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

無人航空機リモートセンシングデータによる侵入植物バイオマス評価

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.

著者スポットライト:効率的な侵入植物バイオマス推定のためのUAVリモートセンシング

コンピュータビジョンに基づく侵入植物バイオマス推定

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.

無人航空機(UAV)から得られたデータを用いた ミカニア・ミクランサ の同定とバイオマス推定

ソフトウェアを準備するには、公式WebサイトからAnacondaをダウンロードしてインストールします。PyCharm IDEプログラムを起動します。次に、Anaconda プロンプトのコマンドラインを開き、「conda create n pytorch python=3.8」と入力して新しい Conda 環境を作成します。環境が作成されたら、「conda info envs」と入力して、PyTorch 環境が存在することを確認します。Anaconda プロンプトを開き、「conda activate pytorch」と入力して PyTorch 環境をアクティブ化します。nvidia-smi と入力して現在の Compute Unified Device Architecture (CUDA) バージョンを確認し、コマンド conda install pytorch=1.8.1 torchvision=0.9.1 torchaudio=0.8.1 cudatoolkit=11.0 c pytorch を実行して PyTorch バージョン 1.8.1 をインストールします。モデル認識を実行するには、画像を前処理してモデル入力用に準備します。表示されたコードを使用して、画像のサイズを 280 x 280 ピクセルから 224 x 224 ピクセルに変更し、モデル サイズの要件を満たすように正規化します。既に作成されているデータセットを使用して多クラス認識モデルをトレーニングするには、イテレーション数を 200 に設定し、初期学習率を 0.0001 に設定します。バッチ サイズを 64 にして、10 回の反復ごとに学習率を 1 分の 1 ずつ減らします。各反復後、最適なモデル パラメーターを自動的に保存します。右クリックして [スクリプトの実行] を押します。次に、細心の注意を払って訓練された認識モデルを採用し、識別のために元の画像を体系的にトラバースします。水平ステップと垂直ステップを 280 ピクセルで正確に構成すると、包括的な分布マップが生成され、分析範囲の境界内に侵入する植物相の存在が強調表示されます。選択した結果を視覚的に表示します。ランダムなサイズ変更された切り抜きとランダムな水平方向の反転機能を使用して、簡単なデータ拡張を実行します。画像セットを拡張し、6 つの植生指数を抽出します。侵入植物のバイオマスを正確に推定するために、出力と抽出された植生指数を入力としてK最近傍回帰モデルを作成し、白い花で飾られた植物、他の植物、道路の上に登るMikania micranthaを観察することができ、付随する要素を背景に均一に描写しました。モデルは赤い部分をMikania micranthaとして認識し、複雑な背景で堅牢な検出を示しました。回帰分析では、R二乗値が0.62、RMSEが10.56グラム/平方メートルという強力な予測性能が示されました。このモデルにより、カモミールバイオマスの推定精度が向上し、空間分布マップはカモミールバイオマスの分布を効果的に捉えました。

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 ...

工学

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

侵略的植物バイオマスの推定のためのUAV画像データセットの作成

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

UAVデータ収集後の Mikania micrantha 試料の地上バイオマスの収集

無人航空機(UAV)から得られたデータを用いた ミカニア・ミクランサ の同定とバイオマス推定

無人航空機(UAV)から得られたデータを用いたミカニア・ミクランサの同定とバイオマス推定