Traditional Manual Method Research Articles

Research on Path Planning of Agricultural UAV Based on Improved Deep Reinforcement Learning

Traditional manual or semi-mechanized pesticide spraying methods often suffer from issues such as redundant coverage and cumbersome operational steps, which fail to meet current pest and disease control requirements. Therefore, there is an urgent need to develop an efficient pest control technology system. This paper builds upon the Deep Q-Network algorithm by integrating the Bi-directional Long Short-Term Memory structure to propose the BL-DQN algorithm. Based on this, a path planning framework for pest and disease control using agricultural drones is designed. This framework comprises four modules: remote sensing image acquisition via the Google Earth platform, task area segmentation using a deep learning U-Net model, rasterized environmental map creation, and coverage path planning. The goal is to enhance the efficiency and safety of pesticide application by drones in complex agricultural environments. Through simulation experiments, the BL-DQN algorithm achieved a 41.68% improvement in coverage compared with the traditional DQN algorithm. The repeat coverage rate for BL-DQN was 5.56%, which is lower than the 9.78% achieved by the DQN algorithm and the 31.29% of the Depth-First Search (DFS) algorithm. Additionally, the number of steps required by BL-DQN was only 80.1% of that of the DFS algorithm. In terms of target point guidance, the BL-DQN algorithm also outperformed both DQN and DFS, demonstrating superior performance.

Technological State and Optimization Analysis of High-Rise Glass Curtain Wall Cleaning Robots

Abstract. With the proliferation of high-rise buildings in urban areas, glass curtain walls are widely used due to their excellent light transmittance and modern appearance. However, these glass curtain walls are prone to accumulating dust, rain stains, and other dirt during use, affecting their aesthetics and transparency, thus requiring regular cleaning. Traditional manual cleaning methods are inefficient and pose significant safety risks. Consequently, high-rise glass curtain wall cleaning robot technology has become a research hotspot. This paper reviews the current research status of high-rise glass curtain wall cleaning robots, which provides a detailed analysis of four main adhesion methods (magnetic adhesion, thrust adhesion, vacuum negative pressure adhesion, and bionic adhesion), four main motion modes (legged, wheeled, tracked, and external assistance), and two main cleaning methods (physical and chemical). For path planning, this paper explores the application status and challenges of regular scanning, random walking, sensor feedback dynamic planning, and bionic path planning. Through the evaluation of the advantages and disadvantages of existing technologies, this paper identifies the main issues faced by each technology and proposes optimization strategies for adhesion technology, motion modes, cleaning efficiency, and path planning. Future directions include improvements in intelligence and automation, multifunctional integration, energy efficiency enhancement, and human-machine collaboration. This paper aims to provide a comprehensive technological review and constructive suggestions to advance high-rise glass curtain wall cleaning robot technology.

Multi-View Fusion-Based Automated Full-Posture Cattle Body Size Measurement

Cattle farming is an important part of the global livestock industry, and cattle body size is the key indicator of livestock growth. However, traditional manual methods for measuring body sizes are not only time-consuming and labor-intensive but also incur significant costs. Meanwhile, automatic measurement techniques are prone to being affected by environmental conditions and the standing postures of livestock. To overcome these challenges, this study proposes a multi-view fusion-driven automatic measurement system for full-attitude cattle body measurements. Outdoors in natural light, three Zed2 cameras were installed covering different views of the channel. Multiple images, including RGB images, depth images, and point clouds, were automatically acquired from multiple views using the YOLOv8n algorithm. The point clouds from different views undergo multiple denoising to become local point clouds of the cattle body. The local point clouds are coarsely and finely aligned to become a complete point cloud of the cattle body. After detecting the 2D key points on the RGB image created by the YOLOv8x-pose algorithm, the 2D key points are mapped onto the 3D cattle body by combining the internal parameters of the camera and the depth values of the corresponding pixels of the depth map. Based on the mapped 3D key points, the body sizes of cows in different poses are automatically measured, including height, length, abdominal circumference, and chest circumference. In addition, support vector machines and Bézier curves are employed to rectify the missing and deformed circumference body sizes caused by environmental effects. The automatic body measurement system measured the height, length, abdominal circumference, and chest circumference of 47 Huaxi Beef Cattle, a breed native to China, and compared the results with manual measurements. The average relative errors were 2.32%, 2.27%, 3.67%, and 5.22%, respectively, when compared with manual measurements, demonstrating the feasibility and accuracy of the system.

Development of ERP System for Outsourcing Company using Internet of Things and Blockchain Technology

Enterprise Resource Planning (ERP) systems are pivotal in optimizing operations within outsourcing industries, providing a unified platform for managing business processes. However, the current generation of ERP platforms exhibits significant limitations in seamlessly integrating with emerging technologies such as the Internet of Things (IoT) and blockchain. the paper highlights the transformative potential of effective integration with IoT can offer real-time visibility and operational efficiency such as attendance of the worker, while blockchain can provide robust security and transparency of worker activity through smart contracts. The implementation of an ERP system compared to the traditional manual method to quantify the efficiency gained through this technological advancement. In this analysis, we collected attendance data for 20 employees, documenting the time taken for both manual and ERP-based attendance in seconds. The result of ERP system significantly reduces attendance time. Efficiency is calculated as the percentage reduction in time achieved by using the ERP system compared to the manual method. An average efficiency of 69.05% shows that, overall, the use of ERP is nearly 70% more efficient than the manual method with IoT data transfer using MQTT to blockchain result is 100% retention rate.

Application of large language model combined with retrieval enhanced generation technology in digestive endoscopic nursing

BackgroundAlthough large language models (LLMs) have demonstrated powerful capabilities in general domains, they may output information in the medical field that could be incorrect, incomplete, or fabricated. They are also unable to answer personalized questions related to departments or individual patient health. Retrieval-augmented generation technology (RAG) can introduce external knowledge bases and utilize the retrieved information to generate answers or text, thereby enhancing prediction accuracy.MethodWe introduced internal departmental data and 17 commonly used gastroenterology guidelines as a knowledge base. Based on RAG, we developed the Endo-chat medical chat application, which can answer patient questions related to gastrointestinal endoscopy. We then included 200 patients undergoing gastrointestinal endoscopy, randomly divided into two groups of 100 each, for a questionnaire survey. A comparative evaluation was conducted between the traditional manual methods and Endo-chat.ResultsCompared to ChatGPT, Endo-chat can accurately and professionally answer relevant questions after matching the knowledge base. In terms of response efficiency, completeness, and patient satisfaction, Endo-chat outperformed manual methods significantly. There was no statistical difference in response accuracy between the two. Patients showed a preference for AI services and expressed support for the introduction of AI. All participating nurses in the survey believed that introducing AI could reduce nursing workload.ConclusionIn clinical practice, Endo-chat can be used as a highly effective auxiliary tool for digestive endoscopic care.

Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality

Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency.

Research on Digital Construction Technology for Special-Shaped Shell Pipe Structures

The aesthetic appeal of special-shaped shell pipe structures makes them highly favored by architects and holds promising prospects for various applications. In the detailed design stage, NURBS curves should be divided into multiple continuous arcs due to the limitations of current steel structure fabrication equipment, which can only accommodate pipes with equal-curvature bends. However, the traditional manual fitting methods suffer from several issues including low efficiency, undercutting at the interface, poor smoothness of curves, and lack of control over tolerances. Furthermore, the weaker out-of-plane stiffness and utilization of bending arc pipe sections pose significant challenges in terms of spatial positioning and installation accuracy that need to be addressed. The study focuses on addressing these challenges by investigating digital construction technology for special-shaped shell pipe structures and developing a parametric algorithm that enables automatic fitting of spatial NURBS curves into multiple arcs, thereby achieving seamless curve fitting. A post-processing program was developed to enable the parametric generation of fabrication and installation information for structural members, which can be seamlessly integrated into the BIM database. Finally, structural position control technology is proposed to improve assembly efficiency and ensure consistency between the completed construction state and the design shape. The above digital construction technology has been applied in projects such as the Haihua Island International Conference Center. It can provide complete technical solutions for modeling of special-shaped shell pipe structures, including establishment of a member information database, fabrication at the workshop and installation on site, construction organization management, as well as installation accuracy control.

Real-Time Monitoring and Control System Enhances Drilling-Fluid Management

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216322, “Next-Generation Drilling-Fluid Management: Real-Time Monitoring and Autonomous Control,” by Zhicai Zhang, Rached M. Rached, SPE, and Yunbo Xun, Sinopec, et al. The paper has not been peer reviewed. _ Accurate measurement and control of drilling-fluid properties are crucial for safe and successful drilling operations. Traditional manual methods are slow and require significant manpower, underscoring the need for real-time monitoring. This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance. The online monitoring system improves drilling-industry safety and success by measuring properties of drilling fluids in an accurate, timely, and automated matter. Introduction The primary objectives of this project are to develop an innovative online system for monitoring drilling fluids with a focus on facilitating intelligent control of drilling-fluid performance. The system aims at measuring 10 key parameters continuously, including apparent viscosity, plastic viscosity, yield point, density, pH, and ion concentrations. By incorporating online, real-time, continuous monitoring capabilities, the system offers several advantages, including improved data quality and frequency, reduced on-site labor requirements, and a corresponding decrease in associated health and safety hazards. System Description Overview of the Online Drilling-Fluid-Monitoring System. The online drilling-fluid-monitoring system is available in four distinct configurations: land modular, land skid-mounted, offshore cabin, and indoor small modular, providing customization options to meet specific space requirements. The system is designed to measure 10 crucial parameters essential for drilling operations. These include rheology factors, density, pH, sulfur ion content, and chloride ion content. Density and rheology measurements are performed at a 1-Hz sampling rate, offering near-continuous monitoring. The system is capable of operating within an ambient temperature range from –35 to 50°C, and the testing temperature can be adjusted between 4 and 80°C. Enabled by real-time data transmission, the system allows for the online, real-time monitoring of drilling-fluid properties. The system has proven its utility extensively, with more than 30 sets having been deployed successfully across more than 200 wells. Ideally, the monitoring system is positioned near the circulation tank to enable efficient fluid transfer (Fig. 1a). When space near the circulation tank is limited, an alternative placement can be on the opposite side of the blowout-prevention pipeline (Fig. 1b). However, this alternative may lead to a slight delay in data because of extended circulation times.

Technology Focus: Drilling and Completion Fluids (November 2024)

This year’s primary selections for the Drilling and Completion Fluids Technology Focus reflect now-well-established industrywide emphases on machine learning (ML), automation, and the achievement of successful drilling of CO2 storage wells. Enhanced methods of modeling and monitoring allow time and cost savings while surpassing safety goals and furthering efforts to optimize CO2 storage. Paper OTC 35433 discusses a study centered on accurate gel-strength estimation. The authors leverage ML techniques, particularly the use of artificial neural networks, to develop predictive models for forecasting the gel strength of synthetic oil-based mud systems at both 10-second and 10-minute intervals, surpassing the precision and speed of techniques based on traditional rotational viscometers. In a similar vein, paper SPE 216322 seeks to bypass traditional manual methods of monitoring drilling fluids, its authors describing a novel online system that measures 10 key parameters continuously. They write that the system has been field-trialed across 200 wells with results that highlight the system’s reliability, with continuous operation for over 125 days. Finally, paper SPE 217711 involves evaluating the effects of an influx of CO2 into drilling fluids, a key task in the drilling of CO2 storage wells. Concentrating on oil-based drilling fluid, the presented study describes the development and integration of models for properties of drilling-fluid/CO2 mixtures into an existing software suite. Recommended-reading papers include an investigation of the use of microwave power in recovering oil from contaminated drill cuttings, reduction of the carbon footprint of drilling fluids, and sedimentation behavior of particles in fibrous sweep fluids. As always, the long-sighted ambition of such research, and the skill applied in its pursuit, reflects the commitment to excellence embodied by the authors of technical papers presented at SPE conferences. Recommended additional reading at OnePetro: www.onepetro.org. SPE 217140 Microwave-Assisted Technique for Oil Recovery From Oily Sludge Shale Drilled Cuttings by A. Agi, Universiti Malaysia Pahang, et al. OTC 34667 Successful Decarbonization of Drilling Fluids by Using Highly Efficient Technology and Reduced Chemical Usage, Logistics, and Casing Sections by Gerardo Jardinez, Pemex, et al. SPE 218631 Application of Machine-Learning Method for Modeling Settling Behavior of a Spherical Particle in Fibrous Drilling Fluids by R.M. Elgaddafi, Australian University, et al.

SecurePass: Intelligent Vehicle Access Management System

Vehicle Access Management is a important aspect of security, especially for any private Organization. The traditional manual methods can be inefficient and prone to human error. The handling of large data becomes difficult. Thus, as the organizations grow, the need for streamlined and automated solution is necessary. “SecurePass: Intelligent Vehicle Access Management System” is designed to address these challenges by using Machine Learning solution for real time vehicle monitoring, number plate detection and validation. The system is designed in such a way that it uses OpenCV, TensorFlow and OCR technologies to accurately detect and extract vehicle registration number. The extracted number plate is validated against a dataset maintained by organization. The SecurePass additionally checks the number plate on basis of the RTO norms and classifies them into personal (if black and white), transportation (if yellow) and electric (if green). It signals the suspicious plate for further inspection. SecurePass consist of 2 modules, first one is the admin itself, who manages the data and the other one is the Security Personnel who can verify the vehicle entries. The Security Personnel the can record the entry and exit of the Vehicle. SecurePass gurantees the security, data accuracy and operational efficiency in Vehicle Access Management.

Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection

Purpose This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork. Design/methodology/approach This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period. Findings Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers. Originality/value The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Machine Learning in Agriculture: KNN-Based Classification of Fruits and Vegetables

In modern agriculture and the food industry, it is essential to classify fruits and vegetables accurately and efficiently to meet growing consumer demand and reduce post-harvest losses. Traditional manual methods are often labor-intensive and error prone, highlighting the need for automated solutions. This paper discusses the application of the k Nearest Neighbor (KNN) algorithm to fruit and vegetable recognition using image processing technology. In this study, image data sets are used for feature extraction using Directional Gradient Histogram (HOG) and dimensionality reduction using Principal Component Analysis (PCA). The accuracy of KNN model on verification set and test set reaches 97%, which proves its validity. Confusion matrix analysis and F1 score evaluation further revealed the strengths and areas of improvement of the model, particularly in distinguishing visually similar categories. The results show that the integration of artificial intelligence (especially KNN) offers great potential for the automation of agricultural classification tasks. Future studies could combine more advanced models, such as CNNS, with larger datasets to improve accuracy and robustness.

A Review of Deep Learning Based Object Detection Algorithms

With the rapid development of artificial intelligence technology, the traditional manual feature-based object detection method has been gradually replaced by deep learning-based object detection technology. Object detection is the basis and prerequisite for many computer vision tasks aimed at recognizing targets in an image and determining their class and location. The purpose of object detection algorithm research based on deep learning is to improve the detection accuracy and detection speed of the detection algorithm, so that the object detection algorithm can be more safe and convenient applied to People's Daily production and life. This article reviews the evolution of object detection algorithms, focusing on two-stage, one-stage and object detection algorithms based on the transformer architecture. In addition, the performance, advantages and disadvantages of different algorithms are compared, and the development trend of object detection algorithms based on deep learning is summarized in the end.

Edge-Cloud Remote Sensing Data-Based Plant Disease Detection Using Deep Neural Networks with Transfer Learning.

The identification and control of plant diseases have become increasingly complex as agricultural lands expand globally. Traditional manual methods of monitoring and diagnosing diseases are not feasible for large-scale farms. The complexity is further heightened by the heterogeneity of data types collected through remote sensing methods, such as images, videos, and sensor readings, which need to be processed in real-time. This paper introduces a novel EdgeCloud Remote Sensing architecture that utilizes Deep Neural Networks (DNNs) with Transfer Learning to enhance the detection of plant diseases using remote sensing data. The proposed system employs a Fuzzy Deep Convolutional Neural Network (FCDCNN) to process multimodal data collected from both edge nodes and satellite point clouds. Transfer learning allows the sharing of model weights between cloud and edge nodes, thus optimizing performance without requiring frequent retraining. Simulations show that the proposed system achieves a 98% detection accuracy and reduces processing time by 25% compared to existing methods. By distributing tasks between edge and cloud resources, the system is able to process large datasets effectively and improve disease detection performance on vast farmlands.

Application and integration of computer vision technologies for automated recognition and recording of chemical experiments

Abstract Advancements in computer vision have significantly improved motion and object recognition accuracy. These advancements should aid the automatic recognition of chemical experiments, potentially contributing to the recording of experiments. Creating an electronic laboratory notebook from experiment filming enhances convenience and allows more detailed information storage compared to traditional manual recording methods. Our previous research focused on employing object detection and action recognition to automate the recognition of chemical experiments. This paper presents a novel system that combines object detection, action recognition, multiple object tracking, and barcode recognition to automatically generate experimental flowcharts. We implemented our system as a graphical user interface-based application for laboratory use that successfully constructs flowcharts from videos of chemical experiments, including simple chemical manipulations.

A Vision-Guided Robotic System for Safe Dental Implant Surgery.

Background: Recent advancements in dental implantology have significantly improved outcomes, with success rates of 90-95% over a 10-year period. Key improvements include enhanced preplanning processes, such as precise implant positioning, model selection, and optimal insertion depth. However, challenges remain, particularly in achieving correct spatial positioning and alignment of implants for optimal occlusion. These challenges are pronounced in patients with reduced bone substance or complex anthropometric features, where even minor misalignments can result in complications or defects. Methods: This paper introduces a vision-guided robotic system designed to improve spatial positioning accuracy during dental implant surgery. The system incorporates advanced force-feedback control to regulate the pressure applied to bone, minimizing the risk of bone damage. A preoperative CBCT scan, combined with real-time images from a robot-mounted camera, guides implant positioning. A personalized marker holder guide, developed from the initial CBCT scan, is used for patient-robot calibration. The robot-mounted camera provides continuous visual feedback of the oral cavity during surgery, enabling precise registration of the patient with the robotic system. Results: Initial experiments were conducted on a 3D-printed mandible using a personalized marker holder. Following successful patient-robot registration, the robotic system autonomously performed implant drilling. To evaluate the accuracy of the robotic-assisted procedure, further tests were conducted on 40 identical molds, followed by measurements of implant positioning. The results demonstrated improved positioning accuracy compared to the manual procedure. Conclusions: The vision-guided robotic system significantly enhances the spatial accuracy of dental implants compared to traditional manual methods. By integrating advanced force-feedback control and real-time visual guidance, the system addresses key challenges in implant positioning, particularly for patients with complex anatomical structures. These findings suggest that robotic-assisted implant surgery could offer a safer and more precise alternative to manual procedures, reducing the risk of implant misalignment and associated complications.

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

Ontology-Guided Generation of Mechanized Construction Plan for Power Grid Construction Project

Mechanized construction is being fully implemented in the electric power infrastructure domain to ensure construction safety, enhance project quality, and improve efficiency. Traditional methods of designing mechanized construction plans are often inefficient due to their labor-intensive processes and heavy reliance on human expertise. This study introduces and evaluates an ontology-guided system designed to automate mechanized construction planning for power grid projects. The developed ontology effectively models domain-specific knowledge, enabling the semantic integration of data from various sources. By leveraging SPARQL queries, the ontology-guided system incorporates knowledge reasoning capabilities that facilitate the automated selection of construction equipment and the generation of comprehensive construction plans. A prototype system incorporating an ontology-guided mechanism has been developed, showcasing marked enhancements in efficiency and accuracy over traditional manual methods, as evidenced by case studies and expert evaluations. The research results emphasize the potential of ontology-guided systems in innovating architectural planning, providing an extensible and standardized approach. Expert evaluation indicates that the system achieves 71.38% effectiveness in generating mechanized construction plans.

Automated Injectors versus Manual Administration: A Comparative Analysis of Radiation Exposure Reduction in 18F-FDG Delivery

Abstract Background Despite the presence of safety protocols, the manual manipulation of radiopharmaceuticals continues to pose a significant occupational radiation risk. Health care professionals in nuclear medicine are at risk of radiation exposure, particularly to their hands and eyes. Despite existing protective measures, manual handling of radiopharmaceuticals remains a significant source of occupational radiation. Objective This study evaluates the effectiveness of automated injectors in reducing radiation exposure among health care workers during fluorine-18 fluorodeoxyglucose (18F-FDG) administrations, compared with traditional manual injection methods. Methods We assessed radiation exposure levels associated with manual versus automated 18F-FDG injection techniques using specialized dosimeters. Measurements focused on whole-body, extremity, and eye-lens radiation doses to evaluate the potential benefits of automation in minimizing exposure. Results Findings reveal that automated injectors significantly reduce radiation exposure, with decreases of 97.97 and 98.96% in left- and right-hand extremity doses, respectively, 43.24% in eye-lens dose, and 91.66% in whole-body dose compared with manual methods. Conclusion Automated injection systems offer considerable advantages in reducing health care worker radiation exposure in nuclear medicine. The substantial reduction in staff doses underscores the necessity of transitioning to such technology to promote safer clinical environments. This study highlights the critical role of automation in enhancing occupational safety standards within diagnostic radiology settings.

IoT- enabled crop waste mulching machine for sustainable farming: perspective of circular economy

Abstract A large amount of crop waste is generated after crop harvesting. A substantial quantity of waste on the farm is burnt to clear the field, contributing to environmental harm and global warming. The study aims to develop a technological solution for crop waste management (CWM) by designing and developing an IoT-enabled crop waste mulching machine. This initiative addresses the environmental and health issues caused by burning crop waste on farms. The research involves designing and developing an IoT-enabled mulching machine that can be attached to the back side of a tractor. The machine consists of power-transmitting elements, supports, and mechanisms to distribute mulching material evenly onto the plant bed. It incorporates advanced IoT load cell sensors, soil moisture sensors, and proximity sensors to optimize the mulching process based on soil moisture content and to prevent machinery blockages. The IoT-enabled mulching machine effectively manages crop waste by utilizing it in mulch, thus preventing the harmful practice of burning waste. This machine ensures a more efficient, safer, and environmentally friendly approach to mulching, offering significant improvements over traditional manual mulching methods.
The novelty of this research lies in integrating IoT technologies within mulching machine. The mulching machine uses sensors to automate and optimize the mulching process, representing a significant technological advancement over conventional methods. The study offers practical benefits by reducing labour requirements, minimizing the risk of injuries, and ensuring a more uniform mulch application. This technology facilitates more sustainable farming practices, aligning with global sustainability goals. The study can potentially revolutionize the agricultural equipment manufacturing industry (AEMI) by shifting the focus toward environmental sustainability. It promotes replacing plastic film mulching machines with more eco-friendly crop waste mulching solutions. This technology has the potential to reduce air pollution, carbon emissions, and mitigate climate. It also enhances soil health through better mulching practices.