Estimation of Wheat Plant Height and Biomass by Combining UAV Imagery and Elevation Data

Wang, Dunliang; Li, Rui; Zhu, Bo; Liu, Tao; Sun, Chengming; Guo, Wenshan

doi:10.3390/agriculture13010009

Cited by 12 publications

(13 citation statements)

References 77 publications

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“…SFs derived from UAVs-based multispectral images are widely used in crop biophysical parameters estimation ( Han et al., 2019 ; Maimaitijiang et al., 2019 ; Wang F. et al., 2022 ; Wang et al., 2023 ). This study compared the RF models’ performance between SFs and TFs for estimating the rice AGB ( Tables 3 – 5 ).…”

Section: Discussionmentioning

confidence: 99%

“…Extensive research has been conducted utilizing spectral information from UAVs imagery to monitor crop AGB, and it has achieved acceptable monitoring accuracy ( Han et al., 2019 ; Maimaitijiang et al., 2019 ; Wang F. et al., 2022 ; Wang et al., 2023 ). However, spectral information is susceptible to interference from water-soil (and weed) background noise during the early stages, while the dense canopy coverage at the later stages can result in spectral saturation ( Li et al., 2022 ; Wang F. et al., 2022 ; Wang Q. et al., 2022 ; Wang W. et al., 2022 ; Zhu et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

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Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery

Liu,

Zhu,

Song

et al. 2023

Front. Plant Sci.

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Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R2 = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R2 =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery

Liu,

Zhu,

Song

et al. 2023

Front. Plant Sci.

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“…To address the limitations of VIs, researchers have integrated plant height (PH) to enhance AGB estimation ( Bendig et al., 2015 ; Panday et al., 2020 ; Wang et al., 2022a ; Yang et al., 2023 ). Three-dimensional point cloud data of the crop canopy can be obtained using photogrammetry or LiDAR technology ( Sun et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

Dai,

Yu,

et al. 2024

Front. Plant Sci.

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IntroductionUnmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice.MethodsTo investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT).ResultsThe results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%–13.5%), demonstrating excellent practical applicability.DiscussionModel evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.

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“…However, the reflectance can be greatly affected by the surface temperature, atmospheric distortions, water content, saturation, landscape heterogeneity, and vegetation type, which can affect the modelling accuracies of actual crop growth [27,31]. Moreover, coarse spatial resolution satellite imagery limits the regression model estimation accuracy due to spectral mixing of different classes [32]. Combining high-resolution UAV-derived vegetation indices with in situ soil properties' data can enhance crop growth modelling.…”

Section: Introductionmentioning

confidence: 99%

Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties

Nduku,

Munghemezulu,

Mashaba-Munghemezulu

et al. 2024

Land

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Monitoring crop growth conditions during the growing season provides information on available soil nutrients and crop health status, which are important for agricultural management practices. Crop growth frequently varies due to site-specific climate and farm management practices. These variations might arise from sub-field-scale heterogeneities in soil composition, moisture levels, sunlight, and diseases. Therefore, soil properties and crop biophysical data are useful to predict field-scale crop development. This study investigates soil data and spectral indices derived from multispectral Unmanned Aerial Vehicle (UAV) imagery to predict crop height at two winter wheat farms. The datasets were investigated using Gaussian Process Regression (GPR), Ensemble Regression (ER), Decision tree (DT), and Support Vector Machine (SVM) machine learning regression algorithms. The findings showed that GPR (R2 = 0.69 to 0.74, RMSE = 15.95 to 17.91 cm) has superior accuracy in all models when using vegetation indices (VIs) to predict crop growth for both wheat farms. Furthermore, the variable importance generated using the GRP model showed that the RedEdge Normalized Difference Vegetation Index (RENDVI) had the most influence in predicting wheat crop height compared to the other predictor variables. The clay, calcium (Ca), magnesium (Mg), and potassium (K) soil properties have a moderate positive correlation with crop height. The findings from this study showed that the integration of vegetation indices and soil properties predicts crop height accurately. However, using the vegetation indices independently was more accurate at predicting crop height. The outcomes from this study are beneficial for improving agronomic management within the season based on crop height trends. Hence, farmers can focus on using cost-effective VIs for monitoring particular areas experiencing crop stress.

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Estimation of Wheat Plant Height and Biomass by Combining UAV Imagery and Elevation Data

Cited by 12 publications

References 77 publications

Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery

Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties

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