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드론 영상 기반 RGB 식생지수 조합 Support Vector Classifier 모델 활용 콩 도복피해율 산정
이현중,고승환,박종화,Lee, Hyun-jung,Go, Seung-hwan,Park, Jong-hwa 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Drone and sensor technologies are enabling digitalization of agricultural crop's growth information and accelerating the development of the precision agriculture. These technologies could be able to assess damage of crops when natural disaster occurs, and contribute to the scientification of the crop insurance assessment method, which is being conducted through field survey. This study was aimed to calculate lodged damage rate from the vegetation indices extracted by drone based RGB images for soybean. Support Vector Classifier (SVC) models were considered by adding vegetation indices to the Crop Surface Model (CSM) based lodged damage rate. Visible Atmospherically Resistant Index (VARI) and Green Red Vegetation Index (GRVI) based lodged damage rate classification were shown the highest accuracy score as 0.709 and 0.705 each. As a result of this study, it was confirmed that drone based RGB images can be used as a useful tool for estimating the rate of lodged damage. The result acquired from this study can be used to the satellite imagery like Sentinel-2 and RapidEye when the damages from the natural disasters occurred.
간척지의 토지이용 현상과 문제점 파악 및 발전방향 - 충남, 전북, 전남 지역 지자체 및 한국농어촌공사 지사 대상 설문조사 -
손재권 ( Son Jae-gwon ),정찬희 ( Jeong Chan-hee ),이동호 ( Lee Dong-ho ),고승환 ( Go Seung-hwan ),송재도 ( Song Jae-do ),이기성 ( Lee Gi-sung ),박종화 ( Park Jong-hwa ) 한국농촌계획학회 2020 농촌계획 Vol.26 No.3
The purpose of this study was to determine the problems of reclamation sites and the prospects of farming in reclamation areas seen by local governments and the KRC branches in Chungnam, Jeonbuk, and Jeonnam provinces. A mail survey method was used. The survey items were set for 15 items regarding the reclamation site situation, problems, and prospects. Seventy-five copies of the questionnaire were distributed to the local government, and 90 copies were sent to the KRC 165 copies in total. In response to the questionnaire, 72 recipients of the local governments responded, showing a 96% response rate, and 74 (82.2%) of the KRC responded. The overall response rate was 88.5%. The opinions on the rental method of the reclaimed land were found to differ according to the geographic conditions of the reclaimed land, the construction conditions, and the time. Regarding the survey on crops preferred for cultivation, rice was highest in both local governments (61%) and KRC (46%). When cultivating field crops in reclaimed land, 56% of local governments and 57% of KRC considered salinity a s t he most problematic or resolvable problem. Regarding growing other field crops in reclaimed land, salt and drainage problems were recognized as the biggest obstacles in all reclaimed land. As for technologies that need to be applied first for the future agriculture of reclamation land, KRC responded with automatic water management (48%) and local governments responded with unmanned agricultural machinery (32%). In order to diversify the land use in the reclamation area, it is necessary to reduce salt damage and ensure systematic maintenance, employing, for example, automatic water management facilities and drainage improvement methods. The results of this study can set a land use direction for reclamation sites and provide useful information for use in various forms.
양파 마늘의 잎 엽록소 함량 추정을 위한 SVM 회귀 활용 RGB 영상 적용성 평가
이동호 ( Dong-ho Lee ),정찬희 ( Chan-hee Jeong ),고승환 ( Seung-hwan Go ),박종화 ( Jong-hwa Park ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.6
AI지능화 농업과 디지털 농업은 농업분야 과학화를 위해서 중요하다. 잎 엽록소 함량은 작물의 생육상태를 파악하는데 매우 중요한 지표 중 하나이다. 본 연구는 양파와 마늘을 대상으로 드론 기반 RGB 카메라와 다중분광(MSP)센서를 활용하여 SVM 회귀 모델을 제작하고, MSP 센서와 비교를 실시하여 RGB 카메라의 LCC 추정 적용성을 검토하고자 하였다. 연구 결과 RGB 기반 LCC 모형은MSP 기반 LCC 모형보다 평균 R<sup>2</sup>에서 0.09, RMSE 18.66, nRMSE 3.46%로 더 낮은 결과를 보였다. 그러나 두 센서 정확도 차이는 크지 않았으며, 다양한 센서와 알고리즘을 활용한 선행연구들과 비교했을 때도 정확도는 크게 떨어지지 않았다. 또한 RGB 기반 LCC 모형은 실제 측정값과 비교하였을 때 현장 LCC 경향을 잘 반영하지만 높은 엽록소 농도에서 과소 추정되는 경향을 보였다. 본 연구로 도출된 결과는 RGB 카메라의 경제성, 범용성을 고려하였을 때 LCC 추정에 적용할 경우 가능성을 확인할 수 있었다. 본 연구에서 얻어진 결과는 인공지능 및 빅데이터 융합 기술을 적용한 AI지능화농업 기술로써 디지털 농업 등에 유용하게 활용될 수 있을 것으로 기대된다. AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents (LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R<sup>2</sup> of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.