Calibration of non-invasive fluorescence-based sensors for the manual and on-the-go assessment of grapevine vegetative status in the field

Diago, María P.; Rey-Caramés, Clara; Moigne, Marine Le; Fadaili, E.M.; Tardáguila, Javier; Cerović, Zoran G.

doi:10.1111/ajgw.12228

Cited by 24 publications

(23 citation statements)

References 44 publications

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“…The N treatment effects were more significant for the readings obtained in the OG mode than those collected in the AC mode (Table 3, Figure 2), which is different from the finding by Diago et al [39] who reported a 20% loss of information occurred when using the Multiplex on-the-go (compared to the AC mode) for N assessment of grapevine. This is because the OG measurements in this study were taken manually by placing the Multiplex sensor right on the top of the rice rather than a small distance above the rice canopy while passing through the rice paddy.…”

Section: Multiplex Measurement Modes and Estimation Of Crop N Indicatcontrasting

confidence: 85%

“…This is because the OG measurements in this study were taken manually by placing the Multiplex sensor right on the top of the rice rather than a small distance above the rice canopy while passing through the rice paddy. In contrast, in the study by Diago et al [39], the Multiplex sensor was mounted onto an all-terrain vehicle and placed 1.5 m above the ground so that the leaves on the mid-part of the canopy were automatically measured at a 20 cm distance, the same measuring distance as their AC mode. In addition, this study revealed that measurements made using the LS mode were the least sensitive to N supply, contrasting to the result by Zhang et al [41] who found Multiplex measurements made from corn leaves were more capable of distinguishing plant N status than those made from above the plants.…”

Section: Multiplex Measurement Modes and Estimation Of Crop N Indicatmentioning

confidence: 99%

“…Furthermore, Multiplex is a canopy sensor that is more efficient than a leaf sensor such as Dualex. It allows rapid large-area measurements with simultaneous GPS data recording for field map generation [39,40]. However, studies based on Multiplex are still limited, especially for rice N status diagnosis and precision N management.…”

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confidence: 99%

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In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

et al. 2019

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Precision nitrogen (N) management requires an accurate and timely in-season assessment of crop N status. The proximal fluorescence sensor Multiplex ® 3 is a promising tool for monitoring crop N status. It performs a non-destructive estimation of plant chlorophyll, flavonol, and anthocyanin contents, which are related to plant N status. The objective of this study was to evaluate the potential of proximal fluorescence sensing for N status estimation at different growth stages for rice in cold regions. In 2012 and 2013, paddy rice field experiments with five N supply rates and two varieties were conducted in northeast China. Field samples and fluorescence data were collected in the leaf scale (LS), on-the-go (OG), and above the canopy (AC) modes using Multiplex ® 3 at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. The relationships between the Multiplex indices or normalized N sufficient indices (NSI) and five N status indicators (above-ground biomass (AGB), leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI)) were evaluated. Results showed that Multiplex measurements taken using the OG mode were more sensitive to rice N status than those made in the other two modes in this study. Most of the measured fluorescence indices, especially the N balance index (NBI), simple fluorescence ratios (SFR), blue-green to far-red fluorescence ratio (BRR_FRF), and flavonol (FLAV) were highly sensitive to N status. Strong relationships between these fluorescence indices and N indicators, especially the LNC, PNC, and NNI were revealed, with coefficients of determination (R 2 ) ranging from 0.40 to 0.78. The N diagnostic results indicated that the normalized N sufficiency index based on NBI under red illumination (NBI_R NSI ) and FLAV achieved the highest diagnostic accuracy rate (90%) at the SE and HE stages, respectively, while NBI_R NSI showed the highest diagnostic consistency across growth stages. The study concluded that the Multiplex sensor could be used to reliably estimate N nutritional status for rice in cold regions, especially for the estimation of LNC, PNC, and NNI. The normalized N sufficiency indices based on the Multiplex indices could further improve the accuracy of N nutrition diagnosis by reducing the influences of inter-annual variations and different varieties, as compared with the original Multiplex indices.3 of 21 their photoprotective roles [31]. These compounds are mainly concentrated in epidermal cells and have typical absorption peaks in the ultraviolet region [31][32][33]. Thus, N status diagnosis is improved by combining the polyphenol and chlorophyll fluorescence [34,35]. Lejealle et al. [36] demonstrated that the N balance index (NBI), the ratio of chlorophyll to flavonol, had a better and more stable correlation with leaf N concentration. Leaf fluorescence sensor Dualex (FORCE-A, Orsay, Paris, France) and canopy fluorescence sensor Multiplex (FORCE-A, Orsay, Paris, France) can be used to estimate plant polyphenol conte...

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Section: Multiplex Measurement Modes and Estimation Of Crop N Indicatcontrasting

confidence: 85%

Section: Multiplex Measurement Modes and Estimation Of Crop N Indicatmentioning

confidence: 99%

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In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

et al. 2019

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“…The use of Mx as an on-the-go sensor mounted on a harvester has also been demonstrated (Bramley et al, 2011). Besides determining Anth, the Mx acts as a tool for characterizing the spatial variability of the vegetative status of the vineyard (Diago et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Prediction models for assessing anthocyanins in grape berries by fluorescence sensors: Dependence on cultivar, site and growing season

et al. 2018

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Fluorescence sensors are useful tools for the non-destructive assessment of grape berry anthocyanins. The Multiplex (Mx) sensor here studied provides two anthocyanin indices: ANTH = log(1/Chl-fluorescence_R) and ANTH = log(Chl-fluorescence_R/Chl-fluorescence_G), based on the chlorophyll (Chl) fluorescence excited with red (R) and green (G) light. These indices were calibrated against wet chemistry. The dependence of anthocyanin prediction models on cultivar, season and site was studied on four cultivars in two Italian regions during three consecutive years. The 2010 global model (all cultivars at both growing sites) gave relative prediction errors on anthocyanin content less than 14.1% (ANTH) and 19.0% (ANTH). The ANTH was independent of season, maintaining a relative error of about 20% in both 2011 and 2012. In field applications of the calibrated Mx, it showed its ability to detect inter-plot and inter-season differences on both growing sites.

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“…The tools normally used in viticulture [the SPAD‐502 (Steele et al , Taskos et al ) and the N‐tester (Tregoat et al )] showed some limitations such as a low sensitivity at early stages and a CHL saturation restricting its use for the detection of overfertilisation [see review by Muñoz‐Huerta et al ()]. In recent years, there has been a growing interest in the application of new optical sensors (Multiplex Research and Dualex Scientific) (Force‐A, Orsay, France) measuring the leaf CHL and flavonol (FLAV) (Cerovic et al , Diago et al ). A ratio, the N balance index (NBI = CHL/FLAV), would be a better indicator of the N status of the plant than the two factors independently (Cartelat et al , Cerovic et al , , Tremblay et al ).…”

Section: Introductionmentioning

confidence: 99%

Grapevine rootstocks result in differences in leaf composition (Vitis viniferaL. cv. Pinot Noir) detected through non-invasive fluorescence sensor technology

Lafontaine

Tittmann

Ghozlen

et al. 2018

Australian Journal of Grape and Wine Research

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Background and Aims:The main objective of the study was to test if differences in leaf composition induced by the rootstock could be detected through non-invasive fluorescence sensor technology. Methods and Results: Multiplex Research measurements on three dates over three consecutive growing seasons showed that Pinot Noir (Vitis vinifera L.) grafted onto high vigour rootstocks (125AA and SO4 with double the amount of pruning mass) had also leaves with high chlorophyll index and low flavonol index when compared to that of Riparia and Schwarzmann. Multiplex measurements 'on-the-go' gave similar results. Amino acids in juices, however, was not directly related to vine vigour as values for R110 and Riparia were significantly lower than that for 125AA. Conclusions: The applicability of new optical sensors used both manually or 'on-the-go' to characterise rootstock influence on scion leaf composition was established and could be related to vine vigour conferred by the rootstock. Significance of the Study: Our study provides evidence that non-invasive fluorescence sensors used for the first time over consecutive growing seasons and dates are able to reveal differences in scion leaf composition induced by the rootstock in relation to vine vigour and to provide the basis for a better understanding of its impact on fruit and wine composition.

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Calibration of non-invasive fluorescence-based sensors for the manual and on-the-go assessment of grapevine vegetative status in the field

Cited by 24 publications

References 44 publications

In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

Prediction models for assessing anthocyanins in grape berries by fluorescence sensors: Dependence on cultivar, site and growing season

Grapevine rootstocks result in differences in leaf composition (Vitis viniferaL. cv. Pinot Noir) detected through non-invasive fluorescence sensor technology

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