Estimation of nitrogen content in wheat from proximal hyperspectral data using machine learning and explainable artificial intelligence (XAI) approach

Singh, Harpinder; Roy, Ajay; Setia, Raj

doi:10.1007/s40808-021-01243-z

Cited by 30 publications

(12 citation statements)

References 27 publications

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“…However, when many characteristic dimensions are used, the correlations between VIs and leaf N status are generally low, and the models are prone to multicollinearity and overfitting, which reduces the accuracy of the estimated N status . To address these issues, machine learning methods can reduce the wide range of co-linear variables and non-correlated factors, and reduce the impact of background effects on model precision (Singh et al, 2021).…”

Section: Dissection Of Hyperspectral Reflectance To Estimate Nitrogen...mentioning

confidence: 99%

“…Most machine learning algorithms are often considered "black boxes" because they provide no information about how they work. Therefore, machine learning can be used to explore the complex nonlinear relationships between spectral features and the N status in wheat plants without a clear understanding of the original data distribution (Singh et al, 2021). This not only provides a multifaceted and flexible direction for data analysis, but also a wider scope for experts to apply their theoretical knowledge to explain the principles in conjunction with algorithms.…”

Section: Dissection Of Hyperspectral Reflectance To Estimate Nitrogen...mentioning

confidence: 99%

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Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review

Zheng

2022

Front. Plant Sci.

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Recent research advances in wheat have focused not only on increasing grain yields, but also on establishing higher grain quality. Wheat quality is primarily determined by the grain protein content (GPC) and composition, and both of these are affected by nitrogen (N) levels in the plant as it develops during the growing season. Hyperspectral remote sensing is gradually becoming recognized as an economical alternative to traditional destructive field sampling methods and laboratory testing as a means of determining the N status within wheat. Currently, hyperspectral vegetation indices (VIs) and linear nonparametric regression are the primary tools for monitoring the N status of wheat. Machine learning algorithms have been increasingly applied to model the nonlinear relationship between spectral data and wheat N status. This study is a comprehensive review of available N-related hyperspectral VIs and aims to inform the selection of VIs under field conditions. The combination of feature mining and machine learning algorithms is discussed as an application of hyperspectral imaging systems. We discuss the major challenges and future directions for evaluating and assessing wheat N status. Finally, we suggest that the underlying mechanism of protein formation in wheat grains as determined by using hyperspectral imaging systems needs to be further investigated. This overview provides theoretical and technical support to promote applications of hyperspectral imaging systems in wheat N status assessments; in addition, it can be applied to help monitor and evaluate food and nutrition security.

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Section: Dissection Of Hyperspectral Reflectance To Estimate Nitrogen...mentioning

confidence: 99%

Section: Dissection Of Hyperspectral Reflectance To Estimate Nitrogen...mentioning

confidence: 99%

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review

Zheng

2022

Front. Plant Sci.

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“…PLSR is an integration of principal component analysis and typical correlation analysis on the basis of linear regression [55]. PLSR was initially used for regression relationships between multiple independent variables and multiple dependent variables, but in later studies it was also commonly used for regression analysis between single dependent variables and multiple independent variables [56,57]. PLSR eliminates possible multi-collinearity among variables and makes that regression analysis performed under conditions of multiple correlations and with a smaller sample size than the number of variables.…”

Section: Non-parametric Modelsmentioning

confidence: 99%

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

Wang

Zhu

et al. 2022

Agriculture

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Aboveground biomass (AGB) is an important basis for wheat yield formation. It is useful to timely collect the AGB data to monitor wheat growth and to build high-yielding wheat groups. However, as traditional AGB data acquisition relies on destructive sampling, it is difficult to adapt to the modernization of agriculture, and the estimation accuracy of spectral data alone is low and cannot solve the problem of index saturation at later stages. In this study, an unmanned aerial vehicle (UAV) with an RGB camera and the real-time kinematic (RTK) was used to obtain imagery data and elevation data at the same time during the critical fertility period of wheat. The cumulative percentile and the mean value methods were then used to extract the wheat plant height (PH), and the color indices (CIS) and PH were combined to invert the AGB of wheat using parametric and non-parametric models. The results showed that the accuracy of the model improved with the addition of elevation data, and the model with the highest accuracy of multi-fertility period estimation was PLSR (PH + CIS), with R2, RMSE and NRMSE of 0.81, 1248.48 kg/ha and 21.77%, respectively. Compared to the parametric models, the non-parametric models incorporating PH and CIS greatly improved the prediction of AGB during critical fertility periods in wheat. The inclusion of elevation data therefore greatly improves the accuracy of AGB prediction in wheat compared to traditional spectral prediction models. The fusion of UAV-based elevation data and image information provides a new technical tool for multi-season wheat AGB monitoring.

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“…Recently, model-agnostic methods have attracted a lot of attention for feature evaluation, such as Shapley Additive Explanation (SHAP) [34] and LIME [28]. Explainable AI techniques in general have been widely used to explain predictions in financial and chemical time-series data [77,78,79,80] vibrational-based Structural Health Monitoring signals [50], hyperspectral imaging [81] and electrocardiogram data [82]. However, to the best of our knowledge, only one recent work focused on using the model-agnostic method (LIME) to explain the non-linear predictions of spectroscopy data to characterize plasma solution conductivity [29].…”

Section: Related Workmentioning

confidence: 99%

Explainable Predictive Modeling for Limited Spectral Data

Akulich¹,

Anahideh²,

Sheyyab³

et al. 2022

Preprint

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Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable for domain experts. Spectroscopy data, which records the interaction between matter and electromagnetic radiation, particularly holds a lot of information in a single sample. Since acquiring such high-dimensional data is a complex task, it is crucial to exploit the best analytical tools to extract necessary information. In this paper, we investigate the most commonly used feature selection techniques and introduce applying recent explainable AI techniques to interpret the prediction outcomes of high-dimensional and limited spectral data. Interpretation of the prediction outcome is beneficial for the domain experts as it ensures the transparency and faithfulness of the ML models to the domain knowledge. Due to the instrument resolution limitations, pinpointing important regions of the spectroscopy data creates a pathway to optimize the data collection process through the miniaturization of the spectrometer device. Reducing the device size and power and therefore cost is a requirement for the real-world deployment of such a sensor-to-prediction system as a whole. Furthermore, we consider a wide range of machine learning models that have been proven to be successful for the prediction of the Cetane Number of fuels. We specifically design three different scenarios to ensure that the evaluation of ML models is robust for the real-time practice of the developed methodologies and to uncover the hidden effect of noise sources on the final outcome. The evaluation is performed for both the full model and reduced models using different feature selection techniques on a real dataset. Finally, we propose a correctness metric for the feature selection techniques to assess the conformance of the selected subset

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Estimation of nitrogen content in wheat from proximal hyperspectral data using machine learning and explainable artificial intelligence (XAI) approach

Cited by 30 publications

References 27 publications

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review

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

Explainable Predictive Modeling for Limited Spectral Data

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