Combining UAV multispectral imagery and ecological factors to estimate leaf nitrogen and grain protein content of wheat

Fu, Zhaopeng; Yu, Shanshan; Zhang, Jiayi; Xi, Hui; Gao, Yang; Lu, Ruhua; Zheng, Hengbiao; Zhu, Yan; Cao, Weixing; Liu, Xiaojun

doi:10.1016/j.eja.2021.126405

Cited by 51 publications

(23 citation statements)

References 73 publications

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“…Because the DON concentration of wheat is at a low level in the early stages and the typical symptoms of Fusarium damage cannot be detected visually, the spectral features are more conducive to observing the early symptoms of wheat infection ( Femenias et al, 2020 ; Zhang et al, 2021 ). The GLCM-based texture feature extraction method was based on Fu et al (2022) . The GLCM method describes the texture by studying the spatial correlation characteristics of the gray levels ( Haralick and Shanmugam, 1973 ).…”

Section: Discussionmentioning

confidence: 99%

“…In fact, texture information can help distinguish the spatial information independent of tone to identify objects or regions of interest in an image, but it is not recommended to use it by itself due to the poor performance of texture parameters ( Sarker and Nichol, 2011 ). Previously, auxiliary texture information was effectively combined with spectral information to significantly improve the accuracy of wheat GPC estimation ( Fu et al, 2022 ). Therefore, in this study, we attempted to fuse texture and spectral features to improve the detection accuracy of field FHB.…”

Section: Discussionmentioning

confidence: 99%

“…The first three bands contain most of the information (the cumulative variance exceeds 97%); thus, the texture features were extracted from the gray images corresponding to the three bands. The extraction of texture features was completed with ENVI 5.3 software, and the specific process occurred in four steps ( Fu et al, 2022 ): (1) select the gray images in “Texture Input File” dialog, (2) select the necessary texture features in the check box, (3) set the processing window size to 3 × 3 (the smallest window size guarantees the highest resolution), and (4) set the output path and calculate the texture values. A total of 24 texture features were calculated.…”

Section: Methodsmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs) are considered a practical detection method for crop pests and diseases. Unlike near-ground and satellite-based remote sensing platforms, applications of UAV have the advantages of large coverage, high efficiency, and flexibility ( Fu et al, 2022 ; Zhu et al, 2022a ). UAV can collect very high-resolution images and data in a cost-effective manner over a short period of time ( Ye et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Zhang

Huang

et al. 2022

Front. Plant Sci.

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Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Zhang

Huang

et al. 2022

Front. Plant Sci.

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“…In this study, most of the VIs extracted from the multispectral images had the highest correlation with yield at the grain-filling stage. The grain-filling period is a critical period for wheat grain formation [31,32], during which dry matter is transferred from plant organs to the seeds and is closely related to the thousand grain weight, so the VIs in this stage exhibit a high correlation with yield. The CWSI derived from canopy temperature information showed desirable yield correlations at both the flowering and grain-filling stages.…”

Section: Correlation Between Features and Yieldmentioning

confidence: 99%

Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

et al. 2022

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Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short-term memory neural network and random forest (LSTM-RF) was proposed for predicting wheat yield using VIs and CWSI from multi-growth stages as predictors. Validation results showed that the R2 of 0.61 and the RMSE value of 878.98 kg/ha was achieved in predicting grain yield using LSTM. LSTM-RF model obtained better prediction results compared to the LSTM with n R2 of 0.78 and RMSE of 684.1 kg/ha, which is equivalent to a 22% reduction in RMSE. The results showed that LSTM-RF considered both the time-series characteristics of the winter wheat growth process and the non-linear characteristics between remote sensing data and crop yield data, providing an alternative for accurate yield prediction in modern agricultural management.

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UAV‐based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates

Walsh

Nambi

Shafian

et al. 2023

Agrosystems Geosci & Env

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The accuracy of the traditional soil and plant‐based techniques for assessing sugarbeet demand for nitrogen (N) and yield prediction is generally low. Refining N and irrigation water management is a key to maximizing return for sugarbeet (Beta vulgaris L.) growers from agronomic, economic, and environmental perspective. The use of Normalized Difference Vegetative Index (NDVI) in combination with the unmanned aerial vehicle (UAV)‐based data collection for in‐season estimation of sugarbeet root yield and sugar concentration has potential for precision N management. Sugarbeet field trials were conducted in Idaho in 2019 and 2020 to assess (1) effects of water and N fertilizer rates on yield and estimated recoverable sugar (ERS) and (2) feasibility of predicting root yield and ERS using UAV NDVI. At the lowest N rate, application of water at 100% level resulted in greater yield, compared to 50%, in both years. At higher N rates, 50% level produced higher yields. At each N level, application of water at 100% level resulted in lower ERS, compared to 50%. The UAV NDVI was strongly correlated with root yield and ERS. The relationship between UAV NDVI and root yield and ERS was stronger in July (60 days after planting) compared to June (40 days after planting). Estimating the yield and ERS potential in late June/early July and topdressing the crop before the end of July may help to improve N use efficiency while optimizing sugarbeet production.

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Combining UAV multispectral imagery and ecological factors to estimate leaf nitrogen and grain protein content of wheat

Cited by 51 publications

References 73 publications

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

UAV‐based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates

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