Search Results (11)

Citrus canker, caused by Xanthomonas citri subsp. citri, and bacterial blight, caused by Xanthomonas citri subsp. malvacearum, results in substantial economic losses worldwide, and searching for new antibacterial agents is a critical challenge. In this study, regional isolates AE28 and RQ3 were obtained from characteristic lesions on Citrus limon and Gossypium hirsutum, respectively. Essential oils extracted by steam distillation from the fresh aerial parts of Pelargonium graveolens and Schinus molle exhibited complete (100%) inhibition of bacterial growth in vitro at a concentration of 1000 ppm, as determined by diffusion tests. To evaluate the potential of these essential oils for controlling Xanthomonas-induced diseases, in vivo assays were conducted on lemon leaves and cotton cotyledons inoculated with the regional AE28 and RQ3 strains. Two treatment approaches were tested: preventive application (24 h before inoculation) and curative application (24 h after inoculation). Preventive and curative treatments with P. graveolens essential oil significantly reduced citrus canker severity, whereas S. molle essential oil did not show a significant reduction compared to the control. In contrast, regardless of the treatment’s timing, both essential oils effectively reduced bacterial blight severity in cotton cotyledons by approximately 1.5-fold. Gas chromatography–mass spectrometry (GC-MS) analysis identified geraniol and citronellol as the major components of P. graveolens essential oil, while limonene and t-cadinol were predominant in S. molle. These findings highlight the promising potential of botanical products as bactericidal agents, warranting further research to optimize their application and efficacy. Full article

Several countries around the world are facing the issue of freshwater availability, where agriculture is highly dependent on irrigation, consuming 70% of this vital resource. Water availability is the most limiting factor for the crop production sector and one of the main regulators of the spatial distribution of plants. It is noted that in recent years, the methods of irrigation water application have been improved. Currently, research is directed towards irrigation strategies that reduce water applications. A partial root drying (PRD) technique involves irrigating one-half of the root zone while leaving the other half in relatively dry soil. This method is used in the production of various crops, such as potatoes and cotton. However, the mechanism of PRD, including the physiological and molecular biological processes involved, is not fully understood. In this study, tomato plants were treated with PRD and nitrogen (N) top-dressing. The results showed that PRD could significantly increase the fruit yield, photosynthetic activities, nitrate reductase activity, and fruit quality in the tomato plants, and PRD could also promote the concentrations of oxygen species (O₂⁻), malondialdehyde (MDA) and proline contents, and activities of antioxidant enzymes. In addition, PRD could enhance stress resistance by increasing disease resistance and NP1 and DRED3 antioxidant enzyme activity. Tomato plants treated with PRD compared to the control showed high photosynthetic activity, high yield, better quality of production, and low leaf blight incidence. Overall, the results indicate that PRD is a feasible approach that could be effectively utilized in tomato fields to improve plant growth and production compared with the control. 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

Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the L*a*b* color space as either a training or a validation sample; finally, these two kinds of images were processed and combined to establish a cotton blight infection inversion model. Results show that the Red, Rededge, and NIR bands of multispectral UAV images were found to be most sensitive to changes in cotton leaf color caused by blight infection; NDVI and GNDVI were verified to be able to infer cotton blight infection information from the UAV images, of which the model calibration accuracy was 84%. Then, the cotton blight infection status was spatially identified with four severity levels. Finally, a cotton blight inversion model was constructed and validated with ground framed photos to be able to explain about 86% of the total variance. Evidently, multispectral UAV images coupled with ground framed cotton canopy photos can improve cotton blight infection identification accuracy and severity classification, and therefore provide a more reliable approach to effectively monitoring such cotton disease damage. Full article

Every nation’s development depends on agriculture. The term “cash crops” refers to cotton and other important crops. Most pathogens that significantly harm crops also impact cotton. Numerous diseases that influence yield via the leaf, such as powdery mildew, leaf curl, leaf spot, target spot, bacterial blight, and nutrient deficiencies, can affect cotton. Early disease detection protects crops from additional harm. Computerized methods perform a vital role in cotton leaf disease detection at an early stage. The method consists of two core steps such as feature extraction and classification. First, in the proposed method, data augmentation is applied to balance the input data. After that, features are extracted from a pre-trained VGG-16 model and passed to 11 fully convolutional layers, which freeze the majority and randomly initialize convolutional features to subsequently generate a score of the anomaly map, which defines the probability of the lesion region. The proposed model is trained on the selected hyperparameters that produce great classification results. The proposed model performance is evaluated on two publicly available Kaggle datasets, Cotton Leaf and Disease. The proposed method provides 99.99% accuracy, which is competent compared to existing methods. Full article

Alternaria alternata botybirnavirus 1 (AaBRV1) was isolated from a strain of Alternaria alternata, causing watermelon leaf blight in our previous research. The effect of AaBRV1 on the phenotype of its host fungus, however, was not determined. In the present study, a novel strain of AaBRV1 was identified in A. tenuissima strain TJ-NH-51S-4, the causal agent of cotton Alternaria leaf spot, and designated as AaBRV1-AT1. A mycovirus AaBRV1-AT1-free strain TJ-NH-51S-4-VF was obtained by protoplast regeneration, which eliminated AaBRV1-AT1 from the mycovirus AaBRV1-AT1-infected strain TJ-NH-51S-4. Colony growth rate, spore production, and virulence of strain TJ-NH-51S-4 were greater than they were in TJ-NH-51S-4-VF, while the sensitivity of strain TJ-NH-51S-4 to difenoconazole, as measured by the EC₅₀, was lower. AaBRV1-AT1 was capable of vertical transmission via asexual spores and horizontal transmission from strain TJ-NH-51S-4 to strain XJ-BZ-5-1_hyg (another strain of A. tenuissima) through hyphal contact in pairing cultures. A total of 613 differentially expressed genes (DEGs) were identified in a comparative transcriptome analysis between TJ-NH-51S-4 and TJ-NH-51S-4-VF. Relative to strain TJ-NH-51S-4-VF, the number of up-regulated and down-regulated DEGs in strain TJ-NH-51S-4 was 286 and 327, respectively. Notably, the expression level of one DEG-encoding cytochrome P450 sterol 14α-demethylase and four DEGs encoding siderophore iron transporters were significantly up-regulated. To our knowledge, this is the first documentation of hypervirulence and reduced sensitivity to difenoconazole induced by AaBRV1-AT1 infection in A. tenuissima. Full article

Agriculture is essential to the growth of every country. Cotton and other major crops fall into the cash crops. Cotton is affected by most of the diseases that cause significant crop damage. Many diseases affect yield through the leaf. Detecting disease early saves crop from further damage. Cotton is susceptible to several diseases, including leaf spot, target spot, bacterial blight, nutrient deficiency, powdery mildew, leaf curl, etc. Accurate disease identification is important for taking effective measures. Deep learning in the identification of plant disease plays an important role. The proposed model based on meta Deep Learning is used to identify several cotton leaf diseases accurately. We gathered cotton leaf images from the field for this study. The dataset contains 2385 images of healthy and diseased leaves. The size of the dataset was increased with the help of the data augmentation approach. The dataset was trained on Custom CNN, VGG16 Transfer Learning, ResNet50, and our proposed model: the meta deep learn leaf disease identification model. A meta learning technique has been proposed and implemented to provide a good accuracy and generalization. The proposed model has outperformed the Cotton Dataset with an accuracy of 98.53%. Full article

Cotton (Gossypium hirsutum) is a global cash crop which has gained importance in earning foreign exchange for each country. Bacterial blight caused by Xanthomonascitri subsp. malvacearum (Xcm) has been a seriousdisease in Pakistan’s cotton belt on multiple occasions. Bacterium was isolated and identified through various biochemical and diagnostic tests wherehypersensitivity reaction, Gram staining, KOH (potassium hydroxide), catalase, starch hydrolysis, lecithinase and Tween 80 hydrolysis tests confirmed bacterium as Gram-negative and plant pathogenic. Xcm perpetuation assays wereevaluated on various cotton varieties under glasshouse conditions in completely randomized design by three different methods, wherein the scratch method proved to be the best upon CIM-496 and showed 83.33% disease incidence as compared with the other two methods, where Bt-3701 responded with 53.33% incidence via the spray gun method, and 50% with the water splash method on CIM-616, as compared with the control. Similarly, for disease severity percentage, Bt-3701 was pragmatic with 47.21% through scratch method, whereas, in the spray gun method, 45.51% disease severity was noted upon Bt-802, and 31.27% was calculated on Cyto-179 through the water splash method. Owing to the unique antibacterial properties of aqueous plant extracts, the poison food technique showed Aloe vera, Mentha piperita, Syzygiumcumini and Azadirachta indica with 17.77, 29.33, 18.33 and 20.22 bacterial colonies counted on nutrient agarmedium petri plate, respectively, as compared with the control. Measurement of the inhibition zone by disk diffusion technique showed Mentha piperita, Syzygiumcumini, Citrus limon, Moringa oleifera and Syzygium aromaticum to present the most promising results by calculating the maximum diameter of the inhibition zone, viz., 8.58, 8.55, 8.52, 8.49 and 8.41 (mm), respectively, at the highest tested concentration (75 ppm, parts per million) compared with the control. It is probable that the decoction’s interaction with the pathogen population on the host plant will need to be considered in future experiments. However, at this moment, more research into the effective management of cotton bacterial blight by plant extracts in terms of concentration determination and development of biopesticides will provide future avenues to avoid environmental pollution. Full article

To obtain chitinase-producing microorganisms with high chitinolytic activity at low temperature, samples collected from Fildes Peninsula in Antarctica were used as sources for bioprospecting of chitinolytic microorganisms. A cold-adapted strain, designated as GWSMS-1, was isolated from marine sediment and further characterized as Pseudomonas. To improve the chitinase production, one-factor-at-a-time and orthogonal test approaches were adopted to optimize the medium components and culture conditions. The results showed that the highest chitinolytic activity (6.36 times higher than that before optimization) was obtained with 95.41 U L⁻¹ with 15 g L⁻¹ of glucose, 1 g L⁻¹ of peptone, 15 g L⁻¹ of colloid chitin and 0.25 g L⁻¹ of magnesium ions contained in the medium, cultivated under pH 7.0 and a temperature of 20 °C. To better understand the application potential of this strain, the enzymatic properties and the antifungal activity of the crude chitinase secreted by the strain were further investigated. The crude enzyme showed the maximum catalytic activity at 35 °C and pH 4.5, and it also exhibited excellent low-temperature activity, which still displayed more than 50% of its maximal activity at 0 °C. Furthermore, the crude chitinase showed significant inhibition of fungi Verticillium dahlia CICC 2534 and Fusarium oxysporum f. sp. cucumerinum CICC 2532, which can cause cotton wilt and cucumber blight, respectively, suggesting that strain GWSMS-1 could be a competitive candidate for biological control in agriculture, especially at low temperature. Full article

Fusarium Head Blight (FHB, scab) is a destructive fungal disease that causes extensive yield and quality losses in wheat and other small cereals. Biological control of FHB is considered to be an alternative disease management strategy that is environmentally benign, durable, and compatible with other control measures. In this study, to screen antagonistic bacteria with the potential to manage FHB, 113 endophytes were isolated from the stems, leaves, panicles, and roots of wheat. Among them, six strains appeared to effectively inhibit Fusarium graminearum growth and one isolate, XS-2, showed a highly antagonistic effect against FHB. An in vitro antagonistic test of XS-2 on wheat heads confirmed that XS-2 could suppress the disease severity of FHB. The 16S rDNA sequence analysis revealed that XS-2 is a strain of Bacillus amyloliquefaciens. Antagonistic spectrum analyses showed that XS-2 had antagonistic effects against two and four types of cotton and fruit tree pathogens, respectively. The fermentation condition assays showed that glucose and peptone are the most suitable nutrient sources for XS-2, and that the optimal pH value and temperature for fermentation were 7.4 and 28 °C, respectively. Our study indicates that XS-2 has a good antagonistic effect on FHB and lays a theoretical foundation for the application of the strain as a biological agent in the field to control FHB. Full article

The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton. Full article