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Comparative Study
. 2021 Jan 7;21(1):28.
doi: 10.1186/s12870-020-02807-4.

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Anna Siedliska  1 , Piotr Baranowski  1 , Joanna Pastuszka-Woźniak  1 , Monika Zubik  2 , Jaromir Krzyszczak  3
Affiliations
Comparative Study

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Anna Siedliska et al. BMC Plant Biol. .

Abstract

Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content.

Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants.

Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

Keywords: Hyperspectral imaging; Phosphorus fertilization; Precision agriculture; Supervised classification.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Measured content of Chlorophyll a, Chlorophyll b, total Chlorophyll and Carotenoids in sugar beet (a), celery (b) and strawberry (c) plants under different P applications. Bars followed by the same letter do not differ statistically by Tukey’s test at p=0.05
Fig. 2
Fig. 2
Effect of phosphorus treatment on N, P, K, Ca and Mg in leaf samples determined by traditional methods. Bars followed by the same letter are not significantly different according to Tukey’s test (p< 0.05)
Fig. 3
Fig. 3
Correlations between carotenoids (Car), chlorophyll (Chltot), magnesium (Mg), calcium (Ca), potassium (K), phosphorus (P), nitrogen (N) content in leaf, leaf mass (mleaf), root mass (mroot) and phosphorus supplementation (Psuppl)
Fig. 4
Fig. 4
General scheme of the procedure to generate spectral characteristics from hyperspectral images of the three studied plants
Fig. 5
Fig. 5
Average reflectance spectra of sugar beet (a), celery (b) and strawberry plants (c) grown under different phosphorus (P) fertilization rates obtained for third development stage. Each line correspond to the spectral characteristics averaged for four plants from each variants of the experiment
Fig. 6
Fig. 6
Second derivative transformed spectra of sugar beet (a), celery (b) and strawberry plants (c) grown under different phosphorus (P) fertilization rates obtained for third development stage
Fig. 7
Fig. 7
Numbers of misclassified cases in a validation dataset for random forest (RF) models of P content in plant treatment for 5 stages of plant growth and 3 studied species
See this image and copyright information in PMC

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