Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses

Agati, Giovanni; Foschi, Lara; Grossi, Nicola; Guglielminetti, Lorenzo; Cerović, Zoran G.; Volterrani, Marco

doi:10.1016/j.eja.2012.10.011

Cited by 78 publications

(74 citation statements)

References 64 publications

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“…In contrast with NBI G and NBI R , FLAV had higher, not lower values for the stressed plants. This agrees well with results from Agati et al [58]. Several authors have reported that FLAV correlates with the flavonoid levels in fruits [13,[60][61][62].…”

Section: Could Nitrogen Deficiency Stress Be Detected By the Sensors?supporting

confidence: 83%

“…The indices NBI G and NBI R clearly differentiated between N-stressed and non-stressed plants, giving lower index values for the stressed than the non-stressed plants. Similar findings have also been reported for bermudagras and turfgrasses [57,58]. Longchamps and Khosla [59] were able to distinguish between all their four N-levels in an experiment with maize, using NBI G and NBI R .…”

Section: Could Nitrogen Deficiency Stress Be Detected By the Sensors?supporting

confidence: 72%

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Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat

et al. 2016

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Abstract:The success of precision agriculture relies largely on our ability to identify how the plants' growth limiting factors vary in time and space. In the field, several stress factors may occur simultaneously, and it is thus crucial to be able to identify the key limitation, in order to decide upon the correct contra-action, e.g., herbicide application. We performed a pot experiment, in which spring wheat was exposed to water shortage, nitrogen deficiency, weed competition (Sinapis alba L.) and fungal infection (Blumeria graminis f. sp. tritici) in a complete, factorial design. A range of sensor measurements were taken every third day from the two-leaf stage until booting of the wheat (BBCH 12 to 40). Already during the first 10 days after stress induction (DAS), both fluorescence measurements and spectral vegetation indices were able to differentiate between non-stressed and stressed wheat plants exposed to water shortage, weed competition or fungal infection. This meant that water shortage and fungal infection could be detected prior to visible symptoms. Nitrogen shortage was detected on the 11-20 DAS. Differentiation of more than one stress factors with the same index was difficult.

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Section: Could Nitrogen Deficiency Stress Be Detected By the Sensors?supporting

confidence: 83%

Section: Could Nitrogen Deficiency Stress Be Detected By the Sensors?supporting

confidence: 72%

Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat

et al. 2016

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“…The fluorescence index indicative of leaf chlorophyll content (SFR_RAD), the normalised indices NVI1 and NVI2, berry weight, pH, TSS, and titratable acidity showed a CV smaller than 10%. NBI has been described as a good indicator of the nitrogen status of grapevine leaves [24,50,51]. Its calculation depends on the concentration of flavonoids and chlorophyll in the leaf, so its variability must be higher than that of chlorophyll and flavonoids alone [51].…”

Section: Spatial Variability Of Spectral Indices Field Variables Anmentioning

confidence: 99%

Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard

et al. 2015

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Implementation of precision viticulture techniques requires the use of emerging sensing technologies to assess the vineyard spatial variability. This work shows the capability of multispectral imagery acquired from a remotely piloted aerial system (RPAS), and the derived spectral indices to assess the vegetative, productive, and berry composition spatial variability within a vineyard (Vitis vinifera L.). Multi-spectral imagery of 17 cm spatial resolution was acquired using a RPAS. Classical vegetation spectral indices and two newly defined normalised indices, NVI1 = (R802 − R531)/(R802 + R531) and NVI2 = (R802 − R570)/(R802 + R570), were computed. Their spatial distribution and relationships with grapevine vegetative, yield, and berry composition parameters were studied. Most of the spectral indices and field data varied spatially within the vineyard, as showed through the variogram parameters. While the correlations were significant but moderate among the spectral indices and the field variables, the kappa index showed that the spatial pattern of the spectral indices agreed with that of the vegetative variables (0.38-0.70) and mean cluster weight (0.40). These results proved the utility of the OPEN ACCESSRemote Sens. 2015, 7 14459 multi-spectral imagery acquired from a RPAS to delineate homogeneous zones within the vineyard, allowing the grapegrower to carry out a specific management of each subarea.

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“…Blue/red (BF/RF) and blue/far-red (BF/FRF) fluorescence ratios and combined fluorescence indices also allow to detect various stresses [8,30] such as water [9] and nitrogen (N) deficiencies [11,31,32] and to monitor changes in chlorophyll and polyphenols [11,33]. However, studies on detecting cereal diseases or estimating chlorophyll content of barley plants are scarce.…”

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confidence: 99%

Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices

Leufen

Hunsche

et al. 2013

Remote Sensing

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Abstract:Leaf diseases, such as powdery mildew and leaf rust, frequently infect barley plants and severely affect the economic value of malting barley. Early detection of barley diseases would facilitate the timely application of fungicides. In a field experiment, we investigated the performance of fluorescence and reflectance indices on (1) detecting barley disease risks when no fungicide is applied and (2) estimating leaf chlorophyll concentration (LCC). Leaf fluorescence and canopy reflectance were weekly measured by a portable fluorescence sensor and spectroradiometer, respectively. Results showed that vegetation indices recorded at canopy level performed well for the early detection of slightly-diseased plants. The combined reflectance index, MCARI/TCARI, yielded the best discrimination between healthy and diseased plants across seven barley varieties. The blue to far-red fluorescence ratio (BFRR_UV) and OSAVI were the best fluorescence and reflectance indices for estimating LCC, respectively, yielding R 2 of 0.72 and 0.79. Partial

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Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses

Cited by 78 publications

References 64 publications

Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat

Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat

Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard

Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices

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