Maturity Classification Research Articles

Measuring sustainable transformation of small and medium‐sized enterprises using management systems standards

AbstractSmall‐ and medium‐sized enterprises (SMEs) represent a significant number of industries in need of moving towards more sustainable development. A sustainability perspective on business activities is today crucial for SMEs to achieve a new level of competitiveness. Identifying which is a suitable set of sustainability indicators for an individual SME is a big challenge. The availability of so many methods and systems can leave SMEs reluctant to implement them in their small business. Therefore, this research aims to create a sustainability maturity model for SMEs to visualize goals and assess progress on their transformation journey. Progress is assessed by indicators, exemplified by indicators of the Global Reporting Initiative and the maturity is verified as stepwise growth along international management system standards. Using the developed model, it is possible to determine the SMEs' sustainability performance. The research has resulted in a stepwise classification of the organization's maturity is compiled into training and auditing material in different regions and sectors.

Comparison Classification Of Tomatoes Ripeness Based On RGB, HSV And CMYK Colors Based On Correlation Coefficient

This article discusses the classification of tomato fruit maturity based on color space. Several studies have been conducted to measure maturity levels using RGB and HSV color spaces. In this article, researchers classify the ripeness of tomatoes using the CMYK color space, which researchers have never done before. Next, the classification results of the CMYK color space are compared with the RGB and HSV color spaces. The CMYK color space is a secondary color commonly seen by the human eye. CMYK colors are colors produced from a combination of RGB colors. Comparison of classification results based on CMYK, RGB, and HSV color spaces was carried out using the correlation coefficient and mean square error (MSE). The correlation coefficient is a method that is often used to measure the similarity between 2 images, where the closer to 0 the correlation value, the better

A Convolutional Neural Network-based Intelligence System for the Identification of Copra Maturity Levels

The North Sulawesi Province, widely recognized as the Coconut Waving Province owing to its substantial coconut tree population, primarily depends on copra production. This research presents a novel methodology for determining copra maturity levels by utilizing a Convolutional Neural Network (CNN) on digital photographs, classifying them into three distinct stages: raw, half-ripe, and ripe. By employing a rigorous 10-fold cross-validation technique, our models demonstrated remarkable performance. Notably, even the model with the lowest performance achieved a commendable accuracy of 87.78% during the training and validation phases. The model that exhibited the highest level of performance achieved a perfect accuracy rate of 100%. Moreover, when subjected to real-world testing situations using novel data, the model with the lowest performance exhibited a noteworthy accuracy of 83.34%. In contrast, the highest-performing model achieved a flawless accuracy of 100%. Based on the findings above, an online system has been built that leverages the most optimal model, facilitating the assessment of copra maturity in real-time. The prospects encompass the integration of this methodology into copra sorting machinery, thereby yielding advantages for both agricultural producers and industrial sectors. This research enhances copra quality control processes and promotes sustainability in the copra industry. Further research could explore refining the CNN model to accommodate a broader range of copra variations and investigating automation possibilities in copra production processes. These endeavors would advance the efficacy and applicability of copra maturity classification methods, fostering continued innovation in the industry.

In-silico evaluation of orthodontic miniscrew-assisted rapid palatal expanders for patients with various stages of skeletal maturation

This study aimed to use a finite element method to assess the stress on the miniscrews, skeletal and dental changes resulting from maxillary expansion using either a tooth-bone-borne (TBB) or a bone-borne (BB) device on patients with various skeletal maturation. Two types of expanders were modeled using SolidWorks. The design of the 2 expanders was similar, with the exception that the BB appliance lacked support for teeth (rods and bands). Both were placed on the palatal bones with the help of 4 miniscrews as bony anchorage. Five skeletal maturation stages were examined using suture maturation classification by Angelieri. A lateral displacement of 0.2 mm was applied to simulate 1 turn of jackscrew per day. The dental and skeletal changes from the treatment were quantified. Stage A sutural maturation exhibited the greatest skeletal movement (0.13 mm/d) with a V-shaped displacement pattern. Stages B and C exhibited a more parallel expansion pattern. Stages D and E exhibited the least amount of skeletal movement with either device. Compared with the BB device, TBB demonstrated greater dental displacement. On average, the BB device exhibited a skeletal-to-dental expansion ratio of 70% from stage A to C compared with 49% with the TBB device. BB device showed more skeletal displacement than TBB, and the latter showed more dental side effects, particularly during stages B and C. The miniscrew-assisted rapid palatal expansion appliance was effective with sutural maturation stages A to C using the classification by Angelieri with a more parallel separation of the midpalatal sutures in stages B and C.

A Method for Sorting High-Quality Fresh Sichuan Pepper Based on a Multi-Domain Multi-Scale Feature Fusion Algorithm.

Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes a multi-scale frequency domain feature fusion module (MSF3M) and a multi-scale dual-domain feature fusion module (MS-DFFM) to construct a multi-scale, multi-domain fusion algorithm for feature fusion of Sichuan pepper images. The MultiDomain YOLOv8 Model network is then built to segment and classify the target Sichuan pepper, distinguishing the maturity level of individual Sichuan peppercorns. A selection method based on the average local pixel value difference is proposed for sorting high-quality fresh Sichuan pepper. Experimental results show that the MultiDomain YOLOv8-seg achieves an mAP50 of 88.8% for the segmentation of fresh Sichuan pepper, with a model size of only 5.84 MB. The MultiDomain YOLOv8-cls excels in Sichuan pepper maturity classification, with an accuracy of 98.34%. Compared to the YOLOv8 baseline model, the MultiDomain YOLOv8 model offers higher accuracy and a more lightweight structure, making it highly effective in reducing misjudgments and enhancing post-harvest processing efficiency in agricultural applications, ultimately increasing producer profits.

Midpalatal Suture Maturation in Relation to Age, Sex, and Facial Skeletal Growth Patterns: A CBCT Study.

The evaluation of midpalatal suture maturation is essential to undertake the most predictable maxillary expansion approach. Several factors, such as age, gender, and facial growth patterns, seem to be involved in midpalatal suture staging and, consequently, in its opening; however, the link between these variables and the stages of midpalatal suture development remains poorly understood. Our study aimed to analyse the midpalatal suture maturation in relation to age, sex, and skeletal growth patterns by CBCT. We enrolled 263 patients (119 males and 144 females) aged from 8 to 20 years. The midpalatal suture maturation was defined according to Angelieri et al.'s classification using a low-dose CBCT. The chi-square test and linear regression were applied to investigate the suture stages by age, sex, and vertical and sagittal growth patterns. Stage A was present in 8- and 9-year-olds with a larger prevalence in boys, while the prevalence of stage E increased progressively with age. Stage D was the most prevalent in our sample. The statistical analysis described that stage A was more likely in the youngest subjects, and stage E in the oldest participants. The males tended to have lower maturation stages. Moreover, the hypodivergent and normodivergent subjects tended to have higher maturation stages, while Class III was more likely in subjects in stages D or E. A total of 127 patients were in stages A, B, and C, showing an unfused suture. In young individuals, the opening of the midpalatal suture leads to a proper facial growth development by correcting the transverse superior hypoplasia. The midpalatal sutural maturation classification was related to age, sex, and divergence.

Pediatric spinal alignment and spinal development

BackgroundKnowledge of the growth spurt and remaining growth is essential for managing musculoskeletal diseases in children. Accurate prediction of curve progression and timely interventions are crucial, particularly for conditions like adolescent idiopathic scoliosis (AIS). MethodsThis study conducted a comprehensive review and synthesis of existing literature on spinal growth, skeletal maturity classifications, and the evolution of sagittal alignment parameters during childhood and adolescence. Key anatomical elements involved in spinal development, natural history of spinal growth, and skeletal maturity assessment systems were analyzed. ResultsThe analysis highlighted that key parameters such as Pelvic incidence (PI), Pelvic tilt (PT), and Lumbar lordosis (LL) increase significantly with growth, especially during the pubertal growth spurt. In contrast, Sacral slope (SS) remains relatively constant, and Thoracic kyphosis (TK) shows a slight increase. Additionally, there is a posterior shift in the center of gravity as children grow, reflecting progressive postural maturation. The study also reviewed and compared various maturity classification systems, noting the reliability and clinical implications of systems like the Sanders Maturity Stage (SMS) and Tanner-Whitehouse III. ConclusionsReliable maturity classification systems, such as the Sanders Maturity Stage (SMS) and Tanner-Whitehouse III, allow for tailored treatments to individual growth patterns. Integrating these classification systems into clinical practice enables precise prediction of curve progression and timely therapeutic interventions. This includes options from bracing to surgical techniques like growing rods or vertebral body tethering (VBT), with growth modulation being a key factor in achieving successful outcomes.

Research on Nondestructive Inspection of Fruits Based on Spectroscopy Techniques: Experimental Scenarios, ROI, Number of Samples, and Number of Features

Spectral technology is a scientific method used to study and analyze substances. In recent years, the role of spectral technology in the non-destructive testing (NDT) of fruits has become increasingly important, and it is expected that its application in the NDT of fruits will be promoted in the coming years. However, there are still challenges in terms of dataset collection methods. This article aims to enhance the effectiveness of spectral technology in NDT of citrus and other fruits and to apply this technology in orchard environments. Firstly, the principles of spectral imaging systems and chemometric methods in spectral analysis are summarized. In addition, while collecting fruit samples, selecting an experimental environment is crucial for the study of maturity classification and pest detection. Subsequently, this article elaborates on the methods for selecting regions of interest (ROIs) for fruits in this field, considering both quantitative and qualitative perspectives. Finally, the impact of sample size and feature size selection on the experimental process is discussed, and the advantages and limitations of the current research are analyzed. Therefore, future research should focus on addressing the challenges of spectroscopy techniques in the non-destructive inspection of citrus and other fruits to improve the accuracy and stability of the inspection process. At the same time, achieving the collection of spectral data of citrus samples in orchard environments, efficiently selecting regions of interest, scientifically selecting sample and feature quantities, and optimizing the entire dataset collection process are critical future research directions. Such efforts will help to improve the application efficiency of spectral technology in the fruit industry and provide broad opportunities for further research.

Research Status and Development Trend of Key Technologies for Pineapple Harvesting Equipment: A Review

Pineapple harvesting is a key step in pineapple field production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper briefly describes the basic situation of pineapple planting worldwide. Based on the degree of automation of mechanized pineapple harvesting equipment, the main structural forms, core technologies, and operation modes of semi-automatic, automatic, and intelligent pineapple harvesting equipment are summarized. The research status and existing problems of key pineapple fruit picking robots, such as fruit recognition, maturity classification, positioning, and separation of pineapple fruits, are analyzed. Considering the problems of pineapple harvesting equipment, such as difficulty entering the ground, low harvesting efficiency, low picking success rate, and fruit damage, innovative future research directions for mechanized pineapple harvesting technology are proposed, such as combining agricultural machinery and agronomical principles, integrating mechanized, automated, and intelligent technology, and developing modular designs and generalized approaches.

Mango crop maturity estimation using meta‐learning approach

AbstractMangos are a significant fruit crop that is widely cultivated in tropical and subtropical regions. However, manual detection of mango crop maturity is time‐consuming and labor‐intensive. It is a vital agricultural commodity, and accurate assessment of the mango maturity stage is critical for harvesting and post‐harvest handling. Accurately detecting mango crop maturity is essential for ensuring optimal harvesting and quality. This study introduced a specialized Meta‐learning method for classifying mango crop images with limited data. The conventional approach to maturity classification, often hindered by limited labeled data, is significantly enhanced by meta‐learning's ability to adapt quickly to new tasks with minimal examples. The proposed approach works as initial image segmentation to isolate mango crop regions, comprehensive feature extraction to capture meaningful data representations, and a critical meta‐training. This phase involves refining a classifier within a metric space by utilizing cosine distance and an adaptable scale parameter, this is followed by a meta‐testing stage. where the adapted classifier is utilized for maturity prediction with minimal support samples. This research holds substantial promise for the agricultural sector, offering practical solutions to optimize harvest management practices and improve mango crop yield predictions.Practical applicationsOptimized Harvest Scheduling: Scenario: Farmers can use crop maturity estimation to schedule harvest times more precisely. Application: Knowing crop maturity optimizes harvest, minimizing losses. Resource Management: Scenario: Efficient use of resources such as water, fertilizers, and pesticides is essential for sustainable agriculture. Application: Crop maturity guides resource use, for example, reducing late‐stage water/nutrients prevents overuse, saves resources. Quality Assurance: Scenario: Crop quality is often linked to its maturity level. Application: Maturity estimation assesses crop quality, guiding post‐harvest planning and determining market value. Data‐Driven Decision Making: Scenario: Modern agriculture relies on data for decision‐making. Application: Maturity estimation promotes data‐driven farming, aiding trend analysis, optimizing planting, and enhancing farm management.

Abstract PR010: Exploratory analysis of tumor tertiary lymphoid structures using a novel artificial intelligence–based approach in patients with muscle-invasive urothelial carcinoma from the CheckMate 274 trial

Abstract Introduction: The phase 3 randomized CheckMate 274 trial in patients (pts) with high-risk muscle-invasive urothelial carcinoma (MIUC) demonstrated improved disease-free survival (DFS) with nivolumab (NIVO) vs placebo (PBO) in intent-to-treat (ITT) and tumor programmed death ligand 1 expression ≥1% pts (primary endpoints). With extended follow-up, continued efficacy was observed, and immature overall survival favored NIVO in both populations. Extending from previous exploratory tumor biomarker analyses (Necchi et al. ESMO 2022), we conducted a post hoc analysis of tertiary lymphoid structures (TLS), which are ectopic lymphoid structures often found within the solid tumor microenvironment. A subset of TLS has been associated with improved outcomes to neoadjuvant immunotherapy in UC. Using artificial intelligence (AI)–based algorithms, we explored the biomarker utility of TLS from CheckMate 274. Methods: We used a reproducible AI-based algorithm to identify and classify TLS in situ using hematoxylin and eosin (H&E) images from pretreatment tumor tissue specimens (n=272/699 treated pts). Images were scored for the presence or absence of lymphoid aggregate (LA), immature TLS (iTLS), and mature TLS (mTLS) and scored as either positive (+) or negative (−) for the respective TLS category. Relationships between TLS categories and DFS were explored (minimum follow-up, 5.9 months). Results: Most tumors exhibited some form of LA/TLS (245/272; 90.1%), with a higher incidence of LA (237/272; 87.1%) compared with mTLS (89/272; 32.7%). There was heterogeneity of TLS within the tumors, with most tumors demonstrating more than 1 form of TLS (198/272; 72.8%). In the PBO arm, median DFS was modest for most TLS categories (5.7–8.3 months) but was substantially improved for mTLS+ tumors (22.1 months; 95% CI, 5.4–not estimable [NE]); n=41), representing a 2.7- to 3.9-fold increase relative to any other category. In contrast, treatment with NIVO resulted in 2.1- to 4.0-fold improvements in median DFS relative to PBO across all but the mTLS+ category (22.9 months; 95% CI,11.9–NE; n=48), which was similar to the PBO arm. The largest increases in median DFS (95% CI) for NIVO vs PBO were observed for LA+ (27.6 [16.1–NE; n=114] vs 8.3 [5.6–11.9; n=123] months; HR, 0.56; 95% CI, 0.39–0.80; P=0.0015), iTLS+ (27.0 [16.1–NE; n=91] vs 8.2 [5.4–21.2; n=105] months; HR, 0.61; 95% CI, 0.41–0.91; P=0.0143), and mTLS− tumors (24.6 [11.4–NE; n=81] vs 6.1 [5.2–8.5; n=102] months; HR, 0.53; 95% CI, 0.36–0.78; P=0.0012]). Conclusion: These results suggest that a novel AI-based TLS maturity classification using H&E images is feasible to investigate the tumor microenvironment in MIUC, with potential broad application in other studies. Citation Format: Johannes Alfred Witjes, Matthew D. Galsky, Jürgen E. Gschwend, Dean F. Bajorin, Matthew I. Milowsky, Gautam Jha, Roger Li, Jun Li, Vanessa Matos-Cruz, Scott Ely, George Lee, Vipul Baxi, Joshua Zhang, Saurabh Gupta, Justin M. David. Exploratory analysis of tumor tertiary lymphoid structures using a novel artificial intelligence–based approach in patients with muscle-invasive urothelial carcinoma from the CheckMate 274 trial [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR010.

SSC and pH prediction and maturity classification of grapes based on hyperspectral imaging

Soluble solids content (SSC) and pH of red globe grapes are crucial measures of quality. In this paper, we used hyperspectral imaging technology to achieve nondestructive detection and distribution visualization of SSC and pH of red globe grapes. First, the hyperspectral images of samples were collected. Then, CARS, SPA, GA, IRIV were used to extract feature variables from raw spectral (RAW) information. The PLSR prediction models of samples were developed. By comparing the different prediction models, RAW-IRIV-PLSR was selected as the optimal model. Finally, the SSC and pH of the samples were calculated to obtain a grayscale image and perform a pseudo-color transformation to visualize the distribution of SSC and pH. By studying the classification of the maturity of samples, it was concluded that the best discriminant classification model of maturity was RAW-IRIV-ELM. Hyperspectral also provided a new method for maturity stage classification of red globe grapes.

Development of Multimodal Fusion Technology for Tomato Maturity Assessment.

The maturity of fruits and vegetables such as tomatoes significantly impacts indicators of their quality, such as taste, nutritional value, and shelf life, making maturity determination vital in agricultural production and the food processing industry. Tomatoes mature from the inside out, leading to an uneven ripening process inside and outside, and these situations make it very challenging to judge their maturity with the help of a single modality. In this paper, we propose a deep learning-assisted multimodal data fusion technique combining color imaging, spectroscopy, and haptic sensing for the maturity assessment of tomatoes. The method uses feature fusion to integrate feature information from images, near-infrared spectra, and haptic modalities into a unified feature set and then classifies the maturity of tomatoes through deep learning. Each modality independently extracts features, capturing the tomatoes' exterior color from color images, internal and surface spectral features linked to chemical compositions in the visible and near-infrared spectra (350 nm to 1100 nm), and physical firmness using haptic sensing. By combining preprocessed and extracted features from multiple modalities, data fusion creates a comprehensive representation of information from all three modalities using an eigenvector in an eigenspace suitable for tomato maturity assessment. Then, a fully connected neural network is constructed to process these fused data. This neural network model achieves 99.4% accuracy in tomato maturity classification, surpassing single-modal methods (color imaging: 94.2%; spectroscopy: 87.8%; haptics: 87.2%). For internal and external maturity unevenness, the classification accuracy reaches 94.4%, demonstrating effective results. A comparative analysis of performance between multimodal fusion and single-modal methods validates the stability and applicability of the multimodal fusion technique. These findings demonstrate the key benefits of multimodal fusion in terms of improving the accuracy of tomato ripening classification and provide a strong theoretical and practical basis for applying multimodal fusion technology to classify the quality and maturity of other fruits and vegetables. Utilizing deep learning (a fully connected neural network) for processing multimodal data provides a new and efficient non-destructive approach for the massive classification of agricultural and food products.

In-field and non-destructive determination of comprehensive maturity index and maturity stages of Camellia oleifera fruits using a portable hyperspectral imager

To efficiently detect the maturity stages of Camellia oleifera fruits, this study proposed a non-invasive method based on hyperspectral imaging technology. First, a portable hyperspectral imager was used for the in-field image acquisition of Camellia oleifera fruits at three maturity stages, and ten quality indexes were measured as reference standards. Then, factor analysis was performed to obtain the comprehensive maturity index (CMI) by analyzing the change trends and correlations of different indexes. To reduce the high dimensionality of spectral data, the successive projection algorithm (SPA) was employed to select effective feature wavelengths. The prediction models for CMI, including partial least squares regression (PLSR), support vector regression (SVR), extreme learning machine (ELM), and convolutional neural network regression (CNNR), were constructed based on full spectra and feature wavelengths; for CNNR, only the raw spectra were used as input. The SPA-CNNR model exhibited more promising performance (RP = 0.839, RMSEP = 0.261, and RPD = 1.849). Furthermore, PLS-DA models for maturity discrimination of Camellia oleifera fruits were developed using full wavelength, characteristic wavelengths and their fusion CMI, respectively. The PLS-DA model using the fused dataset achieved the highest maturity classification accuracy, with the best simplified model achieving 88.6 % accuracy in prediction set. This study indicated that a portable hyperspectral imager can be used for in-field determination of the internal quality and maturity stages of Camellia oleifera fruits. It provides strong support for non-destructive quality inspection and timely harvesting of Camellia oleifera fruits in the field.

Performance of dental students, orthodontic residents, and orthodontists for classification of midpalatal suture maturation stages on cone-beam computed tomography scans – a preliminary study

BackgroundAssessment of midpalatal suture maturation on cone-beam computed tomography (CBCT) scans is performed by visual inspection and is therefore subjective. The extent to which the assessment of midpalatal suture maturation is affected by rater experience has not been adequately explored in the existing literature, thus limiting the availability of evidence-based findings. This study compared the outcomes of classification by dental students, orthodontic residents, and orthodontists.MethodsThree different groups of students, orthodontic residents, and orthodontists evaluated 10 randomly chosen CBCT scans regarding midpalatal suture maturation from a pool of 179 patients (98 female and 81 male patients) aged 8 – 40 years which were previously classified by evaluating CBCT scans. The pool was set as benchmark utilizing midpalatal suture maturation classification by one examiner (OsiriX Lite version 11.0; Pixmeo SARL, Bernex, Switzerland). For assessment of intra-rater reliability of the examiners of each group the randomly chosen subjects were reclassified for midpalatal suture maturation after a wash-out period of two weeks by using the same software. Statistical analysis was performed to evaluate intra- and interrater reliability of the three groups with differing experience level.ResultsGroupwise intra-rater reliability assessment between the classification and reclassification was weak for examiners with a low level of experience (k = 0.59). Orthodontists had highest degree of agreement with regard to benchmark classification with an inter-rater reliability to be considered as moderate (k = 0.68).ConclusionsAssessment of midpalatal suture maturation on CBCT scans appears to be a subjective process and is considerably related to the experience level of the examiner. A high level of clinical experience seems to be favorable but does not necessarily ensure accurate results.

Strawberry Maturity Recognition Based on Improved YOLOv5

Strawberry maturity detection plays an essential role in modern strawberry yield estimation and robot-assisted picking and sorting. Due to the small size and complex growth environment of strawberries, there are still problems with existing recognition systems’ accuracy and maturity classifications. This article proposes a strawberry maturity recognition algorithm based on an improved YOLOv5s model named YOLOv5s-BiCE. This algorithm model is a replacement of the upsampling algorithm with a CARAFE module structure. It is an improvement on the previous model in terms of its content-aware processing; it also widens the field of vision and maintains a high level of efficiency, resulting in improved object detection capabilities. This article also introduces a double attention mechanism named Biformed for small-target detection, optimizing computing allocation, and enhancing content perception flexibility. Via multi-scale feature fusion, we utilized double attention mechanisms to reduce the number of redundant computations. Additionally, the Focal_EIOU optimization method was introduced to improve its accuracy and address issues related to uneven sample classification in the loss function. The YOLOv5s-BiCE algorithm was better at recognizing strawberry maturity compared to the original YOLOv5s model. It achieved a 2.8% increase in the mean average precision and a 7.4% increase in accuracy for the strawberry maturity dataset. The improved algorithm outperformed other networks, like YOLOv4-tiny, YOLOv4-lite-e, YOLOv4-lite-s, YOLOv7, and Fast RCNN, with recognition accuracy improvements of 3.3%, 4.7%, 4.2%, 1.5%, and 2.2%, respectively. In addition, we developed a corresponding detection app and combined the algorithm with DeepSort to apply it to patrol robots. It was found that the detection algorithm exhibits a fast real-time detection speed, can support intelligent estimations of strawberry yield, and can assist picking robots.

Fruits Maturity Classification for Harvesting Robot

Fruits Maturity Classification for Harvesting Robot

KLASIFIKASI KEMATANGAN BUAH ALPUKAT MENTEGA BERDASARKAN FITUR WARNA MENGGUNAKAN SUPPORT VECTOR MACHINE

The ripeness level of the avocado greatly affects the shelf life of the fruit and also determines the taste of the fruit. Determining the proper ripeness of a fruit will play an important role in increasing the nutritional value and ripeness of avocado affects the quality of avocado oil. Classification of fruit maturity manually has many limitations because it is influenced by human subjectivity so that the application of digital image processing needs to be used.This study aims to classify avocado fruit ripeness which is divided into three categories, namely Unripe, Ripe, and Rotten. This research is intended to facilitate the classification of ripeness in fruit through its color. This study used avocado fruit as an experimental material which was carried out using the SVM (Support Vector Machine) method. The colors of the various avocados and in different positions and conditions of light contrast are used as data to classify the types of avocados. Data collection was taken from the selected Keagle Dataset of 150 data. The data is grouped into two data sets, namely dataset-1 (120 training data and 30 test data) and dataset-2 (90 training data and 60 test data). The image will be converted to grayscale to facilitate the classification process and look for HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue) values to classify the fruit. The results from dataset-1 has an accuracy rate of 86%, 90% precision, and 86% Recall, while dataset-2 has an accuracy rate of 85%, 84% precision, and 85% Recall.

The effect of vertical skeletal proportions, skeletal maturation, and age on midpalatal suture maturation: a CBCT-based study

BackgroundMidpalatal suture (MPS) maturation may be influenced by a range of parameters including age, gender, and vertical skeletal pattern. We therefore aimed to evaluate the effect of Frankfurt-mandibular angle (FMA), skeletal maturation, and age on the timing of MPS maturation.MethodsIn this cross-sectional study, cone-beam computed tomography (CBCT) and lateral cephalograms were used to assess the MPS and cervical vertebral maturation (CVM) stage. A proportional odds logistic regression model was used to assess associations between age adjusted for gender and MPS maturation, and a regression analysis was performed to analyze the effect of vertical pattern on these associations.ResultsA total of 201 patients (84 male and 117 female) with a mean age of 13.48 (SD 1.94) were included. With increasing age, the odds of belonging to a higher maturation stage increased (OR: 2.14; 95% CI 1.789; 2.567; P < 0.001); however, no association between FMA and MPS maturation was observed (OR: 1.01; 95% CI 0.964; 1.051; P = 0.76). A strong correlation between MPS maturation and CVM stage was not reported. Males had a higher probability of belonging to a lower MPS maturation stage (OR: 0.24; 95% CI 0.136; 0.415; P < 0.001).ConclusionsBased on this cross-sectional analysis, midpalatal sutural maturation classification is associated with chronological age and occurs later in males. Neither CVM staging nor variation in vertical skeletal proportions were useful predictors of midpalatal maturation stage.

Applying machine learning to predict reproductive condition in fish

Knowledge of reproductive traits in exploited marine populations is crucial for their management and conservation. The maturity status in fish is usually assigned by traditional methods such as macroscopy and histology. Macroscopic analysis is the assessing of maturity stages by naked eye and usually introduces large amount of error. In contrast, histology is the most accurate method for maturity staging but is expensive and unavailable for many stocks worldwide. Here, we use the Random Forest (RF) machine learning method for classification of reproductive condition in fish, using the extensive data from Chilean hake (Merluccius gayi gayi). Gonads randomly collected from commercial industrial and acoustic surveys were classified as immature, mature-active and mature-inactive. A classifier for these three maturity classes was fitted using RFs, with the continuous covariates total length (TL), gonadosomatic index (GSI), condition factor (Krel), latitude, longitude, and depth, along with month as a factor variable. The RF model showed high accuracy (>82%) and high proportion of agreement (>71%) compared to histology, with an OOB error rate lower than 15%. GSI and TL were the most important variables for predicting the reproductive condition in Chilean hake, and to lesser extent, depth when using survey data. The application of the RF shows a promising tool for assigning maturity stages in fishes when covariates are available, and also to improve the accuracy of maturity classification when only macroscopic staging is available.