Modeling of winter wheat fAPAR by integrating Unmanned Aircraft Vehicle-based optical, structural and thermal measurement

Lv, Zhengang; Meng, Ran; Man, Jianguo; Zeng, Linglin; Wang, Meiyv; Xu, Bin; Gao, Renjie; Sun, Rui; Zhao, Feng

doi:10.1016/j.jag.2021.102407

Cited by 16 publications

(29 citation statements)

References 86 publications

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“…In our study, we also tried to predict fAPAR, which is not so commonly used as a parameter in phenotyping experiments. A good result for fAPAR prediction with UAV RGB images and SVR (R 2 of 0.86) was reported by [73]. This result is similar to our study, where the parametric model (3BSI-Wang/polynomial) achieved an R 2 of 0.81 during the cross-validation.…”

Section: Phenotypic Variation and Relationship With Yieldsupporting

confidence: 90%

“…Previous studies indicated that the saturation issue of optical remote sensing can affect the performances of statistical regression models in estimating crop biophysical parameters [73,74]. To overcome the saturation problems inherent in the remotely sensed optical measurements, a multisource remote sensing fusion is proposed [30,73].…”

Section: Phenotypic Variation and Relationship With Yieldmentioning

confidence: 99%

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Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

et al. 2022

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The utility of unmanned aerial vehicles (UAV) imagery in retrieving phenotypic data to support plant breeding research has been a topic of increasing interest in recent years. The advantages of image-based phenotyping are related to the high spatial and temporal resolution of the retrieved data and the non-destructive and rapid method of data acquisition. This study trains parametric and nonparametric regression models to retrieve leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fractional vegetation cover (fCover), leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and grain yield (GY) of winter durum wheat breeding experiment from four-bands UAV images. A ground dataset, collected during two field campaigns and complemented with data from a previous study, is used for model development. The dataset is split at random into two parts, one for training and one for testing the models. The tested parametric models use the vegetation index formula and parametric functions. The tested nonparametric models are partial least square regression (PLSR), random forest regression (RFR), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The retrieved biophysical variables along with traditional phenotypic traits (plant height, yield, and tillering) are analysed for detection of genetic diversity, proximity, and similarity in the studied genotypes. Analysis of variance (ANOVA), Duncan’s multiple range test, correlation analysis, and principal component analysis (PCA) are performed with the phenotypic traits. The parametric and nonparametric models show close results for GY retrieval, with parametric models indicating slightly higher accuracy (R2 = 0.49; MRSE = 0.58 kg/plot; rRMSE = 6.1%). However, the nonparametric model, GPR, computes per pixel uncertainty estimation, making it more appealing for operational use. Furthermore, our results demonstrate that grain filling was better than flowering phenological stage to predict GY. The nonparametric models show better results for biophysical variables retrieval, with GPR presenting the highest prediction performance. Nonetheless, robust models are found only for LAI (R2 = 0.48; MRSE = 0.64; rRMSE = 13.5%) and LCC (R2 = 0.49; MRSE = 31.57 mg m−2; rRMSE = 6.4%) and therefore these are the only remotely sensed phenotypic traits included in the statistical analysis for preliminary assessment of wheat productivity. The results from ANOVA and PCA illustrate that the retrieved remotely sensed phenotypic traits are a valuable addition to the traditional phenotypic traits for plant breeding studies. We believe that these preliminary results could speed up crop improvement programs; however, stronger interdisciplinary research is still needed, as well as uncertainty estimation of the remotely sensed phenotypic traits.

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Section: Phenotypic Variation and Relationship With Yieldsupporting

confidence: 90%

Section: Phenotypic Variation and Relationship With Yieldmentioning

confidence: 99%

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

et al. 2022

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“…UAVs have become an important platform in precision agriculture application [71,72]. The relationships between winter grain yield against ground-measured LAI and the color indices derived from digital images, structural, and thermal information were studied at three key growth stages in this study.…”

Section: Discussionmentioning

confidence: 99%

Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery

et al. 2021

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Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76–0.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92–0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52–0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00–0.40 and R2 = 0.55–0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g/m2 and R2 = 0.89, RMSE = 39.82 g/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.

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“…As an emerging remote sensing platform, UAVs, with their low cost, high efficiency and flexibility, can make up for the inability of satellite remote sensing to acquire data due to factors such as transit time and environment [24]. Rapid advances in UAV technology have made it increasingly easy to acquire remote sensing data at the ultra-high spatial resolution [25,26], and UAVs can carry multiple sensors (e.g., RGB, multispectral and LiDAR) for the task of collecting remote sensing information from multiple sources in a single flight [27].…”

Section: Introductionmentioning

confidence: 99%

“…Although an increasing number of researchers have reported that combining spectral data with SfM-based structural features from UAV data can improve the robustness of predicting crop AGB, as structural features help to mitigate canopy spectral saturation [10,24,36,37], some uncertainties remain. Mao et al [38] demonstrated that the combination of spectral and structural features did not improve the accuracy of biomass estimation in desert shrubs.…”

Section: Introductionmentioning

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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Modeling of winter wheat fAPAR by integrating Unmanned Aircraft Vehicle-based optical, structural and thermal measurement

Cited by 16 publications

References 86 publications

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery

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

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