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Gesture  recognition  technology  is  used  to  detect  movements  through  image processing,   enabling  computers  or digital devices to understand and interpret human  body  movements  as  input  or  commands.   This  technology  has  great potential  to bridge communication between the deaf community and individuals without   hearing   impairments,    enhancing  interaction  and  enriching  mutual understanding between the two.  However,  the accuracy ofgesture recognition is often  affected  by variations in the distance between hand landmarks.  Based on this problem,  this research proposes a methodfor stabilizing the measurement of distances between landmark points  in gesture recognition through a polynomial regression  approach.   Specifically,   the  distance  between  hand  landmarks  is calculated and stabilized using polynomial  regression to improve the accuracy of gesture recognition.  This method is implemented using the MediaPipeframework to detect and track hands in real-time,  and the OpenCV library to manage video. The  research  results  show  that  this  approach  can  significantly  improve  the stability  and accuracy  of gesture detection.   The developed system successfully detects gestures for  letters A  through F with a high accuracy  rate,  averaging above 98,3%.  The use ofpolynomial regression helps enhance detection accuracy by reducing noise in the landmark data.

Sign language recognition is one of the areas of image recognition and image processing technology that is developing rapidly in human-computer interaction. This technology really helps the deaf and speech impaired in communicating with non-disabled people. This research aims to examine the optimization of an object tracking system in sign language using the Gaussian Mixture Model (GMM) and Kalman Filter by including the Region of Interest (ROI). The proposed system consists of three main components, namely hand detection, object extraction, and classification. Hand detection is done using the Kalman Filter to track hand movements accurately. Next, Region of Interest (ROI) features, such as shape, direction and movement features, are extracted from the detected part of the hand. These features are fed into a Gaussian Mixture Model (GMM) classifier, which can recognize sign language based on the extracted features. With the combination of GMM and Kalman Filter in this research, it can increase accuracy in object tracking, reduce interference from the background, and ensure the tracking focus remains on important objects. The dataset used is in the form os SIBI alphabet symbols, namely A-Z with the amount of data for each class, namely 620 images. Based on the research result, model testing using GMM, Kalman Filter and ROI produces higher accuracy of 99%, while model testing using GMM and ROI produces accuracy of 90%.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

Digital images often experience noise disturbances that can reduce visual quality and interfere with the image analysis process. One common type of noise is salt and pepper noise, especially in grayscale images, which is characterized by the random appearance of black and white dots. This study applied the Deep Convolutional Autoencoder (DCAE) method with a skip connection mechanism to eliminate salt and pepper noise in grayscale images measuring 256×256 pixels. The dataset used consists of 300 pairs of clean images and noisy images that have gone through the preprocessing stage, including normalization and data augmentation. The model was trained using an Adam optimizer with a Mean Squared Error (MSE) loss function and validated through a train-test split scheme to avoid overfitting. Model performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The test results showed that the DCAE model with skip connections was able to effectively reduce noise while maintaining the main structure of the image based on the PSNR and SSIM values obtained, and showed better performance than conventional median filters. In addition, the model was successfully implemented into a Streamlit-based application to perform the image denoising process interactively, making it easier for users to experiment and visualize results in real-time.

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.

Millions of people suffer from malaria, one of the most serious parasitic diseases that threatens human life and causes high rates of morbidity and mortality, particularly in tropical and subtropical regions. Traditional diagnostic methods, such as blood smear examination, which can be performed using a microscope, face many challenges due to the inaccuracy of manual analysis and the reliance on individual skills. Therefore, the use of machine learning or deep learning algorithms to automate malaria detection offers promising solutions to improve accuracy, reduce diagnosis time, and enhance scalability. In this paper, a multi-class convolutional neural network (CNN)-based model is designed to classify cells infected with Plasmodium falciparum (P. falciparum) and Plasmodium vivax (P. vivax) and uninfected cells from blood smears, as most severe cases and deaths are caused by P. falciparum and P. vivax. This is achieved by building and training a CNN from scratch, rather than using transfer learning from pre-trained models. The proposed network was trained and tested on the Kaggle dataset, which consists of 27,558 images of infected and uninfected individuals. These images were divided into 13,779 images of uninfected individuals, 6,890 images of individuals with P. falciparum malaria, and 6,889 images of individuals with P. vivax malaria. The images were preprocessed using several operations, including blurring, denoising, and morphological processing. The proposed model achieved the best evaluation accuracy when compared with other deep learning algorithms, with an accuracy rate of 96.5%, a sensitivity rate of 95%, a specificity rate of 97.6%, and an F1-score rate of 96.5%. These results demonstrate the effectiveness of the proposed model as a tool to assist clinicians in malaria diagnosis, reducing reliance on manual analysis.

This study aims to design and implement a damage analysis system for concrete surfaces by utilizing digital image processing based on the Canny edge detection method. The developed system allows users to upload images of concrete surfaces, which are then processed through several stages: conversion to grayscale, transformation to binary images, and crack edge detection using the Canny operator. This process aims to automatically detect crack patterns on the concrete surface. The detection results, represented as edge lines, are used to calculate the percentage of the damaged area. Based on this percentage value, the system automatically classifies the damage level into light, moderate, or severe categories. System testing shows that the Canny method can accurately identify crack patterns, with sufficient detection levels to be used in monitoring the condition of concrete surfaces. The analysis results are then presented in both visual and numerical forms, providing valuable information for assessing the structural condition of concrete. Thus, this system can serve as an efficient and effective tool for early detection of structural damage in concrete infrastructure, ultimately supporting better maintenance and repair efforts.

Digital image processing is a rapidly developing branch of computer science and has many applications in everyday life. One of the fields that most often utilizes this technique is object detection and color identification in images and videos. This study specifically aims to implement the thresholding method in the HSV (Hue, Saturation, Value) color space to detect three basic colors, namely red, green, and blue, in digital images. The research process begins with uploading images using the Google Colab platform, a cloud-based computing environment that makes it easy for users to run Python programs without requiring additional software installation. After the image is uploaded, the next step is to convert it from the RGB (Red, Green, Blue) color space to the HSV color space. This conversion is important because the HSV color space is more suitable for use in the color segmentation process. The Hue value represents the type of color, Saturation shows the level of saturation, while Value describes the level of brightness. Once the image is in the HSV color space, the next step is to determine the HSV value range for each basic color. This range is determined based on experimental results and references from related literature. Using this range, masking is performed to extract the appropriate pixels so that only the red, green, or blue portions of the image are visible, while the other colors are reduced. The results show that the thresholding method in the HSV color space is capable of detecting primary colors with a good level of visual accuracy, especially in simple images with contrasting backgrounds. The implementation of this program is relatively lightweight, easy to run directly in Google Colab, and does not require high-spec hardware. Therefore, this method is very suitable for use as basic learning material for digital image processing, both for students and novice researchers.

This study aims to utilize data mining techniques, specifically the K-Nearest Neighbors (KNN) algorithm, to classify leaf diseases in sugarcane (Saccharum officinarum). Early and accurate detection of leaf disease types is a crucial step in prevention and control strategies, thereby reducing potential crop losses caused by pathogen attacks. Leaf diseases in sugarcane, such as leaf scald, rust, and mosaic virus, are known to affect photosynthesis, inhibit growth, and reduce the quality and quantity of sugarcane produced. The classification process in this study was carried out through image analysis of infected sugarcane leaves, where features such as color, texture, and shape were extracted using digital image processing techniques. The KNN algorithm was chosen because of its non-parametric nature, ease of implementation, and its ability to provide accurate classification results even with limited data size. The working principle of KNN is to determine the class of a new sample based on the majority class of its k nearest neighbors in the feature space, making it very suitable for the case of leaf disease image classification. In addition to building a classification model, this study also examines disease prevention strategies based on the identification results. These strategies include the use of disease-resistant sugarcane varieties, the implementation of appropriate planting patterns, land moisture management, regular plantation sanitation, and the measured and environmentally friendly use of pesticides or fungicides. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics to assess model effectiveness across various data scenarios. The results of this study are expected to not only contribute to the development of decision support systems for farmers and related parties but also support the application of artificial intelligence-based technology in the agricultural sector.

Internet of Things (IoT)-based digital transformation has become a major catalyst in improving the efficiency of operational systems in various sectors, including the modern retail industry. One of the common logistics problems found in supermarket environments is the accumulation of unorganized shopping trolleys, which can hinder service flow and increase staff workload. This study presents a design of an IoT-based autonomous smart trolley system and automatic navigation to address these problems in a structured manner. The system design utilizes the integration of ESP32 and Arduino UNO microcontrollers, ultrasonic sensors for distance detection, line sensors for automatic path navigation, and Raspberry Pi modules for visual image processing in location tracking. The system is designed to be able to independently reposition the trolley to a predetermined parking station. Conceptual analysis shows that this system has significant potential in reducing operational costs, increasing labor efficiency, and strengthening customer service automation. Initial evaluation of technical and economic feasibility aspects strengthens the opportunity for widespread system implementation in the future. This design is the first step in developing a smart retail solution based on adaptive technology that is in line with the principles of Society 5.0. Furthermore, the development of this smart trolley system also considers user safety and comfort through additional features such as anti-collision sensors, an early warning system in the event of technical problems, and a manual control option as an alternative in emergency situations. The integration of Internet of Things-based technology also enables real-time monitoring and management systems through a web-based dashboard or mobile application, which can be accessed by supermarket management for operational analysis. Thus, this system not only addresses internal logistics needs but also contributes to improving the overall customer experience.

This research focuses on the development of an Android-based vegetable detection application by utilizing digital image processing technology and data communication through Application Programming Interface (API). This application is designed to make it easier for users to visually recognize different types of vegetables using the device's camera. The detection process is carried out by sending the image to a cloud server, where the image analysis process is carried out to identify the type of vegetable, displaying its name, characteristics, and benefits. The app's implementation includes an intuitive and user-friendly user interface, with key features such as login, registration, and an interactive dashboard. The dashboard displays user information, location, ambient temperature, vegetable detection history, and direct access to the camera for real-time detection processes. The utilization of cloud computing technology not only keeps application performance lightweight and responsive, but also enables high processing efficiency and data scalability. This allows the application to continue to evolve according to the increasing number of users and incoming data. Image processing is done with machine learning algorithms that are trained to recognize the shape, color, and texture of different types of local vegetables. In addition, this system is also equipped with a periodic data update feature to be able to adjust to the development of new vegetable classifications. The test results show that the app is able to recognize different types of vegetables with a high level of accuracy, as well as provide additional relevant information quickly and accurately. Tests are carried out on a variety of lighting and background conditions to ensure the reliability of the system. The success of the development of this application reflects the integration of modern technology in supporting the digital agriculture sector.

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.

Object detection in digital images is a crucial aspect of image processing and computer vision, with applications ranging from surveillance systems and robotics to image-based search. One commonly used approach is template matching, a technique that compares a template image with sections of the target image to identify similar patterns. This study explores the implementation of the template matching method for object recognition in digital images. The process begins with image preprocessing to enhance data quality, followed by a matching procedure using normalized cross-correlation. Experimental results indicate that this method can accurately detect objects under stable lighting and scale conditions. However, its performance decreases when images undergo rotation or scale variations. Therefore, while template matching proves effective under ideal conditions, further methodological development is needed to improve its robustness against geometric transformations.s

Digital image segmentation is essential in image processing, influencing the accuracy of higher-level tasks. Thresholding is widely used, yet identifying optimal threshold values remains challenging. The Firefly Algorithm with Neighbourhood Attraction (FaNA), a metaheuristic approach, is efficient for color image thresholding but underperforms on grayscale images due to suboptimal thresholds. To overcome this, an enhanced version (eFaNA) was developed by integrating a chaotic tent map for population initialization and a Lévy flight-based random walk for improved exploration. eFaNA was compared with FaNA, fuzzy firefly algorithm (FFA), and the standard Firefly Algorithm (FA) in multilevel thresholding of grayscale images. Results demonstrate that eFaNA achieves superior segmentation quality with minimal detail loss, outperforming the others. The average PSNR obtained by eFaNA, FFA, FaNA, and FA was 25.5320 dB, 25.4075 dB, 24.1522 dB, and 24.4506 dB, respectively; average SSIM was 0.8641, 0.8604, 0.8432, and 0.6703; and execution time was 50.5322, 38.7726, 38.7528, and 107.6340 seconds, respectively. This reflects a PSNR improvement of 5.71% over FaNA, 0.49% over FFA, and 4.42% over FA, and an SSIM gain of 2.48% over FaNA, 0.43% over FFA, and 28.92% over FA. While eFaNA lags behind FFA and FaNA in execution time by ~11.8 seconds, it significantly outperforms FA. The performance gain is attributed to the chaotic tent map’s diverse initialization and the Lévy flight’s enhanced search capability. These improvements enable eFaNA to deliver consistently better threshold values and segmentation results. However, its relatively higher computational cost may limit applicability in real-time image processing.

Facial recognition, a branch of image processing, is widely used in attendance systems to improve efficiency and security. This study develops an employee attendance monitoring system that integrates facial recognition using the Eigenface algorithm in OpenCV. The system records each individual's facial data alongside a password, enabling automated attendance tracking. Testing results indicate that with a database of 10 facial entries, the system achieved 100% accuracy in recognizing individuals. However, as the database expanded beyond 10 entries, accuracy declined to 80%, influenced by factors such as lighting variations, differences in facial structures, and increased data volume. This study employed a Research and Development (R&D) methodology, with expert validation yielding a score of 3.4, categorizing the system as "Highly Valid." User testing with 11 participants resulted in an overall score of 36, classifying the system as "Very Good (Valid)." The findings highlight the potential of facial recognition in improving attendance management while minimizing fraudulent entries. Future research should focus on optimizing recognition accuracy in larger databases through refined preprocessing techniques, image quality adjustments, and deep learning models.

Image processing is a branch of computer science that is growing rapidly and is widely used in various fields, including in security systems. Face identification is one of the main applications of image processing that aims to recognize and distinguish individual faces in a system. The methods used in face identification involve various techniques, such as facial feature detection, characteristic extraction, and classification using machine learning algorithms. This article discusses the application of image processing in a security system based on face identification and the technology used to improve the accuracy and reliability of the system. The results of the study show that the combination of deep learning algorithms with image pre-processing techniques can increase the success rate of face identification in security systems.

Image classification is a key field in digital image processing with broad applications, such as object recognition and disease detection. The use of artificial neural network architectures, such as MobileNetV2, has significantly advanced pattern recognition in large datasets. However, in small datasets, challenges related to accuracy and generalization are often encountered. This study explores an RGB-based approach utilizing MobileNetV2 for image feature extraction and Support Vector Machine (SVM) as the classifier. MobileNetV2 is applied to extract features from RGB images, which are then further processed by SVM to determine image classes. The results indicate that this model achieves an accuracy of 91.67%, precision of 0.9163, recall of 0.9167, and F1-score of 0.9161. Based on the confusion matrix analysis, the model effectively distinguishes between classes, despite slight overlaps. This research contributes to the development of intelligent image classification systems that can be applied in various fields, including the food industry. With these achievements, the RGB approach integrating MobileNetV2 and SVM has proven effective in enhancing image classification accuracy, even with relatively small datasets. These findings open opportunities for applying similar methods in other image processing tasks that require high accuracy in object or disease detection and classification.

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.