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Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

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Abstract

The potential of combining artificial neural networks (ANNs) and image processing for assessing leaf relative water content (RWC) and water content (WC) was addressed. Spathiphyllum wallisii was employed as model species, because it has broad leaves and very responsive stomata. In the course of desiccation, leaves were periodically weighted (to calculate RWC and WC conventionally) and imaged. Image acquisition was performed by a scanner, and was, thus, independent of ambient light environment. Color feature extraction was performed in three color spaces (RGB, HSI, and CIELAB), while six texture statistical features were calculated for each of the (nine) computed color channels. Prior to model development via ANNs, the obtained feature vector underwent feature reduction using principal component analysis. The presented methodology yielded very precise estimations of leaf RWC and WC (correlation coefficient > 0.95). Therefore, the technique under study was proven to be very promising for non-invasive in situ measurements of leaf water status.

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Abbreviations

ANN:

Artificial neural network

B:

Blue

CIE:

International Commission on Illumination

CIE L*a*b* or CIELAB:

CIE lightness: redness and yellowness

e :

Entropy

G:

Green

HSI:

Hue: saturation and intensity

m :

Mean

MAE:

Mean absolute error

MLP:

Multi-layer perceptron

MSE:

Mean square errors

PCA:

Principal component analysis

R:

Red

RGB:

Red: green and blue

R :

Smoothness

R 2 :

Coefficient of determination

R-value:

Pearson’s correlation coefficient

RMSE:

Root mean square error

RWC:

Relative water content

U :

Uniformity

WC:

Water content

δ :

Standard deviation

μ 3 :

Third moment

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Acknowledgements

This research was financed by grants from the Lorestan University (Iran). We thank the laboratory staff for their contributions, continued diligence, and dedication to their craft. The authors also wish to express their gratitude to Dr Amy Kaleita for her constructive recommendations and invaluable assistance. The valuable comments of the editor and two anonymous reviewers are greatly acknowledged.

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Authors and Affiliations

  1. Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran

    Amin Taheri-Garavand & Soodabeh Fatahi

  2. Department of Horticultural Sciences, Faculty of Agriculture, Lorestan University, P.O. Box 465, Khorramabad, Iran

    Abdolhossein Rezaei Nejad & Masoumeh Ahmadi Majd

  3. Laboratory of Quality and Safety of Agricultural Products, Landscape and Environment, Department of Agriculture, School of Agricultural Sciences, Hellenic Mediterranean University, Estavromenos, 71004, Heraklion, Greece

    Dimitrios Fanourakis

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  1. Amin Taheri-Garavand
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  2. Abdolhossein Rezaei Nejad
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  4. Soodabeh Fatahi
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Correspondence to Abdolhossein Rezaei Nejad.

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Taheri-Garavand, A., Rezaei Nejad, A., Fanourakis, D. et al. Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii. Acta Physiol Plant 43, 78 (2021). https://doi.org/10.1007/s11738-021-03244-y

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