Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield

Wang, Fumin; Qian, Yi; Hu, Jinghui; Xie, Lu; Yao, Xiaoping; Xu, Tianyue; Zheng, Jueyi

doi:10.1016/j.jag.2021.102397

Cited by 55 publications

(53 citation statements)

References 48 publications

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“…Although the above-mentioned remote-sensing techniques accurately estimate AGB and allow for real-time monitoring of crop growth, obtaining the data incurs high cost, and data processing is complex, which prevents this approach from gaining wide acceptance in the private sector ( Poley and McDermid, 2020 ). In contrast, UAV digital remote-sensing systems are more acceptable because of their low price, simple data structure, and convenient data processing ( Guo et al, 2021 ; Wang et al, 2021 ). More importantly, UAV-based RGB images may be spliced together to obtain digital surface models (from which a CHM can be developed) and ultrahigh-ground-resolution (GDS) digital orthophoto images (from which crop canopy spectra can be extracted), which provides more avenues to accurately estimate crop parameters.…”

Section: Introductionmentioning

confidence: 99%

Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height

et al. 2022

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Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management and the optimization of planting patterns. Many studies have confirmed that, due to canopy spectral saturation, AGB is underestimated in the multi-growth period of crops when using only optical vegetation indices. To solve this problem, this study obtains textures and crop height directly from ultrahigh-ground-resolution (GDS) red-green-blue (RGB) images to estimate the potato AGB in three key growth periods. Textures include a grayscale co-occurrence matrix texture (GLCM) and a Gabor wavelet texture. GLCM-based textures were extracted from seven-GDS (1, 5, 10, 30, 40, 50, and 60 cm) RGB images. Gabor-based textures were obtained from magnitude images on five scales (scales 1–5, labeled S1–S5, respectively). Potato crop height was extracted based on the generated crop height model. Finally, to estimate potato AGB, we used (i) GLCM-based textures from different GDS and their combinations, (ii) Gabor-based textures from different scales and their combinations, (iii) all GLCM-based textures combined with crop height, (iv) all Gabor-based textures combined with crop height, and (v) two types of textures combined with crop height by least-squares support vector machine (LSSVM), extreme learning machine, and partial least squares regression techniques. The results show that (i) potato crop height and AGB first increase and then decrease over the growth period; (ii) GDS and scales mainly affect the correlation between GLCM- and Gabor-based textures and AGB; (iii) to estimate AGB, GLCM-based textures of GDS1 and GDS30 work best when the GDS is between 1 and 5 cm and 10 and 60 cm, respectively (however, estimating potato AGB based on Gabor-based textures gradually deteriorates as the Gabor convolution kernel scale increases); (iv) the AGB estimation based on a single-type texture is not as good as estimates based on multi-resolution GLCM-based and multiscale Gabor-based textures (with the latter being the best); (v) different forms of textures combined with crop height using the LSSVM technique improved by 22.97, 14.63, 9.74, and 8.18% (normalized root mean square error) compared with using only all GLCM-based textures, all Gabor-based textures, the former combined with crop height, and the latter combined with crop height, respectively. Therefore, different forms of texture features obtained from RGB images acquired from unmanned aerial vehicles and combined with crop height improve the accuracy of potato AGB estimates under high coverage.

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Section: Introductionmentioning

confidence: 99%

Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height

et al. 2022

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“…At present, there are many kinds of unmanned aerial vehicles (UAVs), with significant differences in load, flight time, flight altitude and other parameters; thus, there are different classifications according to different standards. The figures of main UAV platforms and sensors can be found in relevant research [22][23][24][25][26]. Combined with relevant research progress at home and abroad, it can be seen that multirotor UAVs are the most commonly used types [8], mainly including quad-rotor, hexa-rotor and octo-rotor UAVs, among which octo-rotor UAVs are the most widely used.…”

Section: Uav Platforms and Sensorsmentioning

confidence: 99%

Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

Lyu

Dang

et al. 2022

Remote Sensing

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In recent years, the application of unmanned aerial vehicle (UAV) remote sensing in grassland ecosystem monitoring has increased, and the application directions have diversified. However, there have been few research reviews specifically for grassland ecosystems at present. Therefore, it is necessary to systematically and comprehensively summarize the application of UAV remote sensing in grassland ecosystem monitoring. In this paper, we first analyzed the application trend of UAV remote sensing in grassland ecosystem monitoring and introduced common UAV platforms and remote sensing sensors. Then, the application scenarios of UAV remote sensing in grassland ecosystem monitoring were reviewed from five aspects: grassland vegetation monitoring, grassland animal surveys, soil physical and chemical monitoring, grassland degradation monitoring and environmental disturbance monitoring. Finally, the current limitations and future development directions were summarized. The results will be helpful to improve the understanding of the application scenarios of UAV remote sensing in grassland ecosystem monitoring and to provide a scientific reference for ecological remote sensing research.

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“…Textural indices are important indicators in the object detection, image classification and image-processing domains [60,[71][72][73][74]. The most commonly applied textural indices, such as contrast, correlation, energy and homogeneity, can be extracted from the gray-level co-occurrence matrix (GLCM) based on statistical approaches [75][76][77][78]. The textural indices applied in this study was shown in Table 3.…”

Section: Spectral Indicesmentioning

confidence: 99%

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

Guo

Chen

et al. 2022

Remote Sensing

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Accurately identifying the phenology of summer maize is crucial for both cultivar breeding and fertilizer controlling in precision agriculture. In this study, daily RGB images covering the entire growth of summer maize were collected using phenocams at sites in Shangqiu (2018, 2019 and 2020) and Nanpi (2020) in China. Four phenological dates, including six leaves, booting, heading and maturity of summer maize, were pre-defined and extracted from the phenocam-based images. The spectral indices, textural indices and integrated spectral and textural indices were calculated using the improved adaptive feature-weighting method. The double logistic function, harmonic analysis of time series, Savitzky–Golay and spline interpolation were applied to filter these indices and pre-defined phenology was identified and compared with the ground observations. The results show that the DLF achieved the highest accuracy, with the coefficient of determination (R2) and the root-mean-square error (RMSE) being 0.86 and 9.32 days, respectively. The new index performed better than the single usage of spectral and textural indices, of which the R2 and RMSE were 0.92 and 9.38 days, respectively. The phenological extraction using the new index and double logistic function based on the PhenoCam data was effective and convenient, obtaining high accuracy. Therefore, it is recommended the adoption of the new index by integrating the spectral and textural indices for extracting maize phenology using PhenoCam data.

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Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield

Cited by 55 publications

References 48 publications

Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height

Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height

Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

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