The author thanks the Chinese Academy of Agricultural Sciences and Guangxi University for supporting this work. The work was supported by the National Key R&#38;D Program of China (2022YFC2601500 &#38; 2022YFC2601504), the National Natural Science Foundation of China (32272633), Shenzhen Science and Technology Program (KCXFZ20230731093259009)

1. Preparation of datasets
<ol>
	<li>Selecting the research object
	<ol>
		<li>Select test samples based on the focus of the experimental study, considering options like Mikania micrantha or other invasive plants.</li>
	</ol>
	</li>
	<li>Collecting UAV images
	<ol>
		<li>Prepare square plastic frames of size 0.5 m*0.5 m and quantity 25-50, depending on the size of the area studied.</li>
		<li>Employ a random sampling approach to determine soil sampling locations in the study area using a suffi...

Invasive Plant Biomass Assessment by Unmanned Aerial Vehicle Remote Sensing Data

<ol>
	<li>Lian, J. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+obligate+parasite+Cuscuta+campestris+on+the+community+of+its+host+Mikania+micrantha.">Influence of obligate parasite Cuscuta campestris on the community of its host Mikania micrantha.</a> Weed Research. 46, 441-443 (2006).</li><li>Yu, X., Bao, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aboveground+biomass+estimation+of+potato+from+UAV+multispectral+imagery.">Aboveground biomass estimation of potato from UAV multispectral imagery.</a> Remote Sensing. 54 (4), 96-99 (2023).</li><li>Guo, T. C., Wang, Y. H., Feng, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Research+on+wheat+yield+estimation+based+on+multimodal+data+fusion+from+unmanned+aircraft+platforms.">Research on wheat yield estimation based on multimodal data fusion from unmanned aircraft platforms.</a> Acta Agronomica Sinica. 48 (7), 15 (2022).</li><li>Shao, G. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimation+of+transpiration+coefficient+and+aboveground+biomass+in+maize+using+time-series+UAV+multispectral+imagery.">Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery.</a> The Crop Journal. 10 (5), 1376-1385 (2022).</li><li>Jiang, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UAV-based+biomass+estimation+for+rice-combining+spectral,+TIN-based+structural+and+meteorological+features.">UAV-based biomass estimation for rice-combining spectral, TIN-based structural and meteorological features.</a> Remote Sensing. 11 (7), 890 (2019).</li><li>Fei, S. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UAV-based+multi-sensor+data+fusion+and+machine+learning+algorithm+for+yield+prediction+in+wheat.">UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat.</a> Precision Agriculture. 24, 187-212 (2023).</li><li>Shu, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aboveground+biomass+estimation+of+rice+based+on+unmanned+aerial+vehicle+imagery.">Aboveground biomass estimation of rice based on unmanned aerial vehicle imagery.</a> Fujian Journal of Agricultural Sciences. 37 (7), 9 (2022).</li><li>Wu, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UAV+LiDAR-based+biomass+estimation+of+individual+trees.">UAV LiDAR-based biomass estimation of individual trees.</a> Fujian Journal of Agricultural Sciences. 22 (34), 15028-15035 (2022).</li><li>Yang, X., Zan, M., Munire, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimation+of+above+ground+biomass+of+Populus+euphratica+forest+using+UAV+and+satellite+remote+sensing.">Estimation of above ground biomass of Populus euphratica forest using UAV and satellite remote sensing.</a> Transactions of the Chinese Society of Agricultural Engineering. 37 (1), 7 (2021).</li><li>Li, B., Liu, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Forest+biomass+estimation+based+on+UAV+optical+remote+sensing.">Forest biomass estimation based on UAV optical remote sensing.</a> Forest Engineering. 5, 38 (2022).</li><li>Li, Z., Zan, Q., Yang, Q., Zhu, D., Chen, Y., Yu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Remote+estimation+of+mangrove+aboveground+carbon+stock+at+the+species+level+using+a+low-cost+unmanned+aerial+vehicle+system.">Remote estimation of mangrove aboveground carbon stock at the species level using a low-cost unmanned aerial vehicle system.</a> Remote Sensing. 11 (9), 1018 (2019).</li><li>Luo, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fusion+of+airborne+LiDAR+data+and+hyperspectral+imagery+for+aboveground+and+belowground+forest+biomass+estimation.">Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation.</a> Ecological Indicators. 73, 378-387 (2017).</li><li>Li, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Research+of+grassland+aboveground+biomass+inversion+based+on+UAV+and+satellite+remoting+sensing.">Research of grassland aboveground biomass inversion based on UAV and satellite remoting sensing.</a> Remote Sensing Technology and Application. 1, 037 (2022).</li><li>Wengert, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multisite+and+multitemporal+grassland+yield+estimation+using+UAV-borne+hyperspectral+data.">Multisite and multitemporal grassland yield estimation using UAV-borne hyperspectral data.</a> Remote Sensing. 14 (9), 2068 (2022).</li><li>Li, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+season+on+Spartina+alterniflora+identification+and+monitoring.">The effect of season on Spartina alterniflora identification and monitoring.</a> Frontiers in Environmental Science. 10, 1044839 (2022).</li><li>Wang, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimation+of+above-ground+biomass+of+winter+wheat+based+on+consumer-grade+multi-spectral+UAV.">Estimation of above-ground biomass of winter wheat based on consumer-grade multi-spectral UAV.</a> Remote Sensing. 14 (5), 1251 (2022).</li><li>Lu, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+estimation+of+aboveground+biomass+in+wheat+from+RGB+imagery+and+point+cloud+data+acquired+with+a+low-cost+unmanned+aerial+vehicle+system.">Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system.</a> Plant Methods. 15 (1), 17 (2019).</li><li>Chen, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+forest+aboveground+biomass+with+MODIS+and+Fengyun-3C+VIRR+imageries+in+Yunnan+Province,+Southwest+China+using+linear+regression,+K-nearest+neighbor+and+random.">Mapping forest aboveground biomass with MODIS and Fengyun-3C VIRR imageries in Yunnan Province, Southwest China using linear regression, K-nearest neighbor and random.</a> Remote Sensing. 14 (21), 5456 (2022).</li><li>Yan, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biomass+estimation+of+subtropical+arboreal+forest+at+single+tree+scale+based+on+feature+fusion+of+airborne+LiDAR+data+and+aerial+images.">Biomass estimation of subtropical arboreal forest at single tree scale based on feature fusion of airborne LiDAR data and aerial images.</a> Sustainability. 15 (2), 1676 (2023).</li><li>Tian, Y. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aboveground+mangrove+biomass+estimation+in+Beibu+Gulf+using+machine+learning+and+UAV+remote+sensing.">Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing.</a> Science of the Total Environment. 781, 146816 (2021).</li><li>Shrivastava, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beyond+skip+connections:+Top-down+modulation+for+object+detection.">Beyond skip connections: Top-down modulation for object detection.</a> arXiv. , (2016).</li><li>Belkasim, S. O., Shridhar, M., Ahmadi, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pattern+classification+using+an+efficient+KNNR.">Pattern classification using an efficient KNNR.</a> Pattern Recognition. 25 (10), 1269-1274 (1992).</li><li>Joel, S., Jose Luis, A., Shawn, C. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Farming+and+earth+observation:+Sentinel-2+data+to+estimate+within-field+wheat+grain+yield.">Farming and earth observation: Sentinel-2 data to estimate within-field wheat grain yield.</a> International Journal of Applied Earth Observation and Geoinformation. 107, 102697 (2022).</li><li>Tian, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+of+remote+sensing-based+methods+for+forest+aboveground+biomass+estimation:+Progress,+challenges,+and+prospects.">Review of remote sensing-based methods for forest aboveground biomass estimation: Progress, challenges, and prospects.</a> Forests. 14 (6), 1086 (2023).</li><li>Wei, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biomass+estimation:+A+remote+sensing+approach.">Biomass estimation: A remote sensing approach.</a> Geography Compass. 4 (11), 1635-1647 (2010).</li><li>D&#233;bora, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+methodology+for+intertidal+seaweed+biomass+estimation+using+multispectral+data+obtained+with+unoccupied+aerial+vehicles.">New methodology for intertidal seaweed biomass estimation using multispectral data obtained with unoccupied aerial vehicles.</a> Remote Sensing. 15 (13), 3359 (2023).</li><li>Zhang, J. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unmanned+aerial+system-based+wheat+biomass+estimation+using+multispectral,+structural+and+meteorological+data.">Unmanned aerial system-based wheat biomass estimation using multispectral, structural and meteorological data.</a> Agriculture. 13 (8), 1621 (2023).</li><li>Shen, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+the+obligate+parasite+Cuscuta+campestris+on+growth+and+biomass+allocation+of+its+host+Mikania+micrantha.">Influence of the obligate parasite Cuscuta campestris on growth and biomass allocation of its host Mikania micrantha.</a> Journal of Experimental Botany. 56 (415), 1277-1284 (2005).</li></ol>

We present the detailed steps of an experiment on estimating the biomass of invasive plants using UAV remote sensing and computer vision. The main process and steps of this agreement are shown in Figure 7. Proper sample quality is one of the most crucial and challenging aspects of the program. This importance holds true for all invasive plants as well as&#160;any other plant biomass estimation experiments24.
To identify the distribution of ...

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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>DSLR camera</td>
			<td>Nikon</td>
			<td>D850</td>
			<td>Sensor type: CMOS; Maximum number of pixels: 46.89 million; Effective number of pixels: 45.75 million; Maximum resolution 8256 x 5504.</td>
		</tr>
		<tr>
			<td>GPU - Graphics Processing Unit</td>
			<td>NVIDIA&#160;</td>
			<td>RTX3090</td>
			<td></td>
		</tr>
		<tr>
			<td>Hexacopter</td>
			<td>DJI&#160;</td>
			<td>M600PRO</td>
			<td>Horizontal flight: 65 km/h (no wind environment); Maximum flight load: 6000 g</td>
		</tr>
		<tr>
			<td>PyCharm</td>
			<td>Python IDE</td>
			<td>2023.1</td>
			<td></td>
		</tr>
		<tr>
			<td>Python</td>
			<td>Python</td>
			<td>3.8.0</td>
			<td></td>
		</tr>
		<tr>
			<td>Pytorch</td>
			<td>Pytorch</td>
			<td>1.8.1</td>
			<td></td>
		</tr>
	</tbody>
</table>

computer vision-based biomass estimation for invasive plants

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 from the study area, we prepared a sample square assembly to randomize the sampling points. An unmanned aerial camera system was constructed using a drone and camera to acquire continuous RGB images of the study area through automated navigation. After completing the shooting, the aboveground biomass in the sample frame was collected, and all correspondences were labeled and packaged. The sample data was processed, and the aerial images were segmented into small images of 280 x 280 pixels to create an image dataset. A deep convolutional neural network was used to map the distribution of Mikania micrantha in the study area, and its vegetation index was obtained. The organisms collected were dried, and the dry weight was recorded as the ground truth biomass. The invasive plant biomass regression model was constructed using the K-nearest neighbor regression (KNNR)&#160;by extracting the vegetation index from the sample images as an independent variable and integrating it with the ground truth biomass as a dependent variable. The results showed that it was possible to predict the biomass of invasive plants accurately. An accurate spatial distribution map of invasive plant biomass was generated by image traversal, allowing precise identification of high-risk areas affected by invasive plants. In summary, this study demonstrates the potential of combining unmanned aerial vehicle remote sensing with&#160;machine learning techniques to estimate invasive plant biomass. It contributes significantly to the research of new technologies and methods for real-time monitoring of invasive plants and provides technical support for intelligent monitoring and hazard assessment at the regional scale.

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.

We show representative results of a computer vision-based method for the estimation of invasive plants, which is implemented in a programmatic way on a computer. In this experiment, we evaluated the spatial distribution and estimated the biomass of invasive plants in their natural habitats, using Mikania micrantha as a research subject. We utilized a drone camera system to acquire images of the research site, a portion of which is exhibited in Figure 3. We utilized the ResNet101 con...

Read this Scientific Article Protocol about Computer Vision-Based Biomass Estimation for Invasive Plants at JoVE.com

We report detailed procedures for an invasive plant biomass estimation method that utilizes data obtained from unmanned aerial vehicle (UAV) remote sensing to assess biomass and capture the spatial distribution of invasive species. This approach proves highly beneficial for conducting hazard assessment and early warning of invasive plants.

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 ...

computer-vision-based-biomass-estimation-for-invasive-plants

Research

JoVE Journal

Engineering

570 Views.  Guangxi University. Il nostro metodo utilizza il telerilevamento UAV e la visione artificiale per stimare in modo efficiente la biomassa vegetale invasiva, superando le inefficienze e la soggettività dei tradizionali rilievi manuali e della raccolta dati in ambienti complessi. I recenti progressi nel telerilevamento UAV per la stima della biomassa vegetale includono l'imaging ad alta risoluzione e l'integrazione di dati multimodali. Questi sviluppi migliorano la ricerca scientifica, il monitoraggio ambientale e...