KR20180133610A - Insect pest image acquisition method for insect pest prediction system of cash crops - Google Patents

Insect pest image acquisition method for insect pest prediction system of cash crops Download PDF

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KR20180133610A
KR20180133610A KR1020170070631A KR20170070631A KR20180133610A KR 20180133610 A KR20180133610 A KR 20180133610A KR 1020170070631 A KR1020170070631 A KR 1020170070631A KR 20170070631 A KR20170070631 A KR 20170070631A KR 20180133610 A KR20180133610 A KR 20180133610A
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김수진
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주식회사 엘시스
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Abstract

The present invention relates to a disease and insect pest image collecting method and, more specifically, to a disease and inspect image collecting method capable of providing database which can be used for disease and insect pest monitoring, by photographing an image of a special fruit tree; extracting an image feature point of the fruit tree itself, a disease image feature point of the fruit tree, and an insect pest image feature point; and providing the image feature points to a disease and insect pest integrated prediction system.

Description

특용 과수용 병해충 통합 예측 시스템을 위한 병해충 이미지 수집방법{Insect pest image acquisition method for insect pest prediction system of cash crops}Technical Field [0001] The present invention relates to a method for collecting pest insects for an integrated pest-

본 발명은 병해충 이미지 수집방법에 관한 것으로, 보다 구체적으로는 특용 과수의 영상을 촬영하여 과수 자체의 영상 특징점과 과수의 병해 영상 특징점, 해충 영상 특징점을 추출하여 병해충 통합 예측 시스템에 제공함으로써 병해충 예찰에 이용될 수 있는 데이터베이스를 제공할 수 있는 병해충 이미지 수집방법에 관한 것이다.More particularly, the present invention relates to a method for collecting pest insects, and more particularly, to provide a pest insight prediction system by extracting image feature points of fruit trees, To a pest image acquisition method capable of providing a database that can be used.

최근 들어, 농업 기술에 IT 기술의 접목시켜 병해충을 통합적으로 예측함으로써 작물 재배의 효율을 향상시킬 수 있는 시스템들이 속속 개발되어 농업 현장에 적용되고 있으며, 이러한 병해충 통합 예측 시스템은 노지뿐만 아니라 시설 작물 재배(온실 재배) 분야에서 광범위하게 활용되고 있다.In recent years, systems that can improve the efficiency of crop cultivation have been developed and applied to agriculture field by integrating IT technology into agricultural technology, and by anticipating pests collectively, (Greenhouse cultivation).

이러한 병해충 통합 예측 시스템은 여러 가지 센서장치와 제어장치 등을 구비하여 발생가능한 병해충을 미리 예측해 줌으로써 병해충의 발생을 미리 예방할 수 있어 작물에서의 단위 면적당 생산량을 획기적으로 증대시키고 있다. 여기에서, 작물은, 예컨대 채소, 화훼, 과일 등일 수 있으며, 특히 매실과 같은 특용 과수일 수 있다.This pest integrated prediction system is equipped with various sensor devices and control devices to predict pests that can be generated in advance, so that the occurrence of pests can be prevented in advance, and the production per unit area of the crops is dramatically increased. Here, the crops can be, for example, vegetables, flowers, fruits, and the like, and can be special fruit trees such as plums.

또한, 병해충 통합 예측 시스템은 다양한 작물들에 대한 병해충 정보들 및 각 작물별 병해충들에 대한 방제역 정보들을 병해충 데이터베이스로 구축하고, 필요할 때마다 사용자 인터페이스를 통해 병해충 데이터베이스를 검색하여 원하는 병해충 관련 정보를 얻을 수 있도록 함으로써, 작물의 생장시에 발생할 수 있는 병해충에 대해 적절하게 대응하도록 하는 서비스를 제공하고 있다.In addition, the Integrated Pest Pest Incorporation Prediction System builds pest information on various crops and pest control information for each pest by each pest, searches the pest database through the user interface whenever necessary, And to provide a service to respond appropriately to pests that may occur during the growth of crops.

그러나, 과수의 종류별로 병해 및 해충의 정보가 부족하여 정확한 병해충 통합 예측에 어려움이 있다.However, due to the lack of information on pests and diseases, there is a difficulty in predicting the accurate integration of pests.

본 발명은 상술한 문제점을 해결하기 위해 안출된 것으로 본 발명의 목적은 과수별 병해 및 해충의 정보를 영상 특징점으로 추출하여 특징점 데이터베이스로 저장하여 제공함으로써 과수자체의 상태 및 병해뿐만 아니라 해충의 정보까지 통합적으로 제공하여 특용과수의 병해충을 효과적으로 예측할 수 있게 하는 병해충 이미지 수집방법을 제공하는 것이다.It is an object of the present invention to solve the above-mentioned problems, and it is an object of the present invention to provide a method and a system for extracting information on pests and pests of each fruit number by image feature points, To provide a pest-insect image collection method which can integrally provide the pest-insect pests and effectively predict the pest-specific species and the number of pests.

상기의 목적을 달성하기 위하여 본 발명은 과수영상을 실시간으로 촬영하는 단계; 상기 과수영상의 영상 특징점을 추출하는 단계; 상기 영상 특징점을 특징점 데이터베이스로 저장하는 단계;를 포함하는 것을 특징으로 하는 병해충 이미지 수집방법을 제공한다.According to an aspect of the present invention, Extracting image feature points of the fruit image; And storing the image feature points as a feature point database.

바람직한 실시예에 있어서, 상기 영상 특징점은 히스토그램 비교(Comparing histograms) 또는 템플릿 매칭(Template matching)을 통해 과수 영상 특징점, 병해 영상 특징점 및 해충 영상 특징점으로 분류되어 추출된다.In a preferred embodiment, the image feature points are classified and classified into fruit image feature points, diseased image feature points, and pest image feature points through a comparison of histograms (comparison histograms) or template matching (template matching).

바람직한 실시예에 있어서, 상기 과수영상을 촬영하는 위치의 온도 및 습도를 포함하는 환경 정보 측정 단계를 더 포함하고, 상기 영상 특징점은 상기 과수영상이 촬영된 날짜, 시간 및 상기 환경 정보가 맵핑되어 상기 특징점 데이터베이스에 저장된다.In a preferred embodiment, the method further includes an environment information measurement step including a temperature and a humidity at a position where the fruit image is photographed, wherein the image feature point is generated by mapping the date, time and environmental information of the fruit image, The feature points are stored in the database.

본 발명은 다음과 같은 우수한 효과를 가진다.The present invention has the following excellent effects.

본 발명의 병해충 이미지 수집방법에 의하면, 과수 영상을 촬영하여 과수의 영상 특징점, 병해 특징점 및 해충 특징점을 추출하여 데이터베이스로 제공함으써 날짜, 시간 및 환경 정보에 따라 발생할 수 있는 병해충의 정보를 효과적으로 제공할 수 있는 장점이 있다.According to the pest-insect image collecting method of the present invention, a fruit image is photographed to extract image feature points, diseased feature points, and pest feature points of fruit trees and provide them to a database, thereby effectively providing information on pests that may occur according to date, time, and environmental information There is an advantage to be able to do.

도 1은 본 발명의 일 실시예에 따른 병해충 이미지 수집방법을 수행하기 위한 환경을 보여주는 도면,
도 2는 본 발명의 일 실시예에 따른 병해충 이미지 수집방법의 흐름도이다.FIG. 1 is a view showing an environment for performing a pest-insect image collection method according to an embodiment of the present invention;
2 is a flowchart of a pest-insect image collecting method according to an embodiment of the present invention.

본 발명에서 사용되는 용어는 가능한 현재 널리 사용되는 일반적인 용어를 선택하였으나, 특정한 경우는 출원인이 임의로 선정한 용어도 있는데 이 경우에는 단순한 용어의 명칭이 아닌 발명의 상세한 설명 부분에 기재되거나 사용된 의미를 고려하여 그 의미가 파악되어야 할 것이다.Although the terms used in the present invention have been selected as general terms that are widely used at present, there are some terms selected arbitrarily by the applicant in a specific case. In this case, the meaning described or used in the detailed description part of the invention The meaning must be grasped.

이하, 첨부한 도면에 도시된 바람직한 실시예들을 참조하여 본 발명의 기술적 구성을 상세하게 설명한다.Hereinafter, the technical structure of the present invention will be described in detail with reference to preferred embodiments shown in the accompanying drawings.

그러나 본 발명은 여기서 설명되는 실시예에 한정되지 않고 다른 형태로 구체화될 수도 있다. 명세서 전체에 걸쳐 동일한 참조번호는 동일한 구성요소를 나타낸다.However, the present invention is not limited to the embodiments described herein but may be embodied in other forms. Like reference numerals designate like elements throughout the specification.

도 1을 참조하면, 본 발명의 일 실시예에 따른 병해충 이미지 수집방법은 카메라(100), 환경센서(200) 및 병해충 이미지 수집시스템(300)을 통해 이루어진다.Referring to FIG. 1, a pest image collection method according to an embodiment of the present invention is performed through a camera 100, an environmental sensor 200, and a pest insect image collection system 300.

상기 카메라(100)는 과수원에 설치되어 과수(10)의 영상(이하, '과수 영상'이라 함)을 촬영한다.The camera 100 is installed in an orchard and photographs an image of the fruit tree 10 (hereinafter, referred to as a fruit tree image).

또한, 상기 과수 영상에는 병해충(20)의 영상도 포함된다.In addition, the fruit image also includes an image of the pest 20.

또한, 상기 병해충(20)은 상기 과수(10)에서 발생한 병해(disease) 및 해충(insect pest)을 포함한다.In addition, the pest 20 includes diseases and insect pests occurring in the fruit tree 10.

상기 환경센서(200)는 과수원에 설치되어 과수의 재배 환경을 측정하는 센서이다.The environmental sensor 200 is installed in an orchard and measures a cultivation environment of fruit trees.

또한, 상기 환경센서(200)는 온도 센서 및 습도 센서를 포함한다.In addition, the environmental sensor 200 includes a temperature sensor and a humidity sensor.

상기 병해충 이미지 수집시스템(300)은 상기 카메라(100)로부터 과수 영상을 수신하고 상기 환경센서(200)로부터 환경 정보를 수신한다.The pest-insect image collection system 300 receives the fruit image from the camera 100 and receives environment information from the environment sensor 200. [

또한, 상기 병해충 이미지 수집시스템(300)은 상기 과수 영상을 영상 처리하여 상기 과수 영상으로부터 과수 영상 특징점, 병해 영상 특징점 및 해충 영상 특징점을 추출하여 데이터베이스를 생성한다.In addition, the pest-insect image collecting system 300 processes the fruit image to extract a fruit image feature point, a lesion image feature point, and a pest image feature point from the fruit image to generate a database.

즉, 상기 병해충 이미지 수집시스템(300)은 과수 영상으로부터 영상 특징점들을 추출하여 데이터베이스로 생성한 후, 특용 과수의 병해충 예찰에 이용될 수 있게 한다.That is, the pest-insect image collecting system 300 extracts image feature points from a fruit image and generates a database, and then it can be used for pest-inspecting a special fruit tree.

도 2를 참조하여 상기 영상 특징점들을 추출하는 과정을 자세히 설명하면, 먼저, 상기 카메라(100)가 과수(10)의 영상을 실시간으로 촬영한다(S1000)Referring to FIG. 2, the process of extracting the image feature points will be described in detail. First, the camera 100 captures an image of the fruit number 10 in real time (S1000)

동시에 상기 환경 센서(200)는 과수원의 환경 정보를 계측한다(S1100).At the same time, the environmental sensor 200 measures the environmental information of the orchard (S1100).

다음, 상기 병해충 이미지 수집시스템(300)은 상기 카메라(100)로부터 과수 영상을 수신하고, 상기 과수 영상 내의 영상 특징점을 추출한다.Next, the pest-insect image collection system 300 receives the fruit image from the camera 100 and extracts image feature points in the fruit image.

또한, 상기 과수 영상 내의 영상 특징점은 과수를 식별하기 위한 과수 영상 특징점, 과수의 병해에 관한 병해 영상 특징점, 해충에 관한 해충 영상 특징점으로 분류되어 추출된다.In addition, the image feature points in the fruit image are classified into a fruit image feature point for identifying the fruit number, a lesion image feature point for the fruit of the fruit tree, and a pest image feature point for the insect pest.

또한, 상기 영상 특징점들은 영상 히스토그램(histogram), SIFT(Scale-invariant feature transform), HOG(histogram of gradient), Haar(Haar-like features), Ferns, LBP(Local binary patterns) 또는 MCT(Modified Census Transform)의 특징값으로 계산될 수 있다.The image feature points may be classified into image histogram, Scale-invariant feature transform (SIFT), histogram of gradient (HOG), Haar-like features, Ferns, local binary patterns (LBP), or Modified Census Transform ) Can be calculated.

또한, 상기 영상 특징점들을 분류하는 것은 히스토그램 비교(Comparing histograms) 또는 템플릿 매칭(Template matching)을 통해 이미지 패턴을 수집함으로써 이루어질 수 있다.In addition, classification of the image feature points may be performed by collecting image patterns through comparisons histograms or template matching.

또한, 상기 해충 영상 특징점은 과수 영상 내에서 움직임 객체를 검출하여 이루어질 수 있으며, 먼저, 상기 과수 영상 내에서 움직임 객체를 검출한다(S2000).In addition, the pest image feature point may be detected by detecting a motion object in the fruit image. First, a motion object is detected in the fruit image (S2000).

다음, 상기 움직임 객체를 히스토그램 비교(Comparing histograms) 또는 템플릿 매칭(Template matching)을 통해 상기 해충 영상 특징점을 추출한다(S3000)Next, the pest image feature points are extracted through the comparisons histograms or template matching of the motion object (S3000)

다음, 상기 병해충 이미지 수집시스템(300)은 상기 과수 영상 특징점, 상기 병해 영상 특징점 및 상기 해충 영상 특징점을 데이터 베이스에 저장한다(S4000)Next, the pest-insect image collection system 300 stores the fruit image feature point, the lesion image feature point, and the pest image feature point in a database (S4000)

이때, 상기 병해충 이미지 수집시스템(300)은 상기 환경 센서에서 계측된 온도 정보와 습도정보를 상기 영상 특징점들과 함께 매핑하여 저장할 수 있으며, 시간 및 날짜도 함께 매핑되어 저장될 수 있다.At this time, the pest-insect image collecting system 300 can store the temperature information and the humidity information measured by the environmental sensor together with the image minutiae points, and store the mapped time and date together.

즉, 상기 데이터 베이스에는 날짜 및 시간, 환경에 따른 과수 영상, 병해, 해충의 정보가 모두 포함되어 있으므로 추후 날짜 및 시간, 환경에 따라 병해충을 예찰할 수 있게 하는 장점이 있다.That is, since the database includes the date and time, the fruit image according to the environment, the disease, and the pest information, it is possible to observe the pests according to the date, time and environment at a later date.

이상에서 살펴본 바와 같이 본 발명은 바람직한 실시예를 들어 도시하고 설명하였으나, 상기한 실시예에 한정되지 아니하며 본 발명의 정신을 벗어나지 않는 범위 내에서 당해 발명이 속하는 기술분야에서 통상의 지식을 가진 자에 의해 다양한 변경과 수정이 가능할 것이다.While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is clearly understood that the same is by way of illustration and example only and is not to be taken by way of limitation, Various changes and modifications will be possible.

100:카메라 200:환경 센서
300:병해충 이미지 수집 시스템100: camera 200: environmental sensor
300: Pest insect image acquisition system

Claims (3)

과수영상을 실시간으로 촬영하는 단계;
상기 과수영상의 영상 특징점을 추출하는 단계;
상기 영상 특징점을 특징점 데이터베이스로 저장하는 단계;를 포함하는 것을 특징으로 하는 병해충 이미지 수집방법.
Capturing a fruit number image in real time;
Extracting image feature points of the fruit image;
And storing the image feature points as a feature point database.
제 1 항에 있어서,
상기 영상 특징점은 히스토그램 비교(Comparing histograms) 또는 템플릿 매칭(Template matching)을 통해 과수 영상 특징점, 병해 영상 특징점 및 해충 영상 특징점으로 분류되어 추출되는 것을 특징으로 하는 병해충 이미지 수집방법.
The method according to claim 1,
Wherein the image feature points are classified into a fruit image feature point, a disease image feature point, and a pest image feature point through a comparison of histograms or template matching.
제 1 항에 있어서,
상기 과수영상을 촬영하는 위치의 온도 및 습도를 포함하는 환경 정보 측정 단계를 더 포함하고,
상기 영상 특징점은 상기 과수영상이 촬영된 날짜, 시간 및 상기 환경 정보가 맵핑되어 상기 특징점 데이터베이스에 저장되는 것을 특징으로 하는 병해충 이미지 수집방법.The method according to claim 1,
Further comprising an environmental information measuring step including a temperature and a humidity at a position where the fruit image is photographed,
Wherein the image feature points are stored in the feature point database by mapping the date, time, and environment information of the fruit image.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111563885A (en) * 2020-04-29 2020-08-21 广东利元亨智能装备股份有限公司 Visual detection method and device and electronic equipment

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111563885A (en) * 2020-04-29 2020-08-21 广东利元亨智能装备股份有限公司 Visual detection method and device and electronic equipment

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