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

Jiang, Jiale; Johansen, Kasper; Stanschewski, Clara S.; Wellman, Gordon; Mousa, Magdi A. A.; Fiene, Gabriele; Asiry, Khalid A.; Tester, Mark; McCabe, Matthew

doi:10.1007/s11119-021-09870-3

Cited by 38 publications

(25 citation statements)

References 82 publications

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“…Vegetation indices are widely used in crop chlorophyll content (Jiang et al, 2022), LAI (Li et al, 2019), biomass (Gnyp et al, 2014) and yield prediction (Fu et al, 2020;Garcıá-Martıńez et al, 2020). Crop canopy reflectance is easily influenced by leaf pigmentation in the visible bands, which can lead to "oversaturation" of the vegetation indices (Hatfield et al, 2008).…”

Section: Analysis Of Monitoring Lai By Vegetation Indicesmentioning

confidence: 99%

“…Image processing techniques are applied to extract essential information, such as spectral features, texture, and point clouds, which are subsequently used to build models for crop growth parameter monitoring and yield estimation. (Liu et al, 2018;Sarkar et al, 2021;Jiang et al, 2022). The model construction process often involves a combination of non-linear relationships that affect the model's universality.…”

Section: Introductionmentioning

confidence: 99%

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Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features

Sun

Yang

et al. 2023

Front. Plant Sci.

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Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.

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Section: Analysis Of Monitoring Lai By Vegetation Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features

Sun

Yang

et al. 2023

Front. Plant Sci.

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“…In the field of agricultural, the most important ability of HRS is that it can obtain sufficient hyperspectral reflectance data of crops with a non-destructive mean, and with the assistance of various regression modeling algorithms, the relationship between reflectance data and various crop agronomic traits (e.g., leaf nitrogen, chlorophyll, water content, etc.) can be inferred quantitatively ( Weiss et al., 2020 ; Jiang et al., 2022 ). This process is known as “spectral inversion”.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods

Zhang

Chen²,

Gu³

et al. 2023

Front. Plant Sci.

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Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.

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“…These regression methods can be classified into two main groups: (i) traditional linear regression techniques, including multiple linear regression (Wu et al, 2022), partial least squares regression (Rischbeck et al, 2016), ridge regression (Ma et al, 2022), etc. ;and (ii) machine learning algorithms covering back propagation neural networks (Jiang et al, 2022), random forests , and support vector regression (Li et al, 2022). These methods are widely used in estimating crop traits due to (i) the strong correlation between VIs, TEs and crop parameters, and (ii) the ease of accessibility Yue et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images

Yu,

Huo,

et al. 2023

Preprint

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Aims Investigating the potential of combining data dimensionality reduction methods with various linear regression models and machine learning algorithms to improve the accuracy of leaf area index (LAI) and chlorophyll content (LCC) estimation in winter wheat based on UAV RGB imagery. Methods Constructed and compared the performance of three linear regression techniques: multiple linear regression (MLR), ridge regression (RR), and partial least squares regression (PLSR) and three machine learning algorithms: back-propagation neural networks(BP), random forests (RF) and support vector regression (SVR) with spectral vegetation indices (VIs), texture features (TEs) and their combinations extracted from UAV RGB images. Moreover, different data dimensionality reduction methods include principal component analysis (PCA), and stepwise selection (ST) were used to improve the accuracy of LAI and LCC estimation. Results The highest correlation between texture features and LAI, LCC was obtained with window size 5 × 5, orientation 45° and displacement 2 pixels. Combining VIs and TEs improved the accuracy of LAI and LCC estimation for wheat compared to using VIs or TEs alone. The RF model combined with ST_PCA for fusing VIs and TEs achieved the best estimations, with R2 of 0.86 and 0.91, RMSE of 0.26 and 2.01, and MAE of 0.22 and 1.66 for LAI and LCC, respectively. Conclusions The fusing of multiple features improved the accuracy of LAI and LCC estimation. ST_PCA, combined with machine learning algorithms, holds promising potential for monitoring crop physiological and biochemical parameters.

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

Cited by 38 publications

References 82 publications

Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features

Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features

Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods

Estimate leaf area index and leaf chlorophyll content in winter-wheat using image texture and vegetation indices derived from multi-temporal RGB images

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