle method is adopted to carry out gridding treatment, so that the original natural growth form of the blade is reflected;
s203, extracting plant fruit morphological parameters: and (3) performing parameter extraction on different characteristics of the plant fruit at different periods by using three-dimensional related software on the obtained plant fruit three-dimensional visual model.
3. The fruit frost prevention method according to claim 1, wherein the Logistic regression frost prediction model is a probabilistic nonlinear regression model, and obtaining the growth environment parameter prediction value by using the Logistic regression frost prediction model specifically comprises the following steps:
s301, acquiring a data set by surface temperature data acquisition and preprocessing;
s302, taking out a third group in the surface temperature data as a test set for detecting the prediction quality of the sample; taking residual data samples in the surface temperature data as a training set for constructing a Logistic regression model;
s303, training the Logistic regression model by using a training set;
s304, predicting the surface temperature of the growing environment by using the trained Logistic regression model.
4. A method according to claim 3, wherein said pretreatment in S301 comprises the steps of:
s401, eliminating data from the data set or missing data;
s402, normalizing all data in the removed data set by using MinMaxScaler, wherein the formula is as follows:
Z*＝(Z-Zmax)/(Zmax-Zmin) Formula (1)
Wherein Z is*Representing normalized data, ZmaxRepresents the maximum value of data, ZminRepresenting the minimum value of the data.
5. The method for preventing fruit frost according to claim 4, wherein training a Logistic regression model using a training set specifically comprises the steps of:
s501, calculating an output value of the earth surface temperature through an equation (1);
s502, calculating an error term of each earth surface temperature; wherein: l (m-L) is the total number of samples trained; t isiIs a predicted value; y isiIs the true value;
s503, calculating each softwords (T) according to the corresponding error termsi)；
S504, selecting an optimization algorithm based on gradient, and calculating each software word (T)i) And updating the trained model parameters according to a reverse calculation mode to minimize network loss.
6. A frost prevention device applied to the fruit frost prevention method is characterized by comprising a growth state parameter acquisition module, a growth environment temperature acquisition module and an environment temperature regulation module;
the growth state parameter acquisition module is an XTION sensor;
the growth environment temperature acquisition module is an intelligent sensor and is used for detecting surface temperature parameters;
the environment temperature regulation and control module comprises a main control box, a wind power vortex machine and a warm air machine, wherein the main control box is electrically connected with the wind power vortex machine and the warm air machine; the wind power vortex machine and the warm air blower are installed through a support, the support is of a rectangular frame structure, a sliding platform is fixedly installed on the support, and the wind power vortex machine and the warm air blower are installed on the sliding platform in a sliding mode through pulleys.
7. Fruit frost prevention apparatus according to claim 6, wherein a processor and a memory connected to the processor are provided in the main control box, control signal outputs of the processor are connected to the wind vortex machine and the warm air machine, and signal inputs of the processor are connected to the XTION sensor and the intelligent sensor.
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