KR102487666B1 - Leaf wetness duration estimation method using satellite imagery and artificial intelligence - Google Patents

Leaf wetness duration estimation method using satellite imagery and artificial intelligence Download PDF

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KR102487666B1
KR102487666B1 KR1020200128624A KR20200128624A KR102487666B1 KR 102487666 B1 KR102487666 B1 KR 102487666B1 KR 1020200128624 A KR1020200128624 A KR 1020200128624A KR 20200128624 A KR20200128624 A KR 20200128624A KR 102487666 B1 KR102487666 B1 KR 102487666B1
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신주영
박준상
김규랑
김부요
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Abstract

본 발명은 위성영상자료와 인공지능 기법을 적용하여 엽면습윤기간 관측장비가 설치되어 있는 않는 위치에서의 엽면습윤기간 정보를 확보할 수 있고, 표준화 방안이 없어 발생하는 엽면습윤기간 관측자료의 비균질한 품질 문제에 대하여 단일 모형을 넓은 지역에 적용함으로써 동질한 품질의 엽면습윤기간 정보를 얻을 수 있는 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법에 관한 발명이다.The present invention applies satellite image data and artificial intelligence techniques to secure leaf wetting period information at locations where foliar wetting period observation equipment is not installed, and to obtain non-homogeneous foliar wetting period observation data caused by no standardization method. The invention relates to a foliar wetting period prediction method using satellite image data and artificial intelligence techniques that can obtain uniform quality foliar wetting period information by applying a single model to a wide area for quality problems.

Description

위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법{LEAF WETNESS DURATION ESTIMATION METHOD USING SATELLITE IMAGERY AND ARTIFICIAL INTELLIGENCE}Foliar wetting period prediction method using satellite image data and artificial intelligence technique {LEAF WETNESS DURATION ESTIMATION METHOD USING SATELLITE IMAGERY AND ARTIFICIAL INTELLIGENCE}

본 발명은 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법에 관한 것으로, 더욱 상세하게는 천리안 정지궤도의 실시간 위성자료와 인공지능 기법을 이용하여 지상면에 존재하는 식물의 엽면에 습기가 존재하는 시간을 예측하는 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법에 관한 것이다.The present invention relates to a method for predicting leaf surface wetting period using satellite image data and artificial intelligence techniques, and more particularly, by using artificial intelligence techniques and real-time satellite data of a geostationary orbit in Chollian. It is about a method for predicting the leaf wetting period using satellite image data and artificial intelligence techniques that predict the existence time.

식물의 전염병은 농업 및 임업 생산량 증대에 악영향을 주는 주요 원인이 된다.Infectious diseases of plants are a major cause adversely affecting agricultural and forestry productivity growth.

박테리아와 진균류에 의한 식물 전염병은 엽면습윤기간[식물의 잎에 존재하는 수분(엽면습윤)의 지속기간]과 특정 기온에 따라 발병률이 변하는 것으로 알려져 있다.Plant infectious diseases caused by bacteria and fungi are known to change in incidence depending on the foliar wetting period [the duration of moisture (foliar wetting) present on the leaves of plants] and a specific temperature.

농업 및 임업 생산량 증대를 위해서는 식물 전염병에 대한 적절한 예측기술이 개발될 필요가 있다.In order to increase agricultural and forestry production, it is necessary to develop appropriate predictive technologies for plant epidemics.

식물 전염병에 큰 영향 요인인 기온은 세계기상기구에서 정해진 규칙에 따라서 다양한 장소에서 관측되고 있어, 기온자료에 대한 접근성이 높다. 또한, 높은 접근성으로 인하여 많은 연구가 진행되어 다른 기상요소들 보다 높은 예측 정확도를 가진다.Temperature, which is a major factor in plant infectious diseases, is observed in various places according to the rules set by the World Meteorological Organization, so the accessibility to temperature data is high. In addition, due to its high accessibility, many studies have been conducted and it has higher prediction accuracy than other meteorological factors.

하지만, 엽면습윤기간은 세계기상기구에서 지정한 정규 기상요소가 아니라 관측기기, 장비, 설치 위치와 같은 규칙이 정해져 있지 않다. 이런 이유로 많은 기관에서 다른 관측 방법을 사용하고 있어 자료들간의 동질성을 확보하기가 어렵다. 또한, 정규 기상요소가 아니기 때문에 대부분의 기상관측소에서 엽면습윤기간을 관측하고 있지 않다. 이런 이유로 엽면습윤기간 자료에 대한 접근성과 품질이 낮아져 식물의 전염병 연구 및 예측에 많은 문제점으로 여겨지고 있다.However, the leaf wet period is not a regular meteorological factor designated by the World Meteorological Organization, and rules such as observation instruments, equipment, and installation locations are not established. For this reason, many institutions use different observation methods, making it difficult to ensure homogeneity among data. Also, since it is not a regular meteorological factor, most meteorological stations do not observe the leaf wetting period. For this reason, the accessibility and quality of foliar wet period data are low, which is regarded as a problem in research and prediction of plant epidemics.

이와 같이 위에서 언급된 문제점과 한계들을 극복하기 위하여 관측기기가 없는 위치에서도 동질한 품질을 갖는 엽면습윤기간 정보를 획득할 수 있는 방법의 개발이 요구되고 있는 현실이다.As such, in order to overcome the above-mentioned problems and limitations, it is a reality that it is required to develop a method capable of acquiring information on the leaf wet period with the same quality even in a location where there is no observation device.

대한민국 등록특허공보 제1114513호Republic of Korea Patent Registration No. 1114513

본 발명의 목적은 정지궤도 위성영상을 이용한 옆면습윤기간을 예측하여 엽면습윤기간 관측장비가 설치되어 있지 않는 지점에서도 엽면습윤기간 정보를 얻을 수 있는 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법을 제공함에 있다.The purpose of the present invention is to predict the foliar wetting period using geostationary satellite images and predict the foliar wetting period using satellite image data and artificial intelligence techniques that can obtain foliar wetting period information even at points where foliar wetting period observation equipment is not installed in providing a way.

본 발명의 목적은 위성영상과 단일화된 방법으로 넓은 지역의 동질한 품질의 엽면습윤기간 정보를 얻을 수 있는 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법을 제공함에 있다.An object of the present invention is to provide a method for predicting foliar wetting period using satellite image data and artificial intelligence techniques capable of obtaining uniform quality foliar wetting period information in a wide area in a unified way with satellite images.

상기 목적을 달성하기 위한 본 발명에 따른 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법은,The method for predicting the leaf wetting period using satellite image data and artificial intelligence techniques according to the present invention for achieving the above object is,

위성영상자료 및 엽면습윤기간 관측자료를 수집하여 DB(Database)를 구축하는 제1단계와,The first step of building a DB (Database) by collecting satellite image data and foliar wetting period observation data;

상기 DB를 전처리하여 학습, 검증, 평가 자료를 각각 준비하는 제2단계와,A second step of preparing learning, verification, and evaluation data by preprocessing the DB;

상기 학습, 검증, 평가 자료와 인공지능모형을 이용하여 엽면습윤기간 예측모형을 구축하는 제3단계와,A third step of building a foliar wetting period prediction model using the learning, verification, and evaluation data and the artificial intelligence model;

상기 엽면습윤기간 예측모형과 상기 위성영상자료를 이용하여 대상지역의 엽면습윤기간을 예측하는 제4단계를 포함하는 것을 그 기술적 방법상의 기본 특징으로 한다.A basic feature of the technical method is to include a fourth step of predicting the foliar wetting period of the target area using the foliar wetting period prediction model and the satellite image data.

본 발명은 위성영상자료와 인공지능 기법을 적용하여 엽면습윤기간 관측장비가 설치되어 있는 않는 위치에서의 엽면습윤기간 정보를 확보할 수 있는 효과가 있다.The present invention has the effect of securing foliar wetting period information at a location where foliar wetting period observation equipment is not installed by applying satellite image data and artificial intelligence techniques.

본 발명은 관측 방법의 표준화된 방안이 없어 발생하는 엽면습윤기간 관측자료의 비균질한 품질 문제에 대하여 단일 모형을 넓은 지역에 적용함으로써 동질한 품질의 엽면습윤기간 정보를 얻을 수 있는 효과가 있다.The present invention has the effect of obtaining homogeneous quality of foliar wetting period information by applying a single model to a wide area for the non-homogeneous quality problem of foliar wetting period observation data caused by the absence of a standardized method of observation.

도 1은 본 발명에 따른 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법을 나타내는 흐름도.
도 2는 본 발명에 따른 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법을 설명하기 위하여 천리안 2호기의 영상자료와 랜덤포레스트(RF)와 깊은신경망(DNN)을 이용하여 개발한 엽면습윤기간 예측모형의 2020년3월1일부터 동년3월4일까지의 예측 결과 예시도.1 is a flowchart showing a method for predicting a leaf wet period using satellite image data and artificial intelligence techniques according to the present invention.
Figure 2 is a foliar wetting developed using image data of Chollian Unit 2, random forest (RF) and deep neural network (DNN) to explain the method of predicting foliar wetting period using satellite image data and artificial intelligence techniques according to the present invention. An example of forecast results from March 1, 2020 to March 4 of the same year of the period forecasting model.

본 발명에 따른 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법의 바람직한 실시예를 도면을 참조하여 설명하기로 하고, 그 실시예로는 다수 개가 존재할 수 있으며, 이러한 실시예를 통하여 본 고안의 목적, 특징 및 이점들을 더욱 잘 이해할 수 있게 된다.A preferred embodiment of the method for predicting the foliar wetting period using satellite image data and artificial intelligence techniques according to the present invention will be described with reference to the drawings, and there may be a plurality of examples, and through these embodiments, the present invention better understand its purpose, features and benefits.

도 1은 본 발명에 따른 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법을 나타내는 흐름도이다.1 is a flowchart showing a method for predicting a leaf wet period using satellite image data and artificial intelligence techniques according to the present invention.

본 발명에 따른 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법은 도 1에 도시된 바와 같이 위성영상자료 및 엽면습윤기간 관측자료를 수집하여 DB(Database)를 구축하는 제1단계(S10)와, 상기 DB를 전처리하여 학습, 검증, 평가 자료를 각각 준비하는 제2단계(S20)와, 상기 학습, 검증, 평가 자료와 인공지능모형을 이용하여 엽면습윤기간 예측모형을 구축하는 제3단계(S30)와, 상기 엽면습윤기간 예측모형과 상기 위성영상자료를 이용하여 대상지역의 엽면습윤기간을 예측하는 제4단계(S40)를 포함한다.As shown in FIG. 1, the method for predicting foliar wetting period using satellite image data and artificial intelligence techniques according to the present invention collects satellite image data and foliar wetting period observation data to construct a DB (Database) (S10 ), and the second step (S20) of preprocessing the DB to prepare learning, verification, and evaluation data, respectively, and the third step of constructing a foliar wetting period prediction model using the learning, verification, and evaluation data and artificial intelligence model Step S30 and a fourth step S40 of predicting the foliar wetting period of the target area using the foliar wetting period prediction model and the satellite image data.

상기 제1단계(S10)는 해당 분석기간에 대한 위성영상자료를 수집하여 DB를 구축하는 단계(S11)와, 엽면습윤기간 관측장비가 설치되어 있는 지점에서 관측자료 및 관측장비의 정보를 수집하여 DB를 구축하는 단계(S12)를 포함한다.The first step (S10) is the step (S11) of collecting satellite image data for the analysis period and constructing a DB, and collecting observation data and information of observation equipment at the point where observation equipment for the leaf wet period is installed. A step of constructing a DB (S12) is included.

구체적으로, 상기 제1단계(S10)는 해당 분석기간에 대하여 위성영상자료를 수집하여 DB를 구축하는 단계(S11)로 대상지역의 정지궤도 위성 또는 공간 및 시간에 고해상도를 가지는 관측자료를 수집한다. Specifically, the first step (S10) is a step (S11) of constructing a DB by collecting satellite image data for the analysis period, which collects geostationary satellites in the target area or observation data having high resolution in space and time. .

위성영상자료의 시간 해상도는 1시간 이하일 때만 엽면습윤기간 측정에 사용될 수 있고, 위성관측자료는 위성에 탑재되어 있는 분광카메라를 통하여 얻어진 위성영상자료이다.Only when the time resolution of satellite image data is less than 1 hour can be used to measure the leaf wetting period, and satellite observation data are satellite image data obtained through a spectroscopic camera mounted on a satellite.

이 위성영상자료는 수백 ㎚에서 수천 ㎚ 영역의 여러 협대역밴드로 구성된 채널별 영상자료로 물체에서 반사 또는 방출시키는 복사휘도(radiance)를 관측한다. 보통 4㎛ 파장을 기준으로 그 미만은 단파채널 그 이상은 장파채널로 구분하며, 이 채널자료 모두 사용될 수 있다.This satellite image data is image data for each channel consisting of several narrow bands in the range of hundreds of nm to thousands of nm, and observes the radiance reflected or emitted from the object. Generally, based on the 4㎛ wavelength, less than that is classified as a short-wave channel and more than that is classified as a long-wave channel, and all of these channel data can be used.

그리고, 대상지역 근처에 설치되어 있는 엽면습윤기간 관측장비의 정보(위치정도, 장비의 형태 등)와 관측자료를 수집하여 DB를 구축한다.In addition, a DB is constructed by collecting information (position degree, type of equipment, etc.) and observation data of the leaf wetting period observation equipment installed near the target area.

상기 제2단계(S20)는 DB에서 관측장비의 위치와 같은 위치의 위성영상픽셀을 추출하는 단계(S21)와, 위성영상픽셀과 엽면습윤기간 관측정보를 결합하는 단계(S22)와, 이와 같이 결합된 자료로부터 학습, 검증, 평가 자료를 생성하는 단계(S23)를 포함한다.The second step (S20) includes extracting satellite image pixels at the same location as the location of the observation equipment from the DB (S21), combining the satellite image pixels and leaf wetting period observation information (S22), and generating learning, verification, and evaluation data from the combined data (S23).

인공지능기법을 이용하여 엽면습윤을 예측하는 모형을 구축하기 위해서는 인공지능기법 학습에 사용될 학습자료와, 이 학습자료를 통해 만들어진 모형의 초매개변수를 최적화한 자료와, 입력변수 선택을 위한 검증자료와 최종으로 구축된 모형의 성능을 평가하기 위한 평가자료가 필요하다. In order to build a model that predicts leaf wetting using artificial intelligence techniques, learning data to be used for learning artificial intelligence techniques, data optimized for hyperparameters of the model created through this learning data, and verification data for selecting input variables and evaluation data to evaluate the performance of the final model built.

위 세 자료를 만들기 위해서는 구축된 DB에서 관측장비의 위치정보를 이용하여 같은 위치의 위성영상픽셀을 추출하여 입력자료 DB를 구축한다.In order to create the above three data, the input data DB is constructed by extracting the satellite image pixels of the same position using the location information of the observation equipment in the constructed DB.

만들어진 입력자료 DB를 엽면습윤 관측자료 DB와 결합하여 최종 DB를 구축하고, 최종 DB에서 6(학습):2(검증):2(평가)의 비율로 학습, 검증, 평가 자료로 나누어 놓는다.The created input data DB is combined with the leaf wetness observation data DB to construct the final DB, and the final DB is divided into learning, verification, and evaluation data at a ratio of 6 (learning): 2 (verification): 2 (evaluation).

상기 제3단계(S30)는 학습 및 검증 자료를 이용하여 인공지능모형들의 초매개변수(hyperparameter)를 정하는 단계(S31)와, 인공지능모형들의 입력변수(위성 영상 채널자료)를 결정하는 단계(S32)와, 평가 자료를 이용하여 완성된 각 인공지능모형의 성능을 평가하는 단계(S33)와, 이와 같은 평가를 기반으로 최종 엽면습윤기간 예측모형을 결정하는 단계(S34)를 포함한다.The third step (S30) is the step of determining hyperparameters of artificial intelligence models using learning and verification data (S31), and the step of determining input variables (satellite image channel data) of artificial intelligence models ( S32), evaluating the performance of each completed artificial intelligence model using the evaluation data (S33), and determining a final foliar wetting period prediction model based on the evaluation (S34).

구축된 학습 및 검증 자료를 이용하여 다양한 인공지능 모형의 초매개변수를 최적화한다.Optimize the hyperparameters of various artificial intelligence models using the established learning and verification data.

인공지능 모형의 종류로는 인공신경망(artificial neural network), 깊은신경망(deep neural network, DNN), 랜덤포레스트(random forest, RF), 그레디언트부스팅모형(gradient boosted model), 로지스틱 희귀분석(logistic regression), 서포트벡터머신(support vector machine)과 같은 분류(classification) 모형을 사용할 수 있다.Types of artificial intelligence models include artificial neural networks, deep neural networks (DNNs), random forests (RF), gradient boosted models, and logistic regression. , a classification model such as a support vector machine can be used.

분류기(classifier)로서 작동할 수 있는 모든 인공지능기법을 본 발명에서 이용할 수 있고, 인공지능기법에 따라 다른 초매개변수를 가지기 때문에 최적화 방법으로는 다양한 방법론이 적용될 수 있다.All artificial intelligence techniques that can operate as a classifier can be used in the present invention, and since each artificial intelligence technique has different hyperparameters, various methodologies can be applied as an optimization method.

본 발명에서 인공지능 모형의 입력변수는 위성영상자료로서 분광카메라로 촬영된 다양한 밴드의 영상자료를 의미한다. 즉, 위성영상자료의 어떤 밴드 자료를 사용하여 엽면습윤기간을 예측하는 가를 검증 자료를 이용하여 선택하게 된다.In the present invention, the input variable of the artificial intelligence model means image data of various bands taken by a spectroscopic camera as satellite image data. That is, which band data of the satellite image data is used to predict the leaf wetting period is selected using the verification data.

엽면습윤은 일반적으로 밤과 일출시간에 존재하기 때문에 장파채널의 자료와 낮에 태양광에 민감한 단파채널이 엽면습윤을 표현하는 데 유리하다.Since foliar wetting generally exists at night and at sunrise, long-wave channel data and short-wave channels that are sensitive to sunlight during the day are advantageous for expressing foliar wetting.

위성에 탑재된 분광카메라의 특성은 채널마다 다르기 때문에 모든 채널 자료를 입력자료로서의 적합성을 검증자료로 검증하여 선택한다.Since the characteristics of the spectroscopic camera mounted on the satellite are different for each channel, the suitability of all channel data as input data is verified and selected.

최적화된 매개변수와 입력변수 조합을 가지는 인공기법들의 엽면습윤의 예측 성능의 평가자료를 이용하여 평가한다.Evaluate the predictive performance of leaf wetting of artificial techniques with optimized parameter and input variable combinations using evaluation data.

즉, 완성된 모든 인공지능 모형의 엽면습윤 예측 성능과 엽면습윤기간 예측 성능을 제곱근오차, 상대오차, 상관계수, 정확도 등과 같은 평가지표를 이용하여 평가한다.That is, the performance of predicting foliar wetting and foliar wetting period of all completed artificial intelligence models is evaluated using evaluation indicators such as square root error, relative error, correlation coefficient, and accuracy.

평가결과를 기반으로 최고의 성능을 보이는 인공지능 모형을 최종 엽면습윤기간 예측 모형을 결정한다.Based on the evaluation results, the artificial intelligence model with the best performance is determined as the final foliar wetting period prediction model.

상기 제4단계(S40)는 위성영상자료를 취득하는 단계(S41)와, 위성영상자료 및 인공지능모형을 이용하여 대상지역의 엽면습윤기간 정보를 제공하는 단계(S42)를 포함한다.The fourth step (S40) includes acquiring satellite image data (S41) and providing leaf wet period information of the target area using the satellite image data and artificial intelligence model (S42).

즉, 실시간 위성영상자료를 취득하여 완성된 엽면습윤기간 예측 모형에 위성자료를 입력하여 위성영상이 커버하는 지역의 엽면습윤기간 정보를 출력한다.That is, by acquiring real-time satellite image data and inputting the satellite data to the completed foliar wetting period prediction model, the foliar wetting period information of the region covered by the satellite image is output.

도 2는 본 발명에 따른 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법을 설명하기 위하여 천리안 2호기의 영상자료와 랜덤포레스트(RF)와 깊은신경망(DNN)을 이용하여 개발한 엽면습윤기간 예측모형의 2020년3월1일부터 동년3월4일까지의 예측 결과 예시도로서 위성영상자료와 인공지능 기법을 적용하여 엽면습윤기간 관측장비가 설치되어 있는 않는 위치에서의 엽면습윤기간 정보를 확보할 수 있고, 표준화 방안이 없어 발생하는 엽면습윤기간 관측자료의 비균질한 품질 문제에 대하여 단일 모형을 넓은 지역에 적용함으로써 동질한 품질의 엽면습윤기간 정보를 얻을 수 있는 모습을 보여주고 있다.Figure 2 is a foliar wetting developed using image data of Chollian Unit 2, random forest (RF) and deep neural network (DNN) to explain the method of predicting foliar wetting period using satellite image data and artificial intelligence techniques according to the present invention. As an example of prediction results from March 1, 2020 to March 4 of the same year of the period prediction model, satellite image data and artificial intelligence techniques are applied to leaf wetting period information at a location where observation equipment is not installed can be secured, and it is possible to obtain homogeneous quality of foliar wetting period information by applying a single model to a wide area for the non-homogeneous quality problem of foliar wetting period observation data that occurs due to the lack of a standardization method.

본 발명은 관측기기가 없는 위치에서도 동질한 품질을 갖는 엽면습윤기간을 예측할 수 있는 농업 및 임업 분야 관련의 산업에 이용 가능하다.The present invention can be used in industries related to agriculture and forestry that can predict the leaf surface wet period with the same quality even in a location where there is no observation device.

S10 : 제1단계 - DB 구축
S20 : 제2단계 - 학습, 검증, 평가 자료 준비
S30 : 제3단계 - 엽면습윤기간 예측모형 구축
S40 : 제4단계 - 대상지역의 엽면습윤기간 예측S10: Step 1 - DB construction
S20: Step 2 - Preparation of learning, verification, and evaluation materials
S30: Step 3 - Establishment of foliar wetting period prediction model
S40: 4th Step - Prediction of the foliar wetting period in the target area

Claims (5)

위성영상자료 및 엽면습윤기간 관측자료를 수집하여 DB(Database)를 구축하는 제1단계(S10)와, 상기 DB를 전처리하여 학습, 검증, 평가 자료를 각각 준비하는 제2단계(S20)와, 상기 학습, 검증, 평가 자료와 인공지능모형을 이용하여 엽면습윤기간 예측모형을 구축하는 제3단계(S30)와, 상기 엽면습윤기간 예측모형과 상기 위성영상자료를 이용하여 대상지역의 엽면습윤기간을 예측하는 제4단계(S40)를 포함하는 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법에 있어서,
상기 제1단계(S10)는 해당 분석기간에 대한 상기 위성영상자료를 수집하여 상기 DB를 구축하는 단계(S11)와, 엽면습윤기간 관측장비가 설치되어 있는 지점에서 관측자료 및 관측장비의 정보를 수집하여 상기 DB를 구축하는 단계(S12)를 포함하고,
상기 제2단계(S20)는 상기 DB에서 관측장비의 위치와 같은 위치의 위성영상픽셀을 추출하는 단계(S21)와, 상기 위성영상픽셀과 엽면습윤기간 관측정보를 결합하는 단계(S22)와, 상기 결합된 자료로부터 학습, 검증, 평가 자료를 생성하는 단계(S23)를 포함하며,
상기 제3단계(S30)는 상기 학습 및 검증 자료를 이용하여 인공지능모형들의 초매개변수(hyperparameter)를 정하는 단계(S31)와, 상기 인공지능모형들의 입력변수(위성 영상 채널자료)를 결정하는 단계(S32)와, 상기 평가 자료를 이용하여 완성된 각 인공지능모형의 성능을 평가하는 단계(S33)와, 상기 평가를 기반으로 최종 엽면습윤기간 예측모형을 결정하는 단계(S34)를 포함하는 것을 특징으로 하는 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법.The first step (S10) of constructing a DB (Database) by collecting satellite image data and observational data of leaf wetting period, and the second step (S20) of preparing learning, verification, and evaluation data by pre-processing the DB, respectively; The third step (S30) of constructing a foliar wetting period prediction model using the learning, verification, evaluation data and artificial intelligence model, and the foliar wetting period of the target area using the foliar wetting period prediction model and the satellite image data In the method for predicting the leaf wetting period using satellite image data and artificial intelligence techniques, including the fourth step (S40) of predicting,
The first step (S10) is the step (S11) of collecting the satellite image data for the analysis period and constructing the DB, and the observation data and information of the observation equipment at the point where the leaf surface wet period observation equipment is installed Collecting and building the DB (S12),
The second step (S20) includes extracting satellite image pixels at the same location as the location of the observation equipment from the DB (S21), combining the satellite image pixels with leaf wetting period observation information (S22), A step (S23) of generating learning, verification, and evaluation data from the combined data,
The third step (S30) includes the step (S31) of determining hyperparameters of the artificial intelligence models using the learning and verification data, and determining the input variables (satellite image channel data) of the artificial intelligence models. (S32), evaluating the performance of each completed artificial intelligence model using the evaluation data (S33), and determining a final leaf wetting period prediction model based on the evaluation (S34). Foliar wet period prediction method using satellite image data and artificial intelligence technique, characterized in that. 삭제delete 삭제delete 삭제delete 제1항에 있어서,
상기 제4단계(S40)는 상기 위성영상자료를 취득하는 단계(S41)와, 상기 위성영상자료 및 인공지능모형을 이용하여 대상지역의 엽면습윤기간 정보를 제공하는 단계(S42)를 포함하는 것을 특징으로 하는 위성영상자료와 인공지능기법을 이용한 엽면습윤기간 예측 방법.
According to claim 1,
The fourth step (S40) includes the steps of acquiring the satellite image data (S41) and providing leaf wet period information of the target area using the satellite image data and artificial intelligence model (S42). A method for predicting leaf wet period using satellite image data and artificial intelligence techniques.
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