Gossypium Research Articles (Page 1)

Combining random forest and XGBoost models for source apportionment and health risk assessments of heavy metals in suburban farmland soils.

Abstract Background Irrigation has been a strategy used to reduce losses due to drought, which combined with a good supply of nitrogen (N), can improve the protective system of cotton plants. The objective of this study was to investigate the effects of irrigated and rainfed cotton cultivation using different rates and sources of N. Cotton cultivation was carried out in Selvíria-MS field in the 2017/2018 harvest. The experiment was conducted in randomized blocks, which were designed in a 4 × 2 × 2 factorial scheme. The factors were composed of 0, 40, 80, and 150 kg·hm −2 level of N, using two sources of N under rainfed and irrigated systems. Results The provision of irrigation provided an increase in the levels of chlorophylls (Chl) a, Chl b, total Chl, carotenoids, pheophytin, leaf chlorophyll index (LCI), N content, nitrate (NO 3 − ), sucrose (SUC), the number of vegetative and reproductive branches, boll mass, and seed cotton productivity. There was no effect of N sources on any of the characteristics evaluated. Application of 150 kg·hm −2 level of N increased in 11%, 59%, 22%, 15%, 15% and 17% in LCI, NO 3 − , N, total amino acids (TA), SUC, and proline concentration in leaves, compared with 0 kg·hm −2 of N, respectively. Application of 150 kg·hm −2 level of N improved the leaf catalase activity (CAT) under the irrigation system; however, in a rainfed system, the highest CAT was observed at rates of 0 and 150 kg·hm −2 level of N. Irrigation increased in 55%, 117%, 68%, 46%, 8%, 36%, 24%, 118%, 48%, 10%, 11% and 72% in Chl a, Chl b, total Chl, CAR, LCI, pheophytins (Pheo), SUC, NO 3 − , the number of vegetative branches, the number of reproductive branches, mass of 20 bolls and seed cotton yield compared with rainfed system, respectively, however, the antioxidant system and the ammonium content of plants was stimulated by rainfed cultivation. Conclusions Antioxidant responses increased during droughts in cotton farming, which may be connected to oxidative stress-related losses. Better N metabolism, photosynthetic pigments, and manufacturing components were all made possible by irrigated cultivation. The delivery of 150 kg·hm −2 of N in topdressing in cotton agriculture promoted the N metabolism, sucrose, total amino acids, and the plant’s defense mechanism against oxidative stress.

Expression of the APX gene and ROS levels in four Gossypium species are interrelated, functioning in cotton pigment gland development and abiotic stress responses. Ascorbate peroxidases (APXs) represent a crucial family of antioxidant enzymes that play essential roles in plant responses to environmental stresses and the regulation of developmental processes. Pigment glands in cotton are specialized structures formed through programmed cell death (PCD), a process inherently linked to the accumulation of reactive oxygen species (ROS). Despite the well-established functions of APXs in maintaining ROS homeostasis and the recognized involvement of ROS in pigment gland development, the specific interactions between APXs and ROS within these glands remain largely uncharacterized. In this study, bioinformatics approaches were employed to systematically analyze APX genes across multiple cotton varieties. Expression profiling revealed that APX genes exhibited both upregulated and downregulated responses to environmental stresses as well as hydrogen peroxide (H2O2) treatment, suggesting a potential dual-function mechanism for APXs in regulating ROS levels. Significant accumulation of ROS was observed in glands, while negligible levels were detected in glandless cotton. Comparative analysis indicated that most APX genes displayed higher expression levels in glanded cotton plants. Functional validation experiments demonstrated that overexpression and knockdown of GhMYC2-like-a key regulator involved in pigment gland formation-respectively induced and repressed the expression of six APX genes. These findings suggest that members of the APX family are integral components within the regulatory network governing cotton pigment gland formation. This study not only provides novel insights into the role of APXs within cotton biology but also establishes a solid foundation for future investigations into their functions related to ROS-mediated processes.

Identification of the full-length GbERD7 gene family in Gossypium barbadense and functional analysis of the role of the GbERD7g gene in drought and salt tolerance.

Nitric acid-modified biochar enhances saline-alkali soil remediation and cotton growth via regulating soil-plant homeostasis.

Abstract. Monitoring municipal solid waste (MSW) landfills is essential for understanding surface dynamics, vegetation changes, and temperature anomalies that may indicate environmental risks. Traditional ground-based methods are often labor-intensive and spatially limited, making unmanned aerial vehicle (UAV) remote sensing an efficient alternative. This study employs UAV-based RGB and thermal infrared (TIR) imaging to assess landfill conditions in two Czech MSW sites. The first objective is to map the distribution of the invasive plant cotton thistle (Onopordum acanthium L.) using UAV RGB imagery data and a convolutional neural network classifier. The second objective is to analyse apparent temperature variations across landfill zones using repetitive UAV TIR imagery. Results demonstrate that UAV RGB imagery effectively detects vegetation patterns, while UAV TIR imaging identifies temperature anomalies that may be associated with landfill processes. Zones with mixed waste and soil remain the warmest over time, suggesting potential hotspots for gas emissions. UAV-based monitoring offers a multi-dimensional approach to landfill assessment with the early detection of environmental concerns. Future work should focus on mapping other landfill plant species, refining temperature corrections, and integrating additional ground-truthing for emissivity adjustments for precise thermal measurements.

Microbe-induced gene silencing targeting VdEno of Verticillium dahliae for the control of cotton Verticillium wilt

BackgroundSoil salinity poses a serious threat to cotton production worldwide by impairing growth, yield, and fiber quality. Salt stress disrupts key morphological, physiological, and biochemical processes in cotton plants, leading to considerable reductions in productivity. Therefore, identifying salt-tolerant cotton genotypes is essential for improving crop performance in saline environments.MethodsIn this study, fifty-one cotton genotypes were evaluated for their response to salinity stress at the seedling stage. Plants were grown in hydroponic culture under controlled glasshouse conditions and subjected to 200 mM NaCl to simulate salt stress. The experiment followed a completely randomized design (CRD) with three replications, and data were analyzed using two-way analysis of variance (ANOVA) and multivariate approaches, including principal component analysis (PCA), heatmap analysis, and the multi-trait genotype-ideotype distance index (MGIDI).ResultsANOVA showed significant variation among genotypes for all traits. Salt stress caused significant reductions in growth traits, including shoot and root length, fresh and dry biomass, water relation traits, gaseous exchange traits and photosynthetic pigments. In contrast, excised leaf water loss (ELWL), sodium (Na+ )accumulation in roots and shoots, oxidative stress markers like hydrogen peroxide (H₂O₂) and malondialdehyde (MDA), osmolytes including proline, glycine betaine (GB), and saponin, and antioxidant enzyme activities like superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) increased, while potassium contents (K+) and sodium to potassium ratio (K⁺/Na+) decreased. Under control conditions, PCA showed little variation, whereas under salt stress, it explained 64.8% of the variance and separated growth- from stress-related traits. Heatmap analysis confirmed these patterns and grouped genotypes into three clusters based on ion homeostasis and oxidative stress traits. MGIDI index integrated all traits into a single score and identified superior genotypes like G2 (NIAB-868), G22 (NIA-Noori), G32 (FH-530), G3 (NIAB-878-B), G49 (FH-911), G28 (FH-416), G33 (FH-534), and G39 (FH-546).ConclusionThese findings suggest that multivariate and multi-trait screening at the seedling stage is a useful method for identifying cotton germplasm with salt tolerance, providing a foundation for breeding programs and further field evaluation that may contribute to stable yields under saline conditions.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12870-025-07354-4.

Fusarium oxysporum f.sp. vasinfectum (FOV) and Verticillium dahliae occupy a special place among the pathogenic fungi that affect plant productivity, causing annually serious damage to the yield, fiber quality, morpho-biological and agronomic properties of the cotton plants. Therefore, the study of the complex molecular processes that occur between the pathogen and the plant remains one of the most important tasks. This requires molecular geneticists and breeders to fully understand the defense mechanism that has emerged in cotton plants against pathogens and to be able to apply it correctly in practice. To combat pathogenic fungi, a thorough analysis of the natural defense mechanisms of plants, including miRNA, transcription factors (TFs), quantitative trait loci (QTL), regulatory functions of plant cell membranes and proteins, may be of great importance. In this paper, we reviewed the research conducted in recent years to identify miRNAs, TFs and QTLs participating in the defense mechanism against FOV and V. dahliae. This review provides insight to understand research aimed at reducing and controlling the future economic damage caused by pathogenic fungi. Studying those factors by using modern genomic technologies together with OMICS studies has accelerated research in this discipline. As a result, the integration of various methods has emerged, developing new approaches such as multi-omics. Integrating these promising methodologies will enhance our comprehension of the molecular mechanisms underlying wilt resistance in cotton plants, leading to the development of novel resistant varieties.

On August 13, 2024, during a routine insect scouting walkthrough, symptomatic cotton leaves were collected from a field in Washington County, North Carolina (NC), and submitted to the Plant Pathology Laboratory of the corresponding author at North Carolina State University for diagnosis. The samples exhibited circular lesions with yellow halos and concentric circles in brown to reddish hues, characteristic of target spot caused by Corynespora cassiicola. Leaf tissue from lesion margins, was surface sterilized and plated on acidified potato dextrose agar (A-PDA). Plates were incubated at 28°C in the dark for seven days. Emerging fungal colonies were subcultured to obtain pure cultures. The isolates showed dark mycelial growth with a surrounding white halo and produced conidia measuring 60 µm to 120 µm long, on average, with cylindrical to obclavate morphology. Two isolates were recovered. Mycelial fragments from a pure culture were grown in malt extract broth for 48 hours before genomic DNA extraction, PCR, and sequencing. The internal transcribed spacer (ITS) region was amplified using primers ITS4 (5' TCCTCCGCTTATTGATATGC 3') and ITS5 (5' GGAAGTAAAAGTCGTAACAAGG 3') (White et al., 1990). Sequencing showed 100% nucleotide identity with Corynespora cassiicola (GenBank accession MF428364.1). To confirm pathogenicity, Koch's postulates were completed under greenhouse conditions. Cotton seedlings (DP2127 B3XF) were grown for 24 days. A 12-day-old quarter-strength PDA culture of the isolate was covered with 0.01% Tween solution and filtered through four layers of cheesecloth. The resulting spore suspension was adjusted to 4 x 104 spores/ml (Moore et al. 2021). Nine cotton plants were inoculated by pipetting 500 ul of the conidial suspension onto true leaves. Nine control plants received 0.01% Tween solution only. Leaves were covered with Kimwipes, misted with water, and enclosed in plastic bags for 48 hours. Plants were maintained at 81°F with 51.6% relative humidity. At seven days post-inoculation, inoculated plants developed characteristic C. casiicola lesions with concentric rings. The pathogen was successfully reisolated and confirmed by PCR and ITS sequencing, again showing 100% identity to C. casiicola (MF428364.1). This constitutes the first confirmed report of C. casiicola causing target spot in cotton fields in NC. The pathogen has previously been reported in Georgia (Fulmer et al., 2012), Alabama (Conner et al. 2013), Louisiana (Price et al. 2015) and Tennessee (Butler et al. 2016). In 2024, target spot was also observed and diagnosed by the Plant Disease and Insect Clinic (PDIC) in a separate cotton field in Edgecombe County, NC, with 15% incidence on variety 'DP2127 B3XF.' However, isolates from that location did not survive. The Washington County field from which Koch's postulates were completed is the same site where target spot symptoms were informally reported in 2012 with 10% incidence on an unknown variety. Also in 2012, the PDIC confirmed C. cassiicola in variety 'PHY 499 WRF' in Chowan County, with 50% field incidence. This current confirmation supports improved monitoring and management of this emerging disease.

IntroductionPlant type is an important part of plant phenotypic research, which is of great significance for practical applications such as plant genomics and cultivation knowledge modeling. The existing plant type judgment mainly relies on subjective experience, and lacks automatic analysis and identification methods, which seriously restricts the progress of efficient crop breeding and precision cultivation.MethodsIn this study, the digital structure model of cotton plant was constructed based on multi-dimensional vision, and the rapid analysis and identification method of cotton plant type was established. 50 cotton plants were used as experimental objects in this study. Firstly, multi-view images of cotton plants at boll opening stage were collected, and a three-dimensional point cloud model of cotton plants was constructed based on Structure From Motion and Multi View Stereo (SFM-MVS) algorithm. The original cotton point cloud data was preprocessed by coordinate correction, statistical filtering, conditional filtering and down-sampling to obtain a high-quality three-dimensional model. The three-dimensional model is projected in two dimensions to obtain the two-dimensional projection data of cotton plants from multiple perspectives. Secondly, based on the fast convex hull algorithm, the cotton plant two-dimensional convex hull was constructed from multiple perspectives, and the distribution range and corner change rate of each corners of the convex hull were analyzed, and the identification basis of cotton plant type was established.ResultsThe R2 of plant height and width extracted from the model were greater than 0.90, and RMES were 0.372 cm and 0.387 cm, respectively. When the maximum number of point clouds is 75335, the point cloud reading time, cotton multi-view projection time, and convex hull automatic construction time are 0.402 S, 2.275 S, and 0.018 S, respectively. Finally, the cotton cylinder type classification interval is 0-0.2, and the tower type classification interval is 0.4-1.5.DiscussionThe cotton plant type identification method proposed in this study is fast and efficient. It provides a solid theoretical basis and technical support for cotton plant type identification.

Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage.

Cotton is a valuable crop for the textile industry yet, its production is significantly affected by Cotton Leaf Curl Disease (CLCuD), a major cotton constraint. The present study was conducted under field trials and glasshouse conditions to analyze the effect of CLCuD in cotton. Single plant progeny rows (SPPRs) of different cotton accessions were grown in the field. In the glasshouse, two sets of cotton plants were maintained in a controlled environment. One set was kept healthy, while the other was graft-inoculated with a Cotton Leaf Curl Virus (CLCuV) infected plant. After 90 days post-inoculation, SPPRs and grafted plants were screened for symptom development using a disease rating scale from 0 to 6. Estimation of antioxidants and metabolites revealed significant differences in CLCuD-resistant and susceptible varieties. Elevated levels of total phenolic content (TPC), tannins, total oxidant status (TOS), total soluble proteins (TSP), and malondialdehyde (MDA) were observed by CLCuD-susceptible genotypes in the field and glasshouse. In contrast, increased antioxidants for example, peroxidase (POD), ascorbate peroxidase (APX), and, catalase (CAT) were observed in CLCuD- resistant varieties. Under field conditions, CLCuD-resistant varieties showed elevated antioxidant enzymes, with CAT, POD, and APX activities increasing by 32%, 3%, and 8% respectively, while superoxide dismutase (SOD) activity decreased by 25% compared to susceptible lines. Under glasshouse conditions, resistant genotypes showed stronger antioxidant responses than susceptible ones; for instance, POD and APX activities were ~ 62% and ~ 6% higher, respectively, while CAT and SOD increased by 15% and 3%. Principal component analysis (PCA) of the field experiment indicated that five key factors contributed to 80.26% of the variation observed among genotypes. Analysis of the glasshouse experiment explained 74.24% of the total cumulative variability. These factors were identified as the most influential in explaining differences in morphological and biochemical traits. In our study, genotypes Mac-07, T7-1-2, and T7-2-5, showed high chlorophyll a, lycopene, TPC, tannins, MDA, and antioxidant enzymes in the field. Under glasshouse conditions, their un-inoculated plants exhibited elevated level of chlorophyll a and b, total chlorophyll, lycopene, APX, SOD, CAT, and POD. Overall, Mac-07, T7-1-2, and T7-2-5 demonstrated superior performance against CLCuD across both conditions and can be considered strong candidates for future CLCuD-resistant cotton breeding programs.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12870-025-07250-x.

The Gossypium genus, the source of commercial cotton, holds a significant, often-overlooked position in global ethnomedicine. While its economic value is dominated by fiber production, various parts of the cotton plant, including seeds, roots, leaves, and flowers, have a long history of use for treating a spectrum of ailments. The genus is a rich reservoir of bioactive constituents, most notably the polyphenolic sesquiterpenoid aldehyde gossypol, alongside a diverse array of flavonoids, phenolic acids, peptides, and proteins. These compounds contribute to a broad spectrum of biological activities. Scientific validation has increasingly substantiated the plant's traditional applications, revealing potent anticancer, antiprotozoal, antimicrobial, antioxidant, and anti-inflammatory properties. Gossypol, in particular, has been extensively studied as a multi-target anticancer agent. Moreover, constituents from Gossypium species and the cellulose backbone itself are being leveraged in novel material science applications, including functionalized antimicrobial textiles and advanced nanocrystal-based drug delivery systems. This review collates the ethnobotanical context, details the main phytochemical classes, organizes the current pharmacological evidence, and discusses the critical toxicological and sustainability considerations, indicating that the Gossypium genus as a valuable resource for modern therapeutic and biotechnological development.

Cross-domain transfer learning is becoming increasingly popular as an effective technique for disease detection in cotton crops with the help of artificial intelligence. Though the concept promises a lot, there are still a number of constraints and research loopholes that need to be filled. One big challenge is that many models are trained on clean, high-quality images from areas like medical scans or face detection, but real farm images are messy, natural, and often lower in quality. This mismatch makes it hard for the models to perform well in agriculture. Another challenge is the limited number of large labeled datasets. Particularly for cotton plant diseases, this hinders the ability to fine-tune or train models effectively. There are also no standardized benchmarks to assess and compare various models on an equitable basis. These gaps show why we need better models for agriculture, bigger datasets, and common testing methods, so AI tools can truly help farmers in the field. In this study, the performance of different YOLO versions was analyzed for cotton plant disease detection. Experimental results show that YOLOv8 achieved the best overall accuracy (95.14%) for cotton disease detection. The results provide valuable insights for researchers to advance further in this field and explore new opportunities.

Cotton leaf image dataset for disease classification and health monitoring

Cotton is an important crop for the economy and textile sector in arid and semi-arid areas. This study evaluates the physical and chemical quality of cotton fibers grown in the El Meita region of Khenchela, Algeria, focusing on fiber fineness, length, and strength, as well as chemical analysis of the soil and fibers using Fourier transform infrared spectroscopy (FTIR). Samples taken from several experimental plants showed notable variability in fiber quality, highlighting the impact of local soil and climate conditions. FTIR analysis detected essential organic and inorganic compounds, such as lignin, cellulose, calcium, and silica, revealing positive relationships between various soil elements and the mechanical properties of the fibers. These results provide crucial insights for the selection and improvement of local varieties, enabling increased fiber productivity and quality while promoting sustainable agriculture of cotton in the Khenchela region

The threecornered alfalfa hopper (TCAH), Spissistilus festinus (Say), is a sporadic pest in seedling cotton that has the potential to cause significant impact to cotton growth and development. Threecornered alfalfa hopper is a stem girdling pest that generally feeds on the main stem of cotton plants from the two- to eight-leaf growth stages. To better understand the potential impact of this pest on cotton growth and development, trials were conducted in Starkville and Stoneville, MS in 2023 and 2024. Experiments were implemented as a randomized complete block design with a factorial arrangement of treatments with four replications. Factor A was simulated TCAH damage at 0, 10, 20, 30, 40, and 50% increments; factor B was damage timed at the three- and six-leaf growth stage. Damage was simulated by applying low rates of glyphosate to stunt the plant to approximate TCAH damage. There were no significant impacts of simulated TCAH damage level percentage or timing on yield. Therefore, the results from this study suggest that TCAH has little economic impact on cotton seedlings.

Cotton plant point cloud completion by collaborative segmentation and improved completion network

Aquaporin GhPIP2;1 positively regulates salt tolerance in upland cotton (Gossypium hirsutum L.).