Cotton Fields Research Articles

Research on visualization of cotton canopy structure and extraction of feature parameters based on dual-perspective point cloud data

ABSTRACT Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham’s algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients ( R 2 ) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture.

The genomes of two Australian isolates of Verticillium dahliae recovered from cotton fields

Verticillium wilt is a major disease in a wide variety of crops and is caused by the fungus Verticillium dahliae Kleb. In Australian cotton growing regions two pathotypes of V. dahliae are described, namely non-defoliating and defoliating, classified on their ability to cause defoliation in cotton (Gossypium hirsutum) as well as okra (Abelmoschus esculentus) and olives (Olea europaea). Herein we report the genomes of two isolates of V. dahliae, one predicted to be non-defoliating and the other predicted to be defoliating. Phylogenomic analysis places each isolate into separate clades, but the highly aggressive, predicted defoliating, strain lacks the genomic features reported as important for causing defoliation on cotton in other regions.

Modeling of cotton yield responses to different irrigation strategies in Southern Xinjiang Region,China

Southern Xinjiang region represents a prototypical oasis agricultural area, and cotton is the primary cash crop. The current cotton water productivity for this area remains room for improvement. In order to refine cotton drip irrigation strategy in Southern Xinjiang, this study combined field experiment with crop model simulation and prediction. In this experiment, two irrigation methods, furrow irrigation and drip irrigation, and different degrees of deficit irrigation were set up. The impacts of soil moisture on cotton yield and quality were assessed, with the additional utilization of the Root Zone Water Quality Model (RZWQM2) for simulating irrigation scenarios and predicting cotton yield field data showed that during the 2021–2022 growing season, drip irrigation significantly increased boll weight, yield, and Water Productivity compared to furrow irrigation. The deficit irrigation with 90 % of full irrigation during the flowering period resulted in the highest yield among all drip irrigation treatments, while simultaneously improving the fiber quality. The RZWQM2 model was calibrated and validated using 2021 and 2022 cotton field experimental data, respectively. The calibrated and verified RZWQM2 model shows good performance. In terms of soil water storage, cotton yield and evapotranspiration. The simulated value output by the model is close to the measured value in the field. Model prediction data suggest that there is still room for improvement in cotton yield, accompanied by an increase in evapotranspiration. But deficit irrigation below 80 % of the full irrigation quotas poses a notable risk of yield reduction. Targeting higher yields, the predicted scenario suggests an increase in water application (irrigation quota:314.8 mm). Additionally, this study proposes irrigation regimes with water savings of 10 % and 20 %, with irrigation quotas of 290.5 mm and 266.5 mm during the growth period, respectively. Based on this study, we provide a more detailed and reliable water-saving scheme for cotton drip irrigation in southern Xinjiang region.

Unraveling the ecological niche signals of Verticillium dahliae: Insights from Mediterranean landscapes.

Verticillium wilt (VW), caused by the soil-borne plant pathogenic fungus Verticillium dahliae, is a major disease impacting olive crops globally. In view of the lack of effective post-infection treatments, exclusion and avoidance strategies are essential in disease management. Assessing the risks posed by this pathogen is essential to prevent the spread and to ensure selection of suitable sites for new plantations. This study aimed to elucidate the environmental factors driving V. dahliae establishment in the Andalusia region, in southern Spain, an emblematic Mediterranean landscape for olive cultivation. To this end, we explored ecological niche signals for this fungal pathogen by analyzing 62 environmental variables across 1.6 million hectares dedicated to olive and cotton cultivation, using a 15-yr survey data on VW incidence on presence-absence from both olive and cotton fields. To ensure robust identification of ecological niche signals, we employed randomization-based, non-parametric univariate tests to compare presence records with the broader sampling universe (including absence records). Our findings identified key environmental variables that are associated significantly with V. dahliae presence, including temperature range seasonality (including mean diurnal and annual ranges), summer temperature (maximum of the warmest month, mean of the warmest quarter), and moisture and water availability (near-surface humidity, potential evapotranspiration, vapor pressure) as core niche variables for V. dahliae. Our results replicated the pathogen's known distribution, identifying the Guadalquivir Valley as a particularly high-risk area in view of its mild winters and distinct rainy seasons, providing new insights into the specific environmental conditions that facilitate the pathogen's survival and spread. Furthermore, this study introduces a novel approach to niche modeling that prioritizes variables with consistent effects and significant impact on the presence and distribution of V. dahliae and identifies potential data artifacts. This approach enhances our understanding of ecological requirements in V. dahliae and informs targeted management strategies.

Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

ABSTRACT Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring.

Rational optimization of irrigation regimes for drip-irrigated cotton fields without mulch can alleviate the problem of residual film contamination in arid zones

The serious problem of residual film pollution in the arid region of northwestern China has a severe negative impact on both the farmland environment and the benefits of cotton planting. Drip irrigation without mulch (DIWM) has the potential to serve as an effective alternative to mulch drip irrigation and address the issue of residual film pollution. However, there are few studies on how to formulate a rational irrigation regime under DIWM conditions and achieve water savings and productivity gains. Consequently, from 2020 to 2021, we established three DIWM treatments: W4, W6, and W8. None of the treatments were irrigated at the seedling stage, and the irrigation regime was the same for all the treatments at the squaring stage (2 irrigations at an irrigation quota of 45 mm). From the initial flowering stage, the frequency of irrigation was once every 6 d, 8 d, and 12 d for W4, W6, and W8, respectively, and the corresponding total numbers of watering times were 8, 6, and 4, respectively. The irrigation amount was 69 mm at each time point during the flowering and boll-forming stages in all the treatments, and the final irrigation amount was 52.2 mm in W8. The total irrigation amounts were 366 mm, 504 mm and 625.2 mm for W4, W6 and W8, respectively. This study explored the spatiotemporal characteristics of soil moisture via sensors combined with the spatial grid method. Additionally, the growth indices, biomass accumulation, yield components, and water use efficiency (WUE) of cotton were assessed across various treatments. The results revealed that, in 2020 and 2021, the W8 treatment resulted in the highest soil water content (SWC) in the 70–110 cm soil layer and soil water consumption (WC) in the 10–110 cm layer, with the WC in the 10 cm layer being 20 % and 44 % greater than that in W6, respectively. The excessive total irrigation amount (IA) in the W8 treatment led to high vegetative growth of cotton, diminishing the positive impact on yield. Across both years, the WUE observed in the W6 treatment significantly exceeded that of W8, while the seed cotton yield demonstrated only marginal decreases of 5 % and 0.9 % compared with that of W8. The irrigation amount and average WC in the 10–40 cm soil layer were 19 % and 29 % lower in W6 than in W8, respectively, but the economic benefits were only 2 % lower than those in W8. There was a significant positive correlation between the SWC and WC in all the different soil layers, and the effect of the SWC on the WC gradually decreased with increasing soil depth. Overall, the irrigation regime employed in the W6 treatment within the arid zone, devoid of mulch cover, proves to be an effective water-saving strategy, ensuring a consistent cotton yield and enhancing WUE. This study serves as a reference for developing an efficient irrigation system tailored to the DIWM cotton industry in arid zones, contributing to the promotion of green and sustainable agricultural development in this region.

Long-term mulched drip irrigation facilitates soil organic carbon stabilization and the dominance of microbial stochastic assembly processes

Mulched drip irrigation (MDI) is generally accepted as a method to decrease soil salinization and improve crop yields in arid and semi-arid regions. However, there remain gaps in how MDI drives soil organic carbon (SOC) dynamic microbial assembly processes with time, and the mediating role of microorganisms remains unclear. In this study, we investigated the aforementioned issues across soil profiles in cotton fields with different years of MDI. The results showed that MDI did not cause the differences in SOC, particular organic carbon (POC), and mineral-associated organic carbon (MOC) in soil layers. The POC and MOC contents had a parabola relationship with time, and showed an opposite trend in soil. After 15 years of MDI, the ratio of MOC/SOC increased to a peak value of 50 % and 52 % in topsoil and subsoil, respectively; the ratio of POC/SOC decreased to valley values of 50 % and 48 %, respectively (P < 0.05). Long-term MDI reduced the differences in oxidase between soil layers but accelerated SOC loss by increasing polyphenol oxidase activity (P < 0.05). Compared with that of other years, with 10 years of MDI, bacterial Shannon diversity decreased to a valley value, and fungal Shannon diversity reached to a top value in subsoil (P < 0.05). In general, stochastic processes were mainly controlled by dispersal limitation, and undominated processes dominated microbial assembly; however, there was a close relationship between bacterial communities and organic carbon fractions. The high percentage of positive linkages among microorganisms indicated that long-term MDI was beneficial for carbon fixation. Additionally, a decrease of fungal oligotroph/copiotroph ratio, the relative abundance of Ascomycota and Basidiomycota was beneficial for the accumulation of SOC and POC in topsoil (P < 0.05). In conclusion, long-term MDI is useful for the fixation of organic carbon via improving soil POC content and strengthening linkages within community assemblies.

Analysis of test results of the P-20 multi-rotor UAV when spraying pesticides in precision agriculture

Abstract The article provides an overview of the current results of using multi-rotor UAVs when spraying pesticides in precision agriculture. It is noted that, as part of improving the technology for using UAVs, it is necessary to continue work on optimizing and testing the parameters of UAV transport and technological cycles together with a reasonable choice of special reagents and their combinations to improve the spraying effect and increase the level of use of pesticides. This will directly affect the improvement of the quality and efficiency of precision farming systems. The analysis presented in the work is based on testing the parameters of the transport and technological cycle of a multi-rotor UAV of the P-20 type, which was used to spray pesticides on cotton fields to combat aphids and spider mites on cotton at the flowering and boll setting stages. The results are considered that make it possible to analyze and compare the laws of uniform and penetrating deposition and drift of droplets in cotton canopies at different UAV flight altitudes. It has been shown that spraying small volumes of agrochemicals using UAVs at low altitudes differs from operations performed using modern manned aircraft or boom sprayers. The flight altitude of the UAV has a significant impact on the deposition and drift of droplets. The results of studies related to elucidating the effect of defoliant adsorption on cotton leaves are also noted. The adsorption of acetamiprid and spirodiclofen was studied. Methods for statistical processing of the obtained experimental data are presented, in particular, to test the significance of the influence of flight altitude on the experimental results.

FLUCTUACIÓN POBLACIONAL DE LAS PRINCIPALES PLAGAS Y DE SUS ENEMIGOS NATURALES EN ALGODÓN TANGÜIS EN LA MOLINA, LIMA, PERÚ

Population fluctuations of Aphis gossypii, Chloridea virescens, Anthonomus vestitus, Dysdercus peruvianus and Pectinophora gossypiella, besides their natural enemies were studied during the entire phenological period of a Tangüis cotton crop at La Molina (Lima), using three sampling techniques: direct plant evaluation, plant shake falls and pitfall traps. A. gossypii was the most abundant pest, being more important in the vegetative development, followed by D. peruvianus and 1. gossypiella, both with greater importance in the fructification stage. Predatory insects and spiders were recorded during the whole crop period, where the most frequent were, in predatory insects Hippodamia convergens, Hyalochloria denticornis and Rhinacloa forticornis, and in spiders Misumenops sp. morpho 1, Zelotes laetus and Theridion volubile. Parasitoidism was low or scarce in most of the pests, highlighting Lysiphlebus testaceipes as A. gossypii parasitoid due to its abundance in the vegetative development of the cotton field.

Effects of Irrigation Amount and Nitrogen Rate on Cotton Yield, Nitrogen Use Efficiency, and Soil Nitrogen Balance under Drip Irrigation

Water and nitrogen (N) are major constraints for cotton growth and yield formation in arid regions. Irrigation and N application have been widely investigated to improve crop yield and water and N use efficiency (NUE). However, further optimization of water and N management is needed because the effects of N application on soil N balance and N loss under different irrigation levels remain unclear. In this study, a field experiment was conducted in 2020 and 2021 to investigate the effects of different irrigation amounts (full irrigation (100% ETc), moderate irrigation (80% ETc), low irrigation (60% ETc)) and N application rates (control (without N application, N0), low N (150 kg ha−1, N150), medium N (225 kg ha−1 and 300 kg ha−1, N225 and N300), and high N (375 kg ha−1, N375)) on cotton yield, NUE, and soil N balance. The 2-year results showed that under the 60% ETc treatment, cotton dry matter accumulation, N uptake, and yield were significantly enhanced by increasing N application. Under the 80% and 100% ETc treatments, these parameters peaked with the N300 treatment; the N375 treatment showed no significant difference or decrease compared to the N300 treatment. The 80% ETc N300 treatment had the highest cotton yield and NUE, which increased by 17.49–106.57% and 12.28–88.78% compared with other treatments, respectively. Residual soil N accumulation (RSNmin), apparent N loss (ANL), and apparent N surplus (ANS) increased as the N application rate increased under the 60% and 100% ETc treatments. While under the 80% ETc treatment, the ANS did not significantly differ between the N225 and N300 treatments, ANL significantly decreased by 43.51–88.56% when the N application rate increased from 150 to 225 and 300 kg ha−1. The ANL of the 80% ETc N300 treatment was the lowest, but ANS did not significantly increase. The regression analysis and spatial analysis results showed that under irrigation of 336–348 mm and N application of 254–327 kg ha−1, cotton yield, NUE, and WPI reached more than 80% of the maximum value, with less apparent N loss, thus maintaining the soil N balance in drip-irrigated cotton fields. This study helps to improve the utilization of water and N resources in cotton production. Future research on optimizing water and N management needs to more fully consider environmental pollution to achieve sustainable development of agricultural ecosystems.

AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application

To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 × 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of real-time applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests.

Retraction Note: Prediction and detection of harvesting stage in cotton fields using deep adversarial networks

Retraction Note: Prediction and detection of harvesting stage in cotton fields using deep adversarial networks

Prevalence of Tetranychus urticae Koch and T. turkestani (Ugarov & Nikolskii) (Acari: Tetranychidae) and their endosymbiotic bacteria in cotton fields of Aydin, Türkiye

Spider mites are important pests of cotton, a vital fiber crop cultivated in sun-drenched regions. This study investigated the prevalence and distribution of spider mite species infesting cotton fields in Aydin province, Türkiye. Spider mites were found in a significant portion (89%) of the surveyed fields. The two most abundant mite species, were the red form (RF) of Tetranychus urticae (57.1%) and T. turkestani (39.3%). A minor presence of T. urticae green form (GF) (3.6%) was observed. Tetranychus urticae RF was more prevalent in coastal areas, while T. turkestani was more common inland. Field surveys demonstrated that areas with higher T. urticae RF prevalence exhibited more frequent spraying, potentially due to this species’ greater capacity to develop resistance compared to T. turkestani. This study also investigated the prevalence of Wolbachia, Rickettsia, Cardinium and Spiroplasma endosymbionts, which are linked with pesticide resistance due to their ability to degrade pesticides, in these mite populations. The study confirmed the presence of Wolbachia and Rickettsia endosymbionts in spider mite populations in Türkiye. However, no Cardinium or Spiroplasma were detected in any of the populations. Infection rates of the detected endosymbionts differed between T. urticae and T. turkestani. Future studies should investigate the resistance levels of these tetranychid mite species as well as the role of their endosymbiotic bacteria in resistance in cotton-growing areas.

Determination of target site mutations and detoxification enzymes in the abamectin resistance of spider mite Tetranychus urticae collected from cotton fields of Türkiye

The two-spotted spider mite, Tetranychus urticae (Koch, 1836) (Acari: Tetranychidae) causes significant problems in cotton fields. The pest has developed resistance to abamectin, which is used intensely against spider mite populations. A discriminating dose was applied to T. urticae populations collected from Aydın, Adana, Şanlıurfa, and Diyarbakır provinces between 2019 and 2021, where there is significant cotton production and intensive acaricide applications. Populations with a mortality rate of less than 80% at the discriminating dose applied to field populations were included in the bioassay study. Then, the 10 most resistant populations were determined based on their LC50 and LC90 values. It was observed that there was high resistance (HR) in two T. urticae populations collected from Diyarbakır (DIY28 and DIY2) with LC50 values of 24.5 and 33.81 mg L-1, respectively 113 and 156-fold and one population collected from the province of Aydın (AYD4) LC50 value of 23.5 mg L-1 and 109-fold. As a result of the comparisons with a susceptible population in biochemical studies, it was determined that the carboxylesterase (CarE) enzyme of these populations (except for SAN6, SAN7, SAN8, ADA9, and ADA11) had more than 2-fold higher activity compared to the glutathione enzyme (GST), and they were in statistically different groups. When the GST enzyme activities were examined, it was found that the enzyme activities were generally low, except for the ADA11 and ADA16 populations (3.73 and 2.3-fold). In terms of the target site resistance, it was observed that there was no target site mutation (G314D and I321T-G326E) in the glutamate-gated chloride channels (Units GluCl 1 and 3) in these highest 10 resistant populations. It can be said that the resistance levels determined for abamectin from the obtained populations were determined to be low. In brief, out of the total 44 populations brought in and tested, only 30 populations were considered resistant with a mortality rate below 80% after the applied discriminating dose. It was determined that 6.8% of these 30 populations had HR, 31.8% had medium resistance (MR), and 29.5% had low resistance (LR). When examining by regions, it was found that high resistance populations were more prevalent in Diyarbakır and Aydın (15.4% and 10%, respectively) provinces, while the highest percentage of MR populations was found in Şanlıurfa, at 90%. Abamectin still acts as an effective acaricide. However, it is recommended to use acaricides with different modes of action to prevent possible emergence of resistance cases.

Characterization of Caulimovirid-like Sequences from Upland Cotton (Gossypium hirsutum L.) Exhibiting Terminal Abortion in Georgia, USA.

In this study, we investigated the potential involvement of endogenous viral elements (EVEs) in the development of apical tissue necrosis, resulting in the terminal abortion of upland cotton (Gossypium hirsutum L.) in Georgia. The high-throughput sequence analysis of symptomatic and asymptomatic plant tissue samples revealed near-complete EVE-Georgia (EVE-GA) sequences closely related to caulimoviruses. The analysis of EVE-GA's putative open reading frames (ORFs) compared to cotton virus A and endogenous cotton pararetroviral elements (eCPRVE) revealed their similarity in putative ORFs 1-4. However, in the ORF 5 and ORF 6 encoding putative coat protein and reverse transcriptase, respectively, the sequences from EVE-GA have stop codons similar to eCPRVE sequences from Mississippi. In silico mining of the cotton genome database using EVE-GA as a query uncovered near-complete viral sequence insertions in the genomes of G. hirsutum species (~7 kb) but partial in G. tomentosum (~5.3 kb) and G. mustelinum (~5.1 kb) species. Furthermore, cotton EVEs' episomal forms and messenger RNA (mRNA) transcripts were detected in both symptomatic and asymptomatic plants collected from cotton fields. No significant yield difference was observed between symptomatic and asymptomatic plants of the two varieties evaluated in the experimental plot. Additionally, EVEs were also detected in cotton seeds and seedlings. This study emphasizes the need for future research on EVE sequences, their coding capacity, and any potential role in host immunity or pathogenicity.

Boll weevils in dry bolls: is there a performance disadvantage?

BackgroundAnthonomus grandis grandis is the main cotton pest in the Americas, with the potential to become a pest in other continents. The insect uses the plant’s reproductive organs for feeding, oviposition, and survival strategies in the off-season, sheltering in dry bolls. This study aimed to know the reproductive potential, feeding, longevity, and morphometric characteristics of boll weevils from dry bolls, squares, and traps. Adults were obtained from infested squares, dry bolls, and traps installed in cotton fields and the Caatinga area. Thirty weevil pairs were maintained in the laboratory to assess the oviposition, feeding holes, longevity, and morphometric measurements of the parental and F1 generations.ResultsIn the parental generation, boll weevils from traps (the Caatinga area and cotton cultivation) showed greater reproductive potential. Boll weevils from squares and the Caatinga area showed greater feeding activity compared with those from cotton cultivation. Greater rostrum length, thorax + abdomen length, and abdomen width were observed in boll weevils from cotton cultivation and dry bolls. The origin of boll weevils parental generation did not affect progeny emergence time. The F1 generation of boll weevils showed significant differences in average longevity depending on the origin of different microhabitats. Furthermore, differences in rostrum lengths were observed, with the boll weevils coming from traps installed in cotton cultivation, the Caatinga area, and dry bolls being the largest. Regarding thorax + abdomen length and abdomen width, the highest values were observed in those from the Caatinga area and cotton cultivation.ConclusionIt was proven that dry bolls did not affect the reproductive, feeding, and longevity performance of boll weevils, enabling better insect fitness (reproduction and size) in relation to those coming from squares. The use of dry bolls as off-season shelter is an important survival strategy for the boll weevil.

Evaluation of the Genetic Diversity, Haplotype, and Virulence of Fusarium oxysporum f. sp. vasinfectum Field Isolates from Alabama.

The United States is the third largest producer of cotton and the largest exporter of cotton globally. Fusarium wilt, caused by the soilborne fungal pathogen Fusarium oxysporum f. sp. vasinfectum (Fov), was estimated to cause a $21 million cotton yield loss in 2022. Historically, Alabama was an important producer of cotton in the Southeastern United States and was the first state in which Fusarium wilt on cotton was described. To assess the genetic diversity of Fov field isolates in Alabama, 118 field isolates were collected from six counties across the state from 2014 to 2016. Phylogenetic analysis using TEF1 and RPB2 placed the Fov field isolates into 18 haplotypes. Upon profiling the Tfo1 transposon insertion in the NAT gene, it was determined that no race 4 isolates were recovered in Alabama. Representatives of all field isolate haplotypes caused disease on Upland cotton variety Rowden in a hydroponic test tube assay. Two haplotype A isolates were the most aggressive isolates recovered, and haplotype A isolate TF1 was more aggressive than the race 4 isolate 89-1A on Upland cotton and had similar symptom severity on Pima cotton. Karyotype profiling indicted an abundance of small chromosomes characteristic of karyotypes that include accessory chromosomes, with considerable variability between isolates. Collectively, our study indicates that Fov isolates from Alabama are genetically diverse, which may have been promoted by its persistence in cotton fields.

Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field.

Due to the existence of cotton weeds in a complex cotton field environment with many different species, dense distribution, partial occlusion, and small target phenomena, the use of the YOLO algorithm is prone to problems such as low detection accuracy, serious misdetection, etc. In this study, we propose a YOLOv8-DMAS model for the detection of cotton weeds in complex environments based on the YOLOv8 detection algorithm. To enhance the ability of the model to capture multi-scale features of different weeds, all the BottleNeck are replaced by the Dilation-wise Residual Module (DWR) in the C2f network, and the Multi-Scale module (MSBlock) is added in the last layer of the backbone. Additionally, a small-target detection layer is added to the head structure to avoid the omission of small-target weed detection, and the Adaptively Spatial Feature Fusion mechanism (ASFF) is used to improve the detection head to solve the spatial inconsistency problem of feature fusion. Finally, the original Non-maximum suppression (NMS) method is replaced with SoftNMS to improve the accuracy under dense weed detection. In comparison to YOLO v8s, the experimental results show that the improved YOLOv8-DMAS improves accuracy, recall, mAP0.5, and mAP0.5:0.95 by 1.7%, 3.8%, 2.1%, and 3.7%, respectively. Furthermore, compared to the mature target detection algorithms YOLOv5s, YOLOv7, and SSD, it improves 4.8%, 4.5%, and 5.9% on mAP0.5:0.95, respectively. The results show that the improved model could accurately detect cotton weeds in complex field environments in real time and provide technical support for intelligent weeding research.

Monitoring soil moisture in cotton fields with synthetic aperture radar and optical data in arid and semi-arid regions

Monitoring soil moisture in cotton fields with synthetic aperture radar and optical data in arid and semi-arid regions

Impact of insecticides applied in cotton fields on some piercing-sucking insects, pink bollworm, associated predators and Bemisia tabaci parasitoids

Impact of insecticides applied in cotton fields on some piercing-sucking insects, pink bollworm, associated predators and Bemisia tabaci parasitoids