Evaluating Sorghum Senescence Patterns Using Small Unmanned Aerial Vehicles and Multispectral Imaging

Barnhart, Isaac; Rosso, Luiz H. Moro; Secchi, M. A.; Ciampitti, Ignacio A.

doi:10.4148/2378-5977.7799

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hassan et al [36] and Serrano et al [37] showed a positive relationship of NDVI with biomass and grain yield in wheat. Potgieter et al [38] and Barnhart et al [39] used NDRE to characterize the leaf area and senescence patterns of sorghum (Sorghum bicolor). ExG measures the intensity of the greenness of the canopy by utilizing the normalized visible spectrum.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

et al. 2021

View full text Add to dashboard Cite

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Compared with the current studies on plant leaf pigment inversion using leaf spectral reflectance, the inversion accuracy of Chla and Chlb with the 3-D FSIs were better than those of the RT model [38,62] and the spectral index [28], especially in the evaluation metric RMSE (2.96 µg/cm 2 ) of Chla and RMSE (2.01 µg/cm 2 ) of Chlb. Moreover, vegetation spectral can simplify spectral feature and not descale the extraction accuracy of plant information through with a drone multispectral imaging camera in the Barnhart report [63]. Similarly, the 3-D FSIs for leaf chlorophyll a and b could simplify the spectrum characteristics compared with 3-D leaf fluorescence spectrum, and there would a potential chance for the fluorescence images of leaf chlorophyll a and chlorophyll b, using a multi-band imaging camera with a special active fluorescence excitation light source and a few fluorescence imaging channels.…”

Section: New 3-d Fluorescence Indices For Multiple Pigment Content In...mentioning

confidence: 99%

New 3-D Fluorescence Spectral Indices for Multiple Pigment Inversions of Plant Leaves via 3-D Fluorescence Spectra

Tian,

Zhang,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

High-sensitivity fluorescence monitoring has been widely used in agriculture and environmental science. However, the active fluorescence detection information of leaf segments mainly focuses on total chlorophyll, and the fluorescence information of chlorophyll a, chlorophyll b, and some other pigments has not been explored. This only considers the fluorescence spectrum characteristics at a single wavelength or the fluorescence integral from a range of wavelength regions and does not completely consider the linkage relation between the excitation, emission, and interference information. In this paper, the three-dimensional fluorescence spectrum, containing the excitation and emission fluorescence spectra, and the corresponding multiple pigment characteristics from the upgraded LOPEX_ZJU database were collected. The linkages of excitation and emission of the three-dimensional fluorescence spectra of these pigments were analyzed for the newly built multiple pigment 3-D fluorescence spectral indices (3-D FSIs), including those of chlorophyll a, chlorophyll b, carotenoid, anthocyanin, and flavonoid 3-D FSIs. Then, these pigment inversion models were established and validated. The results show that the 3-D FSIs performances for the photosynthetic pigment content inversion (including chlorophyll a and b, and carotenoids) were much better than those for the photo-protective pigments (including anthocyanins and flavonoids) from the 3-D fluorescence spectra of these plant leaves. Here, the 3-D fluorescence normalization index (FNI ((F430,690-F430,763)/(F430,690+F430,763))) for the chlorophyll a inversion model has a high accuracy, the RMSE is 2.96 μg/cm2, and the 3-D fluorescence reciprocal difference index (FRI (F650,704/F650,668) for the chlorophyll b model has an encouraging RMSE (2.01 μg/cm2). The RMSE of the 3-D fluorescence ratio index (FRI (F500,748/F500,717)) for the carotenoid inversion is 3.77 μg/cm2 RMSE. Only FRI (F370,615/F370,438) was selected for the modeling and validating evaluation of the leaf Flas content inversion, but the evaluation metrics were not good, with an RMSE (151.13 μg/cm2). For Ants, although there was a 3-D FSI (FRDI (1/F540,679-1/F540,557)), and its evaluation metrics, with an RMSE (2.8 μg/cm2), were at or over 0.05 level, the validating evaluation metric VC (98.3577%) was not encouraging. These results showed that fluorescence, as a nondestructive and efficient detection method, could determine the contents of chlorophyll a, chlorophyll b, and carotenoid in plant leaves, providing a new method to detect plant information. It can also provide a potential chance for the fluorescence images of fine photo-protective pigments, especially chlorophyll a and b, using the special active fluorescence excitation light source and a few fluorescence imaging channels from the optimal FSIs.

show abstract

Evaluating Sorghum Senescence Patterns Using Small Unmanned Aerial Vehicles and Multispectral Imaging

Cited by 2 publications

References 0 publications

Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

New 3-D Fluorescence Spectral Indices for Multiple Pigment Inversions of Plant Leaves via 3-D Fluorescence Spectra

Contact Info

Product

Resources

About