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

基于无人机遥感数据的入侵植物生物量评估

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.

作者聚焦：用于高效入侵植物生物量估算的无人机遥感

基于计算机视觉的入侵植物生物量估算

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.

使用无人机 （UAV） 获得的数据鉴定和生物量估计 Mikania micrantha

要准备软件，请从其官方网站下载并安装 Anaconda。启动 PyCharm IDE 程序。然后打开 Anaconda 提示符命令行并键入 conda create n pytorch python=3.8 以创建新的 Conda 环境。创建环境后，输入 conda info envs 以确认 PyTorch 环境存在。打开 Anaconda 提示符，然后输入 conda activate pytorch 激活 PyTorch 环境。键入 nvidia-smi 以检查当前的计算统一设备架构或 CUDA 版本，然后通过运行命令 conda install pytorch=1.8.1 torchvision=0.9.1 torchaudio=0.8.1 cudatoolkit=11.0 c pytorch 安装 PyTorch 版本 1.8.1。要运行模型识别，请对图像进行预处理，以便为模型输入做好准备。使用显示的代码，将图像的大小从 280 x 280 像素调整为 224 x 224 像素，并对其进行标准化以确保它们满足模型大小要求。通过将迭代次数设置为 200 并将初始学习率设置为 0.0001，使用已创建的数据集训练多类识别模型。每 10 次迭代将学习率降低 1/3，批处理大小为 64。每次迭代后自动保存最佳模型参数。右键单击并按 run script。然后采用经过精心训练的识别模型，并系统地遍历原始图像以进行识别。在 280 像素处精确配置水平和垂直步长，以生成综合分布图，突出显示研究区域边界内入侵植物群的存在。直观地显示所选结果。使用随机调整大小的裁剪和随机水平翻转函数执行简单的数据增强。扩展图像集并提取 6 个植被指数。为了确保精确估计入侵植物的生物量，使用输出和提取的植被指数作为输入创建一个 K 最近邻回归模型可以观察到 Mikania micrantha 爬在装饰着白色花朵的植物、其他植物以及道路上，伴随元素在背景中统一描绘。该模型将红色部分识别为 Mikania micrantha，展示了在复杂背景下的稳健检测。回归分析显示具有很强的预测性能，R 平方值为 0.62，RMSE 为 10.56 克/平方米。该模型提高了洋甘菊生物量估算的准确性，空间分布图有效地捕捉了洋甘菊生物量的分布。

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 ...

科研

工程学

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

创建用于估计入侵植物生物量的无人机图像数据集

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

无人机数据采集后 Mikania micrantha 样品的地上生物量采集

使用无人机 （UAV） 获得的数据鉴定和生物量估计 Mikania micrantha

使用无人机（UAV）获得的数据鉴定和生物量估计 Mikania micrantha