Computer Vision-Based Biomass Estimation for Invasive Plants

Zhenyu Huang; Zhiyong Xu; Yanzhou Li; Bo Liu; Conghui Liu; Xi Qiao; Wanqiang Qian; Feng Qin; Pingdong Li; Yiqi Huang

doi:10.3791/66067

Engineering

Computer Vision-Based Biomass Estimation for Invasive Plants

Published: February 9, 2024 doi: 10.3791/66067

Zhenyu Huang^1,2, Zhiyong Xu³, Yanzhou Li¹, Bo Liu², Conghui Liu², Xi Qiao^1,2, Wanqiang Qian², Feng Qin^1,2, Pingdong Li⁴, Yiqi Huang¹

¹College of Mechanical Engineering, Guangxi University, ²Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, ³China Lesso Group Holdings Limited, ⁴Shenzhen Agricultural Technology Promotion Center

Abstract

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) 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 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.

Introduction

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.

Subscription Required. Please recommend JoVE to your librarian.

Protocol

1. Preparation of datasets

Selecting the research object
1. Select test samples based on the focus of the experimental study, considering options like Mikania micrantha or other invasive plants.
Collecting UAV images
1. Prepare square plastic frames of size 0.5 m*0.5 m and quantity 25-50, depending on the size of the area studied.
2. Employ a random sampling approach to determine soil sampling locations in the study area using a sufficient number of biomass samples. Position the sample frame horizontally over the vegetation, fully encompassing the plants with a minimum separation distance of 2 m between each plant.
3. Use a drone and a camera to form a UAV remote-sensing filming system, as shown in Figure 1.
4. Use the UAV to plot the route within the specified study area. The route planning setup is shown in Figure 2.
  1. Establish a heading and side overlap rate of 70%, capture photos at uniform time intervals of 2 s, maintain the camera angle perpendicular to the ground at 90°, and position the camera altitude at 30 m. This resulted in continuous visible image data of the study area with a single image resolution measuring 8256 x 5504 pixels, as depicted in Figure 3.
5. Store aerial imagery for subsequent processing with Python software for biomass estimation.
Collecting the aboveground biomass
1. Collect the aboveground biomass of Mikania micrantha manually within each sample plot after completing the drone data collection. Bag them and label each bag accordingly.
  1. When collecting Mikania micrantha, prevent sample plots from moving. First, cut the Mikania micrantha along the inside edge of the sample plot.
  2. Then, cut the rhizome of the Mikania micrantha from the bottom. Remove any dirt, rocks, or other plants that are mixed in. Finally, bag and label the samples.
2. Bring the collected samples of invasive plants from step 1.3.1 to the laboratory. Air-dry all the collected samples to evaporate most of the moisture.
3. To further remove moisture from air-dried samples, use an oven. Set the temperature to 55 °C. Dry the samples for 72 h, then weigh each sample on an electronic balance and record the biomass data in grams (g).
  1. Place the electronic balance in an undisturbed environment, weigh, calibrate, and continue weighing. Place bags of Mikania micrantha on the electronic balance, wait for the readings to stabilize, and record the readings.
  2. Weigh the Mikania micrantha every hour until the mass no longer changes and record the reading minus the weight of the bag as the measured mass of that sample. Calculate the aboveground biomass of the invasive plant using the formula below:
    
    where B represents the biomass of Mikania micrantha in grams per square meter (g/m²), M is the weight of the measured Mikania micrantha, measured in grams (g), S corresponds to the area of the sample plot in square meters (m²).
Creating a dataset
1. Extract the RGB image corresponding to the sample image from the original UAV image. Divide it into a grid of 280 × 280 pixels using Python programming (Supplementray Figure 1).
2. Segment the raw image data into smaller images of the same size as the sample images using Python programming. Use the sliding window method for segmentation, setting the horizontal and vertical steps to 280 pixels.
3. From the small images segmented in step 1.4.2, randomly select 880 invasive plant images and 1500 background images to create a dataset. Then, split this dataset into training, validation, and test sets in a 6:2:2 ratio (Supplementray Figure 2).

2. Identification of Mikania micrantha

Preparing the software
1. Go to Anaconda's official website (https://www.anaconda.com/) and download and install Anaconda. Then, head to PyCharm's website (https://www.jetbrains.com/pycharm/) and download the PyCharm IDE.
Creating a Conda environment.
1. Open the Anaconda Prompt command line after installing Anaconda, then type conda create -n pytorch python==3.8 to create a new Conda environment. After the environment is created, enter conda info --envs to confirm that the pytorch environment exists.
2. Open the Anaconda Prompt and activate the pytorch environment by entering conda activate pytorch. Check the current (Compute Unified Device Architecture)CUDA version by typing nvidia-smi. Then, install PyTorch version 1.8.1 by running the command conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=11.0 -c pytorch.
Runs for model recognition
NOTE: Use PyTorch to build the Mikania micrantha recognition model used in this paper. The network model employed is ResNet101²¹, which remains consistent with the original paper in its architecture. Modifications are made to the network's output section to meet the requirements for chamomile recognition.
1. Preprocess the images to prepare them for model input. Resize the images from 280 x 280 pixels to 224 x 224 pixels and normalize them to ensure they meet the model's size requirements using the following code:
  transform = transforms.Compose([
  transforms.Resize((224, 224)),
  transforms.ToTensor(),
  transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
2. Perform image feature extraction and reduce dimensionality using a convolutional neural network.
  1. First, initialize the convolutional layer for the initial feature extraction through self.conv1. With this convolutional layer, the original image is convolved into a feature map with self.in_channel channels for extracting initial features (Supplementary Figure 3A).
    NOTE: Advanced features are extracted in a convolution operation on the residual passes. These layers are produced by invoking the _make_layer function, which comprises a sequence of residual blocks. Each residual block consists of convolution, batch normalization, and activation functions to gradually extract sophisticated features (Supplementary Figure 3B).
  2. Use the layer's function to modify the channel number for dimensionality reduction via 1x1 convolution. This operation decreases the computational load while preserving significant features (Supplementary Figure 3C).
    NOTE: Overall, ResNet101 performs feature extraction by using various convolutional layers, and dimensionality reduction is achieved through 1x1 convolutional layers within the residual block. This approach allows the network to learn features more deeply and avoid the issue of gradient vanishing, thus enabling more efficient learning and representation of image features for complex tasks.
  3. After convolutions and pooling operations, input the high-quality features into a fully connected layer.
    NOTE: In the ResNet architecture, feature extraction takes place in the convolutional layer. These features are subsequently sent to the fully-connected (FC) layer for classification (Supplementary Figure 4). The operation self.avgpool(x) performs adaptive average pooling to reshape the tensor to a fixed size. The operation torch.flatten(x, 1) spreads the tensor into a one-dimensional vector, and self.fc(x) applies the fully connected layer to the flattened vector, ultimately serving as the final step for classification. This process effectively passes the extracted features through the convolutional layer, transforming them into a format suitable for classification via the fully connected layer.
  4. Use the Softmax function to obtain the final output based on the three classification requirements.
3. Train a multi-class recognition model with the dataset from step 1.4. Set the number of iterations to 200 and an initial learning rate of 0.0001. Reduce the learning rate by a third every 10 iterations with a batch size of 64. Save the optimal model parameters automatically after each iteration (Supplementary Figure 5).
4. Employ a meticulously trained recognition model and systematically traverse the original image from step 1.2.2 for identification purposes.
  1. Configure horizontal and vertical steps precisely at 280 pixels, resulting in the generation of a comprehensive distribution map highlighting the presence of invasive flora within the boundaries of the study area. Present the selected results visually as shown in Figure 4.
    NOTE: The initial image is preprocessed by segmenting it into smaller chunks, classifying each chunk using a trained deep learning model, and combining the results into an output image. If a chunk is classified as an invasive plant, the corresponding location on the output image is set to 255. The resulting output image is saved as a grayscale image file. The specific implementation code is shown in Supplementary Figure 6.

3. Estimation of invasive plant biomass

Perform simple data augmentation with the RandomResizedCrop and RandomHorizontalFlip functions (Supplementary Figure 7) to extend the image set created in step 1.2 and extract the six vegetation indices commonly used for estimating biomass, which are RBRI, GBRI, GRRI, RGRI, NGBDI, and NGRDI. Refer to Table 1 for the calculation formulas for these indices.
Create a K-nearest neighbor regression (KNNR)²² model using the output of the model to ensure precise estimation of the biomass of invasive plants. Use the extracted vegetation indices as inputs for the estimation model.
Use the coefficient of determination R² and Root Mean Square Error (RMSE)²³ to assess the model's accuracy, which is calculated as follows:

NOTE: The K-Nearest Neighbor Regression (KNNR) algorithm is a nonparametric machine learning technique used to solve regression problems. Its fundamental concept is to predict outcomes by determining the closest K neighbors in feature space based on input sample distances. Key benefits of using KNNR include its simplicity and ease of understanding, and it requires no training phase. Additionally, KNNR does not make excessive assumptions about the data's distribution. KNNR can be applied in regression issues to anticipate continuous objective variables and precisely evaluate the biomass of invasive plants.
Employ the aboveground biomass estimation model chosen in step 3.2 and scan through the invasive plant distribution map from step 2.3.4 with horizontal and vertical strides of 280 pixels.

Subscription Required. Please recommend JoVE to your librarian.

Representative Results

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 convolutional neural network to identify the plants present within the study area. Subsequently, we mapped the spatial distribution of invasive plants and illustrated some of our findings in Figure 4. In Figure 3, Mikania micrantha can be observed climbing atop the plant adorned with white flowers. The other plants, as well as the road and accompanying elements, are uniformly depicted in the background. In Figure 4, the model recognizes the red part as Mikania micrantha. Comparing the two sets of images, it is evident that ResNet101 demonstrates robust detection of Mikania micrantha in complex backgrounds. Furthermore, it accurately maps the distribution of Mikania micrantha in the study area with high precision.

The biomass of invasive plants in the study area was estimated by truncating all Mikania micrantha sample plot images from orthophotos at 280 × 280 pixels and extracting the vegetation indices RBRI, GBRI, GRRI, RGRI, NGBDI, and NGBDI. Regression analysis was conducted using the KNNR regression model, with the six indexes as inputs to the estimation model and biomass as the model's output. Figure 5 presents the results: the graph's horizontal coordinates represent the values of the field-measured biomasses, the vertical coordinates represent the values of the model-predicted biomasses, and the gray areas represent the confidence intervals. The results demonstrate strong predictive performance, with an R² value of 0.62 and an RMSE of 10.56 g/m². The model enhances the accuracy of Mikania micrantha biomass estimation, and the spatial distribution map in Figure 6 effectively captures the distribution of Mikania micrantha biomass.

Figure 1: UAV remote sensing systems. Some examples of RGB image data captured by UAV. Please click here to view a larger version of this figure.

Figure 2: Route planning. Study on regional route planning Please click here to view a larger version of this figure.

Figure 3: Invasive plant identification results within the study area. The figure displays the findings of identifying Mikania micrantha in the study zone via ResNet101 convolutional neural network. The red region in the picture indicates the area detected by ResNet101 as Mikania micrantha, while the other background signifies the rest of the study area. These outcomes correspond to the sample images portrayed in Figure 1. They represent the recognition results of Figure 1, respectively. Please click here to view a larger version of this figure.

Figure 4: Spatial distribution of invasive plants. The model recognizes the red part as Mikania micrantha. Please click here to view a larger version of this figure.

Figure 5: Biomass prediction regression results. The horizontal axis displays biomass values observed in the field, while the vertical axis portrays biomass values estimated by the model. The gray-shaded regions denote confidence intervals. The KNNR model attained an R² of 0.65 on the test set, while the lowest root mean square error amounted to 30.59 g/m². In the regression scatter plot of the model, many Mikania micrantha biomass estimations were within the confidence interval, indicating the validity of the biomass prediction. Please click here to view a larger version of this figure.

Figure 6: Spatial distribution of Mikania micrantha biomass. The figure illustrates the estimation of Mikania micrantha biomass throughout the research area utilizing KNNR as the predictive model, along with the extracted Mikania micrantha biomass distribution map. Darker shaded regions represent higher quantities of Mikania micrantha biomass. Please click here to view a larger version of this figure.

Figure 7: Schematic diagram of the main development of this protocol. The figure illustrates the main steps of the protocol presented. Please click here to view a larger version of this figure.

Vegetation Index Name	Calculation Formula
Green Blue Ratio Index	GBRI = DNG/DNB
Green Red Ratio Index	GRRI = DNG/DNR
Red Blue Ratio Index	RBRI = DNR/DNB
Red Green Ratio Index	RGRI = DNR/DNG
Normalized Green Blue Difference Index	NGBDI = (DNG - DNB)/(DNG + DNB)
Normalized Green Red Difference Index	NGRDI = (DNG - DNR)/(DNG + DNR)

Table 1: Vegetation index calculation formula. The vegetation indices used in this protocol and their respective calculation formulas.

Supplementary Figure 1: Cropping an image to 280 x 280 pixels via Python script using the OpenCV library. Please click here to download this File.

Supplementary Figure 2: Partitioning the dataset into a training set, validation set, and test set. Please click here to download this File.

Supplementary Figure 3: Feature extraction and reduction of dimensionality. (A) Initial feature extraction. (B) Convolution operation. (C) Reduction of dimensionality. Please click here to download this File.

Supplementary Figure 4: Conveying the features to the FC layer in ResNet architecture. Please click here to download this File.

Supplementary Figure 5: Setting the parameters. Please click here to download this File.

Supplemnetary Figure 6: The specific implementation code for generating comprehensive distribution map. Please click here to download this File.

Supplementary Figure 7: RandomResizedCrop and RandomHorizontalFlip functions. Please click here to download this File.

Subscription Required. Please recommend JoVE to your librarian.

Discussion

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 any other plant biomass estimation experiments²⁴.

To identify the distribution of invasive plants in the study area, we must first acquire visible and continuous photogrammetry of the study area using remote sensing from UAV. To achieve this, appropriate UAV photographic altitude and camera resolution are necessary. This will ensure that ResNet101, a convolutional neural network, can obtain features such as vegetation index and texture from the image. These features help to identify invasive plants even in complex environmental backgrounds and ultimately map their distribution in the study area.

To estimate the biomass of invasive plants in the study area, it is necessary to create square sample frames²⁵ with 25-50 random sampling points. Once the unmanned aerial system completes the process of taking photographs, it is necessary to gather the invasive plants from the sample frames. It is crucial to note, as stated in the initial section, that collecting the biomass from the frames should be done without moving them and with precision by assigning unique numbers. Additionally, the biomass should be delicately dried and weighed in the laboratory to obtain an accurate measurement. The KNNR regression model¹⁸ was utilized to obtain the spatial distribution of invasive plants, using six vegetation indices extracted from images acquired by the UAV camera system as input to the estimation model and biomass as the model's output.

The methods described in this paper for estimating invasive plant populations are not exhaustive. Additional tools may be utilized to obtain comparable data, and numerous practical or innovative modifications may be employed. A range of drones may be utilized, of which the designated model in the protocol serves as only one option and can be substituted with any drone possessing auto-navigation integration, the capability to adapt to a gimbal, the ability to fly up to 50 meters, a payload capacity exceeding that of the selected camera, and a range covering the entire study area. When choosing a camera for UAV, it is important to consider factors such as maximum resolution, effective image pixels, maximum pixels, and other imaging parameters, as well as the size and weight restrictions of the UAV load. Additionally, adequate range is necessary for the successful completion of the filming process. Furthermore, the model for identification is not the sole method and can be adjusted for multiple invasive plant species, such as woody or diverse types of plants, to enhance the suggested method. This strategy can also be progressed into a regression algorithm that employs fewer samples, acquires additional vegetation indices, and attains a heightened degree of precision. Alternatively, a reduced identification network could be implemented to identify invasive plants, allowing for the practical deployment of intelligent identification methods.

The presented method's primary constraint is the model's dependence on lighting conditions and background. Improvements towards higher accuracy can only come with further incorporation of multispectral²⁶, LiDAR¹², and meteorological data²⁷. Acquiring this sort of data can be challenging and may necessitate pricey equipment, but we can boost the accuracy of our data by avoiding direct sunlight and isolating the backgrounds of plants as we sample the area via our UAV filming system.

The benefit of utilizing this methodology, as opposed to others, lies in its ability to furnish the spatial distribution of invasive plant species in the designated study area. The distribution is determined through regression analysis, utilizing six vegetation indices extracted from images captured via the UAV camera system as inputs, with biomass as the output of the model. This approach utilizes a portable method for estimating biomass in multiple study areas simultaneously, which is fundamentally distinct from prior manual biomass collection methods. The conventional method involves manually conducting surveys and collecting significant quantities, which is inefficient and subjective²⁸, thus failing to provide a practical estimation of biomass given complex conditions.

Estimation of invasive plant biomass using the aforementioned methodology allows for quantification of regional distribution. Furthermore, it offers technical assistance for intelligent monitoring and hazard assessment at the regional level.

Subscription Required. Please recommend JoVE to your librarian.

Disclosures

The authors have nothing to disclose.

Acknowledgments

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&D Program of China (2022YFC2601500 & 2022YFC2601504), the National Natural Science Foundation of China (32272633), Shenzhen Science and Technology Program (KCXFZ20230731093259009)

Materials

Name	Company	Catalog Number	Comments
DSLR camera	Nikon	D850	Sensor type: CMOS; Maximum number of pixels: 46.89 million; Effective number of pixels: 45.75 million; Maximum resolution 8256 x 5504.
GPU - Graphics Processing Unit	NVIDIA	RTX3090
Hexacopter	DJI	M600PRO	Horizontal flight: 65 km/h (no wind environment); Maximum flight load: 6000 g
PyCharm	Python IDE	2023.1
Python	Python	3.8.0
Pytorch	Pytorch	1.8.1

DOWNLOAD MATERIALS LIST

References

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

Engineering