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

Vurdering av invasiv plantebiomasse ved hjelp av fjernmålingsdata fra ubemannede luftfartøyer

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.

Forfatter Spotlight: UAV-fjernmåling for effektiv estimering av invasiv plantebiomasse

Datasynbasert biomasseestimering for invasive planter

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.

Identifikasjon og biomasseestimering av Mikania micrantha ved bruk av data hentet fra et ubemannet luftfartøy (UAV)

For å forberede programvaren, last ned og installer Anaconda fra den offisielle nettsiden. Start PyCharm IDE-programmet. Åpne deretter kommandolinjen for Anaconda-ledeteksten og skriv conda create n pytorch python=3.8 for å opprette et nytt Conda-miljø.Når miljøet er opprettet, skriver du inn conda info envs for å bekrefte at PyTorch-miljøet eksisterer. Åpne Anaconda-ledeteksten og aktiver PyTorch-miljøet ved å angi conda activate pytorch. skriv inn nvidia-smi for å kontrollere gjeldende Compute Unified Device-arkitektur, eller CUDA-versjon, og installer deretter PyTorch versjon 1.8.1 ved å kjøre kommandoen conda install pytorch=1.8.1 torchvision=0.9.1 torchaudio=0.8.1 cudatoolkit=11.0 c pytorch.Hvis du vil kjøre modellgjenkjenningen, forhåndsbehandler du bildene for å klargjøre dem for modellinndata. Bruk den viste koden til å endre størrelsen på bildene fra 280 x 280 piksler til 224 x 224 piksler, og normaliser dem for å sikre at de oppfyller kravene til modellstørrelse. Lær opp en gjenkjenningsmodell med flere klasser med det allerede opprettede datasettet ved å angi antall iterasjoner til 200 og en innledende læringshastighet på 0,0001.Reduser læringshastigheten med en tredjedel hver 10 iterasjoner med en batchstørrelse på 64. Lagre de optimale modellparameterne automatisk etter hver gjentakelse. Høyreklikk og trykk kjør skript.Bruk deretter en omhyggelig trent gjenkjenningsmodell og kryss systematisk det originale bildet for identifikasjonsformål. Konfigurer horisontale og vertikale trinn nøyaktig på 280 piksler for å resultere i generering av et omfattende distribusjonskart, som fremhever tilstedeværelsen av invasiv flora innenfor grensene til studieområdet. Presenter de valgte resultatene visuelt.Utfør enkel dataforsterkning med en tilfeldig endret størrelse og tilfeldige horisontale vendefunksjoner. For å utvide bildesettet og trekke ut de seks vegetasjonsindeksene. For å sikre presis estimering av biomassen til invasive planter, lag en K-nærmeste nabo-regresjonsmodell ved å bruke utgangen og de ekstraherte vegetasjonsindeksene som input Mikania micrantha kunne observeres klatre på toppen av planten utsmykket med hvite blomster, de andre plantene, samt veien, og medfølgende elementer ble jevnt avbildet i bakgrunnen.Modellen gjenkjente den røde delen som Mikania micrantha, og demonstrerte robust deteksjon i komplekse bakgrunner. Regresjonsanalysen viste sterk prediktiv ytelse med en R-kvadratverdi på 0,62 og en RMSE på 10,56 gram per kvadratmeter. Modellen forbedret nøyaktigheten av estimering av kamillebiomasse, og det romlige fordelingskartet fanget effektivt opp fordelingen av kamillebiomasse.

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 ...

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

Lage et UAV-bildedatasett for estimering av invasiv plantebiomasse

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

Innsamling av overjordisk biomasse av Mikania micrantha-prøver etter UAV-datainnsamling