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Epidemiology of coffee leaf rust under the effect of fungicides in the Northern and Northeastern Sierra of Puebla state. Figure 3 - Effect of fungicides on the dynamics of rust (<em>H. vastatrix</em>) severity in coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1 (Costa Rica 95), B) Lipuntahuaca 2 (Typica), C) Escatachuchut 1 (Costa Rica 95), D) Escatachuchut 2 (Garnica), E) Zongozotla 1 (Marsellesa), and F) Zongozotla 2 (Oro Azteca). Arrows indicate the timing of fungicide application: orange: Azoxystrobin, gray: <em>M. alternifolia</em>, and green: <em>B. subtilis</em>. The asterisk (*) indicates significant differences between treatments at each sampling point, according to Tukey's mean comparison test.

Figure 3 - Effect of fungicides on the dynamics of rust (H. vastatrix) severity in coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1 (Costa Rica 95), B) Lipuntahuaca 2 (Typica), C) Escatachuchut 1 (Costa Rica 95), D) Escatachuchut 2 (Garnica), E) Zongozotla 1 (Marsellesa), and F) Zongozotla 2 (Oro Azteca). Arrows indicate the timing of fungicide application: orange: Azoxystrobin, gray: M. alternifolia, and green: B. subtilis. The asterisk (*) indicates significant differences between treatments at each sampling point, according to Tukey's mean comparison test.

Epidemiology of coffee leaf rust under the effect of fungicides in the Northern and Northeastern Sierra of Puebla state. Figure 4 - Effect of fungicides on the incidence dynamics of rust (<em>H. vastatrix</em>) in coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1 (Costa Rica 95), B) Lipuntahuaca 2 (Typica), C) Escatachuchut 1 (Costa Rica 95), D) Escatachuchut 2 (Garnica), E) Zongozotla 1 (Marsellesa), and F) Zongozotla 2 (Oro Azteca). Arrows indicate the timing of fungicide application: orange: Azoxystrobin, gray: <em>M. alternifolia</em>, and green: <em>B. subtilis</em>. The asterisk (*) indicates significant differences between treatments at each sampling point, according to Tukey's mean comparison test.

Figure 4 - Effect of fungicides on the incidence dynamics of rust (H. vastatrix) in coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1 (Costa Rica 95), B) Lipuntahuaca 2 (Typica), C) Escatachuchut 1 (Costa Rica 95), D) Escatachuchut 2 (Garnica), E) Zongozotla 1 (Marsellesa), and F) Zongozotla 2 (Oro Azteca). Arrows indicate the timing of fungicide application: orange: Azoxystrobin, gray: M. alternifolia, and green: B. subtilis. The asterisk (*) indicates significant differences between treatments at each sampling point, according to Tukey's mean comparison test.

Epidemiology of coffee leaf rust under the effect of fungicides in the Northern and Northeastern Sierra of Puebla state. Figure 1 - Temporal dynamics of rust (<em>H. vastatrix</em>) severity on coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla, without fungicide application. A) Severity, B) Incidence. The lines represent disease progression over time: Costa Rica 95 (blue), Typica (orange), Costa Rica 95 (gray), Garnica (yellow), Marsellesa (purple), and Oro Azteca (green).

Figure 1 - Temporal dynamics of rust (H. vastatrix) severity on coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla, without fungicide application. A) Severity, B) Incidence. The lines represent disease progression over time: Costa Rica 95 (blue), Typica (orange), Costa Rica 95 (gray), Garnica (yellow), Marsellesa (purple), and Oro Azteca (green).

First detection of bell pepper endornavirus infecting bell pepper in Guanajuato, Mexico. Figure 1 - Phylogenetic tree constructed using the Maximum Likelihood method based on the bell pepper endornavirus (BPEV, <em>Alphaendornavirus capsici</em>) polyprotein sequence and other Alphaendornavirus sequences available in GenBank. Reference sequences were selected from GenBank based on their annotation as BPEV isolates and sequence availability. The analysis was performed using the corresponding nucleotide substitution model. Bootstrap support values (1,000 replicates) are indicated at the nodes, showing only those greater than 50%. Sequence PZ103604 (indicated by an arrow) clusters with closely related sequences with high bootstrap support, suggesting a close phylogenetic relationship. Sequence DQ399290 (<em>Betaendornavirus</em>) was used as the outgroup. The scale bar represents 0.20 substitutions per site.

Figure 1 - Phylogenetic tree constructed using the Maximum Likelihood method based on the bell pepper endornavirus (BPEV, Alphaendornavirus capsici) polyprotein sequence and other Alphaendornavirus sequences available in GenBank. Reference sequences were selected from GenBank based on their annotation as BPEV isolates and sequence availability. The analysis was performed using the corresponding nucleotide substitution model. Bootstrap support values (1,000 replicates) are indicated at the nodes, showing only those greater than 50%. Sequence PZ103604 (indicated by an arrow) clusters with closely related sequences with high bootstrap support, suggesting a close phylogenetic relationship. Sequence DQ399290 (Betaendornavirus) was used as the outgroup. The scale bar represents 0.20 substitutions per site.

Use of <em>Purpureocillium lilacinum</em> MTL01 and <em>Bacillus velezensis</em> 83 to reduce galling by <em>Meloidogyne enterolobii</em> and improve flowering in tomato. Figure 1 - Isolation and characterization of <em>P. lilacinum</em> MTL01. <strong>A</strong>) MTL01 mycelium growing on an egg mass of <em>M. paranaensis</em>. <strong>B</strong>) Reproductive structures of MTL01: conidiophores, phialides, and conidia. <strong>C</strong> and <strong>D</strong>). Appearance of MTL01 on PDA medium: upper and lower views. <strong>E</strong>) <em>M. enterolobii</em> eggs parasitized by MTL01 on PDA medium. <strong>F</strong>) Parasitized egg from roots in Experiment 1. <strong>G</strong> and <strong>H</strong>) Parasitized adult females from roots in Experiment 1. <strong>I</strong>) Phylogeny of MTL01 compared with other strains and related species.

Figure 1 - Isolation and characterization of P. lilacinum MTL01. A) MTL01 mycelium growing on an egg mass of M. paranaensis. B) Reproductive structures of MTL01: conidiophores, phialides, and conidia. C and D). Appearance of MTL01 on PDA medium: upper and lower views. E) M. enterolobii eggs parasitized by MTL01 on PDA medium. F) Parasitized egg from roots in Experiment 1. G and H) Parasitized adult females from roots in Experiment 1. I) Phylogeny of MTL01 compared with other strains and related species.

Antifungal effect of clove essential oil and its main components on fungi isolated from corn tortillas. Figure 1 - Fungal structure by optic microscopy (40X), isolated from corn tortillas from Morelia, Mich (A: <em>Penicillium</em> sp. and F: <em>Mucor</em> sp.), Monterrey, N.L. (B: <em>Aspergillus</em> sp. and G: <em>Curvularia</em> sp.), Oaxaca, Oax (C: <em>Aspergillus</em> sp.), Hermosillo, Son (D: <em>Penicillium</em> sp. and H: <em>Aspergillus</em> sp.) and Mérida, Yuc (E: <em>Aspergillus</em> sp. and I: <em>Mucor</em> sp.).

Figure 1 - Fungal structure by optic microscopy (40X), isolated from corn tortillas from Morelia, Mich (A: Penicillium sp. and F: Mucor sp.), Monterrey, N.L. (B: Aspergillus sp. and G: Curvularia sp.), Oaxaca, Oax (C: Aspergillus sp.), Hermosillo, Son (D: Penicillium sp. and H: Aspergillus sp.) and Mérida, Yuc (E: Aspergillus sp. and I: Mucor sp.).

Characterization of endophytic bacteria growth-promoting in potato plants (<em>Solanum tuberosum</em>). Figure 1 - Neighbor-Joining Dendogram from the sequences of the gene that codifies the 16S subunit of the rDNA of endophytic bacteria.

Figure 1 - Neighbor-Joining Dendogram from the sequences of the gene that codifies the 16S subunit of the rDNA of endophytic bacteria.

The genus <em>Bacillus</em> as biological control agent against pests and pathogens for sustainable agriculture. Figure 2 - Cry proteins. 1-A) Primary structure, showing the organization of the domains of representative members from each Cry family, 2-A) Conserved tertiary structure, showing the positions of the three domains. B) Mechanism of action of Cry proteins in insects, initiating once the Cry proteins are proteolytically processed by proteases in the midgut of the host, separating an amino acid section in the N-terminal region and at the C-terminal end (depending on the nature of the Cry protein), thus releasing active and toxic fragments that interact with receptor proteins in the insect’s intestinal cells. Modified from Villarreal-Delgado <em>et al</em>., 2018.; de Maagd <em>et al</em>., 2001; Xu <em>et al</em>., 2014.

Figure 2 - Cry proteins. 1-A) Primary structure, showing the organization of the domains of representative members from each Cry family, 2-A) Conserved tertiary structure, showing the positions of the three domains. B) Mechanism of action of Cry proteins in insects, initiating once the Cry proteins are proteolytically processed by proteases in the midgut of the host, separating an amino acid section in the N-terminal region and at the C-terminal end (depending on the nature of the Cry protein), thus releasing active and toxic fragments that interact with receptor proteins in the insect’s intestinal cells. Modified from Villarreal-Delgado et al., 2018.; de Maagd et al., 2001; Xu et al., 2014.

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Vol. 2026, 44(3)
Phytopathological Note

Use of Purpureocillium lilacinum MTL01 and Bacillus velezensis 83 to reduce galling by Meloidogyne enterolobii and improve flowering in tomato

byElena Lugo García , Alejandro Salinas Castro , Gandhi Landa Cadena , Claudia Martínez Anaya , Irán Tapia Vázquez*

Received: 07/May/2026 – Published: 25/August/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2505-3

Abstract Background/Objective. The aim of this study was to determine the combined effect of an environmental strain of Purpureocillium lilacinum (MTL01) together with Bacillus velezensis 83, upon infection by the root-knot nematode Meloidogyne enterolobii.

Experimental development. Three greenhouse experiments were conducted at different times (July–October 2022, February–June 2023, and September 2024–January 2025). In each experiment, treatments included uninoculated controls, plants inoculated only with P. lilacinum MTL01, with B. velezensis 83 only, or with both microorganisms, with and without nematode infection. Certified tomato seeds were inoculated with MTL01 spores or solution as a control and transplanted after germination. B. velezensis 83 was inoculated one week later, followed by two additional applications. Nematodes were introduced three weeks after transplanting, and plants were subsequently maintained in a greenhouse for an additional eight weeks. At harvest, growth parameters and infection levels were quantified (biomass, length, number of flowers, root weight, number of galls, and galling index).

Results. Data of vegetative growth varied among experiments, with no clear evidence of effects associated with microbial inoculation. However, the combination of P. lilacinum MTL01 and B. velezensis 83 showed reproducible results, reducing gall formation caused by M. enterolobii by more than 60% and promoting a consistent tendency toward increased flowering relative to nematode-infested control plants. These results demonstrate the potential of using combined microorganisms to improve tomato performance under greenhouse conditions.

Conclusion. Our findings highlight the importance of mitigating M. enterolobii infection in tomato using a combination of microorganisms, not only to control nematode populations but also to increase flower yield by up to 4.3-fold (Experiment 3) in plants without nematodes, and 1.6-fold in the presence of M. enterolobii. This represents a promising strategy; however, further research is needed to confirm its safety and optimize its application under real agricultural conditions.

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Table 1. Phenological response variables in the presence and absence of <em>M. enterolobii</em> under different treatments, Experiment 1 (July to October 2022).

Table 1. Phenological response variables in the presence and absence of M. enterolobii under different treatments, Experiment 1 (July to October 2022).

Table 2. Phenological response variables in the presence and absence of <em>M. enterolobii</em> under different treatments, Experiment 2 (February to June 2023).

Table 2. Phenological response variables in the presence and absence of M. enterolobii under different treatments, Experiment 2 (February to June 2023).

Table 3. Phenological response variables in the presence and absence of <em>M. enterolobii</em> under different treatments, Experiment 3 (September 2024 – January 2025).

Table 3. Phenological response variables in the presence and absence of M. enterolobii under different treatments, Experiment 3 (September 2024 – January 2025).

Table 4. Number and percentage of gall reduction by treatment in three independent experiments.

Figure 1. Isolation and characterization of <em>P. lilacinum</em> MTL01. <strong>A</strong>) MTL01 mycelium growing on an egg mass of <em>M. paranaensis</em>. <strong>B</strong>) Reproductive structures of MTL01: conidiophores, phialides, and conidia. <strong>C</strong> and <strong>D</strong>). Appearance of MTL01 on PDA medium: upper and lower views. <strong>E</strong>) <em>M. enterolobii</em> eggs parasitized by MTL01 on PDA medium. <strong>F</strong>) Parasitized egg from roots in Experiment 1. <strong>G</strong> and <strong>H</strong>) Parasitized adult females from roots in Experiment 1. <strong>I</strong>) Phylogeny of MTL01 compared with other strains and related species.

Figure 1. Isolation and characterization of P. lilacinum MTL01. A) MTL01 mycelium growing on an egg mass of M. paranaensis. B) Reproductive structures of MTL01: conidiophores, phialides, and conidia. C and D). Appearance of MTL01 on PDA medium: upper and lower views. E) M. enterolobii eggs parasitized by MTL01 on PDA medium. F) Parasitized egg from roots in Experiment 1. G and H) Parasitized adult females from roots in Experiment 1. I) Phylogeny of MTL01 compared with other strains and related species.

Figure 2. Laboratory population of <em>M. enterolobii</em>.<strong> A</strong>) Galling roots after <em>M. enterolobii</em> propagation 90 days after J2 inoculation in a 4-month-old tomato plant. <strong>B</strong>) Gall with an exposed egg mass (white arrow) and eggs (yellow arrow). <strong>C</strong>) Pear-shaped female body (upper arrow) and egg mass (lower arrow). <strong>D</strong>) J2-stage nematodes hatched in water.

Figure 2. Laboratory population of M. enterolobii. A) Galling roots after M. enterolobii propagation 90 days after J2 inoculation in a 4-month-old tomato plant. B) Gall with an exposed egg mass (white arrow) and eggs (yellow arrow). C) Pear-shaped female body (upper arrow) and egg mass (lower arrow). D) J2-stage nematodes hatched in water.

Figure 3. The combination of <em>P. lilacinum</em> MTL01 and <em>B. velezensis</em> 83 promotes flowering and reduces galling in tomato plants infected with M. enterolobii. <strong>A</strong>), <strong>C</strong>), and <strong>E</strong>): Flower counts throughout the life cycle before the end of each experiment. <strong>B</strong>), <strong>D</strong>), and <strong>F</strong>): Root galling by experiment. Different letters indicate statistically significant differences (ANOVA, Tukey’s test; p < 0.05).

Figure 3. The combination of P. lilacinum MTL01 and B. velezensis 83 promotes flowering and reduces galling in tomato plants infected with M. enterolobii. A), C), and E): Flower counts throughout the life cycle before the end of each experiment. B), D), and F): Root galling by experiment. Different letters indicate statistically significant differences (ANOVA, Tukey’s test; p < 0.05).

Vol. 2026, 44(3)
Phytopathological Note

Epidemiology of coffee leaf rust under the effect of fungicides in the Northern and Northeastern Sierra of Puebla state

byMiguel José Luna Alvarez , Martin Aguilar Tlatelpa , Marja Liza Fajardo Franco*

Received: 13/March/2026 – Published: 21/August/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2603-3

Abstract Background/Objective. Coffee leaf rust is the primary disease affecting this crop. The objective was to evaluate the incidence and severity of coffee leaf rust under the influence of a systemic fungicide and two biofungicides on varieties cultivated in the Northern and Northeastern Sierra regions of Puebla.

Experimental development. The effects of Azoxystrobin, Melaleuca alternifolia, and Bacillus subtilis were evaluated across six shade-grown plots featuring the Typica, Costa Rica 95, Garnica, Marsellesa, and Oro Azteca varieties, located in Huehuetla, Ixtepec, and Zongozotla. Sixteen sampling events were conducted, for each, of evaluation rust incidence and severity, shade percentage, the weight of 100 ripe cherries, and altitude were assessed. Additionally, temperature and relative humidity were recorded in Huehuetla. Incidence and severity were compared using the Kruskal-Wallis test and the Dwass-Steel-Critchlow-Fligner (DSCF) method. Furthermore, the correlation of these variables with altitude and shade percentage was analyzed. Cherry weight data underwent analysis of variance and mean comparison, while a risk analysis was performed using the climatic variables.

Results. Rust reached maximum severity and incidence (19.9 and 100%, respectively) in the Typica variety (March 2025). Azoxystrobin significantly reduced the disease, followed by M. alternifolia and B. subtilis. No influence of shade on incidence (r=0.33360) or severity (r=0.30735) was identified. In contrast, altitude showed a moderate negative correlation with incidence (r=-0.4632, p<0.0001) and with severity (r=-0.57433, p<0.0001). The Typica and Garnica varieties were the most susceptible.

Conclusion. The fungicides demonstrated control of the disease compared to the control treatment; therefore, they could be considered for the management of H. vastatrix.

Show Figures and/or Tables

Table 1. Characteristics of experimental plots for the evaluation of incidence, severity, and the effect of fungicides on coffee leaf rust (<em>H. vastatrix</em>) in the Northern and Northeastern Sierra regions of the state of Puebla.

Table 1. Characteristics of experimental plots for the evaluation of incidence, severity, and the effect of fungicides on coffee leaf rust (H. vastatrix) in the Northern and Northeastern Sierra regions of the state of Puebla.

Table 2. Reduction of coffee leaf rust (<em>H. vastatrix</em>) through the use of fungicides in the Northern and Northeastern Sierra of Puebla state.

Table 2. Reduction of coffee leaf rust (H. vastatrix) through the use of fungicides in the Northern and Northeastern Sierra of Puebla state.

Figure 1. Temporal dynamics of rust (<em>H. vastatrix</em>) severity on coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla, without fungicide application. A) Severity, B) Incidence. The lines represent disease progression over time: Costa Rica 95 (blue), Typica (orange), Costa Rica 95 (gray), Garnica (yellow), Marsellesa (purple), and Oro Azteca (green).

Figure 1. Temporal dynamics of rust (H. vastatrix) severity on coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla, without fungicide application. A) Severity, B) Incidence. The lines represent disease progression over time: Costa Rica 95 (blue), Typica (orange), Costa Rica 95 (gray), Garnica (yellow), Marsellesa (purple), and Oro Azteca (green).

Figure 2. Temperature and relative humidity in Huehuetla, Puebla. Period: April 2024 to March 2025. The gray band indicates the optimal temperature range for coffee leaf rust (17 to 26°C), and the green band indicates the optimal relative humidity range (80–100%) reported by SENASICA (2019) and Parada-Molina et al. (2020).

Figure 3. Effect of fungicides on the dynamics of rust (<em>H. vastatrix</em>) severity in coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1 (Costa Rica 95), B) Lipuntahuaca 2 (Typica), C) Escatachuchut 1 (Costa Rica 95), D) Escatachuchut 2 (Garnica), E) Zongozotla 1 (Marsellesa), and F) Zongozotla 2 (Oro Azteca). Arrows indicate the timing of fungicide application: orange: Azoxystrobin, gray: <em>M. alternifolia</em>, and green: <em>B. subtilis</em>. The asterisk (*) indicates significant differences between treatments at each sampling point, according to Tukey's mean comparison test.

Figure 3. Effect of fungicides on the dynamics of rust (H. vastatrix) severity in coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1 (Costa Rica 95), B) Lipuntahuaca 2 (Typica), C) Escatachuchut 1 (Costa Rica 95), D) Escatachuchut 2 (Garnica), E) Zongozotla 1 (Marsellesa), and F) Zongozotla 2 (Oro Azteca). Arrows indicate the timing of fungicide application: orange: Azoxystrobin, gray: M. alternifolia, and green: B. subtilis. The asterisk (*) indicates significant differences between treatments at each sampling point, according to Tukey's mean comparison test.

Figure 4. Effect of fungicides on the incidence dynamics of rust (<em>H. vastatrix</em>) in coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1 (Costa Rica 95), B) Lipuntahuaca 2 (Typica), C) Escatachuchut 1 (Costa Rica 95), D) Escatachuchut 2 (Garnica), E) Zongozotla 1 (Marsellesa), and F) Zongozotla 2 (Oro Azteca). Arrows indicate the timing of fungicide application: orange: Azoxystrobin, gray: <em>M. alternifolia</em>, and green: <em>B. subtilis</em>. The asterisk (*) indicates significant differences between treatments at each sampling point, according to Tukey's mean comparison test.

Figure 4. Effect of fungicides on the incidence dynamics of rust (H. vastatrix) in coffee plants grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1 (Costa Rica 95), B) Lipuntahuaca 2 (Typica), C) Escatachuchut 1 (Costa Rica 95), D) Escatachuchut 2 (Garnica), E) Zongozotla 1 (Marsellesa), and F) Zongozotla 2 (Oro Azteca). Arrows indicate the timing of fungicide application: orange: Azoxystrobin, gray: M. alternifolia, and green: B. subtilis. The asterisk (*) indicates significant differences between treatments at each sampling point, according to Tukey's mean comparison test.

Figure 5. Effect of fungicides on the yield of coffee grown in Huehuetla, Ixtepec, and Zongozotla, Puebla. A) Lipuntahuaca 1: Costa Rica 95, B) Lipuntahuaca 2: Typica, C) Escatachuchut 1: Costa Rica 95, D) Escatachuchut 2: Garnica, E) Zongozotla 1: Marsellesa, and F) Zongozotla 2: Oro Azteca. The bars indicate the treatments: TE = Control, BS = Bacillus subtilis, MA = Melaleuca alternifolia, and AZ = Azoxystrobin. Bars with the same letter are statistically equal (Tukey, p≤0.05).

Vol. 2026, 44(3)
Phytopathological Report

First detection of bell pepper endornavirus infecting bell pepper in Guanajuato, Mexico

byAna Belen Duarte Cruz , Víctor Manuel Martín del Campo Soler , Kristian Stevens , Alfredo Diaz Lara*

Received: 25/May/2026 – Published: 21/August/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2605-3

Abstract Background/Objective. Bell pepper (Capsicum annuum) is one of the most important agricultural crops of Mexico. Globally, over 60 virus species have been reported infecting Capsicum spp., some of them are associated with significant losses in yield and quality. The objective of this report is to determine the presence of bell pepper endornavirus (BPEV, Alphaendornavirus capsici) in bell pepper crops in Guanajuato, Mexico.

Experimental development. Samples of bell pepper plants were collected from a commercial greenhouse in Guanajuato, Mexico, in May 2024. A symptomatic plant (mosaic, leaf yellowing, and dwarfing symptoms) was analyzed by high throughput sequencing (HTS). Detection and identification were performed on individual samples using reverse transcription PCR (RT-PCR) and corroborated by Sanger sequencing. A phylogenetic analysis was conducted to investigate genetic diversity of the virus.

Results. A partial sequence of BPEV was generated by HTS and de novo assembly (GenBank: PZ103604), this virus was part of a mixed infection in the symptomatic plant, including Begomovirus capsicumhuastecoense (pepper huasteco yellow vein virus) and Cucumber mosaic virus. In addition, five bell pepper plants, including symptomatic and asymptomatic, tested positive for BPEV using RT-PCR, the identity of the virus was confirmed through Sanger sequencing and phylogenetic analysis.

Conclusion. The detection of BPEV in commercial bell pepper crops in Guanajuato highlights the importance of continuous monitoring of emerging viruses, including possible mixed infections with other viruses, and further studying their impact on crop health and productivity. Although BPEV was detected in symptomatic plants, the observed symptoms cannot be attributed to it as it was detected as part of a co-infection. To our knowledge, this is the first report of BPEV infecting bell pepper in Mexico.

Show Figures and/or Tables

Figure 1. Phylogenetic tree constructed using the Maximum Likelihood method based on the bell pepper endornavirus (BPEV, <em>Alphaendornavirus capsici</em>) polyprotein sequence and other Alphaendornavirus sequences available in GenBank. Reference sequences were selected from GenBank based on their annotation as BPEV isolates and sequence availability. The analysis was performed using the corresponding nucleotide substitution model. Bootstrap support values (1,000 replicates) are indicated at the nodes, showing only those greater than 50%. Sequence PZ103604 (indicated by an arrow) clusters with closely related sequences with high bootstrap support, suggesting a close phylogenetic relationship. Sequence DQ399290 (<em>Betaendornavirus</em>) was used as the outgroup. The scale bar represents 0.20 substitutions per site.

Figure 1. Phylogenetic tree constructed using the Maximum Likelihood method based on the bell pepper endornavirus (BPEV, Alphaendornavirus capsici) polyprotein sequence and other Alphaendornavirus sequences available in GenBank. Reference sequences were selected from GenBank based on their annotation as BPEV isolates and sequence availability. The analysis was performed using the corresponding nucleotide substitution model. Bootstrap support values (1,000 replicates) are indicated at the nodes, showing only those greater than 50%. Sequence PZ103604 (indicated by an arrow) clusters with closely related sequences with high bootstrap support, suggesting a close phylogenetic relationship. Sequence DQ399290 (Betaendornavirus) was used as the outgroup. The scale bar represents 0.20 substitutions per site.

Vol. 2026, 44(3)
Phytopathological Note

Description of Coffea arabica materials and their tolerance to coffee leaf rust (Hemileia vastatrix) under conditions of Amatlán de los Reyes, Veracruz

byJosé Alan Reyes Pérez , Victorino Morales Ramos*, Ma. de Lourdes Catalina Arévalo Galarza , Dora Angélica Avalos de la Cruz

Received: 09/February/2026 – Published: 13/August/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2602-2

Abstract Backgrounds/Objective. Coffee cultivation is at risk due to the effects of climate change, which has created favorable conditions for the proliferation of diseases such as coffee leaf rust (Hemileia vastatrix). One of the most cost-effective and efficient control measures has been the use of materials that are tolerant or resistant to this fungus. The objective of this study was to analyze the response of 32 promising Coffea arabica accessions to coffee leaf rust and to describe their morphological characteristics, relating them to disease severity, under conditions prevailing in the municipality of Amatlán de los Reyes, Veracruz.

Experimental development. A total of 32 C. arabica accessions were evaluated in a completely randomized block design with three replications, compiling a descriptive profile of the visible, identifiable, and measurable morphological traits of each evaluated accession. The incidence of coffee leaf rust (Hemileia vastatrix) was assessed on a five-level severity scale on a monthly basis over a two-year period. An ordinal mixed-effects model and post hoc tests were computed to identify significant differences in rust incidence. Hierarchical cluster analysis was carried out to group the materials according to susceptibility or severity level, and the relationship between morphological traits and infestation level was analyzed using multiple correspondence analysis.

Results. The materials showed significant differences in tolerance to coffee leaf rust. Tukey's test (α = 0.05) identified three groups based on the severity of rust damage; the Pacamara variety was the most affected, while the H1 Centroamericano, Parainema, Catigua MG2, Col 1, EC 15, IPR 103, Kartila 1, Lempira, Paraíso, S795, and the Arabusta SLN 5B varieties were the least affected. The remaining varieties showed an intermediate severity of infestation.

Conclusion. Under the conditions prevailing at the experimental site of the Colegio de Postgraduados Campus Córdoba, 11 C. arabica materials showed a favorable response to the severity of Hemileia vastatrix (H1 Centroamericano, Parainema, Catigua MG2, Col 1, EC 15, IPR 103, Kartila 1, Lempira, Paraíso, S795, and the Arabusta SLN 5B). This contributes to the options available to coffee growers in terms of production and plant health management against coffee leaf rust.

Show Figures and/or Tables

Table 1. Description of <em>Coffea arabica</em> materials assessed for resistance to coffee leaf rust.

Table 1. Description of Coffea arabica materials assessed for resistance to coffee leaf rust.

Table 2. Morphological descriptors as defined by UPOV (2008) for varieties of <em>C. arabica</em>.

Table 2. Morphological descriptors as defined by UPOV (2008) for varieties of C. arabica.

Table 3. Scale of severity for coffee leaf rust.

Table 4. Morphological description of <em>C. arabica</em> materials in accordance with the UPOV (2008) guidelines.

Table 4. Morphological description of C. arabica materials in accordance with the UPOV (2008) guidelines.

Table 5. Morphological description of the leaf of <em>C. arabica</em> materials according to the UPOV (2008) guidelines.

Table 5. Morphological description of the leaf of C. arabica materials according to the UPOV (2008) guidelines.

Table 6. Morphological description of fruits from <em>C. arabica</em> materials according to the UPOV (2008) guidelines.

Table 6. Morphological description of fruits from C. arabica materials according to the UPOV (2008) guidelines.

Table 7. Groups by severity level (mean ± standard deviation) of coffee leaf rust on coffee materials during the 2023/24 and 2024/25 growing seasons in Amatlán de los Reyes, Veracruz (Tukey, P ≤ 0.05).

Figure 1. A visual guide to assessing the severity of coffee leaf rust based on the proposal by Capucho <em>et al</em>. (2011). A. Healthy leaf with no rust spots (IG 1). B. Infected leaf with chlorotic spots without sporulation covering around 2 per cent of the leaf area (IG 2). C. Infected leaf with sporulating lesions covering around 20 per cent of the leaf area (IG 3). D. Infected leaf with sporulating lesions covering around 45 per cent of the leaf area (IG 4). E. Infected leaf with sporulating lesions covering more than 70 per cent of the leaf area (IG 5).

Figure 1. A visual guide to assessing the severity of coffee leaf rust based on the proposal by Capucho et al. (2011). A. Healthy leaf with no rust spots (IG 1). B. Infected leaf with chlorotic spots without sporulation covering around 2 per cent of the leaf area (IG 2). C. Infected leaf with sporulating lesions covering around 20 per cent of the leaf area (IG 3). D. Infected leaf with sporulating lesions covering around 45 per cent of the leaf area (IG 4). E. Infected leaf with sporulating lesions covering more than 70 per cent of the leaf area (IG 5).

Figure 2. Map of morphological variables of the coffee plant and their relationship with the level of coffee leaf rust infestation.

Figure 3. Materials from <em>C. arabica</em> with a lower level of severity of coffee leaf rust. A. Lempira. B. EC 15. C. Col 1. D. IPR 103.

Figure 3. Materials from C. arabica with a lower level of severity of coffee leaf rust. A. Lempira. B. EC 15. C. Col 1. D. IPR 103.

Figure 4. Materials from <em>C. arabica</em> with a lower level of severity of coffee leaf rust. A. Paraíso. B. Catigua MG2. C. Kartila 1. D. H1 Centroamericano.

Figure 4. Materials from C. arabica with a lower level of severity of coffee leaf rust. A. Paraíso. B. Catigua MG2. C. Kartila 1. D. H1 Centroamericano.

Vol. 2026, 44(3)
Scientific Article

Emerging disease in melon crop associated with a mixed infection by three whitefly-transmitted viruses in La Comarca Lagunera, Mexico

byÁngela Paulina Arce Leal , Edgar Antonio Rodríguez Negrete , Manuel Arturo Méndez De León , Enrique Alejandro Guevara Rivera , Norma Elena Leyva López , Jesús Méndez Lozano*

Received: 14/April/2026 – Published: 05/August/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2604-3

Abstract Background/Objective. Cucurbits are an economic pillar of global and national agriculture. However, their productivity is threatened by the increasing incidence of viral plant diseases. Consequently, coinfections with members of the Begomovirus and Crinivirus genera have been associated with exacerbated symptoms and high incidence rates in crops, leading to emerging disease patterns. The aim of this study was to identify and molecularly characterize the begomoviruses and criniviruses associated with an emerging disease in melon crops in a municipality of the Comarca Lagunera region, Mexico.

Materials and Methods. A targeted sampling of nine melon plants exhibiting symptoms such as severe interveinal chlorosis, mottling, yellow mosaic patterns, and fruit deformation was carried out. For viral detection, DNA extraction was performed using 3% CTAB and RNA extraction using the TRIzol reagent. Begomoviruses and criniviruses were detected using PCR and RT-PCR with specific primers. Complete begomovirus genomes were enriched by rolling circle amplification (RCA). Amplicons derived from Open Reading Frame (ORF) of the capsid protein (CP) of criniviruses were generated using RT-PCR. Subsequently, the genomes and CP amplicons were cloned and sequenced for phylogenetic and evolutionary analysis.

Results. Molecular identification revealed the prevalence of mixed infections in seven of nine samples, comprised of two begomovirus species, cucurbit leaf crumple virus (CuLCrV, Begomovirus cucurbitae) and watermelon chlorotic stunt virus (WmCSV, Begomovirus citrulli), and the crinivirus cucurbit yellow stunting disorder virus (CYSDV, Crinivirus cucurbitae). Genomic analyses confirmed that CuLCrV and WmCSV possess genomic structure typical of begomoviruses. Significant divergence was observed at specific loci (CuLCrV C4 gene, WmCSV NSP gene, and DNA-B intergenic regions of both species), suggesting their potential involvement in evolutionary adaptation processes. Phylogenetically, although the CuLCrV isolate showed close relationship to isolates from Arizona and Sonora, it was placed in a clade distinct from those previously reported in the region, indicating an independent evolutionary course. Furthermore, phylogenetic analysis based on the nucleotide sequence of the CYSDV capsid protein (CP) gene revealed a close relationship with isolates previously reported in Jordan and Mexico.

Conclusions. This study represents the first report in Mexico of a disease associated with WmCSV, CuLCrV, and CYSDV coinfection in cucurbits. The findings amphasize the complexity of emerging diseases and highlight the importance of strengthening molecular epidemiological surveillance to mitigate the impact of these mixed infections on Mexican agriculture.

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Table 1. Molecular detection of begomoviruses and criniviruses in melon leaf tissue. Nine samples were analyzed by PCR for the detection of begomoviruses: cucurbit leaf crumple virus (CuLCrV), watermelon chlorotic stunt virus (WmCSV), squash leaf curl virus (SLCuV), and melon chlorotic leaf curl virus (MCLCuV), and RT-PCR for the criniviruses: cucurbit yellow stunting disorder virus (CYSDV), cucurbit chlorotic yellows virus (CCYV), beet pseudo yellows virus (BPYV), and lettuce infectious virus (LIVY). (+) positive detection, (-) negative detection.

Figure 1. Melon plants and fruits showing viral symptoms collected in the La Comarca Lagunera (CL) region. A total of nine samples (M1-M9) of melon with viral symptoms were collected in the municipality of Tlahualilo. Symptoms observed in the leaf tissue were severe interveinal chlorosis, mottling, and yellow mosaic, while in the fruits, chlorosis, deformation, and cracking.

Figure 2. Complete genomic description of begomoviruses and criniviruses isolated from melon plants in the Comarca Lagunera region. A) Complete genome of CuLCrV-CL (DNA-A of 2631 bp and DNA-B of 2602 bp); B) Complete genome of WmCSV-CL (DNA-A of 2758 bp and DNA-B of 2766 bp). The open reading frames of the DNA-A (CP, AC4, Rep, TrAP, and REn) and DNA-B (NSP and MP) segments, as well as the intergenic regions (IR) of all viral segments, are shown. In the case of WmCSV, the DNA-A segment additionally encodes the AV2 and AC5 genes. C) Genomic organization of RNA segment 2 of CYSDV (7976 bp) (accession number: NC_004810). The open reading frames of Hsp70h, P6, P59, P9, CP, CPm, and p26 are displayed. The region corresponding to the CP gene (753 bp), used for molecular detection of CYSDV, is indicated with red arrows. Created in BioRender.

Figure 3. Phylogenetic trees corresponding to the DNA-A segments of begomoviruses and the CP gene of criniviruses. Neighbor-Joining (NJ) phylogenies were constructed by multiple alignments of the DNA-A segments of the begomoviruses CuLCrV-CL and WmCSV-CL (panels A and B, respectively), as well as by alignments of the complete ORF of the capsid protein (CP) of the crinivirus CYSDV (C). For the NJ phylogenetic trees, the Tamura-3 model with 1000 replicates was used. Isolates derived from this research are marked with a red asterisk. The accession numbers of the different isolates are indicated. The DNA-A of the begomovirus okra mottle virus (OMoV) and the CP of the crinivirus lettuce chlorosis virus (LCV) were used as outgroup sequences.

Vol. 2026, 44(3)
Phytopathological Note

Isolation and molecular characterization of native Trichoderma species in sites with different management practices in Chilpancingo, Guerrero, Mexico

byJatsiris A Meza Silva , Jeiry Toribio Jiménez , Renato León Rodríguez , Nelda Xanath Martínez Galero , Erubiel Toledo Hernández , Roxana Reyes Ríos , Alberto Patricio Hernández*

Received: 15/May/2026 – Published: 29/July/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2605-2

Abstract Background/Objective. The genus Trichoderma comprises fungi widely recognized for their capacity as biological control agents and plant growth promoters; however, information on their molecular diversity in agricultural systems in Guerrero, Mexico, is limited. The objective of this study was to isolate and molecularly identify native Trichoderma species present in different sites within the municipality of Chilpancingo de los Bravo, Guerrero, and to analyze their distribution in relation to management conditions.

Experimental development. During August and September 2025, 125 traps with sterile rice were installed at four sampling points: Palo Blanco (Agroecological system), the Botanical Garden of the Universidad Autónoma de Guerrero (Plant conservation), and two areas of Petaquillas (Unmanaged natural vegetation). Isolates were purified by monosporic culture, morphologically characterized, and identified by ITS region sequencing and maximum likelihood phylogenetic analysis with 1,000 bootstrap replicates.

Results. Seven Trichoderma isolates were obtained, recovered only from the Palo Blanco agroecological plot and the Botanical Garden of the Universidad Autónoma de Guerrero. Phylogenetic analysis identified T. harzianum/Hypocrea lixii, T. asperellum, and T. longibrachiatum. Representatives of T. harzianum/H. lixii and T. longibrachiatum were identified at Palo Blanco, while T. harzianum/H. lixii and T. asperellum were identified at the Botanical Garden. The T. harzianum/H. lixii complex was the predominant taxon, representing 57.1% of the isolates obtained.

Conclusion. Three Trichoderma taxa were identified, with the T. harzianum/Hypocrea lixii complex predominating (57.1%), present in Palo Blanco and the Botanical Garden. The results expand our knowledge of its diversity and distribution in Guerrero, although its biotechnological potential should be confirmed through functional evaluations.

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Table 1. Location of sampling sites and <em>Trichoderma</em> isolates recovered according to site management type.

Table 1. Location of sampling sites and Trichoderma isolates recovered according to site management type.

Table 2. Sequence length, GenBank accession number, and ITS sequence similarity of <em>Trichoderma</em> isolates.

Table 2. Sequence length, GenBank accession number, and ITS sequence similarity of Trichoderma isolates.

Figure 1. Representative morphological characterization of <em>Trichoderma</em>. A) Colony morphology observed on potato dextrose agar (PDA), showing the obverse and reverse sides of the colony. B) Microscopic structures observed using the slide-culture technique and lactophenol cotton blue staining (100×), showing septate hyphae, branched conidiophores, phialides, and conidia characteristic of the genus <em>Trichoderma</em>.

Figure 1. Representative morphological characterization of Trichoderma. A) Colony morphology observed on potato dextrose agar (PDA), showing the obverse and reverse sides of the colony. B) Microscopic structures observed using the slide-culture technique and lactophenol cotton blue staining (100×), showing septate hyphae, branched conidiophores, phialides, and conidia characteristic of the genus Trichoderma.

Figure 2. Phylogenetic tree inferred from ITS-region (ITS1–5.8S–ITS2) sequences of <em>Trichoderma</em> isolates obtained in this study and reference sequences retrieved from GenBank. The analysis grouped the isolates within the clades corresponding to T<em>. longibrachiatum</em>, <em>T. asperelloides</em>, <em>T. asperellum</em>, <em>T. harzianum</em>, and <em>Hypocrea lixii</em>. <em>Aspergillus niger</em> was used as the outgroup because of its phylogenetic position outside the genus <em>Trichoderma</em>, allowing the tree to be rooted and the evolutionary relationships among the analyzed isolates to be interpreted. Node values indicate bootstrap support (%) based on 1,000 replicates.

Figure 2. Phylogenetic tree inferred from ITS-region (ITS1–5.8S–ITS2) sequences of Trichoderma isolates obtained in this study and reference sequences retrieved from GenBank. The analysis grouped the isolates within the clades corresponding to T. longibrachiatum, T. asperelloides, T. asperellum, T. harzianum, and Hypocrea lixii. Aspergillus niger was used as the outgroup because of its phylogenetic position outside the genus Trichoderma, allowing the tree to be rooted and the evolutionary relationships among the analyzed isolates to be interpreted. Node values indicate bootstrap support (%) based on 1,000 replicates.

Vol. 2026, 44(3)
Scientific Article

Natural co-infection of Pospiviroid machoplantae and Begomovirus solanumseveri in tomato

byMaría del Carmen Zuñiga Romano , Daniel Leobardo Ochoa Martínez*, Reyna Isabel Rojas Martínez , Sergio Aranda Ocampo , Candelario Ortega Acosta , Erika Janet Zamora Macorra

Received: 08/March/2026 – Published: 27/July/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2603-1

Abstract Background/Objective. High whitefly populations and incidence of up to 90% of tomato plants showing mosaic, intervenial chlorosis, deformation, rugosity and leaf necrosis, associated with virus and viroids, were observed at a high-tech greenhouse in Atlacomulco, State of Mexico. To identify the viruses or viroids associated with these symptoms, the withefly species, as well as to determine if the insects were carrying any of these pathogens, monthly sampling was conducted over a two-year period, collecting plant leaf tissue and adult whiteflies.

Materials y Methods. A total of 347 plants (211 symptomatic and 136 asymptomatic) were analyzed using immunostrips for impatiens necrotic spot virus (Orthotospovirus impatiensnecromaculae), tomato spotted wilt virus (O. tomatomaculae), tobacco mosaic virus (Tobamovirus tabaci), tomato mosaic virus (T. tomatotessellati), pepino mosaic virus (Potexvirus pepini) and cucumber mosaic virus (Cucumovirus CMV), as well as by RT-PCR and PCR using universal primers for viroids and viruses of the genera Pospiviroid and Begomovirus, respectively. DNA was extracted from the insects and analyzed by PCR with specific primers that amplify a fragment of the mitocondrial gene (mtCOI) to identify the whitefly species, as well as universal primers for begomoviruses to determine if the insects were carriers of these viruses.

Results. The results obtained using immunostrips were negative. The fragments amplified by RT-PCR and PCR were sequenced and corresponded to tomato planta macho viroid (Pospiviroid machoplantae) and tomato severe leaf curl virus (Begomovirus solanumseveri). Of the plants analyzed, 49.56% tested positive for the pospiviroid, 23.9% for the begomovirus, and 8.93% for both. Infection caused solely by the viroid was the only one that resulted in an asymptomatic condition. Bemisia tabaci was identified as the whitefly species and was determined to be a carrier of B. solanumseveri.

Conclusions. Symptoms of mosaic, intervenial chlorosis, leaf deformation and necrosis, blistering, and rugosity observed in tomato plants were associated with individual infection by tomato planta macho viroid (Pospiviroid machoplantae), and tomato severe leaf curl virus (Begomovirus solanumseveri), as well as co-infection by both. This is the first report of natural co-infection of tomato planta macho viroid and tomato leaf curl virus. The presence of asymptomatic plants is a key factor to consider when carrying out cultural practices to minimize mechanical transmission of the viroid.

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Figure 1. Symptoms observed in tomato plants associated with begomoviruses. A. Mosaic; B. Interveinal chlorosis; C. Leaf deformation; D. Necrosis; E. Chlorosis with blistering; F. Chlorosis with leaf deformation; G. Chlorosis with rugosity; H. Rugosity with mild mosaic.

Figure 2. 1% agarose gel electrophoresis of PCR products. A. RT-PCR products obtained using universal primers for viroids of the genus <em>Pospiviroid</em> (⁓200 bp). Lane M: 100 bp molecular marker (Promega®); Lane (+): positive control (sample positive for <em>Pospiviroid machoplantae</em>); Lane (-): negative control (water); Lanes 1-16: processed samples; B. PCR products obtained using universal primers for viruses of the genus <em>Begomovirus</em> (⁓550 bp). Lane M: 100 bp molecular marker (Promega®); Lane (+): positive control (sample positive for <em>Begomovirus abelsmoschusmexicoense</em>); Lane (-): negative control (water); Lanes 1-16: processed samples.

Figure 2. 1% agarose gel electrophoresis of PCR products. A. RT-PCR products obtained using universal primers for viroids of the genus Pospiviroid (⁓200 bp). Lane M: 100 bp molecular marker (Promega®); Lane (+): positive control (sample positive for Pospiviroid machoplantae); Lane (-): negative control (water); Lanes 1-16: processed samples; B. PCR products obtained using universal primers for viruses of the genus Begomovirus (⁓550 bp). Lane M: 100 bp molecular marker (Promega®); Lane (+): positive control (sample positive for Begomovirus abelsmoschusmexicoense); Lane (-): negative control (water); Lanes 1-16: processed samples.

Figure 3. Phylogenetic tree constructed using the Neighbor-Joining method based on partial sequences of <em>Begomovirus solanumseveri</em> and <em>Pospiviroid machoplantae</em> from the isolates obtained in this study. A. For <em>B. solanumseveri</em> the Tajima-Nei model was used with 1,000 bootstrap replicates in the MEGAX program, and <em>Capulavirus medicagonis</em> was used as the outgroup; B. For <em>P. machoplantae</em> the Tamura 3-parameter model was used with 1,000 bootstrap replicates in the MEGAX program, and <em>Coleviroid alphacolei</em> was used as the outgroup.

Figure 3. Phylogenetic tree constructed using the Neighbor-Joining method based on partial sequences of Begomovirus solanumseveri and Pospiviroid machoplantae from the isolates obtained in this study. A. For B. solanumseveri the Tajima-Nei model was used with 1,000 bootstrap replicates in the MEGAX program, and Capulavirus medicagonis was used as the outgroup; B. For P. machoplantae the Tamura 3-parameter model was used with 1,000 bootstrap replicates in the MEGAX program, and Coleviroid alphacolei was used as the outgroup.

Vol. 2026, 44(3)
Scientific Article

Detection of Closterovirus tristezae in Persian lime using machine learning and drones

byMaribel Reyes García , Remigio Anastacio Guzmán Plazola*, Reyna Isabel Rojas Martínez , Victoria Ayala Escobar , Héctor Flores Magdaleno

Received: 03/October/2025 – Published: 17/July/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2510-2

Abstract Background/Objective. Persian lime (Citrus latifolia) production in Mexico is threatened by various diseases. Among the most significant is the citrus tristeza virus (Closterovirus tristezae, CTV), which can infect plants asymptomatically. This research aimed to evaluate the use of drone-acquired multispectral imagery and machine learning algorithms to identify diseased plants as a tool for early, non-destructive detection.

Materials and Methods. The study was conducted in three agricultural fields in Campeche, México: Champotón and Castamay (sections 1 and 2). Multispectral images (green, red, red-edge, and near-infrared) were acquired, and spectral signatures, vegetation indices (NDVI, GNDVI, NDRE, SIPI), and principal components (PCA) were extracted. Supervised classifiers—Random Forest (RF), Maximum Likelihood (MAXLIKE), Support Vector Machine (SVM), and Multilayer Perceptron (MLP)—were applied using hard and soft classification schemes.

Results. RF performed the best. In hard classification, it achieved overall accuracies of 64% in Champotón and Castamay Section 1, and 65% in Section 2, with diseased plant detection rates between 65 and 75%. It yielded better results in soft classification. In Champotón, it achieved 94% overall accuracy and detected 79% of diseased plants. In Castamay Section 1, the figures were 91 and 92%, respectively; and in Section 2, it achieved 99% overall accuracy and 100% disease detection.

Conclusion. Random Forest demonstrated the most robust and stable performance across the various scenarios and fields analyzed—in both hard classification (65%) and soft classification (94%)—confirming its ability to handle non-linear relationships and spectral variability in real agricultural environments. The use of multispectral imagery combined with machine learning algorithms—specifically Random Forest (RF) for soft classification—represents an effective tool for early diagnosis of CTV in Persian lime.

Show Figures and/or Tables

Table 1. Statistical summary of spectral values by band and class across the three fields evaluated in commercial Persian lime plantations.

Table 2. Mean values of variables derived from the images (original bands, principal components, and vegetation indices) in the three fields analyzed in commercial Persian lime plantations.

Figure 1. Spectral density curves by band: comparison between healthy plants and those infected with <em>Closterovirus tristezae</em> (CTV) in the Champotón, Campeche field.

Figure 1. Spectral density curves by band: comparison between healthy plants and those infected with Closterovirus tristezae (CTV) in the Champotón, Campeche field.

Figure 2. Spectral density curves by band: comparison between healthy plants and those infected with <em>Closterovirus tristezae</em> (CTV) in the Castamay Section 1 field, Campeche.

Figure 2. Spectral density curves by band: comparison between healthy plants and those infected with Closterovirus tristezae (CTV) in the Castamay Section 1 field, Campeche.

Figure 3. Spectral density curves by band: comparison between healthy plants and those infected with <em>Closterovirus tristezae</em> (CTV) in the Castamay Section 2 field, Campeche.

Figure 3. Spectral density curves by band: comparison between healthy plants and those infected with Closterovirus tristezae (CTV) in the Castamay Section 2 field, Campeche.

Figure 4. Hard classification for the three crop fields. Champotón field: A1) Overall accuracy considering all correctly classified pixels; A2) Percentage distribution of pixels by class (healthy and diseased plants). Castamay Section 1: B1) Overall accuracy considering all correctly classified pixels; B2) Percentage distribution of pixels by class (healthy and diseased plants). Castamay Section 2: C1) Overall accuracy considering all correctly classified pixels; C2) Percentage distribution of pixels by class (healthy and diseased plants).

Figure 5. Champotón field in Campeche, Mexico, with a Persian lime crop. Soft classification of the best results on probability scales by algorithm. A) RGB drone image of the Persian lime field; B) Training site, data obtained from field analyses using immunostrips; C) RF with a 56% threshold; D) MAXLIKE with a 50% threshold; E) SVM-RBF with a 29% threshold.

Figure 6. Castamay Section 1 in Campeche, Mexico, with a Persian lime crop. Soft classification of the best results based on the algorithm probability scales. A) RGB drone image of the Persian lime crop; B) Training site, data obtained from field analyses using immunostrips; C) RF with 50% threshold; D) SVM-RBF with 50% threshold; E) SVM-LIN with 50% threshold; F) SVM-SIG with 50% threshold; G) MAXLIKE with 50% threshold; H) SVM-POLY with 50% threshold.

Figure 7. Castamay Section 2, Campeche, Mexico, with a Persian lime crop. Soft classification of the best results based on algorithmic probability scales. A) Drone-captured RGB image of the Persian lime field; B) Training site, with data obtained from field analyses using immunostrips; C) Random Forest results at a 50% membership threshold (MT); D) SVM-RBF with 50% MT; E) SVM-SIG with 50% MT; F) SVM-LIN with 50% MT; G) MAXLIKE with 50% MT; H) SVM-POLY with 51% MT.

Vol. 2026, 44(3)
Phytopathological Note

In vitro antifungal activity and sensitivity of Neopestalotiopsis rosae isolates to Baccharis salicifolia and Fouquieria splendens extracts

byJesús Eduardo Ramírez Méndez , Meralida Bartolón Pérez , Francisco Daniel Hernández Castillo , Yisa María Ochoa Fuentes , Laura Castro Rosalez , Marco Antonio Tucuch Pérez*

Received: 19/January/2026 – Published: 13/July/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2601-3

Abstract Background/Objective. Neopestalotiopsis rosae is the causal agent of crown and root rot in strawberry, resulting in significant yield losses. The aim of this study was to evaluate the sensitivity of four N. rosae isolates (466, 323, FREC, and 372) to plant extracts (PEs) obtained from Baccharis salicifolia and Fouquieria splendens, and to determine their in vitro antifungal activity.

Experimental development. A qualitative phytochemical screening was performed to identify the major bioactive compounds present in the PEs. Antifungal activity was evaluated using in vitro microdilution assays at concentrations ranging from 3.9 to 2 000 mg L⁻¹ (10 concentrations). The 50 and 90% inhibitory concentrations (IC₅₀ and IC₉₀) were estimated, and the data were analyzed using analysis of variance (ANOVA) followed by Tukey's multiple comparison test (p ≤ 0.05). The effects of the PEs on fungal cellular structures were also assessed.

Results. Phytochemical screening revealed the presence of phenolic compounds, saponins, and tannins in both plant extracts. Among the N. rosae isolates, isolates 323 and FREC were the most sensitive, exhibiting complete inhibition with both PEs at 250 mg L⁻¹. In contrast, isolates 372 and 466 required higher concentrations to achieve complete inhibition. B. salicifolia was more effective against isolates 466 and 372, achieving complete inhibition at 1 000 mg L⁻¹, whereas F. splendens required 2 000 mg L⁻¹ to completely inhibit isolate 466. Structural analyses revealed that isolate 323 was most susceptible to extract-induced damage at 250 mg L⁻¹ and above, as evidenced by inhibition of conidial germination and severe morphological alterations, including cellular collapse and dehydration. Isolates FREC and 372 exhibited mycelial thickening at 500 mg L⁻¹, whereas isolate 466 was the least sensitive, showing no evident structural damage even at this concentration.

Conclusions. Plant extracts from B. salicifolia and F. splendens contain bioactive phytochemicals with antifungal activity against N. rosae. Isolates 323 and FREC were the most sensitive, exhibiting 100% inhibition and pronounced structural damage at 250 mg L⁻¹, highlighting the potential of these extracts as biorational alternatives for the management of N. rosae. Nevertheless, further greenhouse and field studies are required to validate their efficacy under commercial production conditions.

Show Figures and/or Tables

Table 1. Identification of phytochemicals present in methanolic extracts of <em>Baccharis salicifolia</em> and <em>Fouquieria splendens</em> using colorimetric assays.

Table 1. Identification of phytochemicals present in methanolic extracts of Baccharis salicifolia and Fouquieria splendens using colorimetric assays.

Table 2. Fifty percent and ninety percent inhibitory concentrations (IC₅₀ and IC₉₀) of <em>Neopestalotiopsis rosae</em> isolates treated with <em>Baccharis salicifolia</em> and <em>Fouquieria splendens</em> extracts.

Table 2. Fifty percent and ninety percent inhibitory concentrations (IC₅₀ and IC₉₀) of Neopestalotiopsis rosae isolates treated with Baccharis salicifolia and Fouquieria splendens extracts.

Figure 1. Inhibition percentage of <em>Neopestalotiopsis rosae</em> isolates treated with <em>Baccharis salicifolia</em> extract. *Data followed by the same letter are not significantly different

Figure 1. Inhibition percentage of Neopestalotiopsis rosae isolates treated with Baccharis salicifolia extract. *Data followed by the same letter are not significantly different

Figure 2. Inhibition percentage of <em>Neopestalotiopsis rosae</em> isolates treated with <em>Fouquieria splendens</em> extract. *Data followed by the same letter are not significantly different.

Figure 2. Inhibition percentage of Neopestalotiopsis rosae isolates treated with Fouquieria splendens extract. *Data followed by the same letter are not significantly different.

Figure 3. Effect of <em>Baccharis salicifolia</em> extract on the fungal structures of <em>Neopestalotiopsis rosae</em> isolates. A1–A2: untreated control. B1–B5: isolate 466 (2 000, 1 000, 500, 250, and 125 mg L⁻¹); C1–C5: isolate 323 (2 000, 1 000, 500, 250, and 125 mg L⁻¹); D1–D5: isolate FREC (2 000, 1 000, 500, 250, and 125 mg L⁻¹); E1–E5: isolate 372 (2 000, 1 000, 500, 250, and 125 mg L⁻¹). Microscopic observations were performed at 10× and 40× magnification.

Figure 3. Effect of Baccharis salicifolia extract on the fungal structures of Neopestalotiopsis rosae isolates. A1–A2: untreated control. B1–B5: isolate 466 (2 000, 1 000, 500, 250, and 125 mg L⁻¹); C1–C5: isolate 323 (2 000, 1 000, 500, 250, and 125 mg L⁻¹); D1–D5: isolate FREC (2 000, 1 000, 500, 250, and 125 mg L⁻¹); E1–E5: isolate 372 (2 000, 1 000, 500, 250, and 125 mg L⁻¹). Microscopic observations were performed at 10× and 40× magnification.

Figure 4. Effect of <em>Fouquieria splendens</em> extract on the fungal structures of <em>Neopestalotiopsis rosae</em> isolates. A1–A2: untreated control. B1–B5: isolate 466 (2 000, 1 000, 500, 250, and 125 mg L⁻¹); C1–C5: isolate 323 (2 000, 1 000, 500, 250, and 125 mg L⁻¹); D1–D5: isolate FREC (2 000, 1 000, 500, 250, and 125 mg L⁻¹); E1–E5: isolate 372 (2 000, 1 000, 500, 250, and 125 mg L⁻¹). Microscopic observations were performed using 10×, 40×, and 100× objective lenses

Figure 4. Effect of Fouquieria splendens extract on the fungal structures of Neopestalotiopsis rosae isolates. A1–A2: untreated control. B1–B5: isolate 466 (2 000, 1 000, 500, 250, and 125 mg L⁻¹); C1–C5: isolate 323 (2 000, 1 000, 500, 250, and 125 mg L⁻¹); D1–D5: isolate FREC (2 000, 1 000, 500, 250, and 125 mg L⁻¹); E1–E5: isolate 372 (2 000, 1 000, 500, 250, and 125 mg L⁻¹). Microscopic observations were performed using 10×, 40×, and 100× objective lenses

Vol. 2026, 44(3)
Phytopathological Note

First report of Pseudomonas putida affecting Eryngium spp., case study in Ixtapan de la Sal, State of Mexico

bySantiago Vergara Pineda , Paola S Puga Guzmán , Daniel Mendoza Jiménez , José Antonio Cervantes Chávez*

Received: 09/March/2026 – Published: 22/June/2026
DOI: https://doi.org/10.18781/R.MEX.FIT.2603-2

Abstract Background/Objective. Plant species of the genus Eryngium are native to Mexico; their cultivation as ornamental cut flowers is a recent development, so the pathogens that affect them are largely unknown. In this context, a grower set out to innovate by using this plant as a cut flower but encountered a rot disease that decimated his field; therefore, the objective of this study was to isolate, identify, and characterize the causal agent of soft rot in the stems of Eryngium spp.

Experimental development. Plants exhibiting rot symptoms were collected from a cultivated plot in the municipality of Ixtapan de la Sal, State of Mexico. From ten symptomatic plants kept in a humid chamber, a bacterium was isolated and identified using biochemical analysis. For molecular identification, DNA was extracted, and the 16S ribosomal gene was amplified with primers 27F and 1492R. PCR products were sent to the Advanced Genomics Laboratory at LANGEBIO, CINVESTAV Irapuato, Guanajuato. Koch’s postulates were also performed on twenty asymptomatic Eryngium spp. seedlings, which were inoculated by introducing a bacterial mass at the leaf base, the distal petiole, and the basal vein region.

Results. Based on biochemical analysis, the bacterium was identified as belonging to the genus Pseudomonas; molecular analysis revealed that the bacterium was Pseudomonas putida. According to Koch’s postulates, 100% of the symptoms associated with soft rot were observed.

Conclusion. Pseudomonas putida was confirmed as the causal agent of soft rot in Eryngium spp. Its pathogenicity was confirmed in Eryngium spp. seedlings according to Koch's postulates. This is the first report of P. putida causing this disease in these plants.

Show Figures and/or Tables

Figure 1. Phylogenetic relationship of bacterial strain 1347 and reference species from the genus <em>Pseudomonas</em>. The strain shows a correlation with <em>Pseudomonas putida</em> (MF462927, PP095708). The phylogenetic tree was constructed by the Maximum Likelihood method using 16S ribosomal DNA gene sequences. Bootstrap values at internal nodes represent support from 100,000 replicates. <em>P. brassicacearum</em> AF100322 and <em>P. alabamensis</em> PQ115154 served as the outgroup.

Figure 1. Phylogenetic relationship of bacterial strain 1347 and reference species from the genus Pseudomonas. The strain shows a correlation with Pseudomonas putida (MF462927, PP095708). The phylogenetic tree was constructed by the Maximum Likelihood method using 16S ribosomal DNA gene sequences. Bootstrap values at internal nodes represent support from 100,000 replicates. P. brassicacearum AF100322 and P. alabamensis PQ115154 served as the outgroup.

Figure 2. Stages of soft rot development in <em>Eryngium</em> sp. under field cultivation; A) Leaf blade showing necrosis along the main veins; B) Underside of the same leaf with severe necrosis; C) Leaf petioles with necrotic streaks; D) Wilting and death of leaves; E) Wilting of the entire plant.

Figure 2. Stages of soft rot development in Eryngium sp. under field cultivation; A) Leaf blade showing necrosis along the main veins; B) Underside of the same leaf with severe necrosis; C) Leaf petioles with necrotic streaks; D) Wilting and death of leaves; E) Wilting of the entire plant.

Figure 3. Soft rot in the stem and main root of <em>Eryngium</em> spp. associated with bacteria.

Figure 3. Soft rot in the stem and main root of Eryngium spp. associated with bacteria.

Figure 4. Koch’s postulates in <em>Eryngium</em> spp. seedlings inoculated with bacterial strain 1347; A) Water-soaked lesions at the site of puncture inoculation on the leaf with infectious process the following day; B) Progression of severity on the second day; C) Dead plant on the tenth day; D) Control plant.

Figure 4. Koch’s postulates in Eryngium spp. seedlings inoculated with bacterial strain 1347; A) Water-soaked lesions at the site of puncture inoculation on the leaf with infectious process the following day; B) Progression of severity on the second day; C) Dead plant on the tenth day; D) Control plant.

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