Improving in vivo plant nitrogen content estimates from digital images: Trueness and precision of a new approach as compared to other methods and commercial devices

Confalonieri, Roberto; Paleari, Livia; Movedi, Ermes; Pagani, Valentina; Orlando, Francesca; Foi, Marco; Barbieri, Michela; Pesenti, Michele; Cairati, Oliver; Sala, M.; Besana, Riccardo; Minoli, Sara; Bellocchio, Eleonora; Croci, S.; Mocchi, Silvia; Lampugnani, Francesca; Lubatti, Alberto; Quarteroni, Andrea; Min, Daniele De; Signorelli, Alessandro; Ferri, Alessandro; Ruggeri, Giordano; Locatelli, Simone; Bertoglio, Matteo; Dominoni, P.; Bocchi, Stefano; Sacchi, Gian Attilio; Acutis, Marco

doi:10.1016/j.biosystemseng.2015.04.013

Cited by 29 publications

(26 citation statements)

References 38 publications

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“…The CHL and HNT indices generally showed a statistically significant correlation with N content per sampling date, but for some early dates (25 DAE for Charlotte in 2012 and 13 DAE in 2013) the correlation was not significant or very small. The precision of the NBI index measured by Dualex and the chlorophyll index measured by SPAD, the other version of HNT chlorophyll-meter, was also assessed by Confalonieri et al (2015). In this latter study, the NBI demonstrated the highest correlation (per shoots or plant biomass N content) to the chlorophyll-meter, as was also the case in our study.…”

Section: Effect Of N Rate On the Nitrogen Nutrition Index (Nni)supporting

confidence: 80%

“…With the exception of some Multiplex indices (SFR-G and NBI-G) on the last measurement date in 2012 and some Cropscan indices at 46 DAE, the coefficients of variation indicated an acceptable range of repeatability between repetitions. Confalonieri et al (2015) found that the chlorophyll index (SPAD index measured by chlorophyll-meter) demonstrated higher repeatability than the chlorophyll-and flavonoid-based index (NBI). In this latter study, repeatability was calculated as a function of the standard deviation of repeatability (Sr) and was assessed by performing repeated optical measurements under the same conditions by the same operator.…”

Section: Effect Of N Rate On the Nitrogen Nutrition Index (Nni)mentioning

confidence: 99%

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Comparison of optical indicators for potato crop nitrogen status assessment including novel approaches based on leaf fluorescence and flavonoid content

Abdallah

Philippe

Goffart

2018

Journal of Plant Nutrition

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Some phenolic compounds are proposed as good indicators of crop nitrogen status (CNS). This research compared the use of leaf flavonoid content (LFC) as a potential indicator for evaluating potato CNS with other recognized indicators linked to leaf chlorophyll content (LCC). Three-year trials were conducted in Belgium on two potato cultivars including increasing nitrogen rates. Optical sensors (Dualex, Multiplex, Hydro N-tester, Cropscan) were used to evaluate LFC and/or LCC. Plant tissue samples were analyzed to calculate the biomass nitrogen concentration and the nitrogen nutrition index. The indicators were evaluated based on four criteria: the sensitivity, the earliness of the diagnosis, the accuracy, and the specificity. Apart from the low specificity to nitrogen, which can be improved by the use of a within-field reference plot, the LFC (combined with LCC or individually) matched the required criteria and could be suggested as a valuable indicator for assessing potato CNS.

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Section: Effect Of N Rate On the Nitrogen Nutrition Index (Nni)supporting

confidence: 80%

Section: Effect Of N Rate On the Nitrogen Nutrition Index (Nni)mentioning

confidence: 99%

Comparison of optical indicators for potato crop nitrogen status assessment including novel approaches based on leaf fluorescence and flavonoid content

Abdallah

Philippe

Goffart

2018

Journal of Plant Nutrition

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“…Many studies have investigated the relationships between leaf N concentrations and SPAD readings in the past 20 years, and it has been universally accepted that there were significant relationships between leaf N concentrations and SPAD readings as well as leaf chlorophyll concentrations (Castelli et al, 1996;Huang et al, 2008;Lavres and Monteiro, 2010;Wang et al, 2014). In addition to SPAD-502, visible and near infrared (VIS-NIR) hyperspectral imaging system, 18%-gray dark green color index (DGCI), and so on had been used to diagnose the leaf N status (Rotbart et al, 2013;Zhang et al, 2013;Confalonieri et al, 2015;Yuan et al, 2016). Numerous studies have shown that the application of N could increase chlorophyll concentrations (Li et al, 2009;Gu et al, 2017;Liu et al, 2017).…”

Section: Effects Of Nitrogen and Potassium On Leaf Greennessmentioning

confidence: 99%

Diagnosis of Nitrogen Nutrition in Rice Leaves Influenced by Potassium Levels

Hou

Tränkner

et al. 2020

Front. Plant Sci.

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Evaluation of nitrogen (N) status by leaf color is a kind of classic nutritional diagnostic method. However, the color of leaves is influenced not only by N, but also by other nutrients such as potassium (K). Two-year field trials with a factorial combination of N and K were conducted to investigate the effects of different N and K rates on soil plant analysis development (SPAD) readings and leaf N, K, magnesium (Mg), and chlorophyll concentrations. Visual inspections in leaf greenness revealed darker green leaves with increasing N rates, while paler green leaves with increasing K rates. Data showed that SPAD readings, chlorophyll, N and Mg concentrations, and the chloroplast area increased significantly with raising N rates, while declined sharply with the increase in K rates due to the antagonistic relationships between K + and NH 4 + as well as Mg 2+. It was also probable that the increase in K promoted the growth of leaves and diluted their N and Mg concentrations. The paler leaf appearance resulting from the application of K may overestimate the actual demand for N in the diagnosis of rice N status. The strong antagonistic relationships between K + , NH 4 + , and Mg 2+ should be considered in rice production and fertilization.

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“…Confalonieri et al . [ 21 ] estimated leaf and plant nitrogen content using an 18%-gray dark green color index (DGCI) method. Compared to the DGCI and the corrected DGCI, the new method is considerably more stable with regard to both trueness and precision.…”

Section: Introductionmentioning

confidence: 99%

Preliminary research on total nitrogen content prediction of sandalwood using the error-in-variable models based on digital image processing

Chen

Wang

2018

PLoS ONE

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This paper presents a method for predicting the total nitrogen content in sandalwood using digital image processing. The goal of this study is to provide a real-time, efficient, and highly automated nutritional diagnosis system for producers by analyzing images obtained in forests. Using images acquired from field servers, which were installed in six forest farms of different cities located in northern Hainan Province, we propose a new segmentation algorithm and define a new indicator named “growth status" (GS), which includes two varieties: GSMER (the ratio of sandalwood pixels to the minimum enclosing rectangle pixels) and GSMCC (the ratio of sandalwood pixels to minimum circumscribed circle pixels). We used the error-in-variable model by considering the errors that exist in independent variables. After comparison and analysis, the obtained results show that (1) The b and L channels in the Lab color system have complementary advantages. By combining this system with the Otsu method, median filtering and a morphological operation, sandalwood can be separated from the background. (2) The fitting degree of the models improves after adding the GS indicator and shows that GSMCC performs better than GSMER. (3) After using the error-in-variable model to estimate the parameters, the accuracy and precision of the model improved compared to the results obtained using the least squares method. The optimal model for predicting the total nitrogen content is . This study demonstrates the use of Internet of Things technology in forestry and provides guidance for the nutritional diagnosis of the important sandalwood tree species.

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Improving in vivo plant nitrogen content estimates from digital images: Trueness and precision of a new approach as compared to other methods and commercial devices

Cited by 29 publications

References 38 publications

Comparison of optical indicators for potato crop nitrogen status assessment including novel approaches based on leaf fluorescence and flavonoid content

Comparison of optical indicators for potato crop nitrogen status assessment including novel approaches based on leaf fluorescence and flavonoid content

Diagnosis of Nitrogen Nutrition in Rice Leaves Influenced by Potassium Levels

Preliminary research on total nitrogen content prediction of sandalwood using the error-in-variable models based on digital image processing

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