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

Avaliação da biomassa de plantas invasoras por dados de sensoriamento remoto de veículos aéreos não tripulados

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.

Autor em destaque: Sensoriamento remoto UAV para estimativa eficiente de biomassa de plantas invasoras

Estimativa de biomassa baseada em visão computacional para plantas invasoras

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.

Identificação e estimativa de biomassa de Mikania micrantha usando dados obtidos de um veículo aéreo não tripulado (UAV)

Para preparar o software, baixe e instale o Anaconda em seu site oficial. Inicie o programa PyCharm IDE. Em seguida, abra a linha de comando do prompt do Anaconda e digite conda create n pytorch python=3.8 para criar um novo ambiente Conda.Depois que o ambiente for criado, insira conda info envs para confirmar se o ambiente PyTorch existe. Abra o prompt do Anaconda e ative o ambiente PyTorch digitando conda activate pytorch. digite nvidia-smi para verificar a versão atual da arquitetura de dispositivo unificado de computação ou CUDA e, em seguida, instale o PyTorch versão 1.8.1 executando o comando conda install pytorch=1.8.1 torchvision=0.9.1 torchaudio=0.8.1 cudatoolkit=11.0 c pytorch.Para executar o reconhecimento do modelo, pré-processe as imagens para prepará-las para a entrada do modelo. Usando o código exibido, redimensione as imagens de 280 por 280 pixels para 224 por 224 pixels e normalize-as para garantir que atendam aos requisitos de tamanho do modelo. Treine um modelo de reconhecimento multiclasse com o conjunto de dados já criado definindo o número de iterações como 200 e uma taxa de aprendizado inicial de 0,0001.Reduza a taxa de aprendizado em um terço a cada 10 iterações com um tamanho de lote de 64. Salve os parâmetros ideais do modelo automaticamente após cada iteração. Clique com o botão direito do mouse e pressione executar script.Em seguida, empregue um modelo de reconhecimento meticulosamente treinado e percorra sistematicamente a imagem original para fins de identificação. Configure etapas horizontais e verticais com precisão de 280 pixels para resultar na geração de um mapa de distribuição abrangente, destacando a presença de flora invasora dentro dos limites da área de estudo. Apresente os resultados selecionados visualmente.Execute um aumento de dados simples com um corte redimensionado aleatório e funções de inversão horizontal aleatórias. Para estender o conjunto de imagens e extrair os seis índices de vegetação. Para garantir uma estimativa precisa da biomassa de plantas invasoras, crie um modelo de regressão do vizinho mais próximo K usando a saída e os índices de vegetação extraídos como entradas. Mikania micrantha pode ser observada subindo no topo da planta adornada com flores brancas, as outras plantas, bem como a estrada, e os elementos que a acompanham foram representados uniformemente ao fundo.O modelo reconheceu a parte vermelha como Mikania micrantha, demonstrando detecção robusta em fundos complexos. A análise de regressão demonstrou forte desempenho preditivo com um valor de R ao quadrado de 0,62 e um RMSE de 10,56 gramas por metro quadrado. O modelo aumentou a precisão da estimativa da biomassa de camomila, e o mapa de distribuição espacial capturou efetivamente a distribuição da biomassa de camomila.

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 ...

Engenharia

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

Criando um conjunto de dados de imagens de UAV para a estimativa de biomassa de plantas invasoras

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

Coletando a biomassa aérea de amostras de Mikania micrantha após a coleta de dados de UAV