Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing

Yin, Qian; Zhang, Y; Li, Wei-Long; Wang, Jianjun; Wang, Weiling; Ahmad, Irshad; Zhou, Guisheng; Huo, Zhongyang

doi:10.3390/rs15143595

Cited by 23 publications

(18 citation statements)

References 69 publications

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“…Therefore, the optimal combination of feature selection methods (recursive feature elimination, Pearson, and correlation-based feature selection) and machine learning regression models (random forest), elastic net, extreme gradient boosting (XGBoost), and backpropagation neural network with machine learning algorithms can predict SPAD values at individual growth stages and across growth stages of the crop from the images obtained by UAV (Yin et al, 2023). In other words, machine learning regression models (random forest), partial least squares (PLS) regression, deep neural network, and extreme gradient boosting (XGBoost) were used to establish SPAD estimation models.…”

Section: Soil Plant Analysis Development Analysis For Paddy Growth Mo...mentioning

confidence: 99%

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Mohidem,

Jaafar,

Che’Ya

2024

JST

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Using a conventional approach via visual observation on the ground, farmers encounter difficulties monitoring the entire paddy field area, and it is time-consuming to do manually. The application of unmanned aerial vehicles (UAVs) could help farmers optimise inputs such as water and fertiliser to increase yield, productivity, and quality, allowing them to manage their operations at lower costs and with minimum environmental impact. Therefore, this article aims to provide an overview of the integration of UAV and multispectral sensors in monitoring paddy growth applications based on vegetation indices and soil plant analysis development (SPAD) data. The article briefly describes current rice production in Malaysia and a general concept of precision agriculture technologies. The application of multispectral sensors integrated with UAVs in monitoring paddy growth is highlighted. Previous research on aerial imagery derived from the multispectral sensor using the normalised difference vegetation index (NDVI) is explored to provide information regarding the health condition of the paddy. Validation of the paddy growth map using SPAD data in determining the leaf’s relative chlorophyll and nitrogen content is also being discussed. Implementation of precision agriculture among low-income farmers could provide valuable insights into the practical implications of this review. With ongoing education, training and experience, farmers can eventually manage the UAV independently in the field. This article concludes with a future research direction regarding the production of growth maps for other crops using a variety of vegetation indices and map validation using the SPAD metre values.

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Section: Soil Plant Analysis Development Analysis For Paddy Growth Mo...mentioning

confidence: 99%

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Mohidem,

Jaafar,

Che’Ya

2024

JST

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“…For the prediction accuracy of winter wheat bio-parameters values at the four growth stages, LAI: early filling > booting > late jointing > heading, LCC: heading > early filling > booting > late jointing, CCC: booting > early filling > heading > late jointing. The accuracy of bio-parameter estimation varied with different growth stages of winter wheat, which can be attributed to changes in various factors including crop canopy structure, leaf thickness and cell structure, leaf pigment content, and crop coverage [15,31,36,60]. In addition, the LAI, LCC, and CCC change with the increase in leaf size and number in the vertical distribution of wheat, and the growth and senescence of wheat spikes also affect the estimation of wheat canopy reflectance and bio-parameters.…”

Section: Effects Of Crop Phenology On Bio-parameters Estimationmentioning

confidence: 99%

“…Wang [34] and Wang [35] estimated the wheat LCC using multiple ML models combined with the important ranking of the random forest model. Zhu [15] and Yin [36] used multiple MLs combined with filter-based and RFE feature selection to estimate wheat LCC at different growth stages, respectively. These studies indicated that using the ML model alone cannot achieve optimal model accuracy, and the combination of feature selection and ML model can more accurately estimate the LCC of winter wheat.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Zhang,

Yi,

Wang

et al. 2024

Remote Sensing

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Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R2), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm2 and 2.872 at the heading stage, and 0.968, 0.147 g/m2 and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods.

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“…Yet, LAI estimation based on satellite data falls short in providing refined field-scale monitoring. Unmanned aerial vehicles (UAVs) offer substantial advantages over satellites in terms of enhanced temporal and spatial resolution, alongside greater flexibility ( Li et al., 2019 ; Yin et al., 2023 ). Multispectral cameras are a popular choice among UAV sensors, it can capture spectral information in the red-edge and near-infrared bands, which are crucial for analyzing vegetation ( Yao et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

Zu,

Yang,

Wang

et al. 2024

Front. Plant Sci.

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Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R2 = 0.83), followed by the RF model (R2 = 0.82), with the PLSR model exhibited the lowest accuracy (R2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

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Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing

Cited by 23 publications

References 69 publications

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Integration of Unmanned Aerial Vehicle and Multispectral Sensor for Paddy Growth Monitoring Application: A Review

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

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