CN113191572A - Apple yield prediction method and device, storage medium and electronic equipment - Google Patents
Apple yield prediction method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN113191572A CN113191572A CN202110584375.7A CN202110584375A CN113191572A CN 113191572 A CN113191572 A CN 113191572A CN 202110584375 A CN202110584375 A CN 202110584375A CN 113191572 A CN113191572 A CN 113191572A
- Authority
- CN
- China
- Prior art keywords
- apple
- sample data
- yield
- predicted
- time period
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 241000220225 Malus Species 0.000 claims abstract description 219
- 235000021016 apples Nutrition 0.000 claims abstract description 37
- 238000012549 training Methods 0.000 claims abstract description 18
- 235000011430 Malus pumila Nutrition 0.000 claims description 228
- 235000015103 Malus silvestris Nutrition 0.000 claims description 228
- 235000013399 edible fruits Nutrition 0.000 claims description 140
- 244000141359 Malus pumila Species 0.000 claims description 52
- 230000008014 freezing Effects 0.000 claims description 41
- 238000007710 freezing Methods 0.000 claims description 41
- 230000006378 damage Effects 0.000 claims description 33
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 claims description 23
- 238000001556 precipitation Methods 0.000 claims description 21
- 230000009746 freeze damage Effects 0.000 claims description 20
- 230000002159 abnormal effect Effects 0.000 claims description 17
- 208000027418 Wounds and injury Diseases 0.000 claims description 14
- 208000014674 injury Diseases 0.000 claims description 14
- 238000004891 communication Methods 0.000 claims description 13
- 230000007613 environmental effect Effects 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 5
- 230000003203 everyday effect Effects 0.000 description 12
- 230000008569 process Effects 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 8
- 230000002354 daily effect Effects 0.000 description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 230000000875 corresponding effect Effects 0.000 description 4
- 230000001186 cumulative effect Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 230000034994 death Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 231100000225 lethality Toxicity 0.000 description 3
- 239000002689 soil Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000017074 necrotic cell death Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 101100345347 Arabidopsis thaliana MGD2 gene Proteins 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004040 coloring Methods 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000009313 farming Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002420 orchard Substances 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Agronomy & Crop Science (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Animal Husbandry (AREA)
- Primary Health Care (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a prediction method, a prediction device, a storage medium and electronic equipment for apple yield, wherein the method comprises the following steps: acquiring data to be predicted; the data to be predicted comprises disaster sample data in a target time period and weather sample data of each day; inputting the data to be predicted into a yield prediction model to obtain a prediction result of the yield of the apples in the time period to be predicted; the disaster sample data in the target time period and the meteorological sample data in each day are used for predicting the apple yield in the same year in the time period to be predicted; the yield prediction model is obtained by training a plurality of sample data, and each sample data comprises disaster sample data in a preset time period, meteorological sample data of each day and apple yield sample data of the same year as the preset time period. The apple yield prediction method solves the problems that the existing apple yield prediction method is not universal and cannot accurately predict apple yield.
Description
Technical Field
The application relates to the technical field of agriculture, in particular to a method and a device for predicting apple yield, a storage medium and electronic equipment.
Background
China is the biggest apple producing country and consuming country in the world, the planting area and the yield of apples both account for more than 40% of the total amount of the world, and the apple tree plays an important role in the world apple industry. The current apple yield prediction method usually only considers common meteorological factors such as air temperature, precipitation, illumination and the like, and the selected meteorological factors are limited by regions and climates, so the current apple yield prediction method has no universality and cannot accurately and objectively predict the apple yield.
Disclosure of Invention
The embodiment of the application provides a prediction method, a prediction device, a storage medium and electronic equipment for apple yield, and solves the problems that the current apple yield prediction method is not universal and cannot accurately predict the apple yield.
In order to solve the technical problems, the application comprises the following technical scheme:
in a first aspect, an embodiment of the present application provides a prediction method for apple yield, where the method includes:
acquiring data to be predicted; the data to be predicted comprises disaster sample data in a target time period and weather sample data of each day;
inputting the data to be predicted into a yield prediction model to obtain a prediction result of the yield of the apples in the time period to be predicted; the disaster sample data in the target time period and the meteorological sample data in each day are used for predicting the apple yield in the same year in the time period to be predicted; the yield prediction model is obtained by training a plurality of sample data, and each sample data comprises disaster sample data in a preset time period, meteorological sample data of each day and apple yield sample data of the same year as the preset time period.
In a second aspect, an embodiment of the present application provides an apparatus for predicting apple yield, the apparatus including:
the acquisition module is used for acquiring data to be predicted; the data to be predicted comprises disaster sample data in a target time period and weather sample data of each day;
the prediction module is used for inputting the data to be predicted into a yield prediction model to obtain a prediction result of the yield of the apples in the time period to be predicted; the disaster sample data in the target time period and the meteorological sample data in each day are used for predicting the apple yield in the same year in the time period to be predicted; the yield prediction model is obtained by training a plurality of sample data, and each sample data comprises disaster sample data in a preset time period, meteorological sample data of each day and apple yield sample data of the same year as the preset time period.
In a third aspect, an embodiment of the present application provides an electronic device, where the electronic device includes: processor, memory, and communication interface:
the processor is connected with the memory and the communication interface;
the memory for storing executable program code;
the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the prediction method of apple yield according to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the prediction method for apple yield according to the first aspect.
The method for predicting the apple yield comprehensively considers the influence of hail disasters, freezing disasters, flower thinning and fruit thinning on the apple fruit hanging amount and the influence of precipitation on the single fruit weight of the apples, utilizes historical apple yield data and corresponding meteorological data in a required area to construct an apple yield prediction model for the area, and predicts the apple yield of the year by combining the meteorological data and disaster data of the predicted year.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for predicting apple yield according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for predicting apple yield according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an apparatus for predicting apple yield according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below.
The terms "first," "second," "third," and the like in the description and claims of this application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a method for predicting apple yield according to an embodiment of the present application, the method including:
s101, acquiring data to be predicted; the data to be predicted comprises disaster sample data in a target time period and weather sample data of each day.
Specifically, the user inputs the data to be predicted into the electronic device, and the electronic device obtains the data to be predicted. The data to be predicted comprises disaster sample data in the target time period and weather sample data of each day.
Further, before acquiring the data to be predicted, the method further comprises: acquiring a plurality of sample data; and training a plurality of sample data to obtain the yield prediction model. The sample data comprises a plurality of sample data, and each sample data comprises disaster sample data in a preset time period, weather sample data of each day and apple yield sample data of the same year as the preset time period. Wherein, in the process of model training, the method further comprises the following steps: the parameters in the yield model are solved. Methods of solving for the parameters may include, but are not limited to: and obtaining a plurality of groups of parameter combinations by using a small-batch Gradient Descent algorithm (MGDB), wherein each parameter combination comprises a plurality of parameters, and randomly obtaining partial data in the sample data of the apple yield to estimate a plurality of parameter values in the yield prediction model, wherein the parameter value estimation adopts a least square method, namely, a group of parameters with the minimum error in the parameter combinations is found to minimize the error between the actual value and the simulated value of the apple yield, and the group of parameters is the target parameters required by the scheme.
Specifically, the method selects key environmental elements to construct an apple yield estimation model for a specific main market, and basically assumes that the unit yield of apples is determined by the number of apple trees, the bearing capacity of each apple tree and the weight of each apple. The phenological period of the apple fruits is generally divided into two periods of an exposed red period, a young fruit period and a fruit expansion period, wherein the fruit bearing amount of each apple tree is calculated in the exposed red period and the young fruit period, the weight of each apple is calculated in the fruit expansion period, the apple yield estimation model based on the plant physiological process is used for calculating the step length, and the fruit bearing amount of each apple tree and the weight of each apple are simulated respectively to obtain the yield estimation model shown as follows:
Yield＝Plantnum×Numend×Weightend
wherein Yield is apple Yield; plantnumPlanting a tree for the apple; numendWeight is the amount of fruit bearing per apple treeendFor the weight of each apple.
Further, the yield prediction model may also include a progress coefficient and a prediction year; the progress factor in combination with the predicted year characterizes the effect of non-environmental factors on apple yield. With the improvement of the technological level and the management level of the apples, the yield of the apples is increased continuously. When carrying the progress coefficients, a yield prediction model can be obtained as follows:
Yield＝Plantnum×Numend×Weightend+C×year
wherein Yield is apple Yield; plantnumPlanting a tree for the apple; numendWeight is the amount of fruit bearing per apple treeendFor each apple weight; c is a progress coefficient; year is the year. Wherein the progress coefficient represents the influence of non-environmental factors on the yield of the apples in combination with the prediction year.
And S102, inputting the data to be predicted into a yield prediction model, and obtaining the apple yield prediction result in the time period to be predicted.
Specifically, after the electronic equipment acquires data to be predicted, the data to be predicted is input into a yield prediction model, the data to be predicted comprises disaster sample data in a target time period and meteorological sample data in each day, the yield prediction model inputs the disaster sample data and the meteorological sample data into the yield prediction model, and the yield prediction model analyzes and calculates the data to be predicted to acquire an apple yield prediction result in the time period to be predicted. The method comprises the steps that disaster sample data in a target time period and meteorological sample data of each day are used for predicting the yield of apples in the same time period to be predicted in the same year, a yield prediction model is obtained by training a plurality of sample data, and each sample data comprises the disaster sample data in a preset time period, the meteorological sample data of each day and the apple yield sample data in the same year as the preset time period.
Further, inputting the data to be predicted into a yield prediction model, and obtaining a prediction result of the yield of the apple in the time period to be predicted, wherein the prediction result comprises: inputting the disaster sample data in the time period to be predicted and the meteorological sample data of each day into a yield prediction model, and acquiring the freeze injury mortality, the hail mortality, the flower thinning and fruit thinning rate and the fruit growth rate; determining the fruit bearing amount of each apple tree according to the freeze injury mortality, the hail mortality and the flower thinning and fruit thinning rates; determining the weight of each apple according to the fruit growth rate; and obtaining an apple yield prediction result in a time period to be predicted according to the bearing quantity of each apple tree, the weight of each apple and the trees of the apple trees.
Further, the weather sample data comprises at least one of: the lowest temperature every day, the temperature every day being lower than a preset threshold value for a total duration, the precipitation every day, and the disaster sample data comprise at least one of the following items: the number of times of freezing damage disasters in the apple flowering phase, the number of times of hail disasters in the apple flowering phase, and the fruit hanging amount of each apple tree after the apple trees experience the freezing damage disasters and the hail disasters; inputting the disaster sample data in the time period to be predicted and the weather sample data of each day into a yield prediction model to obtain the freeze injury mortality, the hail mortality, the flower thinning and fruit thinning rate and the fruit growth rate, wherein the method comprises the following steps: obtaining the freezing injury mortality according to the lowest temperature of each day, the duration total time of the temperature of each day being lower than a preset threshold value and the occurrence frequency of the freezing injury disasters in the flowering period of the apples; acquiring the hail mortality according to the occurrence frequency of the hail disaster in the apple flowering phase; obtaining the flower thinning and fruit thinning rate according to the fruit hanging amount of each apple tree after the apple trees are subjected to freezing damage disasters and hail disasters; and acquiring the growth rate of the fruits according to the daily precipitation.
Specifically, the fruit bearing amount of each apple tree depends on the initial flower amount, the freeze injury mortality rate, the hail mortality rate and the flower thinning and fruit thinning rate. The formula for calculating the fruit bearing amount of each apple tree is as follows:
Numend＝Numstart×{(1–Rate1)×(1–Rate2)×…(1–Raten)}×(1-Hail)m×(1-Thin)
wherein, NumstartThe initial flower amount is obtained; rate1-nRespectively the lethality rates of the 1 st to nth freeze injury; hail is Hail mortality; thin is flower thinning and fruit thinning rate; n is the number of times of freezing damage disasters in the flowering phase of the apples; and m is the occurrence frequency of hail disasters in the flowering period of the apples.
Specifically, the lethality of freezing injury is positively correlated to the lowest temperature per day and the total duration of time that the temperature per day is lower than the preset threshold, wherein the preset threshold of the lowest temperature can be, but not limited to, 0 ℃, i.e., the total duration of time that the temperature per day is lower than 0 ℃ is counted. The calculation formula of the freezing injury lethality is as follows:
Ratei＝p×Tmini+q×Houri
wherein, Ratei、HouriTmin for the i-th freezing injury mortalityiFor the daily minimum temperature and HouriFor each day the temperature is below a preset threshold for a total duration; p and q are both undetermined parameters.
Specifically, the adverse effect that the hail caused the apple depends on the size of hail, hail reduction intensity and hail piece falling speed, consequently, for simplifying the calculation, in this application, the apple that the hypothesis caused by different hail disasters is the definite value, and the hail death rate of each time is the definite value promptly, and this definite value is undetermined parameter.
Specifically, flower thinning and fruit thinning can improve the quality and the yield of the apple in the same year to a certain extent, and the flower thinning and fruit thinning rate is influenced by multiple factors, so that the calculation is simplified, and the influence of freezing damage disasters and hail disasters on the dropping amount of the apple is only considered in the setting of the artificial flower thinning and fruit thinning rate in the application. The calculation formula of the flower thinning and fruit thinning rate is as follows:
wherein Thin is flower thinning and fruit thinning rate; numstartThe initial flower amount is obtained; numstateThe fruit bearing capacity is measured after freezing damage and hail disaster; numbaseThe minimum expected fruit bearing amount; thinaAnd ThinbAre parameters to be determined.
Specifically, the diameter of the apple grows in an S-shaped curve, the apple grows in a young fruit period within 30 days after fruit setting, the apple grows fast in the young fruit period, then the apple enters the early fruit expansion period, the cell number is mainly increased in the period, the fruit grows mainly in the longitudinal direction and is a water critical period of the apple, and meanwhile, the growth speed of the apple is generally maximum at about 60 days after fruit setting; and the fruit enters the later fruit expansion stage 75 days after fruit setting, the stage is mainly the increase of the total cell volume, the fruit mainly expands transversely, the growth speed is slow, and the fruit enters the slow growth stage. According to the above analysis, the apple fruit growth stage can be divided into an initial growth stage, an exponential growth stage and a stable growth stage. For calculating the weight of each apple, a Richards theoretical growth equation can be adopted for calculation, wherein the Richards theoretical growth equation is an equation constructed by Richards F J on the basis of Bertalanffy growth theory and is widely used for biological growth simulation. The Richards theory growth equation is a four-parameter nonlinear equation which can better describe the change of the growth and development fruit weight of the apples along with the change of time. The formula for calculating the weight of each apple is as follows:
wherein x represents the number of days of growth, WeightendFor each apple weight; k is the cumulative growth saturation value; a is0Is a growth initial value parameter; b0Growth rate parameters; c. C0For the purpose of the anisotropic growth parameters, and to simplify the calculation, the model will use c0The value is 2.
The above Richards theoretical growth equation was converted to Logistic growth equation as follows:
wherein, b0i、KiAnd WeightiThe growth rate parameters, the cumulative growth saturation value and the apple weight of the apple fruits on the ith day are respectively.
The method is characterized in that in the early stage of the expansion period of apple fruit growth, the apple growth speed is high, which is a key period of apple fruit growth, the soil water content in the period plays a decisive role in the size of apple fruits, the accumulated precipitation in the fruit expansion period directly determines the fruit diameter of the apples, if the precipitation in the period is insufficient, the soil water content is too low, and the apple orchard suffers from drought, so that the fruits are too small, and the fruit weight is influenced; if the precipitation is excessive, the young shoots grow vigorously, the crowns are seriously shielded, and the later coloring of the fruits is influenced, so that the quality of the apple fruits is influenced. The influence of insufficient rainfall on the weight of the apple fruits is only considered in the stage. Wherein, insufficient precipitation mainly affects the cumulative growth saturation value and the growth rate parameter, and the relationship among the daily growth rate parameter, the cumulative growth saturation value and the precipitation per day is respectively shown as the following formula:
wherein, b0iAnd KiRespectively setting the growth rate parameter and the accumulated growth saturation value of the apple fruits on the ith day; apcpiAccumulating precipitation for the ith day and the last 7 days; apcpbaseThe lower limit of the precipitation amount per day is 7, and when the lower limit is lower than the lower limit, the fruit weight increase of the apples is hindered.
Further, inputting the data to be predicted into a yield prediction model, and obtaining a prediction result of the yield of the apple in the time period to be predicted, wherein the prediction result comprises: and inputting the disaster sample data and the weather sample data of each day, the number of apple trees and the year in the time period to be predicted into a yield prediction model, and obtaining the apple yield prediction result in the time period to be predicted. In the yield prediction model provided by the application, if the influence of hail disasters on the apple yield is not considered and only the situation simulation is used for predicting the apple yield, the apple yield of years in the future can be predicted.
Illustratively, if the yield of apples in 9 months of 2021 year is to be predicted, data to be predicted in 1-8 months are acquired, and the data to be predicted comprises disaster sample data in 1-8 months and weather sample data in each day. Wherein the disaster sample data comprises: the number of times of freezing damage disasters in the apple flowering phase, the number of times of hail disasters in the apple flowering phase, and the fruit hanging amount of each apple tree after the freezing damage disasters and the hail disasters are experienced, wherein the meteorological sample data comprises: the lowest temperature every day, the total time when the temperature every day is lower than the preset threshold value and the precipitation every day. When the apples are in the red exposure period and the young fruit period, acquiring the lowest temperature of each day, the total duration time when the temperature of each day is lower than a preset threshold value and the occurrence frequency of hail, obtaining the freeze injury mortality according to the data, obtaining the hail mortality according to the freeze injury mortality, combining the initial flower quantity to obtain the flower and fruit thinning rate, and calculating the fruit hanging quantity of each apple tree according to the freeze injury mortality, the hail mortality, the flower and fruit thinning rate and the initial flower quantity. When the apples are in the expanding period, the growth rate and the accumulated growth saturation value of the apples are calculated according to the daily rainfall, so that the increased weight of the apples every day is obtained, the increased weight every day is accumulated day by day, and finally the weight of each apple is obtained. And if the yield of the apples in one mu of land is required to be calculated, inputting the calculated fruiting amount of each apple tree, the weight of each apple, the trees of the apple trees in one mu of land and the year into an apple yield prediction model, and finally obtaining an apple yield prediction result in the year.
The method for predicting the apple yield comprehensively considers the influence of hail disasters, freezing disasters, flower thinning and fruit thinning on the apple fruit hanging amount and the influence of precipitation on the single fruit weight of the apples, utilizes historical apple yield data and corresponding meteorological data in a required area to construct an apple yield prediction model for the area, and predicts the apple yield of the year by combining the meteorological data and disaster data of the predicted year.
Referring to fig. 2, fig. 2 is a schematic flow chart illustrating another apple yield prediction method according to an embodiment of the present application, the method including:
s201, obtaining a plurality of sample data.
Specifically, the electronic device obtains a plurality of sample data, where the sample data includes disaster sample data in a target time period and weather sample data of each day. Wherein the disaster sample data comprises: the number of times of freezing damage disasters in the apple flowering period, the number of times of hail disasters in the apple flowering period, and the fruit hanging amount of each apple tree after the apple trees experience the freezing damage disasters and the hail disasters. The weather sample data includes: the lowest temperature every day, the temperature every day lower than the preset threshold value for the total time and the precipitation every day.
S202, removing abnormal values of a plurality of sample data by utilizing the box type graph.
Specifically, after the electronic equipment acquires a plurality of sample data, abnormal value elimination is performed on the sample data of the apple yield in the plurality of sample data by using the boxed graph. Outliers include values where the annual yield of apples is less than a preset minimum or greater than a preset maximum. The specific method for eliminating the abnormal value by using the boxed graph can comprise the following steps: after a plurality of sample data are obtained, analyzing and sorting the sample data, for example, associating data corresponding to a certain year, a certain time period and a certain place to obtain a spatiotemporal mixed sample of the apple yield of the certain time period of the certain year of the certain place, analyzing the spatiotemporal mixed sample, and eliminating an abnormal value of the apple yield by using a box type graph, namely presetting a preset minimum value and a preset maximum value of the yield value, wherein when the annual yield of the apples of a certain year is less than the preset minimum value or greater than the preset maximum value, the annual yield value is an abnormal value, and then eliminating the abnormal value. The calculation formula of the preset minimum value and the preset maximum value of the annual yield is as follows:
Outlier_below＝Q1–1.5×(Q3-Q1)
Outlier_up＝Q3+1.5×(Q3-Q1)
wherein, the Outlier _ below is the lower inner limit; outlier _ up is an upper inner limit; q1 is the lower quartile; q3 is the upper quartile.
S203, training a plurality of sample data after the abnormal values are removed to obtain a yield prediction model.
Specifically, the electronic equipment trains a plurality of sample data from which the abnormal values are removed, and trains according to the disaster sample data and the weather sample data of each day to obtain the relationship between the two data and the apple yield data, so as to finally obtain the yield prediction model.
And S204, acquiring disaster sample data in a time period to be predicted and weather sample data of each day, and inputting the disaster sample data and the weather sample data of each day into the yield prediction model.
Specifically, if the yield of apples in 9 to 10 months in a certain year in a certain region is to be predicted, disaster sample data and weather sample data of each day in the region in 1 to 8 months in the same year are acquired, and the data are input into a yield prediction model.
S205, determining the freeze injury mortality, hail mortality, flower thinning and fruit thinning rate and fruit growth rate according to the disaster sample data and the weather sample data of each day.
Specifically, the freeze injury mortality, hail mortality, flower thinning and fruit thinning rate and the fruit growth rate are determined according to the disaster sample data and the weather sample data of each day. The cold resistance of the apples in the flowering period and the young fruit period is weak, the tolerance to low temperature is poor, and flower and fruit dropping is easily caused, so that in the embodiment of the application, the freezing injury mortality can be calculated according to disaster sample data in 1 to 8 months, the flower thinning and fruit thinning rate is determined according to the freezing injury mortality and the hail mortality, the specific formula for calculating the freezing injury mortality, the hail mortality and the flower thinning and fruit thinning rate refers to the embodiment, and the embodiment is not repeated. In the early stage of the apple fruit expansion period, the fruit growth speed is high, which is a key period of the apple growth and development, and the soil water content plays a decisive role in the fruit size in the period, so that the apple fruit growth rate is determined according to daily precipitation in the fruit expansion period, and the specific calculation formula refers to the above embodiment, and the embodiment is not repeated.
S206, determining the fruit bearing amount of each apple tree according to the freeze injury mortality, the hail mortality and the flower thinning and fruit thinning rate, and determining the weight of each apple according to the fruit growth rate.
Specifically, the fruit bearing amount of each apple tree is determined according to the acquired freeze injury mortality, hail mortality and flower and fruit thinning rate, and the weight of each apple is determined according to the fruit growth rate.
The method comprises the steps of setting a freezing disaster, determining the freezing disaster, and determining the death rate of the apple flowers and fruits, wherein the freezing disaster is the probability of apple necrosis caused by the freezing disaster, and the death rate of the apple flowers and fruits is determined by the freezing disaster. The hail mortality is the probability that the hail disaster causes apple necrosis, and the late spring hail mostly occurs from late 4 to late 5, and the hail falls to open petals or young countries in this period of time, causes certain influence to apple output, therefore, the output prediction model that this application provided embodies the hail mortality with the influence of the hail disaster in this period of time to apple output. The flower thinning and fruit thinning rate is the influence of the farming operation of manually thinning flowers and fruits on the fruit bearing quantity of the apples. The fruit growth rate is the growth rate of the apple fruits in the young fruit period and the expansion period every day. The weight of the apple is increased along with the increase of the diameter of the apple, the weight of the apple and the diameter of the apple are in positive correlation, and the weight of the apple can be described by using the change of the diameter of the apple. The growth process of the diameter of the apple is in an S-shaped curve, the young fruit period is within 30 days after the fruit is set, the apple grows faster and then enters the early stage of fruit expansion, the stage mainly comprises the increase of the number of cells, the fruit mainly grows longitudinally and is the water critical period of the apple, and meanwhile, the growth speed of the apple is maximum generally at about 60 days after the fruit is set; and the fruit enters the later fruit expansion stage 75 days after fruit setting, the stage is mainly the increase of the total cell volume, the fruit mainly expands transversely, the growth speed is slow, and the fruit enters the slow growth stage. Therefore, in the examples of the present application, the growth rate of apple fruits per day in the young fruit stage and the enlargement stage of apples was counted.
And S207, inputting the bearing quantity of each apple tree, the weight of each apple, the tree of the apple tree and the prediction year into a yield prediction model, and obtaining an apple yield prediction result.
Specifically, the calculated bearing amount of each apple tree, the calculated weight of each apple tree, the calculated tree of each apple tree and the calculated year are input into a yield prediction model, and the predicted yield of the apple in the year and the area is obtained.
In the embodiment of the application, disaster sample data and weather data of each day are introduced into a yield prediction model, wherein the disaster sample data considers freezing disaster and hail disaster at the same time, conventional environmental factors such as temperature and rainfall are considered in the weather sample data, and the influence of flower thinning and fruit thinning conditions on the yield of apples is also considered, a plurality of sample data carrying the data are simulated, a yield prediction model covering the whole apple growth process is established, and the yield of the apples in the same year can be predicted according to the disaster data and the weather data of a time period to be predicted in the yield prediction process.
Referring to fig. 3, a prediction method based on apple yield is shown, and fig. 3 is a schematic view of a prediction apparatus for apple yield according to an embodiment of the present application, including:
a first obtaining module 301, configured to obtain data to be predicted; the data to be predicted comprises disaster sample data in a target time period and weather sample data of each day;
the prediction module 302 is configured to input the data to be predicted into a yield prediction model, and obtain a prediction result of the yield of the apple in a time period to be predicted; the disaster sample data in the target time period and the meteorological sample data in each day are used for predicting the apple yield in the same year in the time period to be predicted; the yield prediction model is obtained by training a plurality of sample data, and each sample data comprises disaster sample data in a preset time period, meteorological sample data of each day and apple yield sample data of the same year as the preset time period.
In some embodiments, the apparatus further comprises:
a second obtaining module, configured to obtain a plurality of sample data before the first obtaining module 301 obtains the data to be predicted;
and the training module is used for training a plurality of sample data to obtain the yield prediction model.
In some embodiments, the apparatus further comprises:
the removing module is used for removing abnormal values of the sample data after the second acquiring module acquires the plurality of sample data and before the training module trains the plurality of sample data; the abnormal value includes a value where the annual apple yield is less than a preset minimum value or greater than a preset maximum value.
In some embodiments, the culling module is specifically configured to:
and removing abnormal values of the sample data by using the boxed graph.
In some embodiments, the prediction module 302 comprises:
the third acquisition module is used for inputting the disaster sample data in the time period to be predicted and the meteorological sample data in each day into a yield prediction model to acquire the freeze injury mortality, the hail mortality, the flower thinning and fruit thinning rate and the fruit growth rate;
the determining module is used for determining the fruit bearing amount of each apple tree according to the freeze injury mortality, the hail mortality and the flower thinning and fruit thinning rates; determining the weight of each apple according to the fruit growth rate;
and the fourth obtaining module is used for obtaining the apple yield prediction result in the time period to be predicted according to the bearing quantity of each apple tree, the weight of each apple and the trees of the apple trees.
In some embodiments, the weather sample data comprises at least one of: the lowest temperature of each day, the total duration of time when the temperature of each day is lower than a preset threshold value and the precipitation of each day; the disaster sample data comprises at least one of: the number of times of freezing damage disasters in the apple flowering phase, the number of times of hail disasters in the apple flowering phase, and the fruit hanging amount of each apple tree after the apple trees experience the freezing damage disasters and the hail disasters;
the third obtaining module is specifically configured to:
obtaining the freezing injury mortality according to the lowest temperature of each day, the duration total time of the temperature of each day being lower than a preset threshold value and the occurrence frequency of the freezing injury disasters in the flowering period of the apples; acquiring the hail mortality according to the occurrence frequency of the hail disaster in the apple flowering phase; obtaining the flower thinning and fruit thinning rate according to the fruit hanging amount of each apple tree after the apple trees are subjected to freezing damage disasters and hail disasters; and acquiring the growth rate of the fruits according to the daily precipitation.
In some embodiments, the yield prediction model comprises a progress coefficient and a prediction year; the progress factor, in combination with the predicted year, characterizes the effect of non-environmental factors on apple yield;
the prediction module 302 is specifically configured to:
and inputting the disaster sample data and the weather sample data of each day, the number of apple trees and the year in the time period to be predicted into a yield prediction model, and obtaining the apple yield prediction result in the time period to be predicted.
Referring to fig. 4, a schematic structural diagram of an electronic device 400 provided in the embodiment of the present application is shown. The electronic device 400 may comprise at least: at least one processor 401, e.g., a CPU, at least one network interface 404, a user interface 403, a memory 405, at least one communication bus 402. Wherein a communication bus 402 is used to enable connective communication between these components. The user interface 403 may include, but is not limited to, a display, a touch screen, a keyboard, a mouse, a joystick, and the like. The network interface 404 may optionally include a standard wired interface, a wireless interface (e.g., a WIFI interface), and a communication connection may be established with the server through the network interface 404. The memory 402 may be a high-speed RAM memory or a non-volatile memory (e.g., at least one disk memory). As shown in fig. 4, memory 405, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.
It should be noted that the network interface 404 may be connected to an acquirer, a transmitter or other communication module, and the other communication module may include, but is not limited to, a WiFi module, a carrier network communication module, and the like, and it is understood that the electronic device in the embodiment of the present application may also include an acquirer, a transmitter, other communication module, and the like.
acquiring data to be predicted; the data to be predicted comprises disaster sample data in a target time period and weather sample data of each day;
inputting the data to be predicted into a yield prediction model to obtain a prediction result of the yield of the apples in the time period to be predicted; the disaster sample data in the target time period and the meteorological sample data in each day are used for predicting the apple yield in the same year in the time period to be predicted; the yield prediction model is obtained by training a plurality of sample data, and each sample data comprises disaster sample data in a preset time period, meteorological sample data of each day and apple yield sample data of the same year as the preset time period.
Possibly, before the processor 401 acquires the data to be predicted, it is further configured to:
acquiring a plurality of sample data;
and training a plurality of sample data to obtain the yield prediction model.
Possibly, after obtaining a plurality of the sample data, and before training the plurality of the sample data, the processor 401 is further configured to:
removing abnormal values of the sample data; the abnormal value includes a value where the annual apple yield is less than a preset minimum value or greater than a preset maximum value.
Possibly, the processor 401 performs outlier rejection on the sample data, and specifically performs:
and removing abnormal values of the sample data by using the boxed graph.
Possibly, the processor 401 inputs the data to be predicted into a yield prediction model, obtains a prediction result of the yield of the apple in the time period to be predicted, and specifically executes:
inputting the disaster sample data in the time period to be predicted and the meteorological sample data of each day into a yield prediction model, and acquiring the freeze injury mortality, the hail mortality, the flower thinning and fruit thinning rate and the fruit growth rate;
determining the fruit bearing amount of each apple tree according to the freeze injury mortality, the hail mortality and the flower thinning and fruit thinning rates; determining the weight of each apple according to the fruit growth rate;
and obtaining an apple yield prediction result in a time period to be predicted according to the bearing quantity of each apple tree, the weight of each apple and the trees of the apple trees.
Possibly, the weather sample data comprises at least one of: the lowest temperature of each day, the total duration of time when the temperature of each day is lower than a preset threshold value and the precipitation of each day; the disaster sample data comprises at least one of: the number of times of freezing damage disasters in the apple flowering phase, the number of times of hail disasters in the apple flowering phase, and the fruit hanging amount of each apple tree after the apple trees experience the freezing damage disasters and the hail disasters;
the processor 401 inputs the disaster sample data in the time period to be predicted and the weather sample data of each day into a yield prediction model, obtains the freeze injury mortality, the hail mortality, the flower thinning and fruit thinning rate and the fruit growth rate, and specifically executes the following steps:
obtaining the freezing injury mortality according to the lowest temperature of each day, the duration total time of the temperature of each day being lower than a preset threshold value and the occurrence frequency of the freezing injury disasters in the flowering period of the apples; acquiring the hail mortality according to the occurrence frequency of the hail disaster in the apple flowering phase; obtaining the flower thinning and fruit thinning rate according to the fruit hanging amount of each apple tree after the apple trees are subjected to freezing damage disasters and hail disasters; and acquiring the growth rate of the fruits according to the daily precipitation.
Possibly, the yield prediction model comprises a progress coefficient and a prediction year; the progress factor, in combination with the predicted year, characterizes the effect of non-environmental factors on apple yield;
the processor 401 inputs the data to be predicted into a yield prediction model, obtains a prediction result of the apple yield within a time period to be predicted, and specifically executes:
and inputting the disaster sample data and the weather sample data of each day, the number of apple trees and the year in the time period to be predicted into a yield prediction model, and obtaining the apple yield prediction result in the time period to be predicted.
Embodiments of the present application also provide a computer-readable storage medium having stored therein instructions, which when executed on a computer or processor, cause the computer or processor to perform one or more steps of any one of the methods described above. The above prediction apparatus for apple yield and the respective constituent modules of the electronic device may be stored in the computer-readable storage medium if they are implemented in the form of software functional units and sold or used as independent products.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)), or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., Digital Versatile Disk (DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), etc.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. And the aforementioned storage medium includes: various media capable of storing program codes, such as Read-Only Memory (ROM), Random Access Memory (RAM), magnetic disk, and optical disk. The technical features in the present examples and embodiments may be arbitrarily combined without conflict.
The above-described embodiments are merely preferred embodiments of the present application, and are not intended to limit the scope of the present application, and various modifications and improvements made to the technical solutions of the present application by those skilled in the art without departing from the design spirit of the present application should fall within the protection scope defined by the claims of the present application.
Claims (10)
1. A prediction method for apple yield, characterized in that the method comprises the following steps:
acquiring data to be predicted; the data to be predicted comprises disaster sample data in a target time period and weather sample data of each day;
inputting the data to be predicted into a yield prediction model to obtain a prediction result of the yield of the apples in the time period to be predicted; the disaster sample data in the target time period and the meteorological sample data in each day are used for predicting the apple yield in the same year in the time period to be predicted; the yield prediction model is obtained by training a plurality of sample data, and each sample data comprises disaster sample data in a preset time period, meteorological sample data of each day and apple yield sample data of the same year as the preset time period.
2. The method of claim 1, wherein prior to obtaining the data to be predicted, further comprising:
acquiring a plurality of sample data;
and training a plurality of sample data to obtain the yield prediction model.
3. The method of claim 2, wherein after said obtaining a plurality of said sample data and before said training said plurality of said sample data, further comprising:
removing abnormal values of the sample data; the abnormal value includes a value where the annual apple yield is less than a preset minimum value or greater than a preset maximum value.
4. The method of claim 3, wherein said outlier rejection of said sample data comprises:
and removing abnormal values of the sample data by using the boxed graph.
5. The method as claimed in claim 1, wherein the inputting the data to be predicted into a yield prediction model to obtain the apple yield prediction result in the time period to be predicted comprises:
inputting the disaster sample data in the time period to be predicted and the meteorological sample data of each day into a yield prediction model, and acquiring the freeze injury mortality, the hail mortality, the flower thinning and fruit thinning rate and the fruit growth rate;
determining the fruit bearing amount of each apple tree according to the freeze injury mortality, the hail mortality and the flower thinning and fruit thinning rates; determining the weight of each apple according to the fruit growth rate;
and obtaining an apple yield prediction result in a time period to be predicted according to the bearing quantity of each apple tree, the weight of each apple and the trees of the apple trees.
6. The method of claim 5, wherein the weather sample data comprises at least one of: the lowest temperature of each day, the total duration of time when the temperature of each day is lower than a preset threshold value and the precipitation of each day; the disaster sample data comprises at least one of: the number of times of freezing damage disasters in the apple flowering phase, the number of times of hail disasters in the apple flowering phase, and the fruit hanging amount of each apple tree after the apple trees experience the freezing damage disasters and the hail disasters;
inputting the disaster sample data in the time period to be predicted and the weather sample data of each day into a yield prediction model to obtain the freeze injury mortality, the hail mortality, the flower thinning and fruit thinning rate and the fruit growth rate, wherein the method comprises the following steps:
obtaining the freezing injury mortality according to the lowest temperature of each day, the duration total time of the temperature of each day being lower than a preset threshold value and the occurrence frequency of the freezing injury disasters in the flowering period of the apples; acquiring the hail mortality according to the occurrence frequency of the hail disaster in the apple flowering phase; obtaining the flower thinning and fruit thinning rate according to the fruit hanging amount of each apple tree after the apple trees are subjected to freezing damage disasters and hail disasters; and acquiring the growth rate of the fruits according to the daily precipitation.
7. The method of claim 1, wherein the yield prediction model comprises a coefficient of progress and a year of prediction; the progress factor, in combination with the predicted year, characterizes the effect of non-environmental factors on apple yield;
the inputting the data to be predicted into a yield prediction model to obtain the apple yield prediction result in the time period to be predicted comprises the following steps:
and inputting the disaster sample data and the weather sample data of each day, the number of apple trees and the year in the time period to be predicted into a yield prediction model, and obtaining the apple yield prediction result in the time period to be predicted.
8. An apparatus for predicting apple yield, the apparatus comprising:
the first acquisition module is used for acquiring data to be predicted; the data to be predicted comprises disaster sample data in a target time period and weather sample data of each day;
the prediction module is used for inputting the data to be predicted into a yield prediction model to obtain a prediction result of the yield of the apples in the time period to be predicted; the disaster sample data in the target time period and the meteorological sample data in each day are used for predicting the apple yield in the same year in the time period to be predicted; the yield prediction model is obtained by training a plurality of sample data, and each sample data comprises disaster sample data in a preset time period, meteorological sample data of each day and apple yield sample data of the same year as the preset time period.
9. An electronic device comprising a processor, a memory, and a communication interface:
the processor is connected with the memory and the communication interface;
the memory for storing executable program code;
the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory for performing the apple yield prediction method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method for prediction of apple yield as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110584375.7A CN113191572A (en) | 2021-05-27 | 2021-05-27 | Apple yield prediction method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110584375.7A CN113191572A (en) | 2021-05-27 | 2021-05-27 | Apple yield prediction method and device, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113191572A true CN113191572A (en) | 2021-07-30 |
Family
ID=76985392
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110584375.7A Pending CN113191572A (en) | 2021-05-27 | 2021-05-27 | Apple yield prediction method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113191572A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114239849A (en) * | 2021-11-30 | 2022-03-25 | 支付宝(杭州)信息技术有限公司 | Weather disaster prediction and model training method, device, equipment and storage medium |
| CN114742324A (en) * | 2022-05-24 | 2022-07-12 | 北京极星农业有限公司 | Fruit yield estimation method and application thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341577A (en) * | 2017-07-25 | 2017-11-10 | 中国农业科学院农业信息研究所 | A kind of crop yield Forecasting Methodology and system |
| CN109615148A (en) * | 2018-12-29 | 2019-04-12 | 航天信息股份有限公司 | A kind of method and system of determining Maize Meteorological yield |
| CN112215716A (en) * | 2020-10-13 | 2021-01-12 | 浙江省气候中心 | Crop growth intervention method, device, equipment and storage medium |
-
2021
- 2021-05-27 CN CN202110584375.7A patent/CN113191572A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341577A (en) * | 2017-07-25 | 2017-11-10 | 中国农业科学院农业信息研究所 | A kind of crop yield Forecasting Methodology and system |
| CN109615148A (en) * | 2018-12-29 | 2019-04-12 | 航天信息股份有限公司 | A kind of method and system of determining Maize Meteorological yield |
| CN112215716A (en) * | 2020-10-13 | 2021-01-12 | 浙江省气候中心 | Crop growth intervention method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 车向军等: ""甘肃庆阳苹果气象灾害保险指数设计"", 《中国果树》, 31 March 2019 (2019-03-31), pages 104 - 109 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114239849A (en) * | 2021-11-30 | 2022-03-25 | 支付宝(杭州)信息技术有限公司 | Weather disaster prediction and model training method, device, equipment and storage medium |
| CN114742324A (en) * | 2022-05-24 | 2022-07-12 | 北京极星农业有限公司 | Fruit yield estimation method and application thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Holzkämper et al. | Identifying climatic limitations to grain maize yield potentials using a suitability evaluation approach | |
| JP6580334B2 (en) | Information processing apparatus, program, and information processing method | |
| CN110059915B (en) | Winter wheat weather disaster comprehensive risk dynamic evaluation method and device | |
| CN113191572A (en) | Apple yield prediction method and device, storage medium and electronic equipment | |
| Ben Dhiab et al. | Modeling olive-crop forecasting in Tunisia | |
| CN112215716A (en) | Crop growth intervention method, device, equipment and storage medium | |
| Masaki et al. | Geographical variation in climatic cues for mast seeding of Fagus crenata | |
| Measham et al. | Climate, winter chill, and decision-making in sweet cherry production | |
| CN115619583A (en) | Construction method of composite agricultural meteorological disaster monitoring index system | |
| Galera et al. | Analysis of airborne Olea pollen in Cartagena (Spain) | |
| Carini et al. | Nonlinear models for describing lettuce growth in autumn-winter | |
| García-Cervigón et al. | Intra-annual stem increment patterns and climatic responses in five tree species from an Ecuadorian tropical dry forest | |
| CN114461903A (en) | Method and device for determining suitable popularization area of crop variety | |
| CN116451823A (en) | Apple yield prediction method based on meteorological master control factors | |
| Real et al. | A climatology of Vintage Port quality | |
| JP2015000040A (en) | Data extraction apparatus, data extraction method, and program | |
| Baruah et al. | Use of data mining technique for prediction of tea yield in the face of climate change of Assam, India | |
| Fisogni et al. | Long‐term changes in flowering synchrony reflect climatic changes across an elevational gradient | |
| CA2761682C (en) | Real-time process for targeting trait phenotyping of plant breeding experiments | |
| CN116579521B (en) | Yield prediction time window determining method, device, equipment and readable storage medium | |
| CN111881418A (en) | Dichotomy-based meteorological soybean yield prediction method and dichotomy-based meteorological soybean yield prediction system | |
| Watson et al. | Rootstocks affect pear (Pyrus communis) tree growth through extent of node neoformation and flowering with key differences to apple | |
| Risio et al. | Inter-annual variability in Prosopis caldenia pod production in the Argentinean semiarid pampas: a modelling approach | |
| Whiting et al. | Development of bloom phenology models for tree fruits | |
| De Silva et al. | Variation of fruit size and growth within an apple tree and its influence on sampling methods for estimating the parameters of mid-season size distributions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |