NL2029693A - Remote sensing prediction method for fragrant pear maturity period based on multi-source remote sensing data - Google Patents

Remote sensing prediction method for fragrant pear maturity period based on multi-source remote sensing data Download PDF

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NL2029693A
NL2029693A NL2029693A NL2029693A NL2029693A NL 2029693 A NL2029693 A NL 2029693A NL 2029693 A NL2029693 A NL 2029693A NL 2029693 A NL2029693 A NL 2029693A NL 2029693 A NL2029693 A NL 2029693A
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Lv Xifeng
Liu Yiting
Li Xu
Bai Tiecheng
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Univ Tarim
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Abstract

The present invention discloses a remote sensing prediction method for a fragrant pear maturity period based on multi-source remote sensing data. A planting area is manually 5 determined, the selected area is measured, a matching canal system is made, and an irrigation system is improved to prevent soil salinization. A planting climate is determined based on meteorological data. Meteorological conditions are the most important factors that affect the growth of crops and lead to changes in crop maturity period, so traditional crop maturity prediction methods mostly predict the maturity 10 period of annual crops by means of the meteorological conditions of different phenological periods of crops or the appearance time of specific phenological periods. A crop growth model can describe the process of crop growth and yield formation from the growth mechanism driven by crop photosynthesis, a cost function is constructed by using the crop growth model with the optimization of crop yield or quality as the goal, 15 and then the optimized crop harvest time can be reversely solved to predict the maturity period of fruit trees.

Description

REMOTE SENSING PREDICTION METHOD FOR FRAGRANT PEAR MATURITY PERIOD BASED ON MULTI-SOURCE REMOTE SENSING DATA
TECHNICAL FIELD S [01] The present invention relates to the field of multi-source remote sensing technology, in particular to a remote sensing prediction method for a fragrant pear maturity period based on multi-source remote sensing data.
BACKGROUND ART
[02] Korla fragrant pear is well-known at home and abroad for its thin skin, crispy pulp, juiciness, sweetness, crispiness, refreshing taste, rich nutrients, etc. The rapid development of the Korla fragrant pear industry can not only comprehensively reflect the development level of the fruit industry in the Bayingolin Mongolian Autonomous Prefecture, but also has certain influence on the development of Xinjiang’s fruit industry.
Therefore, the analysis and grasp on the planting area and maturity period of fragrant pears have an important impact on local economic benefits.
[03] The method of monitoring the growth of fruit trees by remote sensing technology is to calculate vegetation indexes that can reflect the growth status of fruit trees by using different spectra of remote sensing images, and to determine changes in the health status of the fruit trees through the different vegetation indexes on different dates by multi-day continuous imaging. The monitoring of the remote sensing technology can grasp the distribution of local planting areas of main characteristic fruits more clearly, and can be combined with other factors such as tree age to estimate the output of fragrant pears, which will greatly promote the development of local economy and increase the income of fruit farmers. Through remote sensing monitoring, the current spatial layout, area and output prospects of fragrant pears can be quickly grasped and combined with climatic conditions to analyze and study the impact of disastrous weather on fragrant pear production, thereby improving the management level of fragrant pear cultivation and reducing disaster losses.
SUMMARY
[04] In order to achieve the above objective, the present invention provides the following technical solution:
[05] A remote sensing prediction method for a fragrant pear maturity period based on multi-source remote sensing data, the method including the following steps:
[06] in step 1: manually determining a planting area, measuring the selected area, making a matching canal system, and improving an irrigation system to prevent soil salinization,
[07] in step 2: determining a planting climate based on meteorological data, wherein meteorological conditions are the most important factors that affect the growth of crops and lead to changes in crop maturity period, so traditional crop maturity prediction methods mostly predict the maturity period of annual crops by means of the meteorological conditions of different phenological periods of crops or the appearance time of specific phenological periods;
[08] in step 3: establishing a predictive background database, and analyzing remote sensing image features and farming season differences of different crops in different regions under the support of the background database and in combination with the test area;
[09] in step 4: data analysis, selecting multiple best time-phase remote sensing data from the background database, combining with non-remote sensing data (land use/land cover vector data, GPS sample points, quadrat data, etc.) by means of GIS and GPS, and performing multi-temporal and multi-source data composite analysis on remote sensing images on a large scale, to study an operational method of one-time identification of main fruit trees on the large scale;
[10] in step 5: calculating differences of vegetation indexes to determine the growth status of fruit trees, and comparing the vegetation indexes of remote sensing images on different dates, wherein if the vegetation index increases, the fruit trees grow better, and if the vegetation index decreases, the fruit trees grow worse;
[11] in step 6: image registration, extracting features from two images to obtain feature points; finding matching feature point pairs by similarity measurement; then obtaining image space coordinate transformation parameters through the matching feature point pairs; finally, performing image registration by the coordinate transformation parameters;
[12] instep 7: crop maturity period prediction based on a fragrant pear growth model, analyzing remote sensing image data to estimate a planting area of crops and extract corresponding vegetation indexes of the crops, to monitor the growth status of the crops; constructing a yield per unit estimation model of the vegetation indexes, crop yield and other meteorological and agronomic parameters, and obtaining a total yield by further calculations; and
[13] in step 8: analysis on the feasibility of satellite remote sensing prediction on a crop maturity period, accurately obtaining spatial distribution differences of fragrant pears on a field scale by remote sensing technology, and combining with regular changes of the indicative factors during crop maturation to predict the maturity period of crops.
[14] Preferably, in step 7, the present common remote sensing yield estimation models include the following three: a statistical model of vegetation indexes and yields combined with environmental factors, a yield component prediction model, and a comprehensive yield estimation model with remote sensing as the main information source, among which the comprehensive yield estimation model with remote sensing as the main information source has received the most attention.
[15] Preferably, in step 8, in the actual harvest management of fragrant pears, the influence of factors such as subsequent meteorological conditions, crop rotation patterns and harvesting costs also need to be considered in addition to the maturity of crops.
[16] Preferably, in step 6, the extraction is the key in the registration technology, and accurate feature extraction provides a guarantee for the success of feature matching, so seeking a feature extraction method with good invariance and accuracy is essential for matching accuracy.
[17] Preferably, in step 4, the data collection and editing functions are to integrate,
check and modify remote sensing monitoring background data of agricultural conditions, and the spatial data are mainly collected from the completed data sets, for example, the spatial data such as national land use/land cover and national accumulated temperature, rainfall, and national administrative maps are collected from the existing database of the Agricultural Resources Monitoring Station of the Ministry of Agriculture.
[18] Preferably, in step 3, the attribute data are mainly collected from the existing statistical database, and these data are mainly from the Computing Center of the Chinese Academy of Agricultural Sciences and the Chinese Academy of Meteorological Sciences.
[19] Preferably, in step 8, the data retrieval is one of the important functions of the background database, and the remote sensing identification of crops requires the background database to provide relevant spatial and attribute data. The operation of the database is completed through the data retrieval function, and then data are extracted, and in this research, the data retrieval is mainly implemented by physical query according to the data organization structure.
[20] Compared with the prior art, the beneficial effects of the present invention are:
[21] 1. In the remote sensing prediction method for a fragrant pear maturity period based on multi-source remote sensing data according to the present invention, the crop growth model can describe the process of crop growth and yield formation from the growth mechanism driven by crop photosynthesis, a cost function is constructed by using the crop growth model with the optimization of crop yield or quality as the goal, and then the optimized crop harvest time can be reversely solved to predict the maturity period of fruit trees.
BRIEF DESCRIPTION OF THE DRAWINGS
[22] FIG. 1 is a flowchart of the present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[23] A clear and complete description will be made to the technical solutions in the embodiments of the present invention below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without any creative efforts shall fall within the protection scope of the present invention.
[24] Embodiment 1:
[25] A remote sensing prediction method for a fragrant pear maturity period based on multi-source remote sensing data includes the following steps:
[26] instep 1: a planting area is determined, the selected area is measured, a matching canal system is made, and an irrigation system 1s improved to prevent soil salinization;
[27] in step 2: a planting climate is determined. Meteorological conditions are the most important factors that affect the growth of crops and lead to changes in crop maturity period, so traditional crop maturity prediction methods mostly predict the maturity period of annual crops by means of the meteorological conditions of different phenological periods of crops or the appearance time of specific phenological periods;
[28] in step 3: a predictive background database is established, and remote sensing image features and farming season differences of different crops in different regions are analyzed under the support of the background database and in combination with the test area;
[29] in step 4: data analysis, multiple best time-phase remote sensing data are selected and combined with non-remote sensing data (land use/land cover vector data, GPS sample points, quadrat data, etc.) by means of GIS and GPS, and multi-temporal and multi-source data composite analysis is performed on remote sensing images on a large scale, to study an operational method of one-time identification of main fruit trees on the large scale;
[30] in step S: differences of vegetation indexes are calculated to determine the growth status of fruit trees, and the vegetation indexes of remote sensing images on different dates are compared. If the vegetation index increases, the fruit trees grow better, and if the vegetation index decreases, the fruit trees grow worse;
[31] in step 6: image registration, features are extracted from two images to obtain feature points; matching feature point pairs are found by similarity measurement; then image space coordinate transformation parameters are obtained through the matching feature point pairs; finally, image registration is performed by the coordinate transformation parameters;
[32] instep 7: crop maturity period prediction based on a fragrant pear growth model, remote sensing image data are analyzed to estimate a planting area of crops and extract corresponding vegetation indexes of the crops, to monitor the growth status of the crops; a yield per unit estimation model of the vegetation indexes, crop yield and other meteorological and agronomic parameters is constructed, and a total yield is obtained by further calculations; and
[33] in step 8: analysis on the feasibility of satellite remote sensing prediction on a crop maturity period, and spatial distribution differences of fragrant pears on a field scale are accurately obtained by remote sensing technology, and combined with regular changes of the indicative factors during crop maturation to predict the maturity period of crops.
[34] Embodiment 2:
[35] A remote sensing prediction method for a fragrant pear maturity period based on multi-source remote sensing data includes the following steps:
[36] instep 1: a planting area is manually determined, the selected area is measured, a matching canal system is made, and an irrigation system is improved to prevent soil salinization;
[37] in step 2: a planting climate is determined based on meteorological data. Meteorological conditions are the most important factors that affect the growth of crops and lead to changes in crop maturity period, so traditional crop maturity prediction methods mostly predict the maturity period of annual crops by means of the meteorological conditions of different phenological periods of crops or the appearance time of specific phenological periods;
[38] in step 3: a predictive background database is established, and remote sensing image features and farming season differences of different crops in different regions are analyzed under the support of the background database and in combination with the test area. The attribute data are mainly collected from the existing statistical database, and these data are mainly from the Computing Center of the Chinese Academy of Agricultural Sciences and the Chinese Academy of Meteorological Sciences;
[39] in step 4: data analysis, multiple best time-phase remote sensing data are selected from the background database and combined with non-remote sensing data (land use/land cover vector data, GPS sample points, quadrat data, etc.) by means of GIS and GPS. The data collection and editing functions are to integrate, check and modify remote sensing monitoring background data of agricultural conditions, and the spatial data are mainly collected from the completed data sets, for example, the spatial data such as national land use/land cover and national accumulated temperature, rainfall, and national administrative maps are collected from the existing database of the Agricultural Resources Monitoring Station of the Ministry of Agriculture. Multi-temporal and multi- source data composite analysis is performed on remote sensing images on a large scale, to study an operational method of one-time identification of main fruit trees on the large scale;
[40] in step 5: differences of vegetation indexes are calculated to determine the growth status of fruit trees, and the vegetation indexes of remote sensing images on different dates are compared. If the vegetation index increases, the fruit trees grow better, and if the vegetation index decreases, the fruit trees grow worse;
[41] in step 6: image registration, features are extracted from two images to obtain feature points; matching feature point pairs are found by similarity measurement; then image space coordinate transformation parameters are obtained through the matching feature point pairs; finally, image registration is performed by the coordinate transformation parameters. The extraction is the key in the registration technology, and accurate feature extraction provides a guarantee for the success of feature matching, so seeking a feature extraction method with good invariance and accuracy is essential for matching accuracy;
[42] instep 7: crop maturity period prediction based on a fragrant pear growth model, remote sensing image data are analyzed to estimate a planting area of crops and extract corresponding vegetation indexes of the crops, to monitor the growth status of the crops; a yield per unit estimation model of the vegetation indexes, crop yield and other meteorological and agronomic parameters is constructed, and a total yield is obtained by further calculations. The present common remote sensing yield estimation models include the following three: a statistical model of vegetation indexes and yields combined with environmental factors, a yield component prediction model, and a comprehensive yield estimation model with remote sensing as the main information source. Among them, the comprehensive yield estimation model with remote sensing as the main information source has recerved the most attention; and
[43] in step 8: analysis on the feasibility of satellite remote sensing prediction on a crop maturity period, and spatial distribution differences of fragrant pears on a field scale are accurately obtained by remote sensing technology, and combined with regular changes of the indicative factors during crop maturation to predict the maturity period of crops. Data retrieval is one of the important functions of the background database. The remote sensing identification on crops requires the background database to provide relevant spatial and attribute data. The operation of the database is completed through the data retrieval function, and then data are extracted. In this research, the data retrieval is mainly implemented by physical query according to the data organization structure. In the actual harvest management of fragrant pears, the influence of factors such as subsequent meteorological conditions, crop rotation patterns and harvesting costs also need to be considered in addition to the maturity of crops.
[44] Although the embodiments of the present invention are shown and described above, it should be appreciated by those skilled in the art that many changes, modifications, substitutions and variations may be made to these embodiments without departing from the principle and spirit of the present invention, and the scope of the present invention is defined by the appended claims and equivalents thereof.

Claims (7)

-10- Conclusies l.-10- Conclusions l. Teledetectievoorspellingswerkwijze voor een geurigepeer-rijpheidsperiode op basis van teledetectiedata van meerdere bronnen, met het kenmerk dat deze de volgende stappen omvat:Remote sensing prediction method for a fragrant pear maturity period based on remote sensing data from multiple sources, characterized in that it comprises the following steps: in stap 1: het handmatig bepalen van een aanplantgebied, het meten van het geselecteerde gebied, het maken van een bijpassend kanaalsysteem, en het verbeteren van een irrigatiesysteem om grondverzilting te voorkomen;in step 1: manually determining a planting area, measuring the selected area, making a matching canal system, and improving an irrigation system to prevent soil salinization; in stap 2: het bepalen van een aanplantklimaat op basis van meteorologische data,in step 2: determining a planting climate based on meteorological data, waarin meteorologische omstandigheden de meest belangrijke factoren zijn die de groei van gewassen beïnvloeden en die tot veranderingen in gewasrijpheidsperiode leiden, dus traditionele gewasrijpheidsvoorspellingswerkwijzen voorspellen voornamelijk de rijpheidsperiode van jaargewassen door middel van de meteorologische condities van verschillende fenologische periodes van gewassen of de verschijningstijd van specifieke fenologische periodes;in which meteorological conditions are the most important factors influencing crop growth and leading to changes in crop maturity period, so traditional crop maturity forecasting methods mainly predict maturity period of annual crops through the meteorological conditions of different phenological periods of crops or the appearance time of specific phenological periods ; in stap 3: het tot stand brengen van een voorspellendeachtergronddatabase, en het analyseren van teledetectiebeeldeigenschappen en landbouwseizoenverschillen van verschillende gewassen in verschillende gebieden onder de ondersteuning van de achtergronddatabase en in combinatie met het testgebied,;in step 3: establishing a predictive background database, and analyzing remote sensing image properties and agricultural season differences of different crops in different areas under the support of the background database and in combination with the test area,; in stap 4: data-analyse, het selecteren van meerdere beste tijdfase teledetectiedata uit de achtergronddatabase, het combineren van niet-teledetectiedata (landgebruik/landbedekkingsvectordata, GPS-bemonsteringspunten, kwadrantdata, enz. ) door middel van GIS en GPS, en het uitvoeren van multitemporele- en meerderebronnendatacomposietanalyse op teledetectiebeelden op een grote schaal, om een operationele werkwijze van eenmalige identificatie van voornaamste fruitbomen op de grote schaal te bestuderen;in step 4: data analysis, selecting multiple best time-phase remote sensing data from the background database, combining non-remote sensing data (land use/land cover vector data, GPS sampling points, quadrant data, etc.) by means of GIS and GPS, and performing multi-temporal and multi-source data composite analysis on large-scale remote sensing images to study an operational method of one-time identification of major fruit trees on a large scale; in stap 5: het berekenen van verschillen van vegetatie-indicies om de groeitoestand van fruitbomen te bepalen, en het vergelijken van de vegetatie-indices van teledetectiebeelden op verschillende datums, waarbij als de vegetatie-index toeneemt,in step 5: calculating differences of vegetation indices to determine the growth status of fruit trees, and comparing the vegetation indices of remote sensing images at different dates, where as the vegetation index increases, de fruitboom beter groeit, en als de vegetatie-index afneemt, de fruitboom slechter groeit;the fruit tree grows better, and if the vegetation index decreases, the fruit tree grows worse; in stap 6: beeldregistratie, het extraheren van eigenschappen uit twee beelden om eigenschapspunten te verkrijgen; het vinden van matchende eigenschapspuntparen middels gelijkheidsmeting; vervolgens het verkrijgen vanin step 6: image registration, extracting features from two images to obtain feature points; finding matching property point pairs by similarity measurement; then obtain S11 - beeldruimtecoördinaattransformatieparameters middels de matchende eigenschapspuntparen; ten slotte, het uitvoeren van beeldregistratie middels de coördinaattransformatieparameters;, in stap 7: gewasrijpheidsperiodevoorspelling op basis van een geurigepeer- groeimodel, het analyseren van teledetectiebeelddata om een aanplantgebied van gewassen te schatten en overeenkomstige vegetatie-indices van de gewassen te extraheren, om de groeitoestand van de gewassen te bewaken; het construeren van een opbrengst-per-eenheid-schattingsmodel van de vegetatie-indices, gewasopbrengst en andere meteorologische en landbouwkundige parameters, en het verkrijgen van een totale opbrengst middels verdere berekeningen; en in stap 8: analyse op de haalbaarheid van satelliet-teledetectievoorspelling van een gewasrijpheidsperiode, het nauwkeurig verkrijgen van ruimtelijkeverdelingsverschillen van geurige peren op een veldschaal middels teledetectietechnologie, en het combineren met reguliere veranderingen van de indicatieve factoren tijdens gewasrijping om de rijpheidsperiode van de gewassen te voorspellen.S11 - image space coordinate transformation parameters by the matching property point pairs; finally, performing image registration by the coordinate transformation parameters;, in step 7: crop maturity period prediction based on a fragrant pear growth model, analyzing remote sensing image data to estimate a planting area of crops and extracting corresponding vegetation indices of the crops, to determine the growth status. to monitor the crops; constructing a yield-per-unit estimation model of the vegetation indices, crop yield and other meteorological and agricultural parameters, and obtaining a total yield by further calculations; and in step 8: analysis on the feasibility of satellite remote sensing prediction of a crop maturity period, accurately obtaining spatial distribution differences of fragrant pears on a field scale by remote sensing technology, and combining with regular changes of the indicative factors during crop ripening to measure the maturity period of the crops. to predict. 2. Teledetectievoorspellingswerkwijze voor een geurigepeer-rijpheidsperiode op basis van teledetectiedata van meerdere bronnen volgens conclusie 1, met het kenmerk dat in stap 7, de huidige algemene teledetectie-opbrengstschattingsmodellen de volgende drie omvatten: een statistisch model van vegetatie-indices en opbrengsten gecombineerd met omgevingsfactoren, een opbrengstcomponentvoorspellingsmodel, en een uitvoerig opbrengstschattingsmodel met teledetectie als voornaamste informatiebron, van welke het uitvoerige opbrengstschattingsmodel met teledetectie als de voornaamste informatiebron de meeste aandacht ontvangen heeft.The remote sensing prediction method for a fragrant pear maturity period based on remote sensing data from multiple sources according to claim 1, characterized in that in step 7, the current general remote sensing yield estimation models comprise the following three: a statistical model of vegetation indices and yields combined with environmental factors , a yield component prediction model, and a comprehensive yield estimation model with remote sensing as the main source of information, of which the comprehensive yield estimation model with remote sensing as the main source of information has received the most attention. 3. Teledetectievoorspellingswerkwijze voor een geurigepeer-rijpheidsperiode op basis van teledetectiedata van meerdere bronnen volgens conclusie 1, met het kenmerk dat in stap 8, in het daadwerkelijke oogstbeheer van geurige peren de invloed van factoren zoals opeenvolgende meteorologische condities gewasrotatiepatronen en oogstkosten ook overwogen dienen te worden in aanvulling op de rijpheid van gewassen.The remote sensing prediction method for a fragrant pear ripeness period based on remote sensing data from multiple sources according to claim 1, characterized in that in step 8, in the actual harvest management of fragrant pears, the influence of factors such as successive meteorological conditions, crop rotation patterns and harvest costs should also be considered. in addition to the ripeness of crops. 4. Teledetectievoorspellingswerkwijze voor een geurigepeer-rijpheidsperiode op4. Remote sensing prediction method for a fragrant pear ripeness period on S12 - basis van teledetectiedata van meerdere bronnen volgens conclusie 1, met het kenmerk dat in stap 6, het extraheren de sleutel van de registratietechnologie is, en nauwkeurige eigenschapsextractie een garantie voor het succes van eigenschapsmatching verschaft, dus het zoeken van een eigenschapsonttrekkingswerkwijze met goede invariantie en nauwkeurigheid is essentieel voor passingsnauwkeurigheid.S12 - multi-source remote sensing data basis according to claim 1, characterized in that in step 6, the extraction is the key of the recording technology, and accurate feature extraction provides a guarantee of the success of feature matching, thus seeking a feature extraction method with good invariance and accuracy is essential for fit accuracy. 5. Teledetectievoorspellingswerkwijze voor een geurigepeer-rijpheidsperiode op basis van teledetectiedata van meerdere bronnen volgens conclusie 1, met het kenmerk dat in stap 4, de dataverzameling en -bewerking dienen om teledetectiebewakingsachtergronddata van landbouwkundige condities te integreren, controleren en aan te passen, en waarbij de ruimtelijke data voornamelijk verzameld worden uit de voltooide dataverzamelingen, bijvoorbeeld de ruimtelijke data zoals nationaal landgebruik/landbedekking en nationaal geaccumuleerde temperatuur, regenval, en waarbij nationaal administratieve kaarten verzameld worden uit de bestaande database van het Landbouwkundige Hulpbron Bewakingsstation van het Ministerie van Landbouw.The remote sensing prediction method for a sweet pear ripeness period based on remote sensing data from multiple sources according to claim 1, characterized in that in step 4, the data collection and processing serve to integrate, check and adjust remote sensing monitoring background data of agricultural conditions, and wherein the spatial data is mainly collected from the completed data sets, for example the spatial data such as national land use/land cover and national accumulated temperature, rainfall, and where national administrative maps are collected from the existing database of the Agricultural Resource Monitoring Station of the Ministry of Agriculture. 6. Teledetectievoorspellingswerkwijze voor een geurigepeer-rijpheidsperiode op basis van teledetectiedata van meerdere bronnen volgens conclusie 1, met het kenmerk dat in stap 3, de attribuutdata voornamelijk verzameld worden uit de bestaande statistische database, en waarbij deze data voornamelijk van het Computercentrum van de Chinese Academie van Landbouwkundige Wetenschap en de Chinese Academie voor Meteorologische Wetenschap zijn.The remote sensing prediction method for a fragrant pear maturity period based on remote sensing data from multiple sources according to claim 1, characterized in that in step 3, the attribute data is mainly collected from the existing statistical database, and wherein this data is mainly collected from the Computer Center of the Chinese Academy of Agricultural Science and the Chinese Academy of Meteorological Science. 7. Teledetectievoorspellingswerkwijze voor een geurigepeer-rijpheidsperiode op basis van teledetectiedata van meerdere bronnen volgens conclusie 1, met het kenmerk dat in stap 8, de dataophaling één van de belangrijke functies van de achtergronddatabase is, en de teledetectie-identificatie van gewassen de achtergronddatabase vereist om relevante ruimtelijke en attribuutdata te verschaffen; waarbij de werking van de database voltooid wordt middels de dataophalingsfunctie, en de data dan onttrokken wordt; en in dit onderzoek, de dataophaling voornamelijk geïmplementeerd wordt door fysieke aanvraging volgens de dataorganisatiestructuur.The remote sensing prediction method for a sweet pear ripeness period based on remote sensing data from multiple sources according to claim 1, characterized in that in step 8, the data retrieval is one of the important functions of the background database, and the remote sensing identification of crops requires the background database to provide relevant spatial and attribute data; wherein the operation of the database is completed by the data retrieval function, and the data is then extracted; and in this research, the data retrieval is mainly implemented by physical request according to the data organization structure.
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