TY  - JOUR
AU  - Đalović, Ivica
AU  - Mitrović, Petar
AU  - Trivan, Goran
AU  - Jelušić, Aleksandra
AU  - Pezo, Lato
AU  - Janić Hajnal, Elizabet
AU  - Popović Milovanović, Tatjana
PY  - 2024
UR  - https://plantarum.izbis.bg.ac.rs/handle/123456789/1246
AB  - Infections with phytoplasma present one of the most significant biotic stresses influencing
plant health, growth, and production. The phytoplasma ‘Candidatus Phytoplasma solani’ infects
a variety of plant species. This pathogen impacts the physiological and morphological characteristics
of plants causing stunting, yellowing, leaf curling, and other symptoms that can lead to
significant economic losses. The aim of this study was to determine biochemical changes in peony
(Paeonia tenuifolia L.), mint (Mentha × piperita L.), and dill (Anethum graveolens L.) induced by ‘Ca.
Phytoplasma solani’ in Serbia as well as to predict the impact of the biotic stress using artificial neural
network (ANN) modeling. The phylogenetic position of the Serbian ‘Ca. Phytoplasma solani’ strains
originated from the tested hosts using 16S rRNA (peony and carrot strains) and plsC (mint and dill
strains) sequences indicated by their genetic homogeneity despite the host of origin. Biochemical
parameters significantly differed in asymptomatic and symptomatic plants, except for total anthocyanidins
contents in dill and the capacity of peony and mint extracts to neutralize superoxide anions
and hydroxyl radicals, respectively. Principal Component Analysis (PCA) showed a correlation
between different chemical parameters and revealed a clear separation among the samples. Based on
the ANN performance, the optimal number of hidden neurons for the calculation of TS, RG, PAL, LP,
NBT, •OH, TP, TT, Tflav, Tpro, Tant, DPPH, and Car was nine (using MLP 8-9-13), as it produced
high r2 values (1.000 during the training period) and low SOS values. Developing an effective early
warning system for the detection of plant diseases in different plant species is critical for improving
crop yield and quality.
PB  - MDPI Basel
T2  - Horticulturae
T1  - The Effect of Biotic Stress in Plant Species Induced by ‘Candidatus Phytoplasma solani’—An Artificial Neural Network Approach
IS  - 5
SP  - 426
VL  - 10
DO  - 10.3390/horticulturae10050426
ER  - 

@article{
author = "Đalović, Ivica and Mitrović, Petar and Trivan, Goran and Jelušić, Aleksandra and Pezo, Lato and Janić Hajnal, Elizabet and Popović Milovanović, Tatjana",
year = "2024",
abstract = "Infections with phytoplasma present one of the most significant biotic stresses influencing
plant health, growth, and production. The phytoplasma ‘Candidatus Phytoplasma solani’ infects
a variety of plant species. This pathogen impacts the physiological and morphological characteristics
of plants causing stunting, yellowing, leaf curling, and other symptoms that can lead to
significant economic losses. The aim of this study was to determine biochemical changes in peony
(Paeonia tenuifolia L.), mint (Mentha × piperita L.), and dill (Anethum graveolens L.) induced by ‘Ca.
Phytoplasma solani’ in Serbia as well as to predict the impact of the biotic stress using artificial neural
network (ANN) modeling. The phylogenetic position of the Serbian ‘Ca. Phytoplasma solani’ strains
originated from the tested hosts using 16S rRNA (peony and carrot strains) and plsC (mint and dill
strains) sequences indicated by their genetic homogeneity despite the host of origin. Biochemical
parameters significantly differed in asymptomatic and symptomatic plants, except for total anthocyanidins
contents in dill and the capacity of peony and mint extracts to neutralize superoxide anions
and hydroxyl radicals, respectively. Principal Component Analysis (PCA) showed a correlation
between different chemical parameters and revealed a clear separation among the samples. Based on
the ANN performance, the optimal number of hidden neurons for the calculation of TS, RG, PAL, LP,
NBT, •OH, TP, TT, Tflav, Tpro, Tant, DPPH, and Car was nine (using MLP 8-9-13), as it produced
high r2 values (1.000 during the training period) and low SOS values. Developing an effective early
warning system for the detection of plant diseases in different plant species is critical for improving
crop yield and quality.",
publisher = "MDPI Basel",
journal = "Horticulturae",
title = "The Effect of Biotic Stress in Plant Species Induced by ‘Candidatus Phytoplasma solani’—An Artificial Neural Network Approach",
number = "5",
pages = "426",
volume = "10",
doi = "10.3390/horticulturae10050426"
}

Đalović, I., Mitrović, P., Trivan, G., Jelušić, A., Pezo, L., Janić Hajnal, E.,& Popović Milovanović, T.. (2024). The Effect of Biotic Stress in Plant Species Induced by ‘Candidatus Phytoplasma solani’—An Artificial Neural Network Approach. in Horticulturae
MDPI Basel., 10(5), 426.
https://doi.org/10.3390/horticulturae10050426

Đalović I, Mitrović P, Trivan G, Jelušić A, Pezo L, Janić Hajnal E, Popović Milovanović T. The Effect of Biotic Stress in Plant Species Induced by ‘Candidatus Phytoplasma solani’—An Artificial Neural Network Approach. in Horticulturae. 2024;10(5):426.
doi:10.3390/horticulturae10050426 .

Đalović, Ivica, Mitrović, Petar, Trivan, Goran, Jelušić, Aleksandra, Pezo, Lato, Janić Hajnal, Elizabet, Popović Milovanović, Tatjana, "The Effect of Biotic Stress in Plant Species Induced by ‘Candidatus Phytoplasma solani’—An Artificial Neural Network Approach" in Horticulturae, 10, no. 5 (2024):426,
https://doi.org/10.3390/horticulturae10050426 . .