Sorting System Research Articles

A study of residents’ behavior in participating in smart waste sorting system based on structural equation modeling

A study of residents’ behavior in participating in smart waste sorting system based on structural equation modeling

Artificial Intelligence-Assisted Automatic Raman-Activated Cell Sorting (AI-RACS) System for Mining Specific Functional Microorganisms in the Microbiome.

The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about significant breakthroughs in life sciences and applications. Raman-activated cell sorting (RACS) enables the correlation of phenotype and genotype at the single-cell level, offering a solution to the bottleneck in microbial community functional analysis caused by challenges in cultivating diverse microorganisms. However, current labor-intensive manual procedures fall short in catering to the demands of single-cell functional analysis in microbial communities. To address this issue, we developed an artificial intelligence-assisted Raman-activated cell sorting system (AI-RACS) that integrates precise single-cell positioning, automated data collection, optical tweezers capture, and single-cell printing to elevate microbial single-cell RACS from manual to automated, validating the efficacy of the system by isolating aluminum-tolerant microbes from acidic soil microbiota. Leveraging the AI-RACS framework, we sorted 13 strains from red soil samples under near-in situ conditions, with all demonstrating strong aluminum tolerance. AI-RACS efficiently segregates microbial cells from intricate environmental samples, investigating their functional attributes and presenting a novel tool for microbial research and applications.

Design of automatic sorting system based on capsule neural network

Abstract. This paper proposes a vegetable disease classification system based on Capsule Neural Network (CapsNet), which uses the visual recognition mechanism of deep learning algorithms to improve the efficiency and accuracy of agricultural disease recognition. The CapsNet algorithm outperforms traditional Convolutional Neural Network (CNN) in few-shot learning and generalization due to its unique capsule structure and dynamic routing mechanism. Experimental results show that the proposed system performs well in the accuracy, robustness, and model interpretation of image recognition, and can accurately identify diseases even in the case of poor image quality or noise and occlusion. In addition, the embedded system of this experiment includes an image acquisition module, an algorithm core module, and a logic control module, which realizes the automation of the whole process from image acquisition to recognition result display. This study also discusses the scalability and adaptability of the system, as well as the future application prospects in agricultural intelligence and automation. It is expected that CapsNet will play a more critical role in artificial intelligence, especially in agrarian automation and smart agriculture.

Using a Yolov8-Based Object Detection Model for an Automatic Garbage Sorting System

This study presents the development of an automatic garbage sorting system based on computer vision techniques and the You Only Look Once version 8 (YOLOv8) algorithm. RGB images of garbage, including cans and plastic bottles, were collected by a camera. The YOLOv8 model, trained on a dataset of both RGB images of cans and plastic bottles, acts as the core for detection and classification. During real-time sorting, the classification results and picking points from the YOLOv8 model are used by a gantry robot. The gantry robot is developed and controlled by a Programmable Logic Controller (PLC) to place each type of garbage into its respective categories. A conveyor tracking algorithm was implemented to pick up garbage moving on the conveyor. The system's performance is evaluated using 20 garbage samples for the can and the plastic bottle category. The accuracy, precision, recall, and F1 score were 92.5%, 90.5%, 95%, and 92.7%, respectively. The system proposed in this study can be modified to sort other garbage types.

Vision-based sorting in mixed food-inorganic waste stream

Processing food waste is crucial for environmental conservation and resource recovery, but inadequate sorting can lead to inorganic waste mixing with food waste. The mixed waste stream reduces the efficiency of food waste treatment facilities, and the preliminary sorting relies heavily on manual labor. To address the challenge of a non-homogeneous food-inorganic waste stream, this study proposes a vision-based system for effective sorting. A real-life Mixed Food-Inorganic Waste (MFIW) dataset containing over 13,000 samples and four categories of inorganic waste was created. Based on the dataset analysis, a Waste detection model using Deformable Convolution v3 was employed, and the appropriate positioning and classification algorithm was chosen for optimal detection performance. The Waste detection model achieves an mAP50 of 85.21 %, and the average recalls for packages, trash bags, and animal bones exceed 94 %. Additionally, the model runs at a real-time frame rate of 33.61 FPS, highlighting its suitability for industrial applications.

Review on Automated Storage and Retrieval System for Warehouse

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes.

Carbon footprint impact of waste sorting on the municipal household waste treatment system: A community case study of Hangzhou

Considering the pressing need to reduce carbon emissions, the environmental impacts of waste sorting and its influencing factors remain unverified across many practical scenarios. This study focused on separating perishable and residual waste in a community in Hangzhou, China, examining how waste sorting behavior affects the carbon footprint of subsequent waste treatment systems through a randomized tracking survey and life cycle assessment (LCA) method. The results indicated that the sorted waste treatment system could achieve net carbon emissions ranging from −139.92 to −254.03 kg CO2 eq per day by integrating the anaerobic digestion of sorted perishable waste with the incineration of residual waste. In comparison to the conventional mixed incineration system, which had net carbon emissions between −56.37 and −115.46 kg CO2 eq per day and generated 383 kWh/t of power, the carbon reduction benefits of the sorted system were considerably greater, by a factor of 2.2–2.5. Additionally, the average amount of recovered electrical energy (993 kWh/t) was also higher by a factor of 2.6. Therefore, source separation of waste significantly enhances energy recovery and carbon reduction in household waste treatment systems. In this research system, incineration power generation and oil recovery serve as the primary units for reducing carbon emissions, and the proportion of residual waste generated greatly influences the total carbon emissions of the sorted waste treatment system (p < 0.05). Consequently, increasing the percentage of classified residual waste and optimizing machine efficiency will increase the carbon reduction benefits of waste sorting and treatment systems. These findings provide crucial references and guidance for advancing carbon reduction and waste classification in municipal solid waste (MSW) management systems.

A Tomato Recognition and Rapid Sorting System Based on Improved YOLOv10

In order to address the issue of time-consuming, labor-intensive traditional industrial tomato sorting, this paper proposes a high-precision tomato recognition strategy and fast automatic grasping system. Firstly, the Swin Transformer module is integrated into YOLOv10 to reduce the resolution of each layer by half and double the number of channels, improving recognition accuracy. Then, the Simple Attention Module (SimAM) and the Efficient Multi-Scale Attention (EMA) attention mechanisms are added to achieve complete integration of features, and the Bi-level Routing Attention (BiFormer) is introduced for dynamic sparse attention and resource allocation. Finally, a lightweight detection head is added to YOLOv10 to improve the accuracy of tiny target detection. To complement the recognition system, a single-vertex and multi-crease (SVMC) origami soft gripper is employed for rapid adaptive grasping of identified objects through bistable deformation. This innovative system enables quick and accurate tomato grasping post-identification, showcasing significant potential for application in fruit and vegetable sorting operations.

Apple varieties and growth prediction with time series classification based on deep learning to impact the harvesting decisions

Apples are among the most popular fruits globally due to their health and nutritional benefits for humans. Artificial intelligence in agriculture has advanced, but vision, which improves machine efficiency, speed, and production, still needs to be improved. Managing apple development from planting to harvest affects productivity, quality, and economics. In this study, by establishing a vision system platform with a range of camera types that conforms with orchard standard specifications for data gathering, this work provides two new apple collections: Orchard Fuji Growth Stages (OFGS) and Orchard Apple Varieties (OAV), with preliminary benchmark assessments. Secondly, this research proposes the orchard apple vision transformer method (POA-VT), incorporating novel regularization techniques (CRT) that assist us in boosting efficiency and optimizing the loss functions. The highest accuracy scores are 91.56 % for OFGS and 94.20 % for OAV. Thirdly, an ablation study will be conducted to demonstrate the importance of CRT to the proposed method. Fourthly, the CRT outperforms the baselines by comparing it with the standard regularization functions. Finally, time series analyses predict the ‘Fuji’ growth stage, with the outstanding training and validation RMSE being 19.29 and 19.26, respectively. The proposed method offers high efficiency via multiple tasks and improves the automation of apple systems. It is highly flexible in handling various tasks related to apple fruits. Furthermore, it can integrate with real-time systems, such as UAVs and sorting systems. This research benefits the growth of apple’s robotic vision, development policies, time-sensitive harvesting schedules, and decision-making.

Identifying defects and varieties of Malting Barley Kernels

This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach’s advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.

Investigating the Role of Artificial Intelligence in Developing Eco-Friendly Assistive Technologies for People with Disabilities

Current research indicates that artificial intelligence has immense scope to further the cause of assistive technology in improving the quality of life for persons with disabilities by rendering customized support to mobility aids, visual aids, hearing aids, and smart homes. AI-driven devices make communication, adaptive learning, and independence easier for all, with innovations in prosthetics, wheel chairs, and satellite navigation apps such as Google Maps and Moovit. Voice- activated AI-powered smart devices, like Amazon Echo and Google Home, facilitate independent living with voice activation of light and appliances. AI is also in OrCam to further autonomous living. The study examines that artificial intelligence has immense scope to further the cause of assistive technology in enhancing the quality of life for people with disabilities by providing customized support to mobility aids, visual aids, hearing aids, and smart homes. AI makes environmental sustainability a part of the life cycle of assistive technologies—from design to the use of materials, energy efficiency, and e-waste recycling. Efficient waste management is made possible through AI-based sorting systems and smart recycling bins. Blockchain brings transparency into these processes. It is in social integration and economic efficiency that the following devices and services related to sustainable assistive technology can create environmental sustainability, empowering persons with disabilities, reducing healthcare expenditure, and infusing green practices toward an all-inclusive and sustainable world. UN News, Assistive Ware.

Lab-on-Chip Systems for Cell Sorting: Main Features and Advantages of Inertial Focusing in Spiral Microchannels.

Inertial focusing-based Lab-on-Chip systems represent a promising technology for cell sorting in various applications, thanks to their alignment with the ASSURED criteria recommended by the World Health Organization: Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Delivered. Inertial focusing techniques using spiral microchannels offer a rapid, portable, and easy-to-prototype solution for cell sorting. Various microfluidic devices have been investigated in the literature to understand how hydrodynamic forces influence particle focusing in spiral microchannels. This is crucial for the effective prototyping of devices that allow for high-throughput and efficient filtration of particles of different sizes. However, a clear, comprehensive, and organized overview of current research in this area is lacking. This review aims to fill this gap by offering a thorough summary of the existing literature, thereby guiding future experimentation and facilitating the selection of spiral geometries and materials for cell sorting in microchannels. To this end, we begin with a detailed theoretical introduction to the physical mechanisms underlying particle separation in spiral microfluidic channels. We also dedicate a section to the materials and prototyping techniques most commonly used for spiral microchannels, highlighting and discussing their respective advantages and disadvantages. Subsequently, we provide a critical examination of the key details of inertial focusing across various cross-sections (rectangular, trapezoidal, triangular, hybrid) in spiral devices as reported in the literature.

Real-time Sorting of Broiler Chicken Meat with Robotic arm: XAI-enhanced Deep Learning and LIME Framework for Freshness Detection

Ensuring food safety in poultry processing is crucial due to the risk of contamination, which can lead to health hazards. Traditional methods for sorting chicken meat based on freshness are often manual and prone to error, necessitating the development of an automated, accurate sorting system. This research focuses on integrating Convolutional Neural Networks (CNNs) with Local Interpretable Model-agnostic Explanations (LIME) to enhance the accuracy and efficiency of sorting broiler chicken meat into fresh and rotten categories. This study employed the InceptionV3 CNN model combined with LIME for transparent decision-making. The model was trained and tested on a dataset of chicken meat images to guide a robotic arm for real-time sorting. The system achieved an impressive sorting accuracy of 96.3 %, outperforming traditional manual methods. With the guidance of the CNN-LIME integration, the robotic arm successfully processed 1000 samples of fresh and 300 samples of rotten chicken meat, achieving a precision of 94.19% for fresh meat and 97.24% for rotten meat. This integration significantly improved sorting accuracy and efficiency in poultry processing, providing a reliable method for distinguishing between fresh and rotten chicken meat. This system improves food safety and operational efficiency in automated food processing. It shows the effectiveness of combining explainable AI with robotics, reducing human exposure to harmful contaminants ethically. It also contributes to public health by ensuring safer food products. This research presents a new approach to automated food sorting by integrating CNNs with LIME for transparency and accuracy. This can be beneficial for food processing industries and researchers in the field of AI and robotics.

Mineral classification with X-ray absorption spectroscopy: A deep learning-based approach

The current suite of algorithms used in intelligent mineral sorting equipment is largely generic, lacking the necessary adaptability and the capability for simultaneous non-destructive structural detection and quantitative analysis. To address this, we have developed two specialized algorithms based on X-ray absorption spectroscopy: the Trans-LSTM algorithm based on Transformer and Long Short-Term Memory(LSTM), utilizing knowledge distillation for rough mineral sorting, and the RNN-overhead-xgboost algorithm based on Recurrent Neural Network(RNN) for fine mineral sorting. We collected 3000 X-ray absorption spectra from 15 types of minerals with similar appearances or compositions. We compared the performance of these proposed algorithms with three other general-purpose algorithms for mineral spectral classification. Our study specifically examined the impact of Trans-LSTM on rough mineral sorting and the effect of RNN-overhead-xgboost on fine mineral sorting. In the rough sorting stage, the Trans-LSTM model demonstrated a prediction time of just 0.0171 s per sample, maintaining a classification accuracy of 93.49%, thereby ensuring high precision with high efficiency. During the fine sorting stage, the RNN-overhead-xgboost algorithm significantly improved sorting accuracy to 99.21%, highlighting its effectiveness in achieving precise sorting. These findings underscore the potential of the Trans-LSTM and RNN-overhead-xgboost algorithms to enhance the adaptability and accuracy of intelligent mineral sorting systems, meeting the specific demands of different stages in mineral production.

Incineration Innovation: A Path to Efficient and Sustainable Municipal Solid Waste Management in Kuwait

Municipal solid waste management has become a critical global issue due to the rapid increase in waste generation driven by urbanization and population growth. This surge in waste poses significant environmental, social, and health challenges, exacerbated by inefficient recycling and waste-to-energy facilities. Effective waste management requires comprehensive strategies encompassing waste reduction, efficient collection, sorting systems, and advanced recycling and energy recovery technologies. This study highlights the potential of incineration as a waste-to-energy solution, specifically focusing on Kuwait. By analyzing various waste management technologies and their applicability, this study emphasizes the role of incineration in transforming municipal solid waste into electricity, thereby reducing landfill use and environmental impacts. The research includes a detailed review of the existing technologies, a case study on Kuwait’s waste management practices, and an evaluation of the economic and environmental benefits of implementing waste-to-energy incineration. The findings underscore the importance of tailored waste management solutions to address specific regional challenges, promote sustainability, and enhance public health and well-being.

Grading and Sorting of Capsicum Using Convolutional Neural Networks (CNN): A Review

Abstract: Grading and sorting of capsicum, or bell peppers, is a critical task in the agricultural industry to ensure consistent quality control and efficient distribution. In recent years, Grading &amp; Sorting of Capsicum using Convolutional Neural Networks (CNNs), have shown promise in automating this process. The objective is to automate the Grading and Sorting process, which currently done manually and time-consuming. The proposed approach involves the collection of capturing dataset of capsicum images, extracting relevant features, and training a CNN model to classify the capsicum based on their quality criteria such as size, shape, color, and quality levels. These images are pre-processed to enhance their quality and standardize the inputs. A CNN architecture of 85 trained datasets and 33 tested datasets images using for accurate grading and sorting. After the training phase, the model's accuracy is evaluated using appropriate metrics such as precision, recall, or F1-score. The performance of the CNN model of grading and sorting system is determined based on the accuracy values, providing an objective measure of its effectiveness. Experimental results of testing datasets obtained an accuracy value of above 90% indicates that the system correctly classified, the capsicum into their respective grades or sorted categories 97.43% accuracy achieved

Automated Sorting Conveyor Using Pneumatic Actuators for Industrial Applications at Morowali Metal Industry Polytechnic

The use of automatic sorting systems in industry plays a crucial role in saving time and reducing manual labor through automation. The main challenges are the high error rate and slow manual sorting processes. This research aims to design and develop an automatic sorting conveyor system using Arduino technology and pneumatic actuators to enhance sorting efficiency and accuracy. The proposed solution is a system capable of detecting and sorting objects based on size with very low error rates, thereby increasing productivity and reducing human error. The design and development of this system involve creating a 3D model of components such as the conveyor frame, belt, motor, pneumatic actuators, and sensors, and integrating them into a complete system. Testing was conducted with seven differently sized test objects to evaluate the system's performance in detecting and sorting objects based on size and height. Input and output voltage measurements ensure performance in line with the expected specifications. The research results demonstrate that the developed automatic sorting conveyor system effectively sorts objects with high accuracy. The system can detect and sort objects based on size with a 0% error rate, reducing the time required for the sorting process compared to manual methods. The use of pneumatic actuators and appropriate sensors ensures consistent and reliable system performance. The test results indicate that the system can be integrated into various industrial applications to enhance production efficiency. The developed automatic sorting conveyor system successfully improves the efficiency and accuracy of the sorting process in industry. It shows significant potential for reducing human error and increasing productivity, making it a practical solution for various industrial applications.

AUTORECYCLER: Prototype based on artificial vision to automate the material classification process (Plastic, Glass, Cardboard and Metal)

Environmental protection has gained greater importance over time due to the negative impact and irreversible consequences that have occurred worldwide and stem from pollution. One of the great challenges faced in different parts of the world is the inadequate management and classification of solid waste. In order to contribute to tackling this issue, this paper proposes an automated sorting system based on artificial vision which allows recognition and separation of recyclable materials (Plastic, Glass, Cardboard and Metal) through a webcam connected in real time to the Nvidia® Jetson Nano™ 2 GB programming board, which has a convolutional neural network (CNN) trained for the proper classification of waste. The system had a 95 % accuracy in separating plastic, 96 % in glass and metal, and 94 % in cardboard. With this in mind, we conclude it contributes to the recycling effort, which has an impact on the reduction of environmental pollution worldwide.

The effect of stope size and cut-off grade on the design and scheduling of an underground mining operation in the presence of a pre-concentration system

ABSTRACT Stope layout optimisation is considered one of the main steps in the design and strategic planning of underground mining operations. This paper investigates the influence of stope size and cut-off grade in an underground sublevel stoping operation in the presence of a preconcentration system using a newly developed mathematical optimisation model. The utilisation of a bulk ore sorting system, its effects on the resource utilisation, and the final discounted cash flow of a case study are presented. Results show that incorporating a bulk sorting system to a sublevel stoping operation can significantly increase the viability of a deposit.

Automated Uniform Spheroid Generation Platform for High Throughput Drug Screening Process.

Three-dimensional (3D) spheroid models are crucial for cancer research, offering more accurate insights into tumour biology and drug responses than traditional 2D cell cultures. However, inconsistent and low-throughput spheroid production has hindered their application in drug screening. Here, we present an automated high-throughput platform for a spheroid selection, fabrication, and sorting system (SFSS) to produce uniform gelatine-encapsulated spheroids (GESs) with high efficiency. SFSS integrates advanced imaging, analysis, photo-triggered fabrication, and microfluidic sorting to precisely control spheroid size, shape, and viability. Our data demonstrate that our SFSS can produce over 50 GESs with consistent size and circularity in 30 min with over 97% sorting accuracy while maintaining cell viability and structural integrity. We demonstrated that the GESs can be used for drug screening and potentially for various assays. Thus, the SFSS could significantly enhance the efficiency of generating uniform spheroids, facilitating their application in drug development to investigate complex biological systems and drug responses in a more physiologically relevant context.