Improving estimation of maize leaf area index by combining of UAV-based multispectral and thermal infrared data: The potential of new texture index

Yang, Ning; Zhang, Zhitao; Zhang, Junrui; Guo, Yuhong; Yang, Xizhen; Yu, Guangduo; Bai, Xuqian; Chen, Junying; Chen, Yinwen; Shi, Liangsheng; Li, Xianwen

doi:10.1016/j.compag.2023.108294

Cited by 19 publications

(5 citation statements)

References 73 publications

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“…Sensitive Tm enhanced the response to LNC across multiple growth stages by employing feature combination formulas, which reflected the changes in LNC over these times. In line with Yang et al's [45] findings, our investigation discovered that all 12 TFCIs correlated more with LNC than Tm. It could be attributed to the feature combination formulae highlighting crop canopy information by minimizing interference from soil background, solar angle, terrain, and shadows after optimized band information [62,63].…”

Section: Contribution Of Texture Information To Lnc Estimation Across...supporting

confidence: 92%

“…Furthermore, the study indicated that TFCI T had a higher correlation with LNC than both Tm and TFCI D . In contrast to TFCI D and Tm, TFCI T provides an extra dimension of texture information [45], capturing the changes in wheat LNC over time in more detail and further boosting the responsiveness to LNC.…”

Section: Contribution Of Texture Information To Lnc Estimation Across...mentioning

confidence: 99%

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Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Su,

Nian,

Yue

et al. 2024

Agronomy

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Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R2) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.

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Section: Contribution Of Texture Information To Lnc Estimation Across...supporting

confidence: 92%

Section: Contribution Of Texture Information To Lnc Estimation Across...mentioning

confidence: 99%

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Su,

Nian,

Yue

et al. 2024

Agronomy

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“…Compared to decision tree regression, RFR uses random sampling during the branching process, ensuring that the global optimal decision is returned. Previous work has demonstrated that RFR effectively handles high-dimensional data and a large number of data, without being affected by overfitting [25], and shows a certain degree of robustness for dealing with nonlinear relationships and outliers.…”

Section: Construction Of Regression Modelsmentioning

confidence: 99%

“…Therefore, the capability of texture features in capturing the dynamic changes in crop canopy growth makes it suitable for improving the precision of LAI estimations. Despite the low precision observed when using texture features alone [21,22], the integration of both spectral and texture features has shown superior potential in previous studies related to LAI estimation [22][23][24][25][26] and has been demonstrated to alleviate the phenomenon of spectral saturation [26,27]. For instance, Wang et al [24] linearly combined texture features to form the normalized difference texture index (NDTI), which is integrated with spectral features to effectively enhance the accuracy for retrieving mixed grass LAI.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Monitoring of Peanut Leaf Area Index by Combing UAV Spectral and Textural Characteristics

Qiao,

Yang,

Bai

et al. 2024

Remote Sensing

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The leaf area index (LAI) is a crucial metric for indicating crop development in the field, essential for both research and the practical implementation of precision agriculture. Unmanned aerial vehicles (UAVs) are widely used for monitoring crop growth due to their rapid, repetitive capture ability and cost-effectiveness. Therefore, we developed a non-destructive monitoring method for peanut LAI, combining UAV vegetation indices (VI) and texture features (TF). Field experiments were conducted to capture multispectral imagery of peanut crops. Based on these data, an optimal regression model was constructed to estimate LAI. The initial computation involves determining the potential spectral and textural characteristics. Subsequently, a comprehensive correlation study between these features and peanut LAI is conducted using Pearson’s product component correlation and recursive feature elimination. Six regression models, including univariate linear regression, support vector regression, ridge regression, decision tree regression, partial least squares regression, and random forest regression, are used to determine the optimal LAI estimation. The following results are observed: (1) Vegetation indices exhibit greater correlation with LAI than texture characteristics. (2) The choice of GLCM parameters for texture features impacts estimation accuracy. Generally, smaller moving window sizes and higher grayscale quantization levels yield more accurate peanut LAI estimations. (3) The SVR model using both VI and TF offers the utmost precision, significantly improving accuracy (R2 = 0.867, RMSE = 0.491). Combining VI and TF enhances LAI estimation by 0.055 (VI) and 0.541 (TF), reducing RMSE by 0.093 (VI) and 0.616 (TF). The findings highlight the significant improvement in peanut LAI estimation accuracy achieved by integrating spectral and textural characteristics with appropriate parameters. These insights offer valuable guidance for monitoring peanut growth.

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“…Spectra can focus on the internal optical response of crops [ 11 ], while images capture external morphological information of crops [ 12 ]. Multispectral data provide reflectance of crops in different bands, which correlates with crop leaf moisture to some extent [ 13 ], thus enabling indirect inference of crop moisture status through multispectral data analysis. Vegetation indices, computed based on multispectral data, directly reflect the growth status of crops [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion

Yang,

Li,

Chen

et al. 2024

Plants

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Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021–2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

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Improving estimation of maize leaf area index by combining of UAV-based multispectral and thermal infrared data: The potential of new texture index

Cited by 19 publications

References 73 publications

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Non-Destructive Monitoring of Peanut Leaf Area Index by Combing UAV Spectral and Textural Characteristics

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion

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