Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Kang, Ye-Seong; Nam, Jin-Woo; Kim, Younggwang; Lee, Seong-Tae; Seong, Deok-Gyeong; Jang, Si Hyeong; Ryu, Chung Ho

doi:10.3390/rs13081508

Cited by 16 publications

(8 citation statements)

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“…In this study, ENR is a relatively new machine learning algorithm being used for yield prediction. ENR combines the properties of ridge regression and LASSO ( Ogutu et al, 2012 ), both of which have been successfully applied to crop yield prediction ( Kang et al, 2021 ; Shafiee et al, 2021 ). The incorporation of multiple VIs adds collinearity to the models, and the ENR is robust to severe multicollinearity among the input features ( Ogutu et al, 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data

Fei

Hassan

et al. 2021

Front. Plant Sci.

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Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R2) = 0.527–0.667] compared to using a single multispectral sensor (R2 = 0.130–0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R2 = 0.667, root mean square error (RMSE) = 0.881 t ha–1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha–1. The EWF model outperformed at all the individual growth stages with R2 varying from 0.677 to 0.729. The best prediction result (R2 = 0.729, RMSE = 0.831 t ha–1, RRMSE = 14.3%, and MAE = 0.684 t ha–1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.

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Section: Discussionmentioning

confidence: 99%

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data

Fei

Hassan

et al. 2021

Front. Plant Sci.

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“…Moreover, the aim of this study was not crop phenotyping but instead to predict within-field variability in GY and PC using a single cultivar. Similar to this, Kang et al (2021) showed that data obtained with a five-band spectral camera mounted on a UAV in combination with machine learning (ML) allowed prediction of PC in a rice (Oryza sativa) cultivar with low error. As emphasized by Zhou et al (2021), it is essential to verify obtained results in such studies by considering a wide range of environments.…”

Section: Introductionmentioning

confidence: 54%

“…Similar to this, Kang et al. (2021) showed that data obtained with a five‐band spectral camera mounted on a UAV in combination with machine learning (ML) allowed prediction of PC in a rice ( Oryza sativa ) cultivar with low error. As emphasized by Zhou et al.…”

Section: Introductionmentioning

confidence: 71%

Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging

et al. 2022

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Durum wheat is an important cereal that is widely grown in the Mediterranean basin. In addition to high yield, grain quality traits are of high importance for farmers. The strong influence of climatic conditions makes the improvement of grain quality traits, like protein content, vitreousness, and test weight, a challenging task. Evaluation of quality traits post-harvest is time-and labor-intensive and requires expensive equipment, such as near-infrared spectroscopes or hyperspectral imagers. Predicting not only yield but also important quality traits in the field before harvest is of high value for breeders aiming to optimize resource allocation. Implementation of efficient approaches for trait prediction, such as the use of high-resolution spectral data acquired by a multispectral camera mounted on unmanned aerial vehicles (UAVs), needs to be explored. In this study, we have acquired multispectral image data with an 11-band multispectral camera mounted on a UAV and analyzed the data with machine learning (ML) models to predict grain yield and important quality traits in breeding micro-plots. Combining 11-band multispectral data for 34 cultivars and 16 environments allowed to develop ML models with good prediction capability. Applying the trained models to test sets explained a considerable degree of phenotypic variance with good accuracy showing r squared values of 0.84, 0.69, 0.64, and 0.61 and normalized root mean squared errors of 0.17, 0.07, 0.14, and 0.03 for grain yield, protein content, vitreousness, and test weight, respectively.

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“…It strikes a balance between bias and variance, making it a useful tool in many real-world regression problems (Matdoan et al, 2021). Regression models for predicting rice yield and protein content using unmanned aerial vehicle-based multispectral imagery was employed by Kang et al (2021).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

An,

Wang

2024

Preprint

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Water plays a vital role in the healthy growth of pears. Early detection of water stress can play a significant role in the timely management of water deficiency for pear yield. Most current methods are labour-intensive, time-consuming, only provide point measurements, and could be destructive. In this research, a real-time non-destructive method using a push-broom hyperspectral system (400-1000nm) was used to collect hyperspectral image data and detect the water stress of the pear seedling leaves. To build a reliable prediction model, machine learning techniques were used. The Successive Projections Algorithm (SPA) was applied for optimal wavelength selection. In particular, CNN was applied to obtain the features of key wavelengths. Both the CNN features and key wavelengths were put into RR-MLR, BLR and ENN for analysis. The training accuracy of the three modellings all reach the accuracy above 70% after about 100 epochs, while combination of CNN features outperformed the mere main spectra analysis. This research demonstrated that hyperspectral imaging coupled with machine learning techniques could be applied to predict the water content of pear leaves predict non-destructively.

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Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Cited by 16 publications

References 50 publications

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data

Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging

Use of HSI Coupled with Machine Learning for the Identification of Water Stress in Pear Seedling Leaves

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