Fusarium and verticillium wilt are the primary diseases affecting cotton plants, significantly reducing both the yield and quality of cotton. <i>Paenibacillus</i> spp. are crucial biocontrol strains for controlling plant diseases. In this study, <i>Paenibacillus peoriae</i> MHJL1, which could prevent the pathogenic fungi of fusarium and verticillium wilt and promote cotton growth, was isolated from the rhizosphere soil of cotton plants. Whole-genome analysis of strain MHJL1 identified 16 gene clusters for secondary metabolite synthesis, including fusaricidins with potent antifungal properties. By optimizing the fermentation process, the cell and spore numbers of MHJL1 were increased to 2.14 × 10⁸ CFU/mL and 8.66 × 10⁸ CFU/mL, respectively. Moreover, the antifungal ability of MHJL1 was also increased by 31.48%. In pot experiments conducted with healthy soil, the control rates for MHJL1 against fusarium and verticillium wilt were found to be 44.83% and 58.27%, respectively; in experiments using continuously cropped soil, the control rates were 55.22% against fusarium wilt and 48.46% against verticillium wilt. Our findings provide valuable insights for the biocontrol application and fermentation of <i>P. peoriae</i> MHJL1, while also contributing a new resource for the development of microbial agents.

To address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. The LCDDN-YOLO algorithm is based on YOLOv8n, and replaces part of the convolutional layers in the backbone network with Distributed Shift Convolution (DSConv). The BiFPN network is incorporated into the original architecture, adding learnable weights to evaluate the significance of various input features, thereby enhancing detection accuracy. Furthermore, it integrates Partial Convolution (PConv) and Distributed Shift Convolution (DSConv) into the C2f module, called PDS-C2f. Additionally, the CBAM attention mechanism is incorporated into the neck network to improve model performance. A Focal-EIoU loss function is also integrated to optimize the model’s training process. Experimental results show that compared to YOLOv8, the LCDDN-YOLO model reduces the number of parameters by 12.9% and the floating-point operations (FLOPs) by 9.9%, while precision, mAP@50, and recall improve by 4.6%, 6.5%, and 7.8%, respectively, reaching 89.5%, 85.4%, and 80.2%. In summary, the LCDDN-YOLO model offers excellent detection accuracy and speed, making it effective for pest and disease control in cotton fields, particularly in lightweight computing scenarios.

Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production. The cotton crop, is a major cash crop in Asian and African countries and is affected by different types of weeds leading to reduced yield. Weeds infestation starts with the germination of the crop, due to which diseases also invade the field. Therefore, proper monitoring of the cotton crop throughout the entire phases of crop development from sewing to ripening and reaping is extremely significant to identify the harmful and undesired weeds timely and efficiently so that proper measures can be taken to eradicate them. Most of the weeds and pests attack cotton plants at different stages of growth. Therefore, timely identification and classification of such weeds on virtue of their symptoms, apparent similarities, and effects can reduce the risk of yield loss. Weeds and pest infestation can be controlled through advanced digital gadgets like sensors and cameras which can provide a bulk of data to work with. Yet efficient management of this extraordinarily bulging agriculture data is a cardinal challenge for deep learning techniques too. In the given study, an approach based on deep CNN-based architecture is presented. This work covers identifying and classifying the cotton weeds efficiently alongside a comparison of other already existing CNN models like VGG-16, ResNet, DenseNet, and Xception Model. Experimental results indicate the accuracy of VGG-16, ResNet-101, DenseNet-121, XceptionNet as 95.4%, 97.1%, 96.9% and 96.1%, respectively. The proposed model achieved an accuracy of 98.3% outperforming other models.

Abstract Background Polygalacturonase inhibiting proteins (PGIPs) play a pivotal role in plant defense against plant pathogens by inhibiting polygalacturonase (PG), an enzyme produced by pathogens to degrade plant cell wall pectin. PGIPs, also known as leucine-rich repeat pathogenesis-related (PR) proteins, activate the host’s defense response upon interaction with PG, thereby reinforcing the host defense against plant pathogens attacks. In Egyptian or extra-long staple cotton (Gossypium barbadense), the interaction between PGIP and PG is one of the crucial steps in the defense mechanism against major pathogens such as Xanthomonas citri pv. malvacearum and Alternaria macrospora, which are responsible for bacterial leaf blight and leaf spot diseases, respectively. Results To unravel the molecular mechanisms underlying these PR proteins, we conducted a comprehensive study involving molecular modeling, protein-protein docking, site-specific double mutation (E169G and F242K), and molecular dynamics simulations. Both wild-type and mutated cotton PGIPs were examined in the interaction with the PG enzyme of a bacterial and fungal pathogen. Our findings revealed that changes in conformations of double-mutated residues in the active site of PGIP lead to the inhibition of PG binding. The molecular dynamics simulation studies provide insights into the dynamic behaviour and stability of the PGIP-PG complexes, shedding light on the intricate details of the inhibitory and exhibitory mechanism against the major fungal and bacterial pathogens of G. barbadense, respectively. Conclusions The findings of this study not only enhance our understanding of the molecular interactions between PGs of Xanthomonas citri pv. malvacearum and Alternaria macrospora and PGIP of G. barbadense but also present a potential strategy for developing the disease-resistant cotton varieties. By variations in the binding affinities of PGs through specific mutations in PGIP, this research offers promising avenues for the development of enhanced resistance to cotton plants against bacterial leaf blight and leaf spot diseases.

Abstract Background Energy flows in most food chains in the agroecosystem are crowned with beneficial natural enemies including different species of predatory and parasitic insects, birds and animals. They are utilized in organic and IPM cotton production to replace the conventional insecticides usually applied in cotton production. Results Natural populations of six coccinellids, five staphylinids and two carabids (Coleoptera), three anthocorids and three reduviids (Heteroptera), five syrphids (Diptera, three labidurids (Dermaptera), two chrysopids (Neuroptera) and one thripid (Thysanoptera) species were manipulated in Egyptian clover to aggregate in seed production stripes (stripe technique) adjacent to and across the cotton fields during April–May, 2022. These 30 predatory species represent 112 energy flow routes in food chains preying on tetranychid mites, aphids, thrips, whiteflies and cotton leaf worm attacking cotton plants during vegetative growth stage beginning from April to May 2022. High populations of these predators develop along the clover season (November–May) on different pests where no insecticide applications occur. They aggregate in the flowering clover stripes left for seed production feeding on nectar, pollens and remaining pests. By dryness of the clover stripes, populations of all these predatory species abandon the clover, migrating outwards into the adjacent cotton or corn fields showing an excellent high protection against cotton pests suppressing their populations far away under the level of economic threshold damage during vegetative growth stage. Dressing cotton seeds with Bacillus amyloliquefaciens as antagonist protects the seedlings from soil-borne diseases. Insect pheromone traps detected the first appearance of the pink bollworm, Pectinophora gossypiella (Saund.) moths, the cotton bolls are attacked also by the spiny bollworm, Earias insulana (Boisd.). The egg parasitoid Trichogramma evanescens (West.) was released in 6 successive releases to guide the energy flow in favor of the parasitoid by getting it from egg contents of these two pests, which resulted in high protection of cotton bolls. This study aims better understanding of biodiversity and the routes of energy flow among the complex net of food chains governing the bio-dynamics in the Egyptian agroecosystem, which enabled the development of the present strategy to completely abandon application of the conventional insecticides and chemical fertilization for organic cotton production in Egypt. Conclusion The study is an approach contributing to improvement of the agroecosystem and production of healthy crops.

Verticillium wilt of cotton, caused by Verticillium dahliae, is one of the most devastating soilborne fungal diseases in cotton production, urgently demanding the development of effective control measures. Myxobacteria, a group of higher prokaryotes exhibiting multicellular social behaviors, possess predatory activity against plant pathogenic fungi and bacteria, giving them unique potential for application in plant disease biocontrol. In this study, based on a previously myxobacterial strain collection, a myxobacterial strain, HM-E, exhibiting broad-spectrum antifungal activity was screened. Through morphological observation, physiological and biochemical characterization, and multi-locus sequence analysis, this strain was identified as Cystobacter fuscus HM-E. C. fuscus HM-E not only significantly lysed V. dahliae hyphae but also inhibited its spore germination. Both its cell-free fermentation filtrate and volatile metabolites exhibited certain antifungal activity. Greenhouse pot assays showed that the fermentation broth of C. fuscus HM-E had a control efficacy of only 23.01% against cotton Verticillium wilt, whereas the solid agent formulated with white star flower chafer (Protaetia brevitarsis) frass achieved a significantly higher control efficacy of 70.90%, and the myxobacterial solid agent also significantly promoted cotton seedling growth. Furthermore, the crude extracts concentrated using macroporous resin and acid precipitation showed no antifungal activity against V. dahliae, whereas the crude protein obtained by ammonium sulfate precipitation disrupted not only the cell wall and cell membrane of V. dahliae hyphae, induced intracellular reactive oxygen species (ROS) burst but also lysed spores and inhibited spore germ tube elongation. Enzyme substrate profile assays indicated that several peptidases, lipases, and glycoside hydrolases secreted by C. fuscus HM-E might play important roles in its antifungal process and are potential biocontrol factors. This study suggests C. fuscus HM-E, as a novel biocontrol agent, has great potential for application in the combating of cotton Verticillium wilt.

IntroductionCotton (Gossypium hirsutum L.) plays a vital role in Pakistan’s economy, providing significant employment opportunities and supporting the country’s textile industry. However, cotton productivity is severely impacted by pests and diseases, such as black spots caused by sooty mold, posing critical challenges to sustainable agriculture. This study investigates a novel integration of plant growth-promoting rhizobacteria (PGPR) with recommended NPK fertilizers and micronutrients to enhance cotton growth, yield, disease resistance, and post-harvest soil properties.MethodologyA consortium of Bacillus megaterium (ZR19), Paenibacillus polymyxa (IA7), and Bacillus sp. (IA16) were evaluated under six treatments: control (T1), PGPR (T2), recommended NPK (T3), recommended NPK + PGPR (T4), recommended NPK + micronutrients (T5), and recommended NPK + micronutrients + PGPR (T6).ResultsThe results depicted a significant increase in antioxidant activities of 19% in superoxide dismutase (SOD), 29% peroxidase (POX), 28% peroxidase dismutase (POD), and 14% catalase (CAT) activity under T6 as compared to control. Similarly, growth parameters substantially improved root length (39%), shoot length (19%), and root and shoot biomass by up to 31 and 20%, respectively, under T6. Moreover, the yield attributes like single boll weight and lint percentage were also enhanced by 32 and 13%, respectively, under the integration. In contrast, the PGPR consortium demonstrated considerable biocontrol potential against sooty mold, as disease incidence was reduced by 68% in cotton, the disease index was 75%, and control efficacy reached 75%. The PGPR consortium also substantially improved post-harvest soil biological and chemical properties, including bacterial populations, microbial biomass nitrogen, organic matter, and essential nutrient availability.DiscussionSo, these findings witnessed the dual behavior of the Bacillus and Paenibacillus strains with balanced nutrition and can lead us to the development of an effective biopesticide cum biofertilizer for the sustainable production of cotton in arid conditions by combating sooty mold effectively.

Cropping sequence can have a major impact on diseases, pests, nutrient cycling, crop yield, and overall financial return at the farm level for crops that are grown on an annual basis. In some cases, implementing an effective rotation sequence can allow growers to avoid using nematicides to suppress plant-parasitic nematodes. Two cropping system trials were established with ten rotations each in 1997 and have been maintained through 2022. From 2013 through 2019, rotation sequences were both favorable and unfavorable for peanut (<i>Arachis hypogaea</i> L.) plant health. Peanut (2020), cotton (<i>Gossypium hirsutum</i> L.) (2021), peanut (2022), and corn (<i>Zea mays</i> L.) (2023) were planted in all plots to determine the residual effects of the previous cropping sequence. In 2020, 2021, and 2022, fluopyram at 0.25 kg ai/ha was applied in the seed furrow at planting in the same area of each plot to determine if the response of nematode populations and crop yield to this nematicide differed based on previous crop sequence. Differences in nematode populations in soil and yield of peanut (2020 and 2022) and cotton (2021) were observed when comparing crop rotation sequences regardless of fluopyram treatment. Increasing the number of years peanut was in the rotation or including soybean [<i>Glycine max</i> (L.) Merr.] rather than corn or cotton often resulted in higher populations of nematodes and a lower peanut yield. While fluopyram occasionally reduced nematode populations in soil and root injury from nematode feeding, the yield of peanut did not differ when comparing non-treated and fluopyram-treated peanut. When pooled over crop rotation sequence, peanut yield at Lewiston–Woodville was 5970 kg/ha vs. 6140 kg/ha for these respective treatments. At this location in 2021 and at Rocky Mount in 2019 and 2020, peanut yield for this comparison was 4710 vs. 4550, 5790 kg/ha vs. 6010 kg/ha, and 6060 kg/ha vs. 6120 kg/ha, respectively. These data indicate that previous crop sequences can influence crop yield more than the continuous use of fluopyram. Therefore, fluopyram is not recommended for application in the seed furrow at planting to suppress nematodes in cotton or peanut in North Carolina.

Citrus canker, caused by <i>Xanthomonas citri</i> subsp. <i>citri</i>, and bacterial blight, caused by <i>Xanthomonas citri</i> subsp. <i>malvacearum</i>, 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 <i>Citrus limon</i> and <i>Gossypium hirsutum</i>, respectively. Essential oils extracted by steam distillation from the fresh aerial parts of <i>Pelargonium graveolens</i> and <i>Schinus molle</i> 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 <i>Xanthomonas</i>-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 <i>P. graveolens</i> essential oil significantly reduced citrus canker severity, whereas <i>S. molle</i> 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 <i>P. graveolens</i> essential oil, while limonene and t-cadinol were predominant in <i>S. molle</i>. These findings highlight the promising potential of botanical products as bactericidal agents, warranting further research to optimize their application and efficacy.

ABSTRACT Influenza, as well as other respiratory viruses, can trigger local and systemic inflammation resulting in an overall “cytokine storm” that produces serious outcomes such as acute lung injury (ALI) or acute respiratory distress syndrome (ARDS). We hypothesized that gene therapy platforms could be useful in these cases if the production of an anti-inflammatory protein reflects the intensity and duration of the inflammatory condition. The recombinant protein would be produced and released only in the presence of the inciting stimulus, avoiding immunosuppression or other unwanted side effects that may occur when treating infectious diseases with anti-inflammatory drugs. To test this hypothesis, we developed AdV.C3-Tat/HIV-Box A, an inflammation-inducible cassette that remains innocuous in the absence of inflammation but releases HMGB1 Box A, an antagonist of high mobility group box 1 (HMGB1), in response to inflammatory stimuli such as lipopolysaccharide (LPS) or influenza virus infection. We report here that this novel inflammation-inducible HMGB1 Box A construct in a non-replicative adenovirus (AdV) vector mitigates lung and systemic inflammation therapeutically in response to influenza infection. We anticipate that this strategy will apply to the treatment of multiple diseases in which HMGB1-mediated signaling is a central driver of inflammation.IMPORTANCEMany inflammatory diseases are mediated by the action of a host-derived protein, HMGB1, on Toll-like receptor 4 (TLR4) to elicit an inflammatory response. We have engineered a non-replicative AdV vector that produces HMGB1 Box A, an antagonist of HMGB1-induced inflammation, under the control of an endogenous complement component C3 (C3) promoter sequence, that is inducible by LPS and influenza in vitro and ex vivo in macrophages (Mϕ) and protects mice and cotton rats therapeutically against infection with mouse-adapted and human non-adapted influenza strains, respectively, in vivo. We anticipate that this novel strategy will apply to the treatment of multiple infectious and non-infectious diseases in which HMGB1-mediated TLR4 signaling is a central driver of inflammation.