Characteristics Of Ear Research Articles

Morphologic Variations In External Ear Among Ethnic Meiteis: A Hospital-Based Cross-Sectional Study

The external ear is highly individualised in terms of its shape, size, and other morphological characteristics, making it a useful tool for forensic identification in determining sex and personal identification. These characteristics are believed to be controlled by multiple genes and shared among genetically related individuals. To describe the uniqueness and sexual differences in external ear morphology among the ethnic Meiteis, a cross-sectional study was conducted in the Clinical Section of the Department of Forensic Medicine and Toxicology from May 2022 to April 2024. The study included 422 ethnic Meiteis, and common characteristics observed in both sexes included an oval-shaped pinna in 35.38% of individuals, nodosity of Darwin's tubercle in 74.53%, medium-sized anti-tragus in 50.66%, arched shape of the lobule in 44.67%, and proportionate form of the concha in 74.10%. The tragus types were evenly distributed, with an average of 30% for each type. The shape of the helical fold differed between males and females, with 60.03% of males having a normally rolled shape and 62.16% of females having a wide covering scapha shape, with an average occurrence of 49.14%. There was no significant sexual difference in the external ear characteristics except for the helical fold. Regarding the uniqueness, most of the ethnic Meitei population exhibited the nodosity type of Darwin's tubercle, medium-sized anti-tragus, and proportionate form of the concha, accounting for more than half of the total study population.

Correction of Microtia with Constricted Ear Features Using Helix Costal Cartilage Scaffold and Postauricular Flap Advancement.

Microtia with constricted ear features represents a challenging congenital deformity characterized by the variable shape and large volume of the remnant cartilage and skin involved. This study aims to evaluate the efficacy and safety of a method using helix costal cartilage scaffold and postauricular flap advancement for the correction. This retrospective study analyzed the clinical and postoperative data of 121 patients diagnosed with microtia and constricted ear features, treated at our institution between January 2020 and January 2023. All patients underwent helix reconstruction using the eighth or ninth costal cartilage combined with postauricular flap advancement. Outcomes were assessed based on the incidence of postoperative complications and auricular aesthetic scores. The aesthetic evaluation of the reshaped auricle was based on a four-point Likert scale (i.e. 1 = poor, 2 = fair, 3 = good, 4 = excellent). The postoperative follow-up period ranged from 12 months to 3 years (mean 18.5 months). All patients achieved successful helix expansion, with reconstructed ears showing improved symmetry and shape comparable to the contralateral normal ears. The average aesthetic score was 3.4 out of 4. Complications included suture exposure in 8.3% of cases and cartilage protrusion in 4.1%, both managed without significant issues. There were no instances of skin necrosis, costal cartilage exposure, or infection. The combination of helix costal cartilage scaffold and postauricular advancing flap offers a reliable and effective method for correcting microtia with constricted ear features, providing satisfactory aesthetic outcomes with minimal complications.

Determination of Friction Characteristics of Corn Ears at Varying Factors in Mechanical Peeling

ABSTRACTIn view of the low bract removal and high corn ear damage in the operation of the current peeling device, this work aimed to accurately measure the coefficients of friction (COFs) involved between peeling rollers and corn ears during the corn peeling. Consequently, the movement state of corn ear in a peeling device and the friction behavior involved were analyzed, and the mechanical model was established. Friction tests were conducted by using the modified tilting table, the modified direct shear apparatus, and the self‐built rolling friction apparatus. The value range and change rule of the COFs between corn ears and peeling rollers were obtained. Linear regression models were described for the COFs depending on moisture content. The results show that the coefficient of static friction (COSF) was 0.391–1.396, the coefficient of sliding friction (CODF) was 0.398–1.318, and the coefficient of rolling friction (CORF) was 0.119–0.377. All COFs were positively correlated with moisture content. Besides, the COFs of two rubber rollers were significantly larger than that of the steel roller. Among the two rubber rollers, the COFs were higher between the fish scale rubber roller and bare corn ears and were lower between the fish scale rubber roller and corn ears with bracts. This meant that the fish scale rubber roller was more likely to cause seed damage under the same conditions. Knowledge concerning these coefficients is believed to provide data support for the improvement and optimization of corn peeling devices.

Quantitative Trait Loci Analysis of Maize Husk Characteristics Associated with Gibberella Ear Rot Resistance

Maize (Zea mays L.) is a vital crop susceptible to Gibberella ear rot (GER), a disease caused by Fusarium graminearum, resulting in significant yield losses and mycotoxin production. This study aimed to investigate the correlation between ear characteristics and GER resistance in 74 maize inbred lines (42 with non-stiff stalks and 32 stiff stalks) adapted to the northern Italian environment. Mycotoxin analysis was performed to assess the presence of deoxynivalenol (DON) and zearalenone (ZEA). The results showed a positive correlation between the husk traits, like the husk number and husk cover, and GER resistance in both heterotic groups. A positive correlation was also found between the DON and ZEA concentrations. In addition, we conducted a genome-wide association study (GWAS) which identified novel quantitative trait loci (QTLs) associated with the husk number, husk cover, ear attitude, and infection score. These QTLs can be utilized in marker-assisted selection for breeding new GER-resistant maize varieties. Our study provides valuable insights into the genetic basis of ear traits and their relationship with GER resistance, which can contribute to an improvement in the environmental and economical sustainability of the corn growing system.

Application of graphical analysis and principal components to identify the effect of genotype × trait in maize hybrids

AbstractIn order to identify the effect of genotype × trait, 20 maize (Zea mays L.) hybrids were cultivated and investigated in a randomized complete block design in three replications in the Karaj region. The results of the analysis of variance showed that the effect of genotype in terms of all traits except for the traits of days until the tassel dries, peduncle outside the flag leaf, tassel length, the number of fill seeds, and the depth of the seeds are significantly different. Based on the mean comparison done by Duncan's method, G3, G6, G7, and G4 genotypes were identified as favorable hybrids. Based on the graphic analysis, the genotypes G5, G4, G6, G3, G9, and G14 can be identified as desirable hybrids. The correlation diagram indicated that the grain yield trait has a positive correlation with tassel length, leaf length, leaf width, and leaf surface traits. Based on the principal component analysis, the first 10 components explained more than 74% of the data variance. The traits were classified into 10 components: components of ear characteristics, time characteristics in terms of maturity, leaf characteristics, characteristics of maize plant 1 (cob corn diameter, peduncle length, and grain yield traits), characteristics of maize plant 2 (number of tassel branches, leaf surface, and grain yield traits), physiological characteristics and germination, the crown part of the ear characteristics, grain characteristics, grain yield, and characteristics of the ear head. The experiment results indicated that G8, G15, G1, and G6 hybrids were more favorable in terms of grain yield trait.

A study on creative drawing strategies based on the human head features in children's realistic figure drawings

Creative strategies used in drawing practice determine the visual effects of drawing. Drawing strategies reflect children's understanding, association, and summarisation of the visual features of objects. In the case of the human head, visual features are composed of positional, contour, and structural features. This study assessed the creative strategies of children aged 7–12 years based on their drawings of the visual features of the eyebrows, eyes, nose, mouth, ears, and neck in realistic figures. Children's strategies for creating visual features of the human head were classified into four types: hidden, abstract, summarised, and realistic. The results showed that most children used abstract creation strategies to represent the visual features of the nose and mouth in their drawings. Most children expressed the visual features of the eyes and neck using summarised creative strategies. A few children applied realistic creative strategies to draw the visual features of eyebrows, eyes, nose, mouth, and ears. Children used simplified structural features as a strategy to understand the visual features of the human head before creating their drawings. Most of the children tended to express the overall features of the human head with less curvature or number of contour lines in their drawings. Children regard the eyes and mouth as the major elements when drawing the visual features of the human head. Children regard eyebrows, noses, and ears as secondary elements expressing human heads. Accordingly, teachers need to educate children to understand and summarise the visual features of these secondary elements. Teachers also need to guide children in converting the visual elements from their understanding to the graphical elements in drawings.

High-Throughput Phenotyping: Application in Maize Breeding

In breeding programs, the demand for high-throughput phenotyping is substantial as it serves as a crucial tool for enhancing technological sophistication and efficiency. This advanced approach to phenotyping enables the rapid and precise measurement of complex traits. Therefore, the objective of this study was to estimate the correlation between vegetation indices (VIs) and grain yield and to identify the optimal timing for accurately estimating yield. Furthermore, this study aims to employ photographic quantification to measure the characteristics of corn ears and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in a Complete Randomized Block (CRB) design with three replications across three locations. Vegetation and green leaf area indices were estimated throughout the growing cycle using an unmanned aerial vehicle (UAV) and were subsequently correlated with grain yield. The experiments consistently exhibited high levels of experimental quality across different locations, characterized by both high accuracy and low coefficients of variation. The experimental quality was consistently significant across all sites, with accuracy ranging from 79.07% to 95.94%. UAV flights conducted at the beginning of the crop cycle revealed a positive correlation between grain yield and the evaluated vegetation indices. However, a positive correlation with yield was observed at the V5 vegetative growth stage in Lavras and Ijaci, as well as at the V8 stage in Nazareno. In terms of corn ear phenotyping, the regression coefficients for ear width, length, and total number of grains (TNG) were 0.92, 0.88, and 0.62, respectively, demonstrating a strong association with manual measurements. The use of imaging for ear phenotyping is promising as a method for measuring corn components. It also enables the identification of the optimal timing to accurately estimate corn grain yield, leading to advancements in the agricultural imaging sector by streamlining the process of estimating corn production.

Townes-Brocks Syndrome Revealed by Kidney Gene Panel Testing

Townes-Brocks syndrome (TBS), a rare autosomal dominant genetic condition associated with SALL1 (Spalt like Transcription Factor 1), is reported to be present in 1:238,000 individuals in the general population. TBS is characterized by the triad of anorectal malformations, dysplastic ears, with or without hearing impairment, and hand or thumb anomalies. Although kidney involvement is less common in TBS, the disease can progress to kidney failure. Here, we sought to characterize the incidence of SALL1 variants in individuals undergoing broad-based genetic testing with a kidney gene panel and to quantify the presence of (extra)renal features. A retrospective analysis of the genetic data from a 385-gene panel identified cases with a pathogenic (P) or likely pathogenic (LP) variant in SALL1. Data including age, features, and disease progression were collected. Of 35,044 samples, P or LP variants in SALL1 were identified in 22, yielding a prevalence of 1:1592 among patients tested for monogenic kidney disease, and 1:342 among cases identified with a monogenic kidney disease. Among this cohort, the median patient age was 23 years (range: 3 months-62 years) with chronic kidney disease (CKD) reported in 91% (20/22) of cases. Reported kidney features included renal agenesis/hypoplasia (7/22; 32%), focal segmental glomerulosclerosis (4/22; 18%), and kidney cysts (3/22; 14%). Confirmed extrarenal features included hearing loss and/or ear features (7/22; 32%), anorectal malformations (6/22; 27%) and hand or thumb abnormalities (4/22; 18%). Three patients (3/22; 14%) had both a priori TBS diagnoses and the traditional "triad." Traditionally, a molecular diagnosis was ascertained primarily in individuals presenting with cardinal features of TBS; therefore, individuals with mild or atypical presentations were often overlooked clinically. Our findings reveal that SALL1 P/LP variants could be a consequential contributor to monogenic kidney disease.

A Novel Ear Impression-Taking Method Using Structured Light Imaging and Machine Learning: A Pilot Proof of Concept Study with Patients' Feedback on Prototype.

Taking an ear impression is a minimally invasive procedure. A review of existing literature suggests that contactless methods of scanning the ear have not been developed. We proposed to establish a correlation between external ear features with the ear canal and with this proof of concept to develop a prototype and an algorithm for capturing and predicting ear canal information. We developed a novel prototype using structured light imaging to capture external images of the ear. Using a large database of existing ear impression images obtained by traditional methods, correlation analyses were carried out and established. A deep neural network was devised to build a predictive algorithm. Patients undergoing hearing aid evaluation undertook both methods of ear impression-taking. We evaluated their subjective feedback and determined if there was a close enough objective match between the images obtained from the impression techniques. A prototype was developed and deployed for trial, and most participants were comfortable with this novel method of ear impression-taking. Partial matching of the ear canal could be obtained from the images taken, and the predictive algorithm applied for a few sample images was within good standard of error with proof of concept established. Further studies are warranted to strengthen the predictive capabilities of the algorithm and determine optimal prototype imaging positions so that sufficient ear canal information can be obtained for three-dimensional printing. Ear impression-taking may then have the potential to be automated, with the possibility of same-day three-dimensional printing of the earmold to provide timely access.

Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection

Ear recognition has made good progress as an emerging biometric technology. However, the recognition performance, generalization ability, and feature robustness of ear recognition systems based on hand-crafted features are relatively poor. With the development of deep learning, these problems have been partly overcome. However, the recognition performance of existing ear recognition systems still needs to be improved when facing unconstrained ear databases in realistic scenarios. Another critical problem is that most systems with ear feature template databases are vulnerable to software attacks that disclose users’ privacy and even bring down the system. This paper proposes a software-attack-proof ear recognition system using deep feature learning and blockchain protection to address the problem that the recognition performance of existing systems is generally poor in the face of unconstrained ear databases in realistic scenarios. First, we propose an accommodative DropBlock (AccDrop) to generate drop masks with adaptive shapes. It has an advantage over DropBlock in coping with unconstrained ear databases. Second, we introduce a simple and parameterless attention module that uses 3D weights to refine the ear features output from the convolutional layer. To protect the security of the ear feature template database and the user’s privacy, we use Merkle tree nodes to store the ear feature templates, ensuring the determinism of the root node in the smart contract. We achieve Rank-1 (R1) recognition accuracies of 83.87% and 96.52% on the AWE and EARVN1.0 ear databases, which outperform most advanced ear recognition systems.

Этиология, диа гностика, осложнения и лечение острого среднего отита у детей. Современный взгляд на проблему

The article delves into the etiology, clinical manifestation, diagnosis, and treatment of acute otitis media (AOM), along with measures for preventing its complications. AOM is characterized by the rapid onset of symptoms indicative of inflammation in the middle ear. The paper examines AOM incidence rates across different countries, including Russia, highlighting the significance of this health issue. Various factors can contribute to the development of AOM, such as anatomical features of the middle ear, the cellular biology patterns of the nasopharynx, and variations in immune responses to microbial invasions. The condition is commonly associated with viral and bacterial pathogens, as well as genetic predisposition; however, bacteria are generally considered the primary causative agents. The article also addresses therapeutic approaches and the concern of overdiagnosis, which can lead to the excessive use of antibiotics and subsequent resistance. A retrospective analysis of 90 outpatient records was conducted, focusing on patients who received analgesic ear drops during the pre-perforated stage of AOM. Findings indicate that a combination drug containing lidocaine and phenazone was frequently prescribed in cases of recurrent AOM and for referrals to an otorhinolaryngologist within the initial two days of disease onset, particularly for patients with bilateral AOM. The importance of prompt diagnosis and appropriate treatment of AOM is underscored, as well as the necessity for preventive measures to decrease incidence rates. These insights can be highly beneficial for practitioners and specialists in the field of otorhinolaryngology. KEYWORDS: acute otitis media, otalgia, earache, ear drops, lidocaine, phenazone, compliance, drug choice. FOR CITATION: Kiselyov A.B., Chaukina V.A., Andamova O.V., Avtushko A.S., Vertakova O.V., Rudenskikh Yu.V. Etiology, diagnosis, complications and treatment of acute otitis media in pediatric population. Modern view of the problem. Russian Medical Inquiry. 2024;8(8):493–498 (in Russ.). DOI: 10.32364/2587-6821-2024-8-8-8.

Anthropometric study of ear prints in males of Haryana and Himachal region

Background: The external ear is a highly intricate structure exhibiting considerable variability among individuals. Ear prints, two-dimensional representations of portions of the outer ear that has made contact with a specific surface, have garnered attention in recent years. Some researchers propose that characteristics of the external ear could serve as identifiers with a level of certainty akin to fingerprinting. Aims and Objectives: This study aimed to measure ear height, ear breadth, and auricular index on ear prints and to compare these parameters between the populations of Haryana and Himachal Pradesh. Materials and Methods: The study involved 200 males aged 18–40 years and was conducted at the department of anatomy, MMIMSR, MMU, Mullana. Parameters measured on the ear prints included total ear height, total ear breadth, and the calculation of auricular index. Results: Statistically significant differences were observed in ear length and breadth between the two populations. Haryanvi males exhibited greater ear length and breadth compared to Himachali males. Furthermore, there was a significant difference in auricular indices, with Haryanvi males displaying higher values compared to Himachali males. Conclusion: Upon comparison of ear prints, it was noted that Haryanvi males typically possessed larger and broader ears, whereas Himachali males tended to have medium-sized ears.

Multicomponent tympanometry in ears with cochlear implant

Abstract Introduction: Multifrequency and multicomponent tympanometry procedures enable the evaluation of minute changes in the middle ear system and the estimation of resonant frequency. Changes in the resonant frequency beyond normative values indicate the presence of pathology or change in the natural characteristics of the middle ear. The present study aimed to investigate if there is a difference in the admittance of the middle ear on the side of cochlear implantation and the contralateral ear for different probe tones. The study also investigated if there was a difference in the type of multicomponent tympanogram obtained in ears that have undergone cochlear implantation when compared to the contralateral ear. Methods: Twenty children in the age range of 4–14 years with bilateral severe to profound sensorineural hearing loss, having undergone unilateral cochlear implantation, were included in the study. Tympanometry using four probe-tone frequencies (226 Hz, 678 Hz, 800 Hz, and 1000 Hz) was carried out in each ear. Total compliance (Y) and type of tympanogram were compared across the ear with cochlear implantation and the contralateral ear. Results: It was found that the implanted ears had lower mean admittance values as compared to the contralateral side at all four probe-tone frequencies. Wilcoxon signed-rank test indicated that the difference was statistically significant for 226 Hz, 678 Hz, and 800 Hz probe tones, suggesting increased stiffness in the ear with cochlear implantation. There was a significant difference in the type of tympanograms obtained in the two ears for 800 Hz and 1000 Hz probe-tone frequencies. Conclusions: These results warrant the testing of the contralateral ear for obtaining electrically evoked stapedial reflex thresholds (eSRT).

CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting.

Wheat is the most widely grown crop in the world, and its yield is closely related to global food security. The number of ears is important for wheat breeding and yield estimation. Therefore, automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain yield. However, all existing methods require position-level annotation for training, implying that a large amount of labor is required for annotation, limiting the application and development of deep learning technology in the agricultural field. To address this problem, we propose a count-supervised multiscale perceptive wheat counting network (CSNet, count-supervised network), which aims to achieve accurate counting of wheat ears using quantity information. In particular, in the absence of location information, CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear features. We conduct comparative experiments on a publicly available global wheat head detection dataset, showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error (MAE) and root mean square error (RMSE). This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs, demonstrating its great potential for agricultural counting tasks. The code is available at http://csnet.samlab.cn.

FIDMT-GhostNet: a lightweight density estimation model for wheat ear counting.

Wheat is one of the important food crops in the world, and the stability and growth of wheat production have a decisive impact on global food security and economic prosperity. Wheat counting is of great significance for agricultural management, yield prediction and resource allocation. Research shows that the wheat ear counting method based on deep learning has achieved remarkable results and the model accuracy is high. However, the complex background of wheat fields, dense wheat ears, small wheat ear targets, and different sizes of wheat ears make the accurate positioning and counting of wheat ears still face great challenges. To this end, an automatic positioning and counting method of wheat ears based on FIDMT-GhostNet (focal inverse distance transform maps - GhostNet) is proposed. Firstly, a lightweight wheat ear counting network using GhostNet as the feature extraction network is proposed, aiming to obtain multi-scale wheat ear features. Secondly, in view of the difficulty in counting caused by dense wheat ears, the point annotation-based network FIDMT (focal inverse distance transform maps) is introduced as a baseline network to improve counting accuracy. Furthermore, to address the problem of less feature information caused by the small ear of wheat target, a dense upsampling convolution module is introduced to improve the resolution of the image and extract more detailed information. Finally, to overcome background noise or wheat ear interference, a local maximum value detection strategy is designed to realize automatic processing of wheat ear counting. To verify the effectiveness of the FIDMT-GhostNet model, the constructed wheat image data sets including WEC, WEDD and GWHD were used for training and testing. Experimental results show that the accuracy of the wheat ear counting model reaches 0.9145, and the model parameters reach 8.42M, indicating that the model FIDMT-GhostNet proposed in this study has good performance.

Pigeon Pea Green Manuring and Nitrogen Fertilization Increase Agronomic Efficiency by Improving Yield and Ear Characteristics of Maize

Green legume incorporation is an encouraging, at least unfinished, substitute for chemical fertilizers, particularly for nitrogen (N). The experiment was conducted in an RCB design with a split plot arrangement replicated four times. Pigeon pea green manuring (GM) of 3.4, 6.3 and 7.3 t ha-1 at pre flowering (GM1), at flowering (GM2) and post flowering (GM3) were assigned to the main plots, respectively, and nitrogen levels (N) (0, 70, 100 and 130 kg N ha-1) were allotted to the subplots. Results showed that GM2 significantly improved plant height (183 cm) and leaf area (393.6 cm2). Whereas, GM1 significantly enhanced biological yield (9826 kg ha-1), grain yield (3500 kg ha-1), thousand grain weight (203.6 g), grain ear-1(319), ear length (18 cm) and ear diameter (11.4 cm) as compared to GM2. Similarly, nitrogen application at the rate of 130 kg ha-1 resulted in taller plants, higher leaf area, thousand grain weight, biological and grain yields, harvest index, grains ear-1, ear height, length, weight and diameter than other N levels. The agronomic efficiency (AE) was significantly increased by 13.8 kg kg-1 and 11.8 kg kg-1 at GM1 and 70 kg N ha-1, respectively. It was concluded from the outcomes of the study that pigeon pea GM at pre flowering stage and 130 kg N ha-1 improved maize crop production.

Wideband frequency impedance for diagnosis of ossicular chain abnormality

Background The accurate estimation of the ossicular chain abnormalities using existing functional examinations has been difficult. Aims/Objectives This study aimed to verify the accuracy of preoperative diagnosis of ossicular chain abnormalities using a wideband frequency impedance (WFI) meter, which can measure the dynamic characteristics of the middle ear. Material and Methods Retrospective cohort study. Fourteen ears of patients with ossicular chain abnormalities that were definitively diagnosed surgically were included in this study. The following data were collected for each participant: sound pressure level (SPL) curve measured using the WFI meter and a sweep frequency impedance (SFI) meter, WFI measurements plotted on the resonance frequency (RF)-ΔSPL plane, distribution map of the dynamic characteristics of the middle ear, preoperative audiometry results, and the definitive surgical diagnosis. Results The SPL curve obtained using the WFI meter had lesser noise than that obtained using the SFI meter. The distribution map revealed that the ossicular chain separation range and ossicular chain fixation range were completely separated. The hearing data tended to be poor in cases with small ΔSPL. Conclusions and Significance WFI can potentially enhance the accuracy of SFI. In addition, it can also be used for the classification of ossicular chain separation and fixation as well as the quantification of fixation in cases of ossicular chain anomalies that cannot be diagnosed using conventional tests.

Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance

The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, an efficient real-time model designed to detect, track, and count wheat ears in video sequences. This model uses FasterNet as its foundational feature extraction network, significantly reducing the model’s parameter count and improving the model’s inference speed. We also incorporate deformable convolutions and dynamic sparse attention into the feature extraction network to enhance its ability to capture wheat ear features while reducing the effects of intricate environmental conditions. To address information loss during up-sampling and strengthen the model’s capacity to extract wheat ear features across varying feature map scales, we integrate a path aggregation network (PAN) with the content-aware reassembly of features (CARAFE) up-sampling operator. Furthermore, the incorporation of the Kalman filter-based target-tracking algorithm, Observation-centric SORT (OC-SORT), enables real-time tracking and counting of wheat ears within expansive field settings. Experimental results demonstrate that Wheat-FasterYOLO achieves a mean average precision (mAP) score of 94.01% with a small memory usage of 2.87MB, surpassing popular detectors such as YOLOX and YOLOv7-Tiny. With the integration of OC-SORT, the composite higher order tracking accuracy (HOTA) and counting accuracy reached 60.52% and 91.88%, respectively, while maintaining a frame rate of 92 frames per second (FPS). This technology has promising applications in wheat ear counting tasks.

Genetic diversity and productive potential of starchy corn varieties evaluated in Peruvian highland environments

Corn cultivation is relevant to the province of Tayacaja, a region with the greatest biodiversity in the Huancavelica State, although to date there are no published studies. Phenotypic characterization pursues measuring the genetic diversity of a group of genotypes, using appropriate morphological descriptors. The main objective of this work was to study the agro-morphological characteristics and productive potential of 25 starchy corn varieties, evaluated in four contrasting environments of the province of Tayacaja, Perú, during the crop cycle 2019–2020. For this purpose, 25 plant and ear characteristics were chosen, using principal components (PC) and cluster analysis. The experiments were established in an alpha-lattice design, with three replications, in experimental units of two rows, with 0.20 m between plants and 0.80 m between furrows, 4 m long. The agro-morphological characterization grouped the 25 varieties within 6 well-defined clusters, giving an overview of the existing phenotypic variability among the evaluated starchy corn varieties. The varieties G6, G14, G20, GT3, and G18 were the genotypes with the most outstanding characteristics, which facilitated their separation from the rest of the materials studied. The multivariate analysis brought together representative varieties of certain races, with typical plant and ear characteristics. The study revealed the existence of a good productive and agronomic potential for the development of genetic materials for cancha (toasted kernel grains), choclo (fresh corn), and mote corn production; as well as the presence of anthocyanin in the grain, information that, together with the agro-morphological characterization, could be very useful to enhance gains in the selection process in future genetic improvement work.

Performance of second crop maize hybrids in different population densities

The cultivation of second crop (safrinha) maize (Zea Mays L.) has progressed in Brazil. The maize plant population recommendation for the second crop is drastically reduced. This work aimed to evaluate the morphophysiological characters of ear and yield components in different populations of plants and maize hybrids. The experiment was cultivated in a no-tillage system in the second crop of the 2019/2020 agricultural year in Unaí-MG and carried out according to a randomized block design in subdivided plots, with the five simple maize hybrids KWS (K9105VIP3, K9960VIP3, K9606VIP3, K9555VIP3 and K8774PRO3) considered as parcels and the four populations (35.000, 50.000, 65.000 and 80.000 plants he-1), considered as subplots. The maize hybrids did not influence (P>0,05) the variables. The populations of 35.000, 65.000 and 80.000 plants he-1 resulted in the highest (P<0,05) number of rows, number of grains per row and ear length. The increase in plant population influenced (P<0,05) positively the grain yields up to the population of plant density of 68.200 plants he-1 with grain yield of 9.302 kg he-1. Population densification, even increasing competition for resources negatively influencing most plant and ear characters, increases grain yield due to the greater amount of ear per area.