Utilization of Spectral Indices for High-Throughput Phenotyping

Tayade, Rupesh; Yoon, Ju-Yeon; Lay, Liny; Khan, Abdul Latif; Yoon, Young Mi; Kim, Yoon-Ha

doi:10.3390/plants11131712

Cited by 28 publications

(9 citation statements)

References 126 publications

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“…Furthermore, this type of information can be used as a secondary trait for indirect selection and prediction of the target trait by exploiting the correlation between the traits (Rutkoski et al, 2016; Sun et al, 2017). In addition, spectral vegetation indices (SVI) can provide information about plant status and development (Tayade et al, 2022) and previous studies suggested that SVI calculated from HTP could increase the efficiency of indirect selection for seed yield and can be integrated into sesame breeding programs (Petsoulas et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sabag,

Bi,

Sahoo

et al. 2024

Preprint

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Sesame (Sesamum indicum) is an important oilseed crop with rising demand due to its high oil quality. To meet these future demands, there is an urgent need to develop and integrate new breeding strategies. While genomic resources have advanced genetic research in sesame, implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11 conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

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Section: Introductionmentioning

confidence: 99%

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sabag,

Bi,

Sahoo

et al. 2024

Preprint

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“…1 It is grown and consumed throughout the year because it has numerous health-promoting compounds, such as anthocyanins, carotenoids, chlorophylls, flavonoids, hydroxycinnamic acid derivatives, isorhamnetin, kaempferol, phenolics, quercetin, vitamins, and minerals. 2,3 Glucosinolates are a large group of plant secondary metabolites with nutritional effects and biological activity. Glucosinolates are primarily found in cruciferous plants, including the widely consumed Brassicaceae family.…”

Section: Introductionmentioning

confidence: 99%

Effect of LED Lights on Secondary Metabolites and Antioxidant Activities in Red Pakchoi Baby Leaves

Bungala,

Park,

Nguyen

et al. 2024

ACS Omega

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Pakchoi (Brassica rapa subsp. chinensis) is one of the most widely consumed vegetables in Asian countries, and it is high in secondary metabolites. The availability, quantity, and quality of light play a critical role in the growth and development of plants. In this study, we investigated the effect of LEDs (light-emitting diodes; white, blue, red, and red + blue) on anthocyanin, glucosinolates, and phenolic levels in red pakchoi baby leaves. On the 24th day after sowing (DAS), red baby pakchoi leaves were harvested, and shoot length, root length, and fresh weight were measured. Among the different LED treatments, there was no significant difference in shoot length, whereas the highest root length was achieved in the red + blue LED treatment (23.8 cm). The fresh weight also showed a significant difference among the different LED treatments. In total, 12 phenolic and 7 glucosinolate individual compounds were identified using high-performance liquid chromatography (HPLC) analysis. The highest total glucosinolate (2937 μg/g dry wt) and phenolic (1589 μg/g dry wt) contents were achieved in baby leaves exposed to red + blue light. Similarly, the highest contents of total anthocyanins (1726 μg/g dry wt), flavonoids (4920 μg/g dry wt), and phenolics (5900 μg/g dry wt) were achieved in the red + blue treatment. Plants exposed to red + blue LED light showed the highest accumulation of anthocyanin, glucosinolates, and phenolic compounds. For antioxidant activity, DPPH (2,2-diphenyl-1-picrylhydrazylradical) free radical scavenging, ABTS (2,2-azinobis (3-ethylbenzothiazoline)-6-sulfonic acid) radical scavenging, and reducing power assays were performed, and the antioxidant activity of red pakchoi baby leaves grown under red + blue LED light was found to be the best. The metabolic profiling of the identified metabolites revealed distinct separation based on the secondary metabolites. This research will be helpful for farmers to choose the best LED light combination to increase the secondary metabolic content in pakchoi plants.

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“…Most VIs are ratios or linear combinations of spectral reflectance in two or more wavelength bands [ 39 ] that can be obtained from HSI [ 37 , 40 ]. Consisting of a straightforward transformation of spectral bands, VIs are estimated without bias or presumptions about land cover class, soil type, or climate conditions—enabling the observations of seasonal and long-term changes in a vegetation’s structural, phenological, and biophysical characteristics [ 41 , 42 , 43 ]. VIs have proven to be a valuable tool for monitoring plant health and productivity, and they are widely applied in fields such as agriculture, forestry, and ecology.…”

Section: Introductionmentioning

confidence: 99%

Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System

Pengphorm,

Thongrom,

Daengngam

et al. 2024

Plants

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Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand’s unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (R2=0.78 and RMSE = 2.4 µg∙cm−2), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions.

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Utilization of Spectral Indices for High-Throughput Phenotyping

Cited by 28 publications

References 126 publications

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Effect of LED Lights on Secondary Metabolites and Antioxidant Activities in Red Pakchoi Baby Leaves

Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System

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