Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery

Liu, Ji‐Kai; Zhu, Yongji; Tao, Xinyu; Chen, Xiaofang; Li, Xinwei

doi:10.3389/fpls.2022.1032170

Cited by 18 publications

(18 citation statements)

References 60 publications

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“…This study investigated the potential of two consumer-grad multispectral UAV sensors, PS and P4M, for predicting GY and retrieving four biophysical variables (LAI, fAPAR, fCover, and LCC) in winter barley trails. Both sensors have been previously tested for GY assessment in different crops, providing reasonable results [73][74][75][76].…”

Section: Correlated Response Of Grain Yield and Efficiency Of Indirec...mentioning

confidence: 98%

Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs

Ganeva,

Roumenina,

Dimitrov

et al. 2023

Sensors

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This study tested the potential of parametric and nonparametric regression modeling utilizing multispectral data from two different unoccupied aerial vehicles (UAVs) as a tool for the prediction of and indirect selection of grain yield (GY) in barley breeding experiments. The coefficient of determination (R2) of the nonparametric models for GY prediction ranged between 0.33 and 0.61 depending on the UAV and flight date, where the highest value was achieved with the DJI Phantom 4 Multispectral (P4M) image from 26 May (milk ripening). The parametric models performed worse than the nonparametric ones for GY prediction. Independent of the retrieval method and UAV, GY retrieval was more accurate in milk ripening than dough ripening. The leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction vegetation cover (fCover), and leaf chlorophyll content (LCC) were modeled at milk ripening using nonparametric models with the P4M images. A significant effect of the genotype was found for the estimated biophysical variables, which was referred to as remotely sensed phenotypic traits (RSPTs). Measured GY heritability was lower, with a few exceptions, compared to the RSPTs, indicating that GY was more environmentally influenced than the RSPTs. The moderate to strong genetic correlation of the RSPTs to GY in the present study indicated their potential utility as an indirect selection approach to identify high-yield genotypes of winter barley.

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Section: Correlated Response Of Grain Yield and Efficiency Of Indirec...mentioning

confidence: 98%

Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs

Ganeva,

Roumenina,

Dimitrov

et al. 2023

Sensors

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“…The P4M equipped with five monochrome sensors are used for multispectral images (blue (450nm±16nm, B), green (560nm±16nm,G), red (650nm±16nm, R), red edge (730nm±16nm, RE), and near-infrared (840nm±26nm, NIR) image). Among them, the RE and the NIR are the important selection of crop parameter inversion ( Cui and Kerekes, 2018a ; Liu J. et al., 2022 ).The multispectral sensors record each monochrome for 2.12-megapixel with a 40-mm focal length and 62.7-degree field of view. The multispectral image was geotagged automatically by its built-in multi-frequency high-precision RTK GNSS positioning system which provides about 8 mm and 15 mm accuracy in the vertical and horizontal directions, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Due to the absence of spectral response function (SRF) of camera, it is not possible to quantify the influence of SRF ( Cui and Kerekes, 2018b ). Hence, we employ empirical linear model (ELM) for radiometric correction purposes ( Di Gennaro et al., 2022 ; Liu J. et al., 2022 ). Four standard diffuse panels ( Figure 1D ) were placed on one side of the study area within the UAVs field of view ( Figure 1C ).…”

Section: Methodsmentioning

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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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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“…Early yield estimation could help guide breeding decisions and make the most efficient use of limited land resources. In recent years, with the need for agricultural production and the gradual emergence of UAV-based remote sensing [59], UAV-based remote sensing data have been increasingly used for estimating the yields of staple crops (such as maize [60] and wheat [61]); however, studies on the use of remote sensing data for estimating the phenotypic parameters of faba beans are few, and only two relevant articles have been published [49,62]. Different factors influence the accuracy of faba bean yield estimation models, including the growth period, the growth period data type (single or combined), the adopted ML algorithm, crop varieties, and sensor types (single or combined).…”

Section: Discussionmentioning

confidence: 99%

Faba Bean (Vicia faba L.) Yield Estimation Based on Dual-Sensor Data

Cui

Rong

et al. 2023

Drones

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Faba bean is an important member of legumes, which has richer protein levels and great development potential. Yield is an important phenotype character of crops, and early yield estimation can provide a reference for field inputs. To facilitate rapid and accurate estimation of the faba bean yield, the dual-sensor (RGB and multi-spectral) data based on unmanned aerial vehicle (UAV) was collected and analyzed. For this, support vector machine (SVM), ridge regression (RR), partial least squares regression (PLS), and k-nearest neighbor (KNN) were used for yield estimation. Additionally, the fusing data from different growth periods based on UAV was first used for estimating faba bean yield to obtain better estimation accuracy. The results obtained are as follows: for a single-growth period, S2 (12 July 2019) had the best accuracy of the estimation model. For fusion data from the muti-growth period, S2 + S3 (12 August 2019) obtained the best estimation results. Furthermore, the coefficient of determination (R2) values for RF were higher than other machine learning algorithms, followed by PLS, and the estimation effects of fusion data from a dual-sensor were evidently better than from a single sensor. In a word, these results indicated that it was feasible to estimate the faba bean yield with high accuracy through data fusion based on dual-sensor data and different growth periods.

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Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery

Cited by 18 publications

References 60 publications

Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs

Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs

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

Faba Bean (Vicia faba L.) Yield Estimation Based on Dual-Sensor Data

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