Faculty of Computing-Scopus
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4892
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Item Open Access An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka(SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, MChronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.Item Embargo Enhancing Environmental Awareness for Hard of Hearing Individuals: A Mobile Application Approach(Springer Science and Business Media Deutschland GmbH, 2025) Dharmasiri, K.G; Rathnasooriya, C.V; Balasuriya, M.K; Yapa, L.N; De Silva, D.I; Thilakarathne, TThis research focuses on developing a mobile application to enhance environmental awareness for deaf and hard of hearing individuals. At its core is an advanced audio classification system using a convolutional neural network model optimized for recognizing environmental sounds. Extensive experimentation identified the best performing convolutional neural network architecture, trained on spectrograms to classify diverse environmental sounds accurately. The model balances accuracy and computational efficiency, making it ideal for real-time mobile deployment. The application includes a user-friendly admin interface, enabling individuals without machine learning expertise to manage and train models, ensuring adaptability to various auditory environments. Leveraging cloud technologies like Amazon Web Services for data storage, processing, and model deployment, the platform provides a scalable solution for safe interaction with surroundings. This empowers users to navigate their environments confidently, enhancing awareness of crucial auditory cues. The study demonstrates the potential of mobile technology to improve inclusivity and environmental consciousness for underserved populations through real-time, tailored sound recognition.Item Embargo The Influence of IT Infrastructure and Supply Chain Flexibility on Supply Chain Performance in the Apparel Manufacturing Sector(Institute of Electrical and Electronics Engineers Inc., 2025) Kulasekara, D; Sandaruwan, D; Ekanayake, T; Perera, A; Wisenthige, K; Aluthwala, CIn a developing country, the apparel manufacturing sector needs to improve their performance, reduce costs, and satisfy the demands of a highly competitive global market. Thus, Supply Chain Management (SCM), particularly the influence of IT Infrastructure (ITI) and Supply Chain Flexibility (SCF) in driving Supply Chain Performance (SCP), focuses on their combine within the Sri Lankan apparel manufacturing sector. These relationships were evaluated using a quantitative research approach, with data gathered from Supply Chain (SC) professionals in apparel manufacturing companies. The study reveals that SCF mediates the relationship between ITI and SCP, indicating the importance of a flexible SC in moving ITI investments into functional performance improvements. Considering the current environment, apparel manufacturers should apply methods that relate ITI capabilities to the SC ability to respond to changing demands. Technological foundation influences SCF, enabling organizations to respond effectively to market demand and operational challenges. Consequently, SCF significantly contributes to SCP by improving flexibility, reducing costs and enhancing customer satisfaction. Some of the practical implication include the usage of advanced IT systems including prophetic analytics and enterprise resource planning tools to enhance SC responsiveness. In addition, development of collaborative relationship with suppliers and partners can enhance the effect of SCF and SCP, making Sri Lanka apparel manufacturers more competent to perform international standard. Future studies are encouraged to take these findings to other sectors and regions to explore the influence of new technologies and other external factors. In this way, it would be possible to achieve more advancements in the SCM practices in response to the current global market challenges.Item Embargo Hybrid Model-Based Automated Exterior Vehicle Damage Assessment and Severity Estimation for Insurance Operations(Institute of Electrical and Electronics Engineers Inc., 2025) Jayagoda, N.M; Kasthurirathna, DAfter a vehicle accident, insurance companies face the critical task of assessing the damage sustained by the involved vehicles, a process essential for maintaining the insurer's credibility, building consumer trust, and meeting legal and ethical obligations. This assessment is crucial for ensuring clients' financial protection and proper compensation, upholding the integrity of the insurance process. Traditionally, evaluations have been conducted through manual inspections by experienced professionals who meticulously document vehicle damage. Despite its thoroughness, this approach suffers from significant inefficiencies, high costs, and extended time requirements. Moreover, the method is vulnerable to human errors and subjective biases, which can result in inflated valuations. To overcome these challenges, this research introduces an innovative system designed to leverage technology for analyzing images of damaged vehicles uploaded by the user. This system aims to accurately identify the damaged external components, assess the severity of the damage, and determine the repair needs based on the compromised sections of the vehicle. The findings reveal that the hybrid model used in this research is capable of determining vehicle damage severity with an overall accuracy of 73.3%. This level of accuracy demonstrates the model's robust capability to effectively navigate and analyze complex damage patterns, underscoring its practical applications. By accurately determining damage levels on the first assessment, the model reduces the need for further assessments and disagreements, which frequently cause claim delays. This enhancement increases productivity, reduces administrative costs, and improves the customer experience, resulting in a more efficient, transparent, and satisfactory resolution of vehicle insurance claims.Item Embargo AI-Driven Fault-Tolerant ETL Pipelines for Enhanced Data Integration and Quality(Institute of Electrical and Electronics Engineers Inc., 2025) Wickramaarachchi, C.K; Perera, S.K; Thelijjagoda, SThe reliability and fault tolerance of ETL (Extract, Transform, Load) pipelines are essential for maintaining data integrity in corporate environments. Traditional ETL systems often depend on manual interventions to resolve data inconsistencies, leading to errors, inefficiencies, and increased operational costs. This study introduces an AI-driven framework designed to improve the fault tolerance of ETL processes by automating data cleaning, standardization, and integration tasks. Using machine learning models, the framework reduces the need for human intervention, enhances data quality, and supports scalability across various data formats. Using real-world data sets, the proposed solution demonstrates its ability to improve operational efficiency and reduce errors within corporate data pipelines. This research addresses a crucial gap in ETL automation, offering a scalable and proactive approach to robust data integration in large-scale corporate settings. The findings highlight the ability of the framework to improve fault tolerance, improve data quality, and offer organizations a competitive advantage in managing complex data ecosystems.Item Embargo Predictive Modeling for Identifying Early Warning Signs of Underperformance in Vocational Education(Institute of Electrical and Electronics Engineers Inc., 2025) Hettiarachchi D.S.S; Harshanath S.M.BThis study focuses on developing a predictive modeling system to identify early signs of underperformance in vocational education, critical for building a skilled workforce. Addressing challenges like high dropout rates and inadequate graduate preparedness, the system utilizes machine learning techniques such as Neural Networks, Decision Trees, and Logistic Regression. Implemented in Python, it analyzes key features like academic records, attendance, engagement, and socioeconomic factors. Preprocessing steps, such as data cleaning and feature engineering, were implemented, and transfer learning was employed to adapt the model. This combination of feature engineering and transfer learning enables the transfer of knowledge from academic settings to vocational education by identifying and leveraging shared characteristics between the two domains. The system provides real time insights through automated reports and notifications, enabling targeted interventions to improve retention and graduation rates. This scalable approach advances educational technology and informs policies to enhance vocational education outcomes.Item Open Access Designing Culturally Adaptive Emotional Gestures to Enhance Child-Robot Interaction with NAO Robots in ASD Therapy(Institute of Electrical and Electronics Engineers Inc., 2025) Manukalpa, C.S; Pulasinghe, K; Rajapakshe, SIntegrating social robots into human-robot interactions demands advancements in natural language processing, navigation, computer vision, and expressive gestures to foster meaningful interactions. However, a gap remains in designing culturally relevant and developmentally appropriate gestures, particularly in the Sri Lankan context. Autism Spectrum Disorder (ASD), a neurodevelopmental condition impacting early education, often remains underdiagnosed, exacerbating learning challenges. This study introduces a novel approach utilizing robot-child interactions for ASD screening to minimize such delays. Expressive gestures were developed for the NAO6 humanoid robot to engage Sinhala-speaking children aged 2 to 6 years, including those with ASD, in Sri Lanka. Using the NAOqi Python API and Choregraphe simulator, culturally aligned gestures expressing emotions like happiness, sadness, fear, anger, and more were designed and synchronized with voice and LED effects. Pilot studies with typical children demonstrated the significance of linguistic and cultural alignment in enhancing engagement, emotional response, and trust. By addressing cultural nuances and advancing early ASD screening, this framework holds potential for broader applications in education, therapy, and diagnosis, improving human-robot interactions globally.Item Open Access Evaluation of Machine Learning Models in Student Academic Performance Prediction(Institute of Electrical and Electronics Engineers Inc., 2025) Sandeepa A.G.R.; Mohottala, SThis research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach.Item Embargo Intelligent Adaptive Lighting Control: Reinforcement Learning-Based Optimization for Smart Home Energy Efficiency(Institute of Electrical and Electronics Engineers Inc., 2025) Hewakapuge M.M; Gamage W.G.T; Surendra D.M.B.G.D; Thejan K.G.T; Rajapaksha, S; Rajendran, KThis study introduces a novel research paper outlining a behavioral-based adaptive lighting system that aims to revolutionise smart home lighting by integrating user behavior tracking to enhance energy efficiency and user comfort. Unlike traditional motion-sensor-based lighting, the novelty of this approach is the ability to adapt dynamically to evolving user behaviors through reinforcement learning. The system utilises Wi-Fi-based positioning, GPS and accelerometer data to monitor user movements and classify different areas of the house. Users initially calibrate the home layout through a mobile application, marking room locations and lighting configurations. The system then collects movement data over time to predict optimal lighting schedules based on user routines and refines the predictions and updates lighting adjustments accordingly, minimising energy wastage while maximising user convenience. A serverless backend architecture ensures scalability, cost-effectiveness, and seamless data processing. The adaptive framework continuously refines lighting automation, responding to evolving behavioral patterns.Item Embargo Autonomous Water Quality Monitoring: Integrating UWB Ad-Hoc Networks, Sensor Calibration, and Kubernetes Cloud Architecture(IEEE Computer Society, 2025) Tharindu, K; Abeysinghe, M; Karunarathne, S; Dilshan, K; Primal, D; Jayakody, AWater quality monitoring plays a critical role in ensuring environmental sustainability and public health. Traditional methods, while accurate, are time-consuming and lack the ability to provide real-time insights. This study proposes a secure, scalable IoT-based solution utilizing autonomous sensor-equipped boats designed to measure pH, turbidity, and temperature in aquatic environments. The boats navigate predefined grid coordinates generated through a Python-based script and communicate data using UWB in a decentralized ad hoc network operating under the AODV routing protocol. Preprocessed sensor data is transmitted to a base station and securely forwarded to a Kubernetes-based cloud infrastructure for real-time processing and visualization. Communication between the base station and cloud services is secured using HTTPS/TLS encryption. Experimental trials confirm reliable navigation, high sensor accuracy, low latency, and robust security. The system remains cloud-agnostic and is compatible with a range of open-source Kubernetes distributions, enabling deployment flexibility across various environments. This research demonstrates an effective, autonomous approach to real-time water quality monitoring, advancing scalable and sustainable environmental managementItem Embargo Precision Agriculture with Centralized IoT-Enabled Greenhouse Management for Sustainable Vanilla Production(Institute of Electrical and Electronics Engineers Inc., 2025) Karunathilaka M.M.D.N; Samaraweera H.M.C.D; Balachandra B.A.D.K.M; Thenabandu W.S.D; Silva, S; Fernando, HAfter saffron, vanilla is the second most significant spice in terms of economic impact worldwide. The vanilla business faces challenges from pests, illnesses, and environmental variables, especially fungal diseases like fusarium wilt and unfavorable climatic circumstances that can significantly reduce productivity and lower bean quality. This study offers a clever remedy that helps all parties involved by identifying and categorizing plant illnesses, predicting vanilla bean growth and quality, vanilla bean market value analysis and future prediction and build cost prediction and improve operational efficiency. Stakeholders can also obtain forecasts for the quality and growth of vanilla beans in the future. Deep learning algorithms are used in the suggested solution to track the location of diseased areas, diagnose and classify plant diseases in real-time, and apply pesticides or growth-regulating chemicals selectively. For sustainable vanilla production, machine learning algorithms are used to forecast yields, advise ideal greenhouse conditions, and recommend the best vanilla beans. In precision agriculture, the types, applications, and monitoring of IoT devices and sensors are also discussed. Data analysis and management, disease and pest control, fertilization and irrigation management, and environmental monitoring are a few examples. The suggested method produced high accuracy rates in identifying illnesses, evaluating bean quality, estimating yields, and optimizing greenhouse conditions in controlled studies using data from vanilla farms and greenhouses. This technology could assist the vanilla business, producers, and sustainable agricultural practices. It could also boost productivity and production of vanilla, decrease yield loss, and maintain constant bean quality with the help of our suggesting vanilla greenhouse application.Item Embargo MindBridge: Early Identification of Learning Difficulties in Children as a Supporting Tool for Teachers(Institute of Electrical and Electronics Engineers Inc., 2025) Mapa, N; Deshapriya, M; Premathilake, M; Samarakoon, S; Thelijjagoda, S; Vidanaralage, A.JLearning difficulties in children significantly impede academic success by affecting information processing, mathematical performance, and the learning of proper reading and writing. This paper proposes a Progressive Web Application (PWA) based on artificial intelligence (AI) and machine learning (ML) for identifying potential learning barriers. In contrast with standard diagnostic instruments, the proposed system is designed as a prediction tool with the potential for teachers to conduct timely and focused interventions. By automating feature extraction and reducing manual processing, the system overcomes the limitations of existing learning systems and improves early detection accuracy. Preliminary evaluations indicate that the PWA can effectively identify at-risk students and improve intervention methods and overall academic performance. This research contributes to the integration of computational methods and pedagogy, offering a scalable and low-cost solution for helping slow learners overcome their learning challenges.Item Embargo Enhancing the Performance of Supply Chain using Artificial Intelligence(Institute of Electrical and Electronics Engineers Inc., 2025) Wijedasa, S; Gnanathilake, K; Alahakoon, T; Warunika, R; Krishara, J; Tissera, WOptimizing warehouse operations is essential to meet dynamic customer demands while maintaining efficiency in the rapidly changing supply chain landscape. Using four key components, this research presents a comprehensive AI-based approach to improve supply chain management performance. The first component uses Long Short-Term Memory (LSTM) networks to predict demand and returns, allowing for accurate forecasting of product demand and returns based on historical sales data. The second component uses Q-learning, a Reinforcement Learning (RL) technique that optimizes the scheduling of product replenishments by prioritizing critical stock shortages based on inventory levels and predicted demand. The third component analyzes customer purchasing patterns using FP Growth and clustering algorithms to analyze customer buying patterns, strategically placing items in aisles to reduce selection time and improve picking efficiency. The final component involves customer churn prediction using machine learning techniques to identify at-risk customers and facilitate proactive retention strategies. To bridge the gap between complex AI models and practical warehouse operations, a web-based application named 'OptiFlow AI' has been developed. This platform provides warehouse workers with user-friendly interfaces to access demand forecasts, replenishment priorities, optimized product placements, and customer retention insights. The proposed system significantly enhances operational efficiency, reduces time delays, and improves customer satisfaction, contributing to a more resilient and intelligent supply chain ecosystem.Item Embargo OrchiZen: Hybrid Integrated Smart Farming System for Orchid Plantations(Institute of Electrical and Electronics Engineers Inc., 2025) Wijendra, D; Jayasinghearachchi, V; Dilshan O.A.P.; Herath H.M.K.C.B; Yapa Y.M.T.N.S; Rathnasiri K.D.M.M.OrchiZen is a hybrid integrated smart farming system designed for orchid cultivation, leveraging Machine Learning (ML) and Internet of Things (IoT) technologies to address key horticultural challenges, including irrigation, disease treatment, choice of species, lighting, and nutrients. The OrchiZen has smart irrigation advisory, species recommendation, Ultraviolet (UV) based disease treatment, light optimization, and fertilizer advisory. The priorities are given to specific species such as Dendrobium, Vanda, and Phalaenopsis. The realities of telemonitoring, data processing, and forecasting increase organizational productivity and contribute to better environmental management. The outcomes illustrate that existing modern technologies can enhance the output and ecology of the orchid production to a significant extent, redefining the conventional technologies.Item Embargo Advancing Speech Therapy for Sinhala-Speaking Children with Autism Spectrum Disorder Through an Intelligent Dialog System(Institute of Electrical and Electronics Engineers Inc., 2025) Jayawardena, A; Pulasinghe, K; Rajapakshe, SThis paper presents a dialog system integrated with a NAO socially assistive robot, designed to support Sinhala-speaking children with Autism Spectrum Disorder (ASD). The system leverages a pipeline-based architecture implemented using the RASA framework, consisting of Natural Language Understanding (NLU), Dialog Management (DMU), and Natural Language Generation (NLG) units. The NLU unit processes user input by identifying intents, entities, and dialogue acts, incorporating custom tools like the SpokenSinhalaVerbTokenizer for handling spoken Sinhala. The DMU includes a Dialog State Tracker (DST) to maintain conversation context and a Dialog Policy Generator, which employs rule-based, TED, and UnexpecTED policies to adapt conversation flows dynamically. The NLG unit generates natural responses to foster interactive and goal-oriented conversations. Integrated with the NAO robot, the system engages children through meaningful dialogues, such as discussing toy preferences, aiming to enhance social interaction and communication skills. This work highlights the potential of conversational AI and robotics in therapeutic interventions for ASD in low-resource languages.Item Embargo An Explainable Deep Learning Framework for Coconut Disease Detection Using MobileNetV2, Super-Resolution, and Grad-CAM++(Institute of Electrical and Electronics Engineers Inc., 2025) Balasooriya R.C.; Adithya E.L.A.Y; Gunarathne M.M.S.U; Silva T.C.D; Lokuliyana, S; Wijesiri, PCoconut production is a significant industry in Sri Lanka's economy and food security. However, it is constantly under threat from diseases such as Grey Leaf Spot and pests such as Coconut Mites (Aceria guerreronis). Detection must be early, but it is difficult, especially in field conditions where image quality is low and symptoms are not visually distinguishable. This paper proposes a two-stage deep learning solution to enhance and automate disease and pest recognition with a lightweight and mobile system. The system combines Real-ESRGAN based image super-resolution to restore visual detail in poor-quality mobile images and MobileNetV2-based classification, a lightweight convolutional neural network. The model recognizes grey leaf spot with over 97% accuracy and greatly enhanced mite recognition performance when combined with super-resolution preprocessing. In the interest of transparency and trust for users, the Grad-CAM++ and LIME interpretation techniques are utilized, and visual explanations of the predictions are presented. A mobile application was created with React Native and integrated with a Flask-based backend to enable real-time image enhancement and classification to facilitate practical deployment. Smartphone-captured field-level photos were preprocessed and categorized into healthy, diseased, and non-coconut samples. Farmers can use the proposed system in real time because it maintains good accuracy while being computationally efficient. This framework provides a scalable method for intelligent and sustainable agriculture.Item Embargo Intelligent Water Quality Monitoring and Prediction System(Institute of Electrical and Electronics Engineers Inc., 2025) Shiraz, S; Karunasena, K; Mudelige, H; Kumarasinghe, O; Nawinna, D; Perera, JThe paper aims to develop an integrated approach to improve water treatment processes using predictive modeling and SCADA integration in order to meet the specific needs of water purification systems in Sri Lanka. The current systems utilized for this need are outdated since these systems are based on traditional technologies and do not have the means for predictions or real-time data accessibility outside the system. The proposed solution will focus on raw water quality prediction, optimization of chemical usage to bring in efficiency, sustainability, and resource management, ensuring seamless access to all the relevant data required to manage and monitor. In order to achieve this, past data provided by the Meewatura water plant in Sri Lanka, sourced from the Mahawali river, is utilized for the relevant predictions alongside of the data gathered through the SCADA system. The data is not directly accessible since the SCADA system is mainly built for monitoring, and in order to get the data, a MODBUS connection through the PLC is utilized alongside of an IOT device. In addition to the extracted data, past data that was provided by the water plant is also incorporated. The combined data set is utilized for the predictions while continuously improving itself with new data. The present study contributes to the establishment of sustainable and adaptable water treatment frameworks for a wide range of operational needs within the water plants by addressing the gaps in the existing water quality management systems and improving upon them.Item Embargo Developing Predictive Models for Future Stress Likelihood and Recovery Time Using Behavioral and Emotional Data(Institute of Electrical and Electronics Engineers Inc., 2025) Weerasinghe W.P.D.J.N; Gunasekera H.D.P.M; Wickramasinghe B.G.W.M.C.R; Jayathunge K.A.D.T.R; Wijesiri, P; Dassanayake, TStress has a serious impact on mental and physical well-being, but treatments as usual are often unavailable and not effective over the long term. The AyurAura application combines imaginative Ayurvedic therapies with modern AI techniques to deliver customized stress reduction by way of Mandala art and music. This research develops two predictive models for the application. In its first model, the stress prediction probability is estimated from users' behavior in a questionnaire and the result can be used to proactively intervene. The second model forecasts time needed for recovery into a stress-free state by using the changes in daily emotional state and participation in app activities. Machine learning algorithms are used to prepare behavioral and emotional data for improved prediction performance. Trained on multi-institution datasets, both models delivered 90-95% accuracy, enabling the user to detect behavior eliciting stress and the degree needed for recovery. These results highlight the possibility of combining conventional therapeutics with contemporary tech for ongoing, affordable stress relief interventions with personalized needs in mind.Item Embargo Hybrid Motion Prediction for Autonomous Vehicles using GNN-Transformer Architecture(Institute of Electrical and Electronics Engineers Inc., 2025) Akalanka, A; Athukorala, D; Ganepola, N; Tharindu, I; Rathnayake, SAccurate perception and scene understanding are pivotal in enabling autonomous vehicles to navigate safely and intelligently. This paper presents an integrated perception module comprising three core subcomponents: real-time object detection using YOLOv5, lane-keeping using a CNN-based steering predictor, and a novel motion prediction architecture based on a hybrid Graph Neural Network (GNN) and Transformer design. The system is deployed and validated within the CARLA simulation environment, with custom data generation pipelines designed to mimic real-world behavioral patterns of nearby agents. The novelty lies in the hybrid GNN-Transformer model, which effectively captures both spatial and temporal interactions of dynamic objects for behavior classification. Experimental results demonstrate a high accuracy of 98.75% in classifying behaviors into four categories: Going, Coming, Crossing, and Stopped. This paper details the architecture, dataset creation, training methodology, and performance evaluation, highlighting the hybrid model's potential to improve trajectory planning modules in autonomous systems.Item Embargo "Cropmaster" - Real-Time Coordination of Multirobot Systems for Autonomous Crop Harvesting: Design and Implementation(Institute of Electrical and Electronics Engineers Inc., 2025) Pramod, I; Arachchi, A.M; Rashen, C; Chinthaka, G; Pandithage, D; Gamage, NThe CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sensors to measure sunlight, humidity, pH, NPK content, and soil moisture, the rover securely transmits analyzed data to a web-based dashboard. LIDAR technology enables efficient autonomous navigation, allowing the rover to move around fields and avoid obstacles. The MQTT protocol facilitates communication between multiple rovers, preventing duplicate measurements and ensuring data is sent to the dashboard for comprehensive data collection across large areas. TensorFlow's machine learning models allow the rover to accurately assess crop health and detect early-stage diseases, followed by automated pesticide and fertilizer application through a spraying system. To maintain reliability, the rover's operations, including data transfer and task execution, are continuously monitored for Quality of Service (QoS). All collected data is stored in the cloud for long-term access. Built with a lightweight aluminum and plastic chassis and robotic arms, the rover is designed for adaptability and operational efficiency, aiming to improve crop management and increase yields across extensive agricultural fields.