Analysis and Evaluation of the Image Preprocessing Process of a Six-Band Multispectral Camera Mounted on an Unmanned Aerial Vehicle for Winter Wheat Monitoring

Abstract

Unmanned aerial vehicle (UAV)-based multispectral sensors have great potential in crop monitoring due to their high flexibility, high spatial resolution, and ease of operation. Image preprocessing, however, is a prerequisite to make full use of the acquired high-quality data in practical applications. Most crop monitoring studies have focused on specific procedures or applications, and there has been little attempt to examine the accuracy of the data preprocessing steps. This study focuses on the preprocessing process of a six-band multispectral camera (Mini-MCA6) mounted on UAVs. First, we have quantified and analyzed the components of sensor error, including noise, vignetting, and lens distortion. Next, different methods of spectral band registration and radiometric correction were evaluated. Then, an appropriate image preprocessing process was proposed. Finally, the applicability and potential for crop monitoring were assessed in terms of accuracy by measurement of the leaf area index (LAI) and the leaf biomass inversion under variable growth conditions during five critical growth stages of winter wheat. The results show that noise and vignetting could be effectively removed via use of correction coefficients in image processing. The widely used Brown model was suitable for lens distortion correction of a Mini-MCA6. Band registration based on ground control points (GCPs) (Root-Mean-Square Error, RMSE = 1.02 pixels) was superior to that using PixelWrench2 (PW2) software (RMSE = 1.82 pixels). For radiometric correction, the accuracy of the empirical linear correction (ELC) method was significantly higher than that of light intensity sensor correction (ILSC) method. The multispectral images that were processed using optimal correction methods were demonstrated to be reliable for estimating LAI and leaf biomass. This study provides a feasible and semi-automatic image preprocessing process for a UAV-based Mini-MCA6, which also serves as a reference for other array-type multispectral sensors. Moreover, the high-quality data generated in this study may stimulate increased interest in remote high-efficiency monitoring of crop growth status.

Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

“…According to the principle of light absorption and reflectance, technologies of spectral analysis, imaging spectroscopy, and other nondestructive methods have been widely used in crop monitoring [4][5][6][7][8]. Combined with the development of airborne or unmanned aerial vehicle (UAV) platforms [9], imaging spectroscopy obtained with high spatial and temporal resolution has become a preferred method and research topic in farmland estimation owing to its advantages of high efficiency and non-invasion [10][11][12]. Thus, this article aims to use the multispectral sensor carried by the UAV to collect maize canopy spectral data in the field and conduct a rapid diagnosis of the chlorophyll content to estimate the growth status and guide the field management.…”

“…Most current studies on spectral image focus on the diagnosis of chlorophyll content [11][12][13]. The three directions of these studies include the analysis of spectral response [14][15][16], quantification and selection of sensitive parameters [17,18], and optimization of models [19][20][21][22] on the basis of the visible and near-infrared images.…”

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach

“…While UAV imagery has not previously been used for predicting LAI and chlorophyll content of quinoa plants, it has been used extensively to map LAI and chlorophyll content of other crops. For example, multispectral UAV imagery has been used to predict winter wheat LAI at critical growth stages (Jiang, et al, 2019b), seasonal leaf area dynamics of sorghum breeding lines (Potgieter et al, 2017), soil-plant analysis development (SPAD) measured chlorophyll content values of maize (Deng et al, 2018) and the leaf chlorophyll content of a potato crop (Roosjen et al, 2018). Vegetation indices (VIs) extracted from UAV-based imagery have been the most commonly-used information to predict LAI and chlorophyll content of crops (Jin et al, 2020).…”

Direct reflectance transformation methodology for drone-based hyperspectral imaging

“…With the Tetracam Micro-MCA, the sixth channel presents an irradiance sensor, called incident light sensor (ILS), which contains a band pass filter and an optical fiber (Tetracam, 2020). Jiang et al (2019) assessed the Micro-MCA for monitoring winter wheat crops. They performed radiometric calibration on a Micro-MCA and compared the reflectance transformation using panels and ELM versus the direct reflectance using the camera irradiance sensor.…”