The Assessment of Leaf Nitrogen in Corn from Digital Images

Rorie, Robert L.; Purcell, Larry C.; Karcher, Douglas E.; King, C. Andy

doi:10.2135/cropsci2010.12.0699

Cited by 57 publications

(54 citation statements)

References 20 publications

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“…Two spectral indices were selected for analysis, the dark green color index (DGCI) and the triangular greenness index (TGI), because both indices are calculated with red, green, and blue bands (Karcher and Richardson, 2003;Rorie et al, 2011aRorie et al, , 2011bHunt et al, 2011Hunt et al, , 2013. The DGCI was calculated by first transforming R, G, and B values into hue, saturation, and brightness values: …”

Section: Image Analysesmentioning

confidence: 99%

“…Videography and photography have become widespread for low-cost analysis of green leaf color (Spomer et al, 1988;Kawashima and Nakatani, 1998;Adamsen et al, 1999;Scharf and Lory, 2002;Karcher and Richardson, 2003;Dani et al, 2005;Rorie et al, 2011aRorie et al, , 2011b. Our hypothesis was that digital color photographs of leaves with the LCC could provide an objective, reproducible, and potentially automated method for determining LCC values in maize.…”

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confidence: 99%

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Assessment of Leaf Color Chart Observations for Estimating Maize Chlorophyll Content by Analysis of Digital Photographs

Friedman

Hunt

Mutters³

2016

Agronomy Journal

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Developed as a nondestructive aid for estimating the N content in rice (Oryza sativa L.) crops, leaf color charts (LCCs) are a numbered series of plastic panels that range from yellow‐green to dark green. By visual comparison, the panel value closest in color to a leaf indicates whether N is deficient, sufficient, or in excess. Because the selected values depend on subjective decisions by an observer, our goal was to determine whether spectral reflectances or digital color photographs could provide an objective, reproducible, and potentially automated method for determining LCC values. Maize (Zea mays L.) leaves were collected on two dates from an ongoing N fertilization experiment. Observed LCC panel values of selected leaves were highly correlated to chlorophyll content and chlorophyll meter values. Spectral reflectances and digital photographs were analyzed to predict the LCC panel value that was observed for each leaf. Supervised classifications of digital photographs using minimum distance provided reasonable predictions of the LCC value, but the spectral angle mapper did not. The dark green color index and the triangular greenness index predicted LCC panel values well. Uncorrected digital photographs (raw) produced better agreement with observed LCC panel values when using spectral indices, whereas color‐corrected photographs (JPEG) produced better agreement using the supervised classification methods. We concluded that subjective visual observations using a LCC were not worse than the objective methods for estimating leaf chlorophyll content. Visual observations using LCCs may be an easy‐to‐use and low‐cost method for managing N at smaller scales.

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Section: Image Analysesmentioning

confidence: 99%

mentioning

confidence: 99%

Assessment of Leaf Color Chart Observations for Estimating Maize Chlorophyll Content by Analysis of Digital Photographs

Friedman

Hunt

Mutters³

2016

Agronomy Journal

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“…The nitrogen content in the leaves can also be determined by analysing the distribution of colour components of the image of a single leaf or group of plants (Kawashima, Nakatan, 1998;Mercado-Luna et al, 2010;Rorie et al, 2011;Yuzhu et al, 2011;Wang et al, 2014). The aim of the study was to evaluate whether the concentration of nitrogen in apple trees leaves could be reliably estimated with an image analysis method using a flatbed scanner.…”

Section: Introductionmentioning

confidence: 99%

Possibilities of using image analysis to estimate the nitrogen nutrition status of apple trees

Treder

Klamkowski

Kowalczyk

et al. 2016

Zemdirbyste-Agriculture

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Precise timing and rate of nitrogen (N) fertilizer application play a major role for crop yield and quality. Prediction of N requirements is necessary for efficient utilization of nitrogen fertilizers. A user-friendly method for estimating foliar nitrogen concentration, based on an analysis of leaf blade colour, was developed in the study. Apple tree (Malus domestica Borkh.) was used as a model species for the validation of the proposed method. Digital recording of adaxial leaf surface was performed using a flatbed scanner. The acquired images were analysed to obtain average values of basic colours: red, green and blue (RGB). Analyses of the relationships between N concentration in the leaves, leaf greenness indices (determined with SPAD-502 and CCM-200 meters) and RGB colour components were performed. The best fitted models were: a multiple regression model based on RGB primary colour values and a model that included the values of the red and blue colours as well as a parameter describing the age of the leaf. The performed tests showed that the analysis of scanned images provided a more precise determination of leaf nitrogen content in apple trees compared to the measurements conducted with leaf greenness meters.

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“…For example, Normalized Green Red difference index (NGRDI) shows sensitivity to different chlorophyll concentration in leaves (Hunt et al, 2005). Rorie et al, (2011a;2011b) found that Dark Green Colour index (DGCI) have a close association with leaf N in corn. Furthermore, Hunt (2013) developed the Triangular Greenness Index (TGI) based on Vis bands for determining status of chlorophyll content in crops (R 2 = 0.85).…”

Section: Introductionmentioning

confidence: 99%

Novel Approach for Estimating Nitrogen Content in Paddy Fields Using Low Altitude Remote Sensing System

Saberioon¹,

Gholizadeh²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/VbKEY WORDS: Low altitude remote sensing, Unmanned aerial vehicle, Rice, Vegetation index, Nitrogen ABSTRACT:Concerns over the use of nitrogen have been increasing due to the high cost of fertilizers and environmental pollutions caused by excess nitrogen application in agricultural fields. Several methods are available to assess the amount of nitrogen in crops, however, they are expensive, time-consuming, inaccurate, and/or require specialists to operate the tools. Researcher recently suggested remote sensing and specifically Low Altitude Remote Sensing (LARS) system of chlorophyll content in crop canopies as a low-cost alternative to estimate plant nitrogen status. The main objective of this study was to develop and test a new Vegetation Index (VI) to determine the status of nitrogen and chlorophyll content in rice leaf by analysing and considering all Visible (Vis) bands. Besides, capability of introduced VI has compared with all known VIs in both Vis and Near Infrared (NIR) bands in canopy scale. To develop the VI, images from 6-pannel leaf colour chart were acquired using Basler Scout scA640-70fc under light-emitting diode lighting, in which principal component analysis was used to retain the lower order principal component to develop a new index called I PCA . A conventional digital camera mounted to an Unmanned Aerial Vehicle (UAV) was also used to acquire images over the rice canopy in Vis bands. Simultaneously, Tetracam agriculture digital camera was employed to acquire rice canopy image in Vis-NIR bands. The results indicated that the proposed index at canopy (r = 0.78) scale could be used as a sensor to determine the status of chlorophyll content consequently for monitoring nitrogen in rice plant through different growth stages. Moreover, results confirmed that a lowcost LARS system would be suited for high spatial and temporal resolution images and data analysis for proper assessment of key nutrients in crop farming in a fast, inexpensive and non-destructive way.

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The Assessment of Leaf Nitrogen in Corn from Digital Images

Cited by 57 publications

References 20 publications

Assessment of Leaf Color Chart Observations for Estimating Maize Chlorophyll Content by Analysis of Digital Photographs

Assessment of Leaf Color Chart Observations for Estimating Maize Chlorophyll Content by Analysis of Digital Photographs

Possibilities of using image analysis to estimate the nitrogen nutrition status of apple trees

Novel Approach for Estimating Nitrogen Content in Paddy Fields Using Low Altitude Remote Sensing System

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