Search Results (19)

Endophytic bacteria of the Bacillus subtilis species complex are known for plant growth promotion and antifungal activity, although strain-specific traits remain poorly understood. This study characterized the Bacillus siamensis strain BACIII, isolated from asymptomatic soybean roots in a charcoal rot-affected area, using whole-genome analysis and phenotypic assays. Genome sequencing identified BACIII as Bacillus siamensis and revealed a metabolically versatile genome containing twelve biosynthetic gene clusters linked to antimicrobial compounds such as difficidin, fengycin, and surfactin. Additionally, genomic islands associated with mobile elements, regulation, and stress response suggest adaptive potential. Inoculation with the BACIII strain significantly accelerated germination and increased early growth, biomass accumulation, and chlorophyll content in soybean and sunn hemp (p < 0.05), whereas no significant effects were observed in cotton or sunflower, indicating host-dependent responses. In vitro assays demonstrated consistent inhibition of several phytopathogenic fungi, including Sclerotinia sclerotiorum, Fusarium spp., and Macrophomina phaseolina, with variable intensity. Overall, BACIII combines host-specific plant growth promotion with broad antifungal activity, supporting its potential for biological control in sustainable agriculture. Full article

The necrotrophic pathogen Sclerotinia sclerotiorum compromises the physiological and anatomical integrity of cotton, leading to substantial economic losses due to rapid tissue necrosis, stem blight, boll rot, and leaf wilting. In this context, the use of endophytic microorganisms emerges as a promising strategy for the biocontrol of white mold. This study tested the hypothesis that endophytic fungal strains isolated from the roots of Butia purpurascens, a palm tree endemic to the Cerrado biome, could mitigate disease symptoms in Gossypium hirsutum L. To evaluate this, cotton plants were subjected to biotic stress imposed by S. sclerotiorum to assess the effectiveness of seven fungal strains in attenuating disease. The impact of the pathogen was monitored through growth variables, gas exchange, leaf temperature, chlorophyll a fluorescence, antioxidant enzyme activity, proline and malondialdehyde (MDA) levels, and the incidence of rot in petioles, leaves, and flower buds. Overall, inoculation with endophytic fungi significantly alleviated the effects of the phytopathogen, promoting vegetative growth and optimizing physiological performance. Treated plants exhibited alleviated stress in primary photochemistry, reduced non-photochemical energy dissipation, and stable carbon fixation. Additionally, efficient modulation of the antioxidant system and preservation of anatomical structures were observed, minimizing the severe symptoms of white mold. Notably, the non-pathogenic strains BP10EF (Gibberella moniliformis), BP16EF (Penicillium purpurogenum), and BP33EF (Hamigera insecticola) acted as potent physiological modulators, yielding responses similar to those of healthy plants. These results highlight the biotechnological potential of these endophytic strains, which can be explored as both growth promoters and resistance inducers in cotton against white mold. Full article

In chili cultivation, obstacles to continuous cropping significantly compromise crop yield and soil health, whereas crop rotation can enhance the microbial environment of the soil and reduce disease incidence. However, its effects on the diversity of rhizosphere soil microbial communities are not clear. In this study, we analyzed the composition and characteristics of rhizosphere soil microbial communities under chili continuous cropping (CC) and chili–cotton crop rotation (CR) using high-throughput sequencing technology. CR treatment reduced the alpha diversity indices (including Chao1, Observed_species, and Shannon index) of bacterial communities and had less of an effect on fungal community diversity. Principal component analysis (PCA) revealed distinct compositional differences in bacterial and fungal communities between the treatments. Compared with CC, CR treatment has altered the structure of the soil microbial community. In terms of bacterial communities, the relative abundance of Firmicutes increased from 12.89% to 17.97%, while the Proteobacteria increased by 6.8%. At the genus level, CR treatment significantly enriched beneficial genera such as RB41 (8.19%), Lactobacillus (4.56%), and Bacillus (1.50%) (p < 0.05). In contrast, the relative abundances of Alternaria and Fusarium in the fungal community decreased by 6.62% and 5.34%, respectively (p < 0.05). Venn diagrams and linear discriminant effect size analysis (LEfSe) further indicated that CR facilitated the enrichment of beneficial bacteria, such as Bacillus, whereas CC favored enrichment of pathogens, such as Firmicutes. Fusarium solani MG6 and F. oxysporum LG2 are the primary chili root-rot pathogens. Optimal growth occurs at 25 °C, pH 6: after 5 days, MG6 colonies reach 6.42 ± 0.04 cm, and LG2 5.33 ± 0.02 cm, peaking in sporulation (p < 0.05). In addition, there are significant differences in the utilization spectra of carbon and nitrogen sources between the two strains of fungi, suggesting their different ecological adaptability. Integrated analyses revealed that CR enhanced soil health and reduced the root rot incidence by optimizing the structure of soil microbial communities, increasing the proportion of beneficial bacteria, and suppressing pathogens, providing a scientific basis for microbial-based soil management strategies in chili cultivation. Full article

Okra (Abelmoschus esculentus) is an important agricultural crop of the Malvaceae family, cultivated across tropical, subtropical, and warm temperate regions. However, okra production faces numerous challenges from diverse pest species, including insects, nematodes, arachnids, and mites, that significantly reduce its yield. Major economic pests include the cotton aphid, cotton spotted bollworm, Egyptian bollworm, cotton mealybug, whitefly, cotton leafhopper, cotton bollworm, two-spotted spider mite, root-knot nematode, reniform nematode, cotton leaf roller, and flea beetle. Additionally, less prevalent pests such as the blister beetle, okra stem fly, red cotton bug, cotton seed bug, cotton looper, onion thrips, green plant bug, and lesion nematode are also described. This review also addresses fungal and oomycete diseases that present high risks to okra production, including damping-off, powdery mildew, Cercospora leaf spot, gray mold, Alternaria leaf spot and pod rot, Phyllosticta leaf spot, Fusarium wilt, Verticillium wilt, collar rot, stem canker, anthracnose, and fruit rot. In addition to these fungal diseases, okra is also severely affected by several viral diseases, with the most important being okra yellow vein mosaic disease, okra enation leaf curl disease, and okra mosaic disease, which can cause significant yield losses. Moreover, okra may also suffer from bacterial diseases, with bacterial leaf spot and blight, caused primarily by Pseudomonas syringae, being the most significant. This manuscript synthesizes the current knowledge on these pests. It outlines various management techniques and strategies to expand the knowledge base of farmers and researchers, highlighting the key role of integrated pest management (IPM). Full article

Black root rot (BRR) and Verticillium wilt of cotton are caused by soilborne Berkeleyomyces rouxiae and Verticillium dahliae, respectively, and can individually cause yield loss of a 10–50% in New South Wales (NSW). Both diseases were first detected in a northern valley of NSW but are now present across the state. ‘Come Clean Go Clean’ is a widely practiced biosecurity measure used to minimize the risk of further introducing the pathogens from one field to another by restricting the movement of soil-contaminated farm equipment and machinery. We rely on cleaning agents to effectively wash down and decontaminate the equipment and machinery. In this study, we examined 12 locally available, commercial disinfectants for their efficacy against B. rouxiae and V. dahliae reproductive structures with and without soil contamination of 10% (w/v). We found a significant interaction between pathogens, disinfectants, and soil amendment (p < 0.01). The germination of B. rouxiae chlamydospores and endoconidia in both the presence and absence of soil contamination was completely suppressed even after a short 10 sec exposure to 70% ethanol and 25% bleach. Both 70% ethanol and bleach were highly lethal at 10 sec exposures in similar assays against V. dahliae microsclerotia and conidia. Some other commercial products were able to reduce the germination rate significantly but did not completely kill microsclerotia and conidia even after 30 min of exposure. The lethal effect against B. rouxiae and V. dahliae warrants further exploration of both 70% ethanol and bleach to improve their field applications. Additionally, the efficacy of most tested commercial disinfectants was time-dependent; however, this is not recommended on the labels for their effectiveness. This study provides an additional integrated disease management option aiming to limit the spread of the pathogens. Full article

Cotton (Gossypium spp.) is the most important fibre crop worldwide. Black root rot and Fusarium wilt are two major diseases of cotton caused by soil-borne Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum (Fov), respectively. Phenotyping plant symptoms caused by soil-borne pathogens has always been a challenge. To increase the uniformity of infection, we adapted a seedling screening method that directly uses liquid cultures to inoculate the plant roots and the soil. Four isolates, each of B. rouxiae and Fov, were collected from cotton fields in Australia and were characterised for virulence on cotton under controlled plant growth conditions. While the identities of all four B. rouxiae isolates were confirmed by multilocus sequencing, only two of them were found to be pathogenic on cotton, suggesting variability in the ability of isolates of this species to cause disease. The four Fov isolates were phylogenetically clustered together with the other Australian Fov isolates and displayed both external and internal symptoms characteristic of Fusarium wilt on cotton plants. Furthermore, the isolates appeared to induce varied levels of plant disease severity indicating differences in their virulence on cotton. To contrast the virulence of the Fov isolates, four putatively non-pathogenic Fusarium oxysporum (Fo) isolates collected from cotton seedlings exhibiting atypical wilt symptoms were assessed for their ability to colonise cotton host. Despite the absence of Secreted in Xylem genes (SIX6, SIX11, SIX13 and SIX14) characteristic of Fov, all four Fo isolates retained the ability to colonise cotton and induce wilt symptoms. This suggests that slightly virulent strains of Fo may contribute to the overall occurrence of Fusarium wilt in cotton fields. Findings from this study will allow better distinction to be made between plant pathogens and endophytes and allow fungal effectors underpinning pathogenicity to be explored. Full article

Cotton Verticillium wilt is a common fungal disease during the growth of cotton, leading to the yellowing of leaves, stem dryness, and root rot, severely affecting the yield and quality of cotton. Current monitoring methods for Verticillium wilt mainly rely on manual inspection and field investigation, which are inefficient and costly, and the methods of applying pesticides in cotton fields are singular, with issues of low pesticide efficiency and uneven application. This study aims to combine UAV remote sensing monitoring of cotton Verticillium wilt with the precision spraying characteristics of agricultural drones, to provide a methodological reference for monitoring and precision application of pesticides for cotton diseases. Taking the cotton fields of Shihezi City, Xinjiang as the research subject, high-resolution multispectral images were collected using drones. Simultaneously, 150 sets of field samples with varying degrees of Verticillium wilt were collected through ground data collection, utilizing data analysis methods such as partial least squares regression (PLSR) and neural network models; additionally, a cotton Verticillium wilt monitoring model based on drone remote sensing images was constructed. The results showed that the estimation accuracy $R^2$ of the PLSR and BP neural network models based on EVI, RENDVI, SAVI, MSAVI, and RDVI vegetation indices were 0.778 and 0.817, respectively, with $RMSE$ of 0.126 and 0.117, respectively. Based on this, an analysis of the condition of the areas to be treated was performed, combining the operational parameters of agricultural drones, resulting in a prescription map for spraying against cotton Verticillium wilt. Full article

Charcoal rot disease (CRD), caused by the phytopathogenic fungus Macrophomina phaseolina, is a significant threat to cotton production in Israel and worldwide. The pathogen secretes toxins and degrading enzymes that disrupt the water and nutrient uptake, leading to death at the late stages of growth. While many control strategies were tested over the years to reduce CRD impact, reaching that goal remains a significant challenge. The current study aimed to establish, improve, and deepen our understanding of a new approach combining biological agents and chemical pesticides. Such intervention relies on reducing fungicides while providing stability and a head start to eco-friendly bio-protective Trichoderma species. The research design included sprouts in a growth room and commercial field plants receiving the same treatments. Under a controlled environment, comparing the bio-based coating treatments with their corresponding chemical coating partners resulted in similar outcomes in most measures. At 52 days, these practices gained up to 38% and 45% higher root and shoot weight and up to 78% decreased pathogen root infection (tracked by Real-Time PCR), compared to non-infected control plants. Yet, in the shoot weight assessment (day 29 post-sowing), the treatment with only biological seed coating outperformed (p < 0.05) all other biological-based treatments and all Azoxystrobin-based irrigation treatments. In contrast, adverse effects are observed in the chemical seed coating group, particularly in above ground plant parts, which are attributable to the addition of Azoxystrobin irrigation. In the field, the biological treatments had the same impact as the chemical intervention, increasing the cotton plants’ yield (up to 17%), improving the health (up to 27%) and reducing M. phaseolina DNA in the roots (up to 37%). When considering all treatments within each approach, a significant benefit to plant health was observed with the bio-chemo integrated management compared to using only chemical interventions. Specific integrated treatments have shown potential in reducing CRD symptoms, such as applying bio-coating and sprinkling Azoxystrobin during sowing. Aerial remote sensing based on high-resolution visible-channel (RGB), green–red vegetation index (GRVI), and thermal imaging supported the above findings and proved its value for studying CRD control management. This research validates the combined biological and chemical intervention potential to shield cotton crops from CRD. Full article

The soil fungus Macrophomina phaseolina, the charcoal rot disease agent, poses a major threat to cotton fields. In Israel, highly infected areas are also inhabited by the maize pathogen Magnaporthiopsis maydis. This study reveals the relationships between the two pathogens and their impact on cotton sprouts. Infecting the soil 14 days before sowing (DBS) with each pathogen or with M. phaseolina before M. maydis caused a strong inhibition (up to 50–65%) of the sprouts’ development and survival, accompanied by each pathogen’s high DNA levels in the plants. However, combined or sequence infection with M. maydis first led to two distinct scenarios. This pathogen acted as a beneficial protective endophyte in one experiment, leading to significantly high emergence and growth indices of the plants and a ca. 10-fold reduction in M. phaseolina DNA in the sprouts’ roots. In contrast, M. maydis showed strong virulence potential (with 43–69% growth and survival suppression) in the other experiment, proving its true nature as an opportunist. Interestingly, soil inoculation with M. phaseolina first, 14 DBS (but not at sowing), shielded the plants from M. maydis’ devastating impact. The results suggest that the two pathogens restrict each other, and this equilibrium may lead to a moderate disease burst. Full article

Black root rot (BRR) is an economically important disease of cotton and other crops, especially in cooler regions with short growing seasons. Symptoms include black discoloration of the roots, reduced number of lateral roots and stunted or slow plant growth. The cultivated tetraploid Gossypium species are susceptible to BRR. Resistance to BRR was identified in G. arboreum accession BM13H and is associated with reduced and restricted hyphal growth and less sporulation. Transcriptome analysis indicates that BM13H responds to infection at early time points 2- and 3-days post-inoculation, but by day 5, few differentially expressed genes are observed between infected and uninfected roots. Inheritance of BM13H resistance to BRR was evaluated in an F₆ recombinant inbred population and shows a single semi-dominant locus conferring resistance that was fine mapped to a region on chromosome 1, containing ten genes including five putative resistance-like genes. Full article

Cotton root rot is a destructive cotton disease and significantly affects cotton quality and yield, and accurate identification of its distribution within fields is critical for cotton growers to control the disease effectively. In this study, Sentinel-2 images were used to explore the feasibility of creating classification maps and prescription maps for site-specific fungicide application. Eight cotton fields with different levels of root rot were selected and random forest (RF) was used to identify the optimal spectral indices and texture features of the Sentinel-2 images. Five optimal spectral indices (plant senescence reflectance index (PSRI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI1), moisture stressed index (MSI), and renormalized difference vegetation index (RDVI)) and seven optimal texture features (Contrast 1, Dissimilarity 1, Entory 2, Mean 1, Variance 1, Homogeneity 1, and Second moment 2) were identified. Three binary logistic regression (BLR) models, including a spectral model, a texture model, and a spectral-texture model, were constructed for cotton root rot classification and prescription map creation. The results were compared with classification maps and prescription maps based on airborne imagery. Accuracy assessment showed that the accuracies of the classification maps for the spectral, texture, and spectral-texture models were 92.95%, 84.81%, and 91.87%, respectively, and the accuracies of the prescription maps for the three respective models were 90.83%, 87.14%, and 91.40%. These results confirmed that it was feasible to identify cotton root rot and create prescription maps using different features of Sentinel-2 imagery. The addition of texture features had little effect on the overall accuracy, but it could improve the ability to identify root rot areas. The producer’s accuracy (PA) for infested cotton in the classification maps for the texture model and the spectral-texture model was 2.82% and 1.07% higher, respectively, than that of the spectral model, and the PA for treatment zones in the prescription maps for the two respective models was 8.6% and 8.22% higher than that of the spectral model. Results based on the eight cotton fields showed that the spectral model was appropriate for the cotton fields with relatively severe infestation and the spectral-texture model was more appropriate for the cotton fields with low or moderate infestation. Full article

Cotton root rot (CRR), caused by the fungus Phymatotrichopsis omnivora, is a destructive cotton disease that mainly affects the crop in Texas. Flutriafol fungicide applied at or soon after planting has been proven effective at protecting cotton plants from being infected by CRR. Previous research has indicated that CRR will reoccur in the same regions of a field as in past years. CRR-infected plants can be detected with aerial remote sensing (RS). As unmanned aerial vehicles (UAVs) have been introduced into agricultural RS, the spatial resolution of farm images has increased significantly, making plant-by-plant (PBP) CRR classification possible. An unsupervised classification algorithm, PBP, based on the Superpixel concept, was developed to delineate CRR-infested areas at roughly the single-plant level. Five-band multispectral data were collected with a UAV to test these methods. The results indicated that the single-plant level classification achieved overall accuracy as high as 95.94%. Compared to regional classifications, PBP classification performed better in overall accuracy, kappa coefficient, errors of commission, and errors of omission. The single-plant fungicide application was also effective in preventing CRR. Full article

The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas. Full article

Cotton root rot (CRR) is a persistent soilborne fungal disease that is devastating to cotton in the southwestern U.S. and Mexico. Research has shown that CRR can be prevented or at least mitigated by applying a fungicide at planting, but the fungicide should be applied precisely to minimize the quantity of product used and the treatment cost. The CRR-infested areas within a field are consistent from year to year, so it is possible to apply the fungicide only at locations where CRR is manifest, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing (RS) from manned aircraft is an effective means of delineating CRR-infested field areas. Applying various classification methods to moderate-resolution (1.0 m/pixel) RS images has recently become the conventional way to delineate CRR-infested areas. In this research, an unmanned aerial vehicle (UAV) was used to collect high-resolution remote sensing (RS) images in three Texas fields known to be infested with CRR. Supervised, unsupervised, and combined unsupervised classification methods were evaluated for differentiating CRR from healthy zones of cotton plants. Two new automated classification methods that take advantage of the high resolution inherent in UAV RS images were also evaluated. The results indicated that the new automated methods were up to 8.89% better than conventional classification methods in overall accuracy. One of these new methods, an automated method combining k-means segmentation and morphological opening and closing, provided the best results, with overall accuracy of 88.5% and the lowest errors of omission (11.44%) and commission (16.13%) of all methods considered. Full article

Plant secondary metabolites including phenolics and flavonoidsare synthesized through phenylpropanoid and phenylpropanoid–acetate pathways and significantly contribute against adverse effect of abiotic and biotic stresses. Herein, we present the development and execution of a novel and expeditious ultra-performance liquid chromatographic-photodiode array (UPLC–PDA) method for qualitative and quantitative analysis of 28 phenolic compounds comprising of flavonoids, phenolic acids, aldehydes and alcohols. The method is able to separate phenolic compounds in just 17 min with the separation of isobaric species such as 3,4 dihydroxybenzoic acid and 3,5 dihydroxy benzoic acid; quercetin and taxifolin. Linear curves concentrations ranged from 6–18 µg/mL (3,5 dihydroxy benzoic acid), 4–12 µg/mL (catechin and salicylic acid) and 2–6 µg/mL for rest of the compounds and correlation coefficients were >0.994. The limit of detection (LOD) varied from 0.04–0.45 µg/mL. Cotton root samples were used to assess the method in terms of recovery efficiency (85–120%), precision (0.12–4.09%) and intermediate precision (0.32–4.0%).Phenolics and flavonoidsin root samples of healthy and diseased plants as well as leaf samples of healthy plants were successfully quantified using this novel method without an expensive Mass Spectrometer. Full article