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

Valutazione della biomassa delle piante invasive mediante dati di telerilevamento di veicoli aerei senza pilota

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.

Autore in primo piano: Telerilevamento UAV per una stima efficiente della biomassa delle piante invasive

Stima della biomassa basata sulla visione artificiale per impianti invasivi

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.

Identificazione e stima della biomassa di Mikania micrantha utilizzando i dati ottenuti da un veicolo aereo senza pilota (UAV)

Per preparare il software, scarica e installa Anaconda dal suo sito ufficiale. Avvia il programma IDE PyCharm. Quindi apri la riga di comando del prompt di Anaconda e digita conda create n pytorch python=3.8 per creare un nuovo ambiente Conda.Dopo aver creato l'ambiente, immettere conda info envs per confermare che l'ambiente PyTorch esiste. Apri il prompt Anaconda e attiva l'ambiente PyTorch inserendo conda activate pytorch. digita nvidia-smi per controllare l'attuale versione dell'architettura del dispositivo unificato di calcolo, o CUDA, quindi installa PyTorch versione 1.8.1 eseguendo il comando conda install pytorch=1.8.1 torchvision=0.9.1 torchaudio=0.8.1 cudatoolkit=11.0 c pytorch.Per eseguire il riconoscimento del modello, pre-elaborare le immagini per prepararle per l'input del modello. Utilizzando il codice visualizzato, ridimensionare le immagini da 280 x 280 pixel a 224 x 224 pixel e normalizzarle per assicurarsi che soddisfino i requisiti di dimensione del modello. Addestra un modello di riconoscimento multiclasse con il set di dati già creato impostando il numero di iterazioni su 200 e una velocità di apprendimento iniziale di 0,0001.Riduci il tasso di apprendimento di un terzo ogni 10 iterazioni con una dimensione del batch di 64. Salvate automaticamente i parametri ottimali del modello dopo ogni iterazione. Fare clic con il pulsante destro del mouse e premere Esegui script.Quindi utilizza un modello di riconoscimento meticolosamente addestrato e attraversa sistematicamente l'immagine originale a scopo di identificazione. Configura i passaggi orizzontali e verticali con precisione a 280 pixel per generare una mappa di distribuzione completa, evidenziando la presenza di flora invasiva all'interno dei confini dell'area di studio. Presenta visivamente i risultati selezionati.Esegui un semplice aumento dei dati con un ritaglio ridimensionato casuale e funzioni di capovolgimento orizzontale casuale. Per estendere il set di immagini ed estrarre i sei indici di vegetazione. Per garantire una stima precisa della biomassa delle piante invasive, creare un modello di regressione K-nearest neighbor utilizzando l'output e gli indici di vegetazione estratti come input Mikania micrantha potrebbe essere osservata arrampicarsi sulla pianta adornata con fiori bianchi, le altre piante, così come la strada, e gli elementi di accompagnamento sono stati uniformemente raffigurati sullo sfondo.Il modello ha riconosciuto la parte rossa come Mikania micrantha, dimostrando una robusta rilevazione in sfondi complessi. L'analisi di regressione ha dimostrato una forte performance predittiva con un valore R al quadrato di 0,62 e un RMSE di 10,56 grammi per metro quadrato. Il modello ha migliorato l'accuratezza della stima della biomassa della camomilla e la mappa di distribuzione spaziale ha catturato efficacemente la distribuzione della biomassa della camomilla.

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 ...

Ricerca

Ingegneria

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

Creazione di un set di dati di immagini UAV per la stima della biomassa vegetale invasiva

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

Raccolta della biomassa fuori terra di campioni di Mikania micrantha dopo la raccolta dei dati UAV