Computer Vision-Based Biomass Estimation for Invasive Plants

Summary

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.

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 invasiv....

Protocol

1. Preparation of datasets

1. 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.
2. 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 suffi.......

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 .......

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