CN113128779A - Decision support system for optimal harvest period of oranges - Google Patents
Decision support system for optimal harvest period of oranges Download PDFInfo
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- 238000003306 harvesting Methods 0.000 title claims abstract description 75
- 235000020971 citrus fruits Nutrition 0.000 claims abstract description 30
- 241000207199 Citrus Species 0.000 claims abstract description 29
- 230000003993 interaction Effects 0.000 claims abstract description 7
- 238000005070 sampling Methods 0.000 claims abstract description 7
- 235000013399 edible fruits Nutrition 0.000 claims description 14
- 238000000034 method Methods 0.000 claims description 9
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 claims description 7
- 239000002420 orchard Substances 0.000 claims description 5
- 238000013340 harvest operation Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims 1
- 241000675108 Citrus tangerina Species 0.000 abstract 2
- 239000002253 acid Substances 0.000 description 6
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000011084 recovery Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
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- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012625 in-situ measurement Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000009659 non-destructive testing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
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Abstract
The invention provides a decision support system for an optimal citrus harvesting period, which comprises a knowledge base module, a man-machine interaction module, a nondestructive detector, a temperature sensor and a harvesting period prediction model base module; and the harvest time prediction model library module comprises an optimal harvest time prediction model based on internal quality and an optimal harvest time prediction model based on effective accumulated temperature. The invention calls the optimal harvest time prediction model based on the internal quality through the nondestructive detector to predict the optimal harvest time of the sampled oranges based on the internal quality. And calling an optimal harvesting period prediction model based on the effective accumulated temperature through a temperature sensor to predict the optimal harvesting period of the sampling oranges based on the accumulated temperature. The invention carries out information complementation from the internal quality and the effective accumulated temperature of the oranges and tangerines, and implements the prediction of the optimal harvest time of the oranges and tangerines.
Description
Technical Field
The invention belongs to the technical field of information systems, and particularly relates to a decision support system for an optimal harvest time of oranges.
Background
Near Infrared (NIR) has the characteristics of rapidness, no damage, in-situ measurement and the like, and provides a new technical approach for the prediction of the harvesting period. NIR combines GPS sensor, can realize the measurement of the oranges on the tree in situ, takes the fruit tree as the basic unit to generate a harvest prescription chart, is a more accurate harvest period prediction scheme, but is difficult to be suitable for the full growth period, such as from flowering to fruit expansion period. The temperature directly influences the growth rate of the oranges, the picking date can be estimated through effective accumulated temperature (the sum of effective temperatures in the growth period of the oranges; the effective temperature refers to the difference value between the activity temperature and the biological lower limit temperature, and when the daily average temperature is higher than the biological zero temperature (the lowest temperature required by growth), the temperature factor plays a role in promoting the growth and development of the oranges).
Therefore, the spectrum information and the temperature sensor information tested by a nondestructive testing instrument are comprehensively utilized to complement information from the internal quality and the development accumulated temperature of the fruits, the change rule of the typical internal quality characters of the oranges and the response characteristics of the typical internal quality characters in a specific NIR wave band are clear, and the data-driven harvesting period prediction is implemented, so that the method is an optimal scheme solution. At present, there is no accepted model, method and technical system for harvesting period prediction, and basic research of application is first developed in the field, which is beneficial to supporting the orange industry to realize quality improvement, income increase and transformation upgrading.
Disclosure of Invention
The invention aims to provide a citrus optimal harvest time decision support system which is used for realizing citrus harvest time prediction more accurately.
The technical scheme of the invention is as follows: a decision support system for optimal harvest time of citrus is characterized by comprising:
the knowledge base module is used for storing a data table of flowering date, fruit expansion date, historical accumulated temperature data, harvesting standard and geographical position information of the fruit trees and is used by the harvesting period prediction model base module;
the human-computer interaction module is used for performing human-computer interaction and optimizing each data table in the knowledge base module;
the nondestructive detector is used for predicting the internal quality of the citrus;
the temperature sensor is used for monitoring the real-time accumulated temperature of the citrus orchard;
the harvest time prediction model library module comprises an optimal harvest time prediction model based on internal quality and an optimal harvest time prediction model based on effective accumulated temperature;
calling an optimal harvest time prediction model based on the internal quality through a nondestructive detector, and predicting the optimal harvest time of the sampled oranges based on the internal quality;
and calling an optimal harvesting period prediction model based on the effective accumulated temperature through a temperature sensor to predict the optimal harvesting period of the sampling oranges based on the accumulated temperature.
The method further comprises a spatial interpolation module and a geographic information system, wherein the optimal harvest time and GPS information of the sampling oranges based on the internal quality are uploaded to the spatial interpolation module, and a harvest operation decision support prescription map based on the internal quality is generated by combining a common kriging interpolation method.
Further, the optimal harvest time prediction model based on the internal intrinsic quality comprises a calculation method of an internal quality index increase speed, and an optimal harvest time of the plot is predicted by combining citrus internal quality index threshold values collected by a nondestructive detector.
Further, the effective accumulated temperature prediction model driven by real-time and historical accumulated temperature data is established on the basis of the effective accumulated temperature prediction model for the optimal recovery period, and the optimal recovery period is predicted by using the information of the temperature sensor.
Compared with the prior art, the invention has the following beneficial effects:
and (3) comprehensively utilizing information of nondestructive detection and a temperature sensor, performing information complementation from the internal quality and the development accumulated temperature of the fruits, clarifying the change rule of typical internal quality characters of the oranges and the response characteristic of the typical internal quality characters in a specific NIR wave band, and implementing data-driven harvest period prediction.
Drawings
FIG. 1 is a schematic diagram of the structure of a decision support system for optimal harvest time of citrus fruits;
FIG. 2 is a flow chart of the operation of a citrus optimal harvest time decision support system.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1:
a decision support system for optimal harvest time of citrus is characterized by comprising:
the knowledge base module is used for storing a data table of flowering date, fruit expansion date, historical accumulated temperature data, harvesting standard and geographical position information of the fruit trees and is used by the harvesting period prediction model base module;
the human-computer interaction module is used for performing human-computer interaction and optimizing each data table in the knowledge base module;
the nondestructive detector is used for predicting the internal quality of the citrus;
the temperature sensor is used for monitoring the real-time accumulated temperature of the citrus orchard;
the harvest time prediction model library module comprises an optimal harvest time prediction model based on internal quality and an optimal harvest time prediction model based on effective accumulated temperature;
calling an optimal harvest time prediction model based on the internal quality through a nondestructive detector, and predicting the optimal harvest time of the sampled oranges based on the internal quality;
and calling an optimal harvesting period prediction model based on the effective accumulated temperature through a temperature sensor to predict the optimal harvesting period of the sampling oranges based on the accumulated temperature.
Preferably, the system further comprises a spatial interpolation module and a geographic information system, wherein the optimal harvest time and GPS information of the sampling citrus based on the internal quality are uploaded to the spatial interpolation module, and a harvest operation decision support prescription chart based on the internal quality is generated by combining a common kriging interpolation method.
Preferably, the model for predicting the optimal harvest time based on the internal intrinsic quality comprises a calculation method of the increase speed of the internal quality index, and the optimal harvest time of the plot is predicted by combining citrus internal quality index threshold values collected by a nondestructive detector.
Preferably, the effective accumulated temperature prediction model based on the effective accumulated temperature is established as a real-time and historical accumulated temperature data-driven effective accumulated temperature prediction model, and the temperature sensor information is used for predicting the optimal recovery period.
The establishment process of the optimal harvest time prediction model based on the internal quality comprises the following steps:
establishing a prediction model of specific internal quality indexes-sugar-acid ratio through a nondestructive detector, periodically predicting the sugar-acid ratio of the citrus on the tree from the fruit expansion period of the citrus according to a fruit expansion date data table every week, subtracting a time interval from the predicted value of the sugar-acid ratio of the citrus in two adjacent weeks, calculating the growth speed of the sugar-acid ratio of the citrus, and predicting the optimal harvesting period of the sampled citrus tree by combining with the optimal harvesting standard threshold of the sugar-acid ratio;
sampling the harvesting period of the citrus trees and uploading GPS information to a spatial interpolation module, and combining a common kriging interpolation method to generate a harvest operation decision support prescription chart based on a sugar-acid ratio.
The establishment process of the optimal harvest time prediction model based on the effective accumulated temperature is as follows:
monitoring the temperature of an orchard from flowering to harvesting in real time from the flowering stage of a citrus tree through a temperature sensor according to a fruit flowering date data table, and calculating effective accumulated temperature; accumulating the historical accumulated temperature corresponding to the future date according to a historical accumulated temperature data table; analyzing the correlation between the effective accumulated temperature and the internal quality index of the fruits according to a harvest standard data table, and determining an effective accumulated temperature harvest standard threshold value required by the ripening of regional oranges; accumulating real-time accumulated temperature and historical accumulated temperature, and predicting a collected operation decision support formula diagram based on the accumulated temperature by combining an effective accumulated temperature collection standard threshold; and analyzing the difference of the effective accumulated temperatures of different geographical positions of the orchard according to the geographical position information data table, and further optimizing the node layout of the temperature sensor network.
Although embodiments of the present invention have been described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A decision support system for optimal harvest time of citrus is characterized by comprising:
the knowledge base module is used for storing a data table of flowering date, fruit expansion date, historical accumulated temperature data, harvesting standard and geographical position information of the fruit trees and is used by the harvesting period prediction model base module;
the human-computer interaction module is used for performing human-computer interaction and optimizing each data table in the knowledge base module;
the nondestructive detector is used for predicting the internal quality of the citrus;
the temperature sensor is used for monitoring the real-time accumulated temperature of the citrus orchard;
the harvest time prediction model library module comprises an optimal harvest time prediction model based on internal quality and an optimal harvest time prediction model based on effective accumulated temperature;
calling an optimal harvest time prediction model based on the internal quality through a nondestructive detector, and predicting the optimal harvest time of the sampled oranges based on the internal quality;
and calling an optimal harvesting period prediction model based on the effective accumulated temperature through a temperature sensor to predict the optimal harvesting period of the sampling oranges based on the accumulated temperature.
2. The citrus optimal harvest time decision support system according to claim 1, further comprising a spatial interpolation module and a geographic information system, wherein the citrus optimal harvest time and GPS information of the sampled citrus based on internal quality are uploaded to the spatial interpolation module, and combined with a common kriging interpolation method, a harvest operation decision support recipe map based on internal quality is generated.
3. The citrus optimal harvest time decision support system according to claim 1, wherein the internal quality-based optimal harvest time prediction model comprises a calculation method of an internal quality index increase speed, and a citrus internal quality index threshold value collected by a nondestructive inspection instrument is combined to predict the optimal harvest time of the plot.
4. The citrus optimal harvest time decision support system according to claim 1, wherein the optimal harvest time prediction model based on effective accumulated temperature builds an effective accumulated temperature prediction model driven by real-time and historical accumulated temperature data, and uses temperature sensor information to predict the optimal harvest time.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592193A (en) * | 2021-08-19 | 2021-11-02 | 中化现代农业有限公司 | Crop harvest time prediction method and device and storage medium |
| WO2024084755A1 (en) * | 2022-10-20 | 2024-04-25 | 国立研究開発法人農業・食品産業技術総合研究機構 | Prediction method, prediction program, environmental adjustment method, and environmental adjustment program |
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|---|---|---|---|---|
| JP2013191107A (en) * | 2012-03-14 | 2013-09-26 | Fujitsu Ltd | Method for estimating harvesting period and program |
| CN103971176A (en) * | 2014-05-07 | 2014-08-06 | 中国农业科学院柑桔研究所 | Method and system for optimizing harvesting decision of citrus fruits |
| CN110263969A (en) * | 2019-05-07 | 2019-09-20 | 西北农林科技大学 | A kind of shelf life apple quality Dynamic Forecasting System and prediction technique |
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2021
- 2021-04-29 CN CN202110470294.4A patent/CN113128779A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013191107A (en) * | 2012-03-14 | 2013-09-26 | Fujitsu Ltd | Method for estimating harvesting period and program |
| CN103971176A (en) * | 2014-05-07 | 2014-08-06 | 中国农业科学院柑桔研究所 | Method and system for optimizing harvesting decision of citrus fruits |
| CN110263969A (en) * | 2019-05-07 | 2019-09-20 | 西北农林科技大学 | A kind of shelf life apple quality Dynamic Forecasting System and prediction technique |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592193A (en) * | 2021-08-19 | 2021-11-02 | 中化现代农业有限公司 | Crop harvest time prediction method and device and storage medium |
| WO2024084755A1 (en) * | 2022-10-20 | 2024-04-25 | 国立研究開発法人農業・食品産業技術総合研究機構 | Prediction method, prediction program, environmental adjustment method, and environmental adjustment program |
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