Combining texture, color, and vegetation indices from fixed-wing UAS imagery to estimate wheat growth parameters using multivariate regression methods

Zhang, Jiayi; Qiu, Xiaolei; Wu, Yueting; Zhu, Yan; Cao, Qiang; Liu, Xiaojun; Cao, Weixing

doi:10.1016/j.compag.2021.106138

Cited by 82 publications

(49 citation statements)

References 36 publications

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“…In this study, the vegetation indices and texture features were combined to compensate for the saturation of vegetation index when the yield was high by virtue of the accuracy of vegetation index texture features in color, so as to improve the accuracy of the model to a certain extent. In previous studies, Zhang et al (2021) used texture features, color and vegetation index to estimate wheat growth parameters, and also used texture features to compensate for index saturation and effectively improve the accuracy of the model. Zhou et al (2021) used drones to diagnose water stress in winter wheat.…”

Section: Discussionmentioning

confidence: 99%

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Zhang

et al. 2022

Front. Plant Sci.

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Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R2 was 0.9109, and RMSE was 0.91277 t.ha−1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

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Section: Discussionmentioning

confidence: 99%

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Zhang

et al. 2022

Front. Plant Sci.

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“…SIs in MSI [48] Note: R b , R g , R r , R re , R nir , represented the reflectance (0-1) of images at NIR, RE, R, G and B band, respectively, in MSI; R b , R g , R r , represented gray values (0-255) of R, G, B channel in RGB, respectively.…”

Section: Type Index Name Acronym Formula Referencementioning

confidence: 99%

A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

Cao

Liu

et al. 2021

Remote Sensing

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High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.

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“…Texture analysis plays an important role in image processing and is usually used to define the variability of pixel values between adjacent pixels of the analysis window ( Kelsey and Neff, 2014 ). In recent years, many studies have considered image texture features when estimating LAI ( Zhang et al, 2021 , 2022 ), AGB ( Hlatshwayo et al, 2019 ; Yue et al, 2019 ), N nutrition parameters ( Zheng et al, 2020 ), and plant potassium accumulation (PKA) ( Lu et al, 2021 ). Li S. et al (2019) used the UAV and RGB images to build the NDTI model.…”

Section: Introductionmentioning

confidence: 99%

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

et al. 2022

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Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R2) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.

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Combining texture, color, and vegetation indices from fixed-wing UAS imagery to estimate wheat growth parameters using multivariate regression methods

Cited by 82 publications

References 36 publications

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

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