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

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.

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

Computer Vision-Based Biomass Estimation for Invasive Plants

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.

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

To prepare the software, download and install Anaconda from its official website. Launch the PyCharm IDE program. Then open the Anaconda prompt command line and type conda create n pytorch python=3.8 to create a new Conda environment.After the environment is created, enter conda info envs to confirm that the PyTorch environment exists. Open the Anaconda prompt and activate the PyTorch environment by entering conda activate pytorch. type nvidia-smi to check the current compute unified device architecture, or CUDA, version, then install PyTorch version 1.8.1 by running the command conda install pytorch=1.8.1 torchvision=0.9.1 torchaudio=0.8.1 cudatoolkit=11.0 c pytorch.To run the model recognition, pre-process the images to prepare them for model input. Using the displayed code, resize the images from 280 by 280 pixels to 224 by 224 pixels, and normalize them to ensure they meet the model size requirements. Train a multi-class recognition model with the already created dataset by setting the number of iterations to 200 and an initial learning rate of 0.0001.Reduce the learning rate by a third every 10 iterations with a batch size of 64. Save the optimal model parameters automatically after each iteration. Right-click and press run script.Then employ a meticulously trained recognition model and systematically traverse the original image for identification purposes. Configure horizontal and vertical steps precisely at 280 pixels to result in the generation of a comprehensive distribution map, highlighting the presence of invasive flora within the boundaries of the study area. Present the selected results visually.Perform simple data augmentation with a random resized crop and random horizontal flip functions. To extend the image set and extract the six vegetation indices. To ensure precise estimation of the biomass of invasive plants, create a K-nearest neighbor regression model using the output and the extracted vegetation indices as inputs Mikania micrantha could be observed climbing atop the plant adorned with white flowers, the other plants, as well as the road, and accompanying elements were uniformly depicted in the background.The model recognized the red part as Mikania micrantha, demonstrating robust detection in complex backgrounds. The regression analysis demonstrated strong predictive performance with an R squared value of 0.62 and an RMSE of 10.56 grams per square meter. The model enhanced the accuracy of chamomile biomass estimation, and the spatial distribution map effectively captured the distribution of chamomile biomass.

Research

JoVE Journal

Engineering

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 ...