Cotton Flower Research Articles

GhASHH1.A and GhASHH2.A Improve Tolerance to High and Low Temperatures and Accelerate the Flowering Response to Temperature in Upland Cotton (Gossypium hirsutum).

The trithorax group (TrxG) complex is an important protein in the regulation of plant histone methylation. The ABSENT, SMALL, OR HOMEOTIC DISCS 1 (ASH1) gene family, as important family members of the TrxG complex, has been shown to regulate tolerance to abiotic stress and growth and development in many plants. In this study, we identified nine GhASH1s in upland cotton. Bioinformatics analysis revealed that GhASH1s contain a variety of cis-acting elements related to stress resistance and growth and development. The transcriptome expression profiles revealed that GhASHH1.A and GhASHH2.A genes expression were upregulated in flower organs and in response to external temperature stress. The results of virus-induced gene silencing (VIGS) indicated that GhASHH1.A and GhASHH2.A genes silencing reduced the ability of cotton to adapt to temperature stress and delayed the development of the flowering phenotype. We also showed that the silencing of these two target genes did not induce early flowering at high temperature (32 °C), suggesting that GhASHH1.A and GhASHH2.A might regulate cotton flowering in response to temperature. These findings provide genetic resources for future breeding of early-maturing and temperature-stress-tolerant cotton varieties.

Repurposing Cotton Gin Trash for Cellulose Nanofibril-Silver Hybrid and Ultralight Silver-Infused Aerogel.

Cellulose nanofibril-silver (CNF-Ag) hybrid and ultralight silver-infused aerogel were produced using cotton gin trash (CGT), an abundant agro-waste material. This repurposing of CGT was achieved by exploiting its potential for CNF extraction and the in situ synthesis of silver nanoparticles (Ag NPs). CNFs were extracted from CGT through a mechanical shearing process. These CNFs served as a multifunctional nanotemplate for the controlled reduction of Ag ions, efficient nucleation, and stabilization of NPs, resulting in the production of a high concentration of Ag NPs (ca. 19 wt %) within the CNFs. Transmission electron microscopy images of cross-sectioned CNFs confirmed the uniform dispersion of NPs (ca. 18 nm diameter) inside the CNFs. Rietveld refinement analysis of X-ray diffraction patterns revealed that CNFs produced smaller Ag crystallites compared to CGT microparticles. The CNF-Ag hybrid was then fabricated into an aerogel using freeze-drying, with its weight being light enough to rest on a cotton flower's stamen. The infusion of Ag NPs led to approximately 20% reductions in the specific surface area and pore volume of the aerogel.

Integrating RTM-GWAS and meta‑QTL data revealed genomic regions and candidate genes associated with the first fruit branch node and its height in upland cotton.

Two genomic regions associated with FFBN and HFFBN and a potential regulatory gene (GhE6) of HFFBN were identified through the integration of RTM-GWAS and meta‑QTL analyses. Abstract The first fruit branch node (FFBN) and the height of the first fruit branch node (HFFBN) are two important traits that are related to plant architecture and early maturation in upland cotton. Several studies have been conducted to elucidate the genetic basis of these traits in cotton using biparental and natural populations. In this study, by using 9,244 SNP linkage disequilibrium block (SNPLDB) loci from 315 upland cotton accessions, we carried out restricted two-stage multilocus and multiallele genome-wide association studies (RTM-GWASs) and identified promising haplotypes/alleles of the four stable and true major SNPLDB loci that were significantly associated with FFBN and HFFBN. Additionally, a meta-quantitative trait locus (MQTL) analysis was conducted on 274 original QTLs that were reported in 27 studies, and 40 MQTLs associated with FFBN and HFFBN were identified. Through the integration of the RTM-GWAS and meta‑QTL analyses, two stable and true major SNPLDBs (LDB_5_15144433 and LDB_16_37952328) that were distributed in the two MQTLs were identified. Ultimately, 142 genes in the two genomic regions were annotated, and three candidate genes associated with FFBN and HFFBN were identified in the genomic region (A05:14.64-15.64Mb) via RNA-Seq and qRT‒PCR. The results of virus-induced gene silencing (VIGS) experiments indicated that GhE6 was a key gene related to HFFBN and that GhDRM1 and GhGES were important genes associated with early flowering in upland cotton. These findings will aid in the future identification of molecular markers and genetic resources for developing elite early-maturing cultivars with ideal plant characteristics.

Three-view cotton flower counting through multi-object tracking and RGB-D imagery

Monitoring the number of cotton flowers can provide important information for breeders to assess the flowering time and the productivity of genotypes because flowering marks the transition from vegetative growth to reproductive growth and impacts the final yield. Traditional manual counting methods are time-consuming and impractical for large-scale fields. To count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform. The tracking-by-detection algorithm was employed to track flowers from three views simultaneously and remove duplicated counting from single views. Specifically, an object detection model (YOLOv8) was trained to detect flowers in RGB images and a deep learning-based optical flow model Recurrent All-pairs Field Transforms (RAFT) was used to estimate motion between two adjacent frames. The intersection over union and distance costs were employed to associate flowers in the tracking algorithm. Additionally, tracked flowers were segmented in RGB images and the depth of each flower was obtained from the corresponding depth image. Those flowers tracked with known depth from two side views were then projected onto the middle image coordinate using camera calibration parameters. Finally, a constrained hierarchy clustering algorithm clustered all flowers in the middle image coordinate to remove duplicated counting from three views. The results showed that the mean average precision of trained YOLOv8x was 96.4%. The counting results of the developed method were highly correlated with those counted manually with a coefficient of determination of 0.92. Besides, the mean absolute percentage error of all 25 testing videos was 6.22%. The predicted cumulative flower number of Pima cotton flowers is higher than that of Acala Maxxa, which is consistent with what breeders have observed. Furthermore, the developed method can also obtain the flower number distributions of different genotypes without laborious manual counting in the field. Overall, the three-view approach provides an efficient and effective approach to count cotton flowers from multiple views. By collecting the video data continuously, this method is beneficial for breeders to dissect genetic mechanisms of flowering time with unprecedented spatial and temporal resolution, also providing a means to discern genetic differences in fecundity, the number of flowers that result in harvestable bolls. The code and datasets used in this paper can be accessed on GitHub: https://github.com/UGA-BSAIL/Multi-view_flower_counting.

H3K36 methyltransferase GhKMT3;1a and GhKMT3;2a promote flowering in upland cotton

BackgroundThe SET domain group (SDG) genes encode histone lysine methyltransferases, which regulate gene transcription by altering chromatin structure and play pivotal roles in plant flowering determination. However, few studies have investigated their role in the regulation of flowering in upland cotton.ResultsA total of 86 SDG genes were identified through genome-wide analysis in upland cotton (Gossypium hirsutum). These genes were unevenly distributed across 25 chromosomes. Cluster analysis revealed that the 86 GhSDGs were divided into seven main branches. RNA-seq data and qRT‒PCR analysis revealed that lysine methyltransferase 3 (KMT3) genes were expressed at high levels in stamens, pistils and other floral organs. Using virus-induced gene silencing (VIGS), functional characterization of GhKMT3;1a and GhKMT3;2a revealed that, compared with those of the controls, the GhKMT3;1a- and GhKMT3;2a-silenced plants exhibited later budding and flowering and lower plant heightwere shorter. In addition, the expression of flowering-related genes (GhAP1, GhSOC1 and GhFT) significantly decreased and the expression level of GhSVP significantly increased in the GhKMT3;1a- and GhKMT3;2a-silenced plants compared with the control plants.ConclusionA total of 86 SDG genes were identified in upland cotton, among which GhKMT3;1a and GhKMT3;2a might regulate flowering by affecting the expression of GhAP1, GhSOC1, GhFT and GhSVP. These findings will provide genetic resources for advanced molecular breeding in the future.

Apical meristem transcriptome analysis identifies a role for the blue light receptor gene GhFKF1 in cotton architecture development

Cotton architecture is determined by the differentiation fate transition of axillary meristem (AM), and influences cotton yield and the efficiency of mechanized harvesting. We observed that the initiation of flowering primordium was earlier in early-maturing than that in late-maturing cultivars during the differentiation and development of AM. The RNA-Seq and expression level analyses showed that genes FLAVIN BINDING, KELCH REPEAT, F-BOX1 (GhFKF1), and GIGANTEA (GhGI) were in response to circadian rhythms, and involved in the regulation of cotton flowering. The gene structure, predicted protein structure, and motif content analyses showed that in Arabidopsis, cotton, rapseed, and soybean, proteins GhFKF1 and GhGI were functionally conserved and share evolutionary origins. Compared to the wild type, in GhFKF1 mutants that were created by the CRISPR/Cas9 system, the initiation of branch primordium was inhibited. Conversely, the knocking out of GhGI increased the number of AM differentiating into flower primordium, and there were much more lateral branch differentiation and development. Besides, we investigated that proteins GhFKF1 and GhGI can interact with each other. These results suggest that GhFKF1 and GhGI are key regulators of cotton architecture development, and may collaborate to regulate the differentiation fate transition of AM, ultimately influencing plant architecture. We describe a strategy for using the CRISPR/Cas9 system to increase cotton adaptation and productivity by optimizing plant architecture.

Study on plant protection unmanned aerial vehicle spraying technology based on the thrips population activity patterns during the cotton flowering period.

Over the years, thrips have transitioned from a minor nuisance to a major problem, significantly impacting the yield and quality of cotton. Unmanned aerial vehicles (UAVs) for plant protection have emerged as an effective alternative to traditional pesticide spraying equipment. UAVs offer advantages such as avoiding crop damage and enhancing pesticide deposition on the plants and have become the primary choice for pesticide application in cotton fields. In this study, a 2-year field experiment found that the thrips population in a cotton field in Xinjiang, China, exhibited gradual growth during the early flowering phase, peaking in late July. The thrips population gradually shifted from the lower canopy to the upper canopy as the cotton flowers opened layer by layer. From 09:00 to 11:00 (GMT+8) and 19:00 to 21:00 (GMT+8), thrips mainly flew outside the flowers, while from 17:00 to 19:00 (GMT+8), they mostly inhabited the inner whorls of flowers. The insecticides 10% cyantraniliprole oil dispersion and 10% spinetoram suspension concentrate, sprayed by UAV, had the best control effect on thrips, with 80.51% and 79.22% control effect after 7 days of spraying, respectively. The optimal spraying time for 10% cyantraniliprole oil dispersion was 19:00 (GMT+8), and the control effect on thrips reached 91.16% at 7 days of spraying. During the cotton flowering period, thrips inhabited flowers in the evening and flew outside during the day. The best control effect on thrips was achieved with UAV-sprayed 10% cyantraniliprole oil dispersion at 19:00 (GMT+8).

Sustaining the Yield of Maize, Blackgram, Greengram, Groundnut, Cotton, Sugarcane, and Coconut through the Application of Nutrients and Plant Growth Regulator Mixture.

The foliar application of nutrients and plant growth regulators (PGRs) at critical crop growth periods can improve the yield of field crops. Hence, the present study was conducted to quantify the effects of the combined application of nutrients and PGRs (crop-specific formulation) on maize, blackgram, greengram, groundnut, cotton, sugarcane, and coconut yield. In all the crops except coconut, the treatments included (i) a foliar spray of crop-specific nutrients and PGR combinations and (ii) an unsprayed control. In coconut, the treatments included (i) the root feeding of coconut-specific nutrients and PGR combinations and (ii) an untreated control. Crop-specific nutrient and PGR formulations were sprayed, namely, Tamil Nadu Agricultural University (TNAU) maize maxim 1.5% at the tassel initiation and grain-filling stages of maize, TNAU pulse wonder 1.0% at the peak flowering stage of green gram and black gram, TNAU groundnut-rich 1.0% at the flowering and pod-filling stages of groundnut, TNAU cotton plus 1.25% at the flowering and boll development stages of cotton, and TNAU sugarcane booster 0.5% at 45 days after planting (DAP), 0.75% at 60 DAP, and 1.0% at 75 DAP of sugarcane. The results showed that the foliar application of TNAU maize maxim, TNAU pulse wonder, TNAU groundnut-rich, TNAU cotton plus and TNAU sugarcane booster and the root feeding of TNAU coconut tonic increased the yield of maize, pulses, groundnut, cotton, sugarcane, and coconut, resulting in higher economic returns.

Sublethal effects of a commercial Bt product and Bt cotton flowers on the bollworm (Helicoverpa zea) with impacts to predation from a lady beetle (Hippodamia convergens).

Insecticidal Bacillus thuringiensis Berliner (Bt) toxins produced by transgenic cotton (Gossypium hirsutum L.) plants have become an essential component of cotton pest management. Bt toxins are the primary management tool in transgenic cotton for lepidopteran pests, the most important of which is the bollworm (Helicoverpa zea Boddie) (Lepidoptera: Noctuidae) in the United States (U.S.). However, bollworm larvae that survive after consuming Bt toxins may experience sublethal effects, which could alter interactions with other organisms, such as natural enemies. Experiments were conducted to evaluate how sublethal effects of a commercial Bt product (Dipel) incorporated into artificial diet and from Bt cotton flowers impact predation from the convergent lady beetle (Hippodamia convergens Guérin-Méneville) (Coleoptera: Coccinellidae), common in cotton fields of the mid-southern U.S. Sublethal effects were detected through reduced weight and slower development in bollworm larvae which fed on Dipel incorporated into artificial diet, Bollgard II, and Bollgard 3 cotton flowers. Sublethal effects from proteins incorporated into artificial diet were found to significantly alter predation from third instar lady beetle larvae. Predation of bollworm larvae also increased significantly after feeding for three days on a diet incorporated with Bt proteins. These results suggest that the changes in larval weight and development induced by Bt can be used to help predict consumption of bollworm larvae by the convergent lady beetle. These findings are essential to understanding the potential level of biological control in Bt cotton where lepidopteran larvae experience sublethal effects.

A cotton mitochondrial alternative electron transporter, GhD2HGDH, induces early flowering by modulating GA and photoperiodic pathways.

D-2-hydroxyglutarate dehydrogenase (D2HGDH) is a mitochondrial enzyme containing flavin adenine dinucleotide FAD, existing as a dimer, and it facilitates the specific oxidation of D-2HG to 2-oxoglutarate (2-OG), which is a key intermediate in the tricarboxylic acid (TCA) cycle. A Genome-wide expression analysis (GWEA) has indicated an association between GhD2HGDH and flowering time. To further explore the role of GhD2HGDH, we performed a comprehensive investigation encompassing phenotyping, physiology, metabolomics, and transcriptomics in Arabidopsis thaliana plants overexpressing GhD2HGDH. Transcriptomic and qRT-PCR data exhibited heightened expression of GhD2HGDH in upland cotton flowers. Additionally, early-maturing cotton exhibited higher expression of GhD2HGDH across all tissues than delayed-maturing cotton. Subcellular localization confirmed its presence in the mitochondria. Overexpression of GhD2HGDH in Arabidopsis resulted in early flowering. Using virus-induced gene silencing (VIGS), we investigated the impact of GhD2HGDH on flowering in both early- and delayed-maturing cotton plants. Manipulation of GhD2HGDH expression levels led to changes in photosynthetic pigment and gas exchange attributes. GhD2HGDH responded to gibberellin (GA3) hormone treatment, influencing the expression of GA biosynthesis genes and repressing DELLA genes. Protein interaction studies, including yeast two-hybrid, luciferase complementation (LUC), and GST pull-down assays, confirmed the interaction between GhD2HGDH and GhSOX (Sulfite oxidase). The metabolomics analysis demonstrated GhD2HGDH's modulation of the TCA cycle through alterations in various metabolite levels. Transcriptome data revealed that GhD2HGDH overexpression triggers early flowering by modulating the GA3 and photoperiodic pathways of the flowering core factor genes. Taken together, GhD2HGDH positively regulates the network of genes associated with early flowering pathways.

Modeling the effect of fertilizers on cotton yields and cotton fiber quality on saline soils in so uthern Kazakhstan

Yntymak st.,26, Kazakhstan Cotton in Kazakhstan is one of the strategically important crops and is cultivated in the extreme south of the republic in the Turkestan region, in the northernmost cotton-growing zone on the globe. The aridity of the climate, the shortage of irrigation water and the growing threat of the risk of increasing saline lands in cotton plantations, on the one hand, and the insufficient and unjustified use of minerals, on the other, are the main limiting factors in cotton production. On the industrial plantations of the peasant farm "Sabyr", v. Atakent, Maktaaral district, Turkestan region, field experiments were carried out on serozems of slightly and moderately salinity in 2022. In experiments on two salinity backgrounds, 9 identical treatments of fertilizers with different doses and ratios of nitrogen, phosphorus and potassium were studied. During the phases of budding, flowering and fruit formation of cotton, biometric studies and selection of plant samples were carried out to study photosynthetic and yield indicators depending on fertilizers on 2 salinity backgrounds. The effect and interaction of fertilizers and the amount of salts in the soil on the formation of leaves in various phases of cotton development isquite accurately described (R2 = 0,957-0,972) by regression equations. At the same time, the effect of single action of nitrogen and potassium fertilizers was negative, and that of phosphorus fertilizers was positive. During the growing season, double combinations of all the studied factors, with the exception of nitrogen and potassium, had a negative effect on the dynamics of accumulation of cotton plant biomass. The gross cotton yield is 86% determined by the complex total influence of nitrogen, phosphorus and potassium fertilizers and the degree of soil salinity. At the same time, the unilateral effect of nitrogen and potassium fertilizers and soil salinity on cotton yield was negative, and that of phosphorus fertilizer was positive. There is a high correlation between potassium nutrition and fiber quality indicators (r = 0,38-0,62). Changes in fiber quality indicators generally had a strong relationship with the amount of salts in the topsoil (r = 0,51-0,61).

GhCOL2 Positively Regulates Flowering by Activating the Transcription of GhHD3A in Upland Cotton (Gossypium hirsutum L.).

Plants have evolved sophisticated signaling networks to adjust flowering time, ensuring successful reproduction. Two crucial flowering regulators, FLOWERING LOCUS T (FT) and CONSTANS (CO), play pivotal roles in regulating flowering across various species. Previous studies have indicated that suppressing Gossypium hirsutum CONSTANS-LIKE 2 (GhCOL2), a homolog of Arabidopsis CO, leads to delayed flowering in cultivated cotton. However, the underlying regulatory mechanisms remain unknown. In this study, a yeast one-hybrid and dual-LUC expression assays were used to elucidate the molecular mechanism through which GhCOL2 regulates the transcription of GhHD3A. RT-qPCR was used to examine the expression of GhCOL2 and GhHD3A.Our findings reveal that GhCOL2 directly binds to CCACA cis-elements and atypical CORE (TGTGTATG) cis-elements in the promoter regions of HEADING DATE 3A (HD3A), thereby activating GhHD3A transcription. Notably, GhCOL2 and GhHD3A exhibited high expression levels in the adult stage and low levels in the juvenile stage. Interestingly, the expression of GhCOL2 and GhHD3A varied significant between the two cotton varieties (Tx2094 and Maxxa). In summary, our study enhances the understanding of the molecular mechanism by which cotton GhCOL2-GhHD3A regulates flowering at the molecular level. Furthermore, it contributes to a broader comprehension of the GhCOL2-GhHD3A model in G. hirsutum.

Estimation model of potassium content in cotton leaves based on hyperspectral information of multileaf position

Potassium (K) is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution. This indirectly leads to variations in the leaf potassium content (LKC, %) at different leaf positions. However, owing to the interaction between light and leaf age, leaf sensitivity at different positions to this change varies, including the reflection and absorption of the spectrum. How to selecting the optimal monitoring leaf position is an important factor in quickly and accurately evaluation of cotton LKC using spectral remote sensing technology. Therefore, this study proposes a comprehensive multileaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom. This is aimed at achieving an accurate estimation of cotton LKC and optimizing the strategy for selecting the monitored leaf position. Between 2020 and 2021, we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom (Li, i=1, 2, 3, …, n), during the cotton budding, flowering, and boll setting stages. Vertical distribution characteristics, sensitivity differences, and spectral correlations of LKC at different leaf positions were investigated. Additionally, the optimal range of the dominant leaf position for monitoring was determined. Partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and the entropy weight method (EWM) were used to establish LKC estimation models for single leaf and multileaf positions. The results showed a vertical heterogeneous distribution of cotton LKC, with LKC initially increasing and then gradually decreasing from top to bottom, and the average LKC of cotton reaches its maximum value at flowering stage. The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum. The selected dominant leaf positions for the three growth stages were L1–L5, L1–L4, and L1–L2, respectively. Based on the dominant leaf position monitoring range, the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4, PLSR-L1, and SVR-L2, with The coefficient of determination of the validation set (R2val) of 0.786, 0.580, and 0.768, and the root-mean-square error of the validation set (RMSEval) of 0.168, 0.197, and 0.191, respectively. The multileaf position LKC estimation model was constructed by EWM with R2val of 0.887, 0.728, and 0.703, and RMSEval of 0.134, 0.172, and 0.209, respectively. In contrast, the newly developed multileaf position comprehensive estimation model yielded superior results, improving the stability of the model on the basis of high accuracy, especially during the budding and flowering stages. These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.

POORANA CHANDROTHAYA CHENTHURAM (PCC), A SIDDHA SYSTEM OF MEDICINE PROTECTS AGAINST SCOPOLAMINE‐INDUCED COGNITIVE IMPAIRMENTS IN EXPERIMENTAL RATS

AbstractBackgroundIndian Systems of Medicine (ISM) are considered to be of native medical systems which were assimilated into Indian culture. ISM comprising of Ayurveda, Yoga, and Naturopathy, Unani and Siddha which are in practice since time immemorial much before the recognized health system came into existence. In Siddha system, higher order pharmaceutical forms of medicine called PCC are predominantly prepared with metals and minerals through well‐coined pharmaceutical procedures and techniques that have been inscribed in the Siddha literature. The methods which convert the toxic metals/ minerals into compatible form that fits for human consumption. It has the privilege that these drug forms can cure many chronic diseases, in precise dosage. PCC is an imperative medicine which contains gold, mercury and sulphur in defined quantity. As given in Siddha vaithiyathirattu PCC is prepared from Gold, Mercury, and Sulphur in a ratio of 1:8:16 and processed with the required amount of red cotton flower juice and plantain stem pith juice. It is well known for its effectiveness in respiratory disorders especially bronchitis, hepatic disorders, renal diseases, neurological disorders, and skin diseases. However, there are no reports on whether PCC may improve cognitive dysfunction.MethodsWe used RAM and NORT models to evaluate cognitive improvement. In addition, the acetylcholinesterase enzyme was assessed in the brain tissues. Further, protein expression of BDNF was also assessed in hippocampus tissues after the behavioral tasks.ResultRepeated administration of PCC (2.5, 5 and 10 mg/kg., p.o) before the acquisition trial (T1) explored the object significantly more than the familiar one during the discrimination trial (T2) respectively, showing an increase in memory retention. In the radial arm‐maze task, scopolamine‐treated rats with PCC showed decreased working memory errors as compared to scopolamine‐alone treated rats. Treatment with different doses of PCC showed up‐regulation of BDNF with a significant effect by inhibiting the elevation of acetylcholinesterase activity when compared to the vehicle‐treated group.ConclusionsThese results suggest that the PCC ameliorates scopolamine‐induced impairment of cognitive functions in rats. These data further support that the anti‐amnestic effect of PCC recommends it as a promising candidate for clinical trials in patients with cognitive impairment.

Early Identification of Cotton Fields Based on Gf-6 Images in Arid and Semiarid Regions (China)

Accurately grasping the distribution and area of cotton for agricultural irrigation scheduling, intensive and efficient management of water resources, and yield estimation in arid and semiarid regions is of great significance. In this paper, taking the Xinjiang Shihezi oasis agriculture region as the study area, extracting the spectroscopic characterization (R, G, B, panchromatic), texture feature (entropy, mean, variance, contrast, homogeneity, angular second moment, correlation, and dissimilarity) and characteristics of vegetation index (normalized difference vegetation index/NDVI, ratio vegetation index/DVI, difference vegetation index/RVI) in the cotton flowering period before and after based on GF-6 image data, four models such as the random forests (RF) and deep learning approach (U-Net, DeepLabV3+ network, Deeplabv3+ model based on attention mechanism) were used to identify cotton and to compare their accuracies. The results show that the deep learning model is better than that of the random forest model. In all the deep learning models with three kinds of feature sets, the recognition accuracy and credibility of the DeepLabV3+ model based on the attention mechanism are the highest, the overall recognition accuracy of cotton is 98.23%, and the kappa coefficient is 96.11. Using the same Deeplabv3+ model based on an attention mechanism with different input feature sets (all features and only spectroscopic characterization), the identification accuracy of the former is much higher than that of the latter. GF-6 satellite image data in the field of crop type recognition has great application potential and prospects.

Anchor-free deep convolutional neural network for tracking and counting cotton seedlings and flowers

Accurate counting of plants and their organs in natural environments is essential for breeders and growers. For breeders, counting plants during the seedling stage aids in selecting genotypes with superior emergence rates, while for growers, it informs decisions about potential replanting. Meanwhile, counting specific plant organs, such as flowers, forecasts yields for different genotypes, offering insights into production levels. The overall goal of this study was to investigate a deep convolutional neural network-based tracking method, CenterTrack, for cotton seedling and flower counting from video frames. The network is extended from a customized CenterNet, which is an anchor-free object detector. CenterTrack predicts the detections of the current frame and displacements of detections between the previous frame and the current frame, which are used to associate the same object in consecutive frames. The modified CenterNet detector achieved high accuracy on both seedling and flower datasets with an overall AP50 of 0.962. The video tracking hyperparameters were optimized for each dataset using orthogonal tests. Experimental results showed that seedling and flower counts with optimized hyperparameters highly correlated with those of manual counts (R2 = 0.98 andR2 = 0.95) and the mean relative errors of 75 cotton seedling testing videos and 50 flower testing videos were 5.5 % and 10.8 %, respectively. An average counting speed of 20.4 frames per second was achieved with an input resolution of 1920 × 1080 pixels for both seedling and flower videos. The anchor-free deep convolution neural network-based tracking method provides automatic tracking and counting in video frames, which will significantly benefit plant breeding and crop management.

A novel carbon reducing natural composite phase change material for effective energy storage

The development of fatty acid made from natural composite materials Lac, Rosin, Flowers of silk cotton, Red ochre, Cinnabar, Beeswax, and Butter as a novel bio-based Natural Composite Phase Change Material (NCPCM) is the subject of the present study. This bio-based material was produced for the first time using natural, readily available, inexpensive, and eco-friendly with a peak phase transition temperature of 75 °C and 194 J/g as the latent heat capacity of fusion. Three distinct compositions, 2 %, 3 % and 5 % of NCPCM to the weight of the binder, were produced by the direct incorporation method into the natural hydraulic lime matrix. The newly developed NCPCM-incorporated lime mortar specimens were characterized based on their physical, mechanical, and thermal properties, and their results were compared with reference lime mortar (RLM). The compressive strength of NCPCM2 and NCPCM3 at 28 days increased by 22.64 % and 47.42 %, respectively. The produced NCPCM retains good thermal stability in its temperature operating range. The XRD findings showed that adding Lac and Rosin containing carboxylic acid esters of fatty acids increased the calcium carbonate formation. The produced FTIR validated the produced material's chemical stability, and SEM images demonstrated that the developed matrix's morphology successfully retains the added bio-additives. From an environmental perspective, the prepared NCPCM in lime mortar can reduce CO2 emissions by 296 kg·CO2/kg and 286 kg·CO2/kg. Furthermore, adding the produced NCPCM as bio-additives to lime mortar can explore the creation of sustainable mortar, which leads to environmental and economic benefits.

Genome‑wide association study identifies GhF3'H affects the spiraeoside biosynthesis in waste cotton (Gossypium hirsutum L.) flowers

Cotton flowers are rich in various flavonoids, exhibiting multiple biological activities, including antioxidant, anti-inflammatory, and anti-tumor effects. The biosynthetic mechanism of flavonoids in cotton flowers remains undefined. In this study, six macro-flavonoids were identified and quantified in the flowers of 373 G. hirsutum accessions based on HPLC technology. These flavonoids included quercetin (0–2.94 mg/g), isoquercetin (9.69–33.61 mg/g), hyperoside (2.54–8.78 mg/g), spiraeoside (0.87–7.10 mg/g), quercimeritrin (5.70–20.29 mg/g), and quercetin-3′-O-β-D-glucoside (4.23–21.87 mg/g). Several candidate genes associated with spiraeoside and quercimeritrin were identified through GWAS analysis. The study also revealed a significant upregulation of GhF3'H expression and an elevated content of spiraeoside in haplotype II compared to haplotype I. The ectopic overexpression of GhF3'H in tobacco led to a significant increase in dihydroquercetin, quercetin, and spiraeoside levels, and a decrease in naringenin content. LUC experiments confirmed the potential positive regulation of GhF3'H by GhTT2. This study provides vital insights into the biosynthetic mechanism of macro-flavonoids in the flowers of upland cotton population. The identified elite haplotype and candidate genes hold great promise as valuable tools for enhancing flavonoid content in cotton, thus facilitating the efficient utilization of cotton by-products in the future.

Green synthesis of cotton flower shaped nickel oxide nanoparticles: Anti-bacterial and tribological studies

The goal of this study was to synthesize nickel oxide nanoparticles (NiO-NPs) by the sol–gel method, which involved the use of Cassia Fistula is a herbal and medicinal plant. The Cassia Fistula (CF) plant extract acts as a stabilizing and capping agent. Further, NiO-NPs were characterized using UV–Visible spectroscopy (UV–Vis), Powder X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Scanning electron microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX). According to the results of UV–Visible, the absorption spectroscopy has displayed that the band gap is 3.5 eV. The biological properties were studied for prepared NiO-NPs. The antibacterial testes were examined using different bacterial strains such as Escherichia coli, Staphylococcus aureus and Staphylococcus Epidermis and dose-dependent inhibition response was reported. The tribological tests are carried out using four ball testers in accordance with ASTM D4172-18 requirements. NiO-NPs are utilised as lubricating additives in poly alpha olefin (PAO4) base oil. NiO-NPs in PAO4 at a concentration of 0.5 wt% improved the coefficient of friction, and positive results are observed.

Foliar melatonin ameliorates drought-induced alterations in enzyme activities of sugar and nitrogen metabolisms in cotton leaves.

Sugar and nitrogen metabolisms help plants maintain cellular homeostasis, stress tolerance, and sustainable growth in drought conditions. Melatonin, a potent antioxidant and signaling molecule, appears to mitigate the negative impacts of drought on plants. This study aimed to investigate the potential role of foliar-applied melatonin in ameliorating drought-induced alterations in leaf sugar and nitrogen metabolisms' enzyme activities during cotton flowering and boll formation. To date, no study has examined drought-induced sugar and nitrogen metabolisms' enzyme activity changes in cotton treated with foliar melatonin. Drought levels (FC1 = 75 ± 5%, FC2 = 60 ± 5%, and FC3 = 45 ± 5%) were maintained between 3 and 35 days after flowering (DAF), and melatonin (M) concentrations (0, 25, 50, and 100 μmol L-1 ) were applied at 3 and 21 DAF in a completely randomized design. M100 concentrations at low FC levels significantly enhanced leaf sugar and N-metabolic enzyme activities, such as sucrose synthase (65.56%) and glutamine synthetase (55.24%), compared to plants not treated with melatonin; peaking between 7 and 21 DAF and declining gradually with crop growth. Moreover, the M100 concentrations at all FC levels, particularly FC3, significantly increased the relative expression of GhSusB, GhSusC, SPS1, and SPS3 genes, indicating that melatonin improves leaf sugar and N-metabolism enzymatic activities under drought stress. Therefore, applying M100 concentrations to cotton foliage under FC3 conditions during reproductive stages improves leaf water status, sugar, and N-metabolism enzyme activities, demonstrating melatonin's potent anti-stress, osmoregulatory, and growth-promoting properties in overcoming drought stress in cotton crops. Future research into the molecular mechanisms of melatonin-mediated sugar and nitrogen metabolism enzyme activities in cotton leaves may lead to biotechnological methods to improve drought resilience in cotton and other crops.