Leaf Colour Chart vis-a-vis Nitrogen Management in Different Rice Genotypes

Sen, Avijit; Srivastava, Vinod K.; Singh, Manoj Kumar; Singh, Ram Kumar; Kumar, Suneel

doi:10.4236/ajps.2011.22024

Cited by 25 publications

(8 citation statements)

References 13 publications

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“…In case of the rice, the medium rate (25 kg ha −1 ) of N topdressing at medium SPAD demonstrated grain and straw yields comparable to FTNM and saved 33.3% fertilizer N. Whereas, in case of wheat, the medium rate produced 13.4% higher grain yield with18.8% less N fertilizer as compared to FTNM. In SPAD-based N management treatments, higher or comparable grain yield with less fertilizer N to that of the FTNM implies a better translation of fertilizer N into grain yield when applied on the basis of the need of the crop (Ghosh et al, 2013;Li et al, 2009;Sen et al, 2011). Similar yield advantages with 20-30% less N fertilizer use were demonstrated previously in rice (Wang et al, 2001;Singh et al, 2010) as compared to wheat where the yield advantages were achieved with 10-12% less N fertilizer use (Diacono et al, 2013;Jia et al, 2007;Peng et al, 2012) due to SPAD-based N management in RTNM over that of FTNM.…”

Section: Discussionmentioning

confidence: 99%

Optimizing chlorophyll meter (SPAD) reading to allow efficient nitrogen use in rice and wheat under rice-wheat cropping system in eastern India

Ghosh

Swain

Jha

et al. 2020

Plant Production Science

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Conventional agricultural practices that rely heavily on blanket fertilizer recommendation, eventually leading to deteriorated partial factor productivity and N use efficiency. We investigated the effect of SPAD-based N-management on productivity and N use efficiency of rice and wheat in eastern India. Here, in the experiment three SPAD thresholds (34, 36 and 38 in rice and 38, 40 and 42 in wheat) using three N levels (15, 25 and 35 kg N ha −1 ) in split were incorporated as real-time N management (RTNM), one fixed-time N management (FTNM), farmers' fertilizer practice (FFP) and control (No fertilizer) were introduced in wet and dry seasons for rice and wheat, respectively, during the years 2010 to 2012. Topdressing with 25 kg N ha −1 at medium SPAD (S 36 in rice and S 40 in wheat) increased soil N availability, leaf N content and grain yield of rice (5215 kg ha −1 ) and wheat (4483 kg ha −1 ) over the grain yield recorded under a low rate of N topdressing at low SPAD. While saving 33.3% N in rice and 18.8% N in wheat, the agronomic N use efficiency (58.5% in both rice and wheat) and nitrogen recovery efficiency (32.2% in rice and 15.1% in wheat) can be increased when compared with conventional FTNM. The SPAD-based management strategy showed great promise in efficient management of N fertilizer, and we estimated the optimal SPAD threshold for rice and wheat as 37.5 and 41.8, respectively.

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Section: Discussionmentioning

confidence: 99%

Optimizing chlorophyll meter (SPAD) reading to allow efficient nitrogen use in rice and wheat under rice-wheat cropping system in eastern India

Ghosh

Swain

Jha

et al. 2020

Plant Production Science

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“…Assim como para o clorofilômetro, a tonalidade da cor crítica na cartela de cores precisa ser determinada, para adequar o manejo das aplicações de N (Singh et al, 2002), e também é necessário avaliar a capacidade da cartela de cores de estimar o teor de N no tecido vegetal. Sen et al (2011) encontraram correlação positiva entre os valores do clorofilômetro e de LCC, em três cultivares de arroz.…”

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Diagnose nutricional de nitrogênio em folhas de feijoeiro pela análise digital de imagens

Haim

Zoffoli

Zonta

et al. 2012

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar o uso da análise digital de imagens na diagnose nutricional de N no feijoeiro. Foram avaliados quatro tratamentos, em que se combinaram duas doses de N e de P aplicadas ao solo. Na emissão de vagens, determinou-se o índice de clorofila Falker, digitalizaram-se as imagens dos trifólios e determinou-se o teor foliar de N. Nas imagens, foi atribuída uma nota com o programa AFSoft, baseada na área ocupada por padrões de verde. O teor foliar de N correlacionou-se ao índice de clorofila Falker e à nota atribuída com o AFSoft, mas a correlação entre o índice de clorofila e a nota AFSoft foi superior.Termos para indexação: Phaseolus vulgaris, AFSoft, fósforo, identificação de cores, índice de clorofila. Diagnosis of nitrogen nutrition in common bean leaves by digital image analysisAbstract -The objective of this work was to evaluate the use of digital image analysis for the N nutritional diagnosis in common bean. Four treatments combining two levels of N and P applied to the soil were evaluated. At the pod setting stage, the Falker chlorophyll index was determined, the images of trifoliates were scanned, and leaf-N concentration was measured. In the images, a note with the software AFSoft was assigned based on the area occupied by green standards. Leaf-N content correlated to the Falker chlorophyll index and the AFSoft note, but the correlation between chlorophyll index and AFSoft note was higher.

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“…This chart has different shades of green, which are matched to the leaf color to get an estimate of nitrogen content in the leaf. Every crop has its leaf color chart with different shades of green according to its possible leaf colors [37]. This method is simple but not very accurate [21].…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves

et al. 2021

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Nitrogen is an essential nutrient element required for optimum crop growth and yield. If a specific amount of nitrogen is not applied to crops, their yield is affected. Estimation of nitrogen level in crops is momentous to decide the nitrogen fertilization in crops. The amount of nitrogen in crops is measured through different techniques, including visual inspection of leaf color and texture and by laboratory analysis of plant leaves. Laboratory analysis-based techniques are more accurate than visual inspection, but they are costly, time-consuming, and require skilled laboratorian and precise equipment. Therefore, computer-based systems are required to estimate the amount of nitrogen in field crops. In this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops. The system takes an image of the crop leaf as input and estimates the amount of nitrogen in it. The image is captured by placing the leaf on a specially designed slate that contains the reference green and yellow colors for that crop. The proposed algorithm automatically extracts the leaf from the image and computes its color similarity with the reference colors. In particular, we define a green color value (GCV) index from this analysis, which serves as a nitrogen indicator. We also present an evaluation of different color distance models to find a model able to accurately capture the color differences. The performance of the proposed system is evaluated on a Spinacia oleracea dataset. The results of the proposed system and laboratory analysis are highly correlated, which shows the effectiveness of the proposed system.

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Leaf Colour Chart vis-a-vis Nitrogen Management in Different Rice Genotypes

Abstract: ABSTRACT

Cited by 25 publications

References 13 publications

Optimizing chlorophyll meter (SPAD) reading to allow efficient nitrogen use in rice and wheat under rice-wheat cropping system in eastern India

Optimizing chlorophyll meter (SPAD) reading to allow efficient nitrogen use in rice and wheat under rice-wheat cropping system in eastern India

Diagnose nutricional de nitrogênio em folhas de feijoeiro pela análise digital de imagens

A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves

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