identification and biomass estimation of mikania micrantha using data obtained from an unmanned aerial vehicle (uav)

무인 항공기 원격 감지 데이터에 의한 침입 식물 바이오매스 평가

In this protocol, the proposed method of invasive biomass estimation based on UAV remote sensing and computer vision can reflect the distribution of invasive organisms and predict the degree of invasive biohazard. Estimates of the distribution and biomass of invasive organisms are critical to the prevention and control of these organisms. Once invasive plants invade, they can damage the ecosystem and cause huge economic losses. Quickly and accurately identifying invasive plants and estimating key invasive plant biomass are major challenges in invasive plant monitoring and control. In this protocol, we take Mikania micrantha as an example to explore an invasive plant biomass estimation method based on unmanned aerial remote sensing and computer vision, which provides a new approach and method for the ecological research of invasive plants and promotes the ecological research and management of invasive plants.
At present, the biomass measurement of Mikania micrantha is mainly done by manual sampling1. The traditional methods of biomass measurement need a lot of workforce and material resources, which are inefficient and limited by the terrain; it is difficult to meet the needs of regional biomass estimation of Mikania micrantha. The major advantage of using this protocol is that it provides a method for quantifying regional invasive plant biomass and spatial distribution of invasive plants in a way that does not take into account the sampling limitations of the area and eliminates the need for manual surveys.
UAV remote sensing technology has achieved certain results in plant biomass estimation and has been widely used in agriculture2,3,4,5,6,7, forestry8,9,10,11, and grassland12,13,14. UAV remote sensing technology has the advantages of low cost, high efficiency, high precision, and flexible operation15,16, which can efficiently obtain remote sensing image data in the study area; then, the texture feature and vegetation index of remote sensing image are extracted to provide data support for the estimation of plant biomass in large area. Current plant biomass estimation methods are mainly categorized into parametric and nonparametric models17. With the development of machine learning algorithms, nonparametric machine learning models with higher accuracy have been widely used in remote sensing estimation of plant biomass. Chen et al.18 utilized mixed logistic regression (MLR), KNNR, and random forest regression (RFR) to estimate the aboveground biomass of forests in Yunnan Province. They concluded that the machine learning models, specifically KNNR and RFR, resulted in superior outcomes compared to MLR. Yan et al.19 employed RFR and extreme gradient boosting (XGBR) regression models to assess the accuracy of estimating subtropical forest biomass using various sets of variables. Tian et al.20 utilized eleven machine-learning models to estimate the aboveground biomass of varying mangrove forest species in Beibuwan Bay. The researchers discovered that the XGBR method was more effective in determining the aboveground biomass of mangrove forests. Plant biomass estimation using man-machine remote sensing is a well-established practice, however, the use of UAV for biomass estimation of the invasive plant Mikania micrantha has yet to be reported both domestically and internationally. This approach is fundamentally different from all previous methods of biomass estimation for invasive plants, especially Mikania micrantha.
To sum up, UAV remote sensing has the advantages of high resolution, high efficiency, and low cost. In the feature variable extraction of remote sensing images, texture features combined with vegetation indexes can obtain better regression prediction performance. Nonparametric models can obtain more accurate regression models than parametric ones in plant biomass estimation. Therefore, to calculate the null distribution of invasive plants and their biomass precisely, we suggest the following outlined procedures for the invasive plant biomass experiment that relies on remote sensing using UAVs and computer vision.

저자 스포트라이트: 효율적인 침입 식물 바이오매스 추정을 위한 UAV 원격 감지

침입 식물에 대한 컴퓨터 비전 기반 바이오매스 추정

Our method utilizes UAV remote sensing and computer vision to efficiently estimate invasive plant biomass, overcoming the inefficiencies and subjectivity of traditional manual surveys and data collection in complex environments. Recent advancement in UAV remote sensing for plant biomass estimation include high-resolution imaging and multimodal data integration. These developments enhance scientific research, environmental monitoring, and agricultural management by offering more precise data support.Advancements in UAV remote sensing for plant biomass estimation encompass high-resolution sensors, multispectral hyperspectral imaging, GPS, INS, specialized data processing software, AI and deep learning, improved batteries, enhanced data storage, and cloud computing. Relating to a traditional plant biomass is traditional methods. Our proposed method doesn't require service and data combing and for other multispectral hyperspectral estimation methods, we don't need a special requirement.Our method can accurately estimate a plant's biomass without experience requirement, which is our advantage. Our laboratory will continue to study UAV remote sensors plant biomass estimation. In the future we'll focus on firmware of amazing deep learning algorithms, applying in remotely stored data filtering.Continuing deep research on thrive preference and the sensor technology innovations.

무인 항공기(UAV)에서 얻은 데이터를 사용한 Mikania micrantha 의 식별 및 바이오매스 추정

소프트웨어를 준비하려면 공식 웹 사이트에서 Anaconda를 다운로드하여 설치하십시오. PyCharm IDE 프로그램을 시작합니다. 그런 다음 Anaconda 프롬프트 명령줄을 열고 conda create n pytorch python=3.8을 입력하여 새 Conda 환경을 만듭니다.환경을 만든 후 conda info envs를 입력하여 PyTorch 환경이 있는지 확인합니다. Anaconda 프롬프트를 열고 conda activate pytorch를 입력하여 PyTorch 환경을 활성화합니다. nvidia-smi를 입력하여 현재 CUDA 버전인 Compute Unified Device Architecture를 확인한 다음, conda install pytorch=1.8.1 torchvision=0.9.1 torchaudio=0.8.1 cudatoolkit=11.0 c pytorch 명령을 실행하여 PyTorch 버전 1.8.1을 설치합니다.모델 인식을 실행하려면 이미지를 사전 처리하여 모델 입력을 준비합니다. 표시된 코드를 사용하여 이미지의 크기를 280 x 280 픽셀에서 224 x 224 픽셀로 조정하고 모델 크기 요구 사항을 충족하도록 정규화합니다. 반복 횟수를 200으로 설정하고 초기 학습률을 0.0001로 설정하여 이미 생성된 데이터 세트로 다중 클래스 인식 모델을 학습시킵니다.배치 크기가 64인 경우 10회 반복할 때마다 학습률을 1/3씩 줄입니다. 각 반복 후에 최적의 모델 매개변수를 자동으로 저장합니다. 마우스 오른쪽 버튼을 클릭하고 run script를 누릅니다.그런 다음 꼼꼼하게 훈련된 인식 모델을 사용하고 식별을 위해 원본 이미지를 체계적으로 탐색합니다. 수평 및 수직 단계를 280픽셀에서 정확하게 구성하여 포괄적인 분포 지도를 생성하여 연구 영역 경계 내에 침입성 식물군의 존재를 강조합니다. 선택한 결과를 시각적으로 표시합니다.무작위 크기 조정 자르기 및 무작위 수평 대칭 이동 기능을 사용하여 간단한 데이터 증강을 수행합니다. 을 클릭하여 영상 세트를 확장하고 6개의 식생 지수를 추출합니다. 침입 식물의 바이오매스를 정확하게 추정하기 위해 출력과 추출된 식생 지수를 입력으로 사용하여 K-최근접 이웃 회귀 모델을 만듭니다. Mikania micrantha는 흰 꽃으로 장식된 식물 꼭대기를 오르는 것을 관찰할 수 있었고, 다른 식물과 도로, 그리고 동반 요소가 배경에 균일하게 묘사되었습니다.이 모델은 빨간색 부분을 Mikania micrantha로 인식하여 복잡한 배경에서 강력한 감지를 보여주었습니다. 회귀 분석은 R 제곱 값이 0.62이고 RMSE가 제곱미터당 10.56g으로 강력한 예측 성능을 보여주었습니다. 이 모델은 카모마일 바이오매스 추정의 정확도를 향상시켰으며, 공간 분포 맵은 카모마일 바이오매스의 분포를 효과적으로 포착했습니다.

Research

JoVE Journal

Engineering

Identification and Biomass Estimation of Mikania micrantha Using Data Obtained from an Unmanned Aerial Vehicle (UAV)

Invasive Plant Biomass Assessment by Unmanned Aerial Vehicle Remote Sensing Data

Computer Vision-Based Biomass Estimation for Invasive Plants

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

We report on the detailed steps of a method to estimate the biomass of invasive plants based on UAV remote sensing and computer vision. To collect samples ...

연구

공학

Creating a UAV Image Dataset for the Estimation of Invasive Plant Biomass

침입 식물 바이오매스 추정을 위한 UAV 이미지 데이터 세트 생성

Collecting the Aboveground Biomass of Mikania micrantha Samples After UAV Data Collection

UAV 데이터 수집 후 Mikania micrantha 샘플의 지상 바이오매스 수집