“…The evaporation of water is increased by global warming, which ultimately contributes to the severity of droughts. It is reasonable to predict that by the end of the twenty-first century, heat waves will become more frequent and intense [12]. Because of the temperature rise, there is a possibility that some significant changes in atmospheric conditions will occur, including sweltering summers and periods of drought [13].…”
This study aimed to assess the susceptibility of three strawberry cultivars (“Festival”, “Fortuna”, and “Rubygem”) to drought stress induced by varying polyethylene glycol (PEG) concentrations in the culture medium. Plantlets were cultivated on a solid medium supplemented with 1 mg/L BAP, and PEG concentrations (0, 2, 4, and 6 mg/L) were introduced to simulate drought stress. Morphological changes were observed, and morphometric analysis was conducted. Additionally, artificial neural network (ANN) analysis and machine learning approaches were integrated into this study. The results showed significant effects of PEG concentrations on plant height and multiplication coefficients, highlighting genotype-specific responses. This study employed various machine learning models, with random forest consistently demonstrating superior performance. Our findings revealed the random forest model outperformed others with a remarkable global diagnostic accuracy of 91.164%, indicating its superior capability in detecting and predicting water stress effects in strawberries. Specifically, the RF model excelled in predicting root length and the number of roots for “Festival” and “Fortuna” cultivars, demonstrating its reliability across different genetic backgrounds. Meanwhile, for the “Rubygem” cultivar, the multi-layer perceptron (MLP) and Gaussian process (GP) models showed particular strengths in predicting proliferation and plant height, respectively. These findings highlight the potential of ML models, particularly RF, to enhance agricultural breeding and cultivation strategies through accurate phenotypic predictions, suggesting a promising direction for future research to improve these predictions further. This research contributes to understanding strawberry responses to drought stress and emphasizes the potential of machine learning in predicting plant characteristics.
“…The evaporation of water is increased by global warming, which ultimately contributes to the severity of droughts. It is reasonable to predict that by the end of the twenty-first century, heat waves will become more frequent and intense [12]. Because of the temperature rise, there is a possibility that some significant changes in atmospheric conditions will occur, including sweltering summers and periods of drought [13].…”
This study aimed to assess the susceptibility of three strawberry cultivars (“Festival”, “Fortuna”, and “Rubygem”) to drought stress induced by varying polyethylene glycol (PEG) concentrations in the culture medium. Plantlets were cultivated on a solid medium supplemented with 1 mg/L BAP, and PEG concentrations (0, 2, 4, and 6 mg/L) were introduced to simulate drought stress. Morphological changes were observed, and morphometric analysis was conducted. Additionally, artificial neural network (ANN) analysis and machine learning approaches were integrated into this study. The results showed significant effects of PEG concentrations on plant height and multiplication coefficients, highlighting genotype-specific responses. This study employed various machine learning models, with random forest consistently demonstrating superior performance. Our findings revealed the random forest model outperformed others with a remarkable global diagnostic accuracy of 91.164%, indicating its superior capability in detecting and predicting water stress effects in strawberries. Specifically, the RF model excelled in predicting root length and the number of roots for “Festival” and “Fortuna” cultivars, demonstrating its reliability across different genetic backgrounds. Meanwhile, for the “Rubygem” cultivar, the multi-layer perceptron (MLP) and Gaussian process (GP) models showed particular strengths in predicting proliferation and plant height, respectively. These findings highlight the potential of ML models, particularly RF, to enhance agricultural breeding and cultivation strategies through accurate phenotypic predictions, suggesting a promising direction for future research to improve these predictions further. This research contributes to understanding strawberry responses to drought stress and emphasizes the potential of machine learning in predicting plant characteristics.
“…50 Despite the fact that they all belong to the pomelo group, they have genetic features that separate them into separate sub-groups, where C. auratifolia and C. latifolia are the closest, followed by C. sinensis , the most distant in a phylogenetic way. 50–52 Figure 4.Raman spectrum from different citrus species positive for HLB: Persian Lime ( C. latifolia ), Sweet Orange ( C. sinensis ), and Mexican Lime ( C. aurantifolia ).…”
This research describes the application of portable field Raman spectroscopy combined with a statistical analysis of the resulting spectra, employing principal component analysis (PCA) and linear discriminant analysis (LDA), in which we determine that this method provides a high degree of reliability in the early detection of Huanglongbing (HLB) on Sweet Orange, disease caused by the bacteria Candidatus Liberibacter asiaticus. Symptomatic and asymptomatic plant samples of Sweet Orange (Citrus sinensis), Persian Lime (C. latifolia), and Mexican Lime (C. aurantifolia) trees were collected from several municipalities, three at Colima State and three at Jalisco State (HLB presence). In addition, Sweet Orange samples were taken from two other Mexican municipalities, one at San Luis Potosí and the other at Veracruz (HLB absent). All samples were analyzed by real-time PCR to determine its phytosanitary condition, and its spectral signatures were obtained with an ID-Raman mini. Spectral anomalies in orange trees HLB-positive, were identified in bands related to carbohydrates (905 cm(-1), 1043 cm(-1), 1127 cm(-1), 1208 cm(-1), 1370 cm(-1), 1272 cm(-1), 1340 cm(-1), and 1260-1280 cm(-1)), amino acids, proteins (815 cm(-1), 830 cm(-1), 852 cm(-1), 918 cm(-1), 926 cm(-1), 970 cm(-1), 1002 cm(-1), 1053 cm(-1), and 1446 cm(-1)), and lipids (1734 cm(-1), 1736 cm(-1), 1738 cm(-1), 1745 cm(-1), and 1746 cm(-1)). Moreover, PCA-LDA showed a sensitivity of 86.9 % (percentage of positives, which are correctly identified), a specificity of 91.4 % (percentage of negatives, which are correctly identified), and a precision of 89.2 % (the proportion of all tests that are correct) in discriminating between orange plants HLB-positive and healthy plants. The Raman spectroscopy technique permitted rapid diagnoses, was low-cost, simple, and practical to administer, and produced immediate results. These are essential features for phytosanitary epidemiological surveillance activities that may conduct a targeted selection of highly suspicious trees to undergo molecular DNA analysis.
Phenylalanine ammonia lyase (PAL) is a specific branch point enzyme of primary and secondary metabolism. The Citrus reticulata Blanco PAL gene was cloned and designated as CrPAL1. The cDNA sequence of CrPAL1 was 2 166 bp, encoding 721 amino acid residues. Sequence alignment indicates that CrPAL1 shared a high identity with PAL genes found in other plants. Both the dominant and catalytic active sites of CrPAL1 were similar to PAL proteins observed in Petroselinum crispum. Phylogenetic tree analysis indicates that CrPAL1 was more closely related to PALs in Citrus clementina × C. reticulata and Poncirus trifoliata than to those from other plants. Subcellular localization reveals that CrPAL1-green fluorescent protein fusion protein was specifically localized in the plasma membrane. Activity of PAL as well as CrPAL1 expression increased under Fe deficiency. A similar result was noted for total phenolic content. The root exudates of C. reticulata strongly promoted reutilization of apoplastic Fe in roots. Furthermore, Fe was more desorbed from the cell wall under Fe deficiency than in sufficient Fe supply.
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