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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction

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Healthcare Analytics 2024年 6卷 100362-100362页

作者： Ahmed, Rasel Fahad, Nafiz Miah, Md Saef Ullah Hossen, Md. Jakir Morol, Md. Kishor Mahmud, Mufti Mostafizur Rahman, M. Faculty of Science and Technology American International University-Bangladesh Kuratoli Khilkhet Dhaka 1229 Bangladesh Faculty of Information Science and Technology (FIST) Multimedia University Melaka Malaysia Faculty of Engineering and Technology (FET) Multimedia University Jalan Ayer Keroh Lama 75450 Bukit Beruang Melaka Malaysia Department of Computer Science Nottingham Trent University Nottingham NG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Nottingham NG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Nottingham NG11 8NS United Kingdom Department of Mathematics American International University-Bangladesh Dhaka 1229 Bangladesh Department of Computing and Information Science Cornell University NY United States EliteLab.Ai NY United States DataTech Research and Innovation Learning Lab Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia

Dementia is a major global health issue that significantly impacts millions of individuals, families, and societies worldwide, creating a substantial burden on healthcare systems. This study introduces a novel approach for predicting dementia by employing the Logistic Regression (LR) model, enhanced with Recursive Feature Elimination (RFE), applied to a unique dataset comprising 1000 patients, with 49.60% male and 50.40% female. The LR model, recognized for its simplicity and effectiveness in binary classification tasks, is optimized through RFE, a technique that iteratively eliminates less significant features to improve model performance. The model's effectiveness was assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Kappa score. Furthermore, SHapley Additive exPlanations (SHAP) values were employed to increase the interpretability of the model, providing insights into the most influential features for dementia prediction. To address the issue of overfitting, a standardization technique was implemented, which enhanced the model's predictive performance. The findings of this study hold potential implications for early dementia detection, informing intervention strategies, and optimizing healthcare resource allocation. © 2024 The Authors

关键词： Dementia prediction Early detection Logistic regression Recursive feature elimination Shapley Additive exPlanations (SHAP)

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Toward a Comparison Framework for Interactive Ontology Enrichment Methodologies 7

Toward a Comparison Framework for Interactive Ontology Enric...

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7th International Workshop on the visualization and Interaction for Ontologies and Linked Data, VOILA! 2022

作者： Vrolijk, Jarno Reklos, Ioannis Vafaie, Mahsa Massari, Arcangelo Mohammadi, Maryam Rudolph, Sebastian Plantage Muidergracht 12 Amsterdam1018 TV Netherlands Department of Informatics King's College London Bush House 30 Aldwych LondonWC2B 4BG United Kingdom FIZ Karlsruhe - Leibniz Institute for Information Infrastructure Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Research Centre for Open Scholarly Metadata Department of Classical Philology and Italian Studies University of Bologna Bologna Italy Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Minderbroedersberg 4-6 Maastricht6211 LK Netherlands Computational Logic Group TU Dresden Dresden01062 Germany

The growing demand for well-modeled ontologies in diverse application areas increases the need for intuitive interaction techniques that support human domain experts in ontology modeling and enrichment tasks, such that quality expectations are met. Beyond the correctness of the specified information, the quality of an ontology depends on its (relative) completeness, i.e., whether the ontology contains all the necessary information to draw expected inferences. On an abstract level, the Ontology Enrichment problem consists of identifying and filling the gap between information that can be logically inferred from the ontology and the information expected to be inferable by the user. To this end, numerous approaches have been described in the literature, providing methodologies from the fields of Formal Semantics and Automated Reasoning targeted at eliciting knowledge from human domain experts. These approaches vary greatly in many aspects and their applicability typically depends on the specifics of the concrete modeling scenario at hand. Toward a better understanding of the landscape of methodological possibilities, this position paper proposes a framework consisting of multiple performance dimensions along which existing and future approaches to interactive ontology enrichment can be characterized. We apply our categorization scheme to a selection of methodologies from the literature. In light of this comparison, we address the limitations of the methods and propose directions for future work. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ontology

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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High-technology agriculture system to enhance food security: A concept of smart irrigation system using Internet of Things and cloud computing

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Journal of the Saudi Society of Agricultural Sciences 2024年

作者： Morchid, Abdennabi Muhammad Alblushi, Ishaq G. Khalid, Haris M. El Alami, Rachid Sitaramanan, Surendar Rama Muyeen, S.M. Department of Physics Laboratory of Informatics Signals Automation and Cognitivism (LISAC) Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University BP 1796 Fes-Atlas Fez 30003 Morocco Department of Electrical and Electronics Engineering Higher Colleges of Technology Sharjah 7947 United Arab Emirates College of Engineering and Information Technology University of Dubai Academic City Dubai 14143 United Arab Emirates Department of Electrical and Electronic Engineering Science University of Johannesburg Aukland Park 2006 South Africa Samsung Austin Research and Development Centre (SARC) Advanced Computing Lab (ACL) San Jose CA United States Department of Electrical Engineering Qatar University Doha 2713 Qatar

Context: Food security is highly reliant on agricultural activity to drive the world economy. However, this activity is in great danger due to climatic changes and improper use of irrigation techniques. Consequently, the lives of numerous individuals worldwide are in jeopardy. In light, this paper investigates the promise of smart irrigation systems based on new technology. Objective: To meet the growing demand for water in agriculture, this study presents an intelligent irrigation system that uses cutting-edge technologies of (1) cloud computing, (2) embedded systems, and (3) Internet-of-Things (IoT). The main objective is to demonstrate how this innovative strategy can effectively manage water resources, supporting food security through cutting-edge agricultural technology. Methods: This paper proposes a smart irrigation system based on cutting-edge technologies like the embedded system, Internet of Things (IoT), and cloud computing as a groundbreaking strategy to improve food security through the implementation of advanced agricultural technology. This system supervises real-time monitoring of crucial environmental factors such as (1) moisture, (2) humidity, (3) temperature, and (4) water levels, in smart agriculture practices. In addition, this system employs the latest sensors, including the module (DHT22), water level sensor, and moisture sensors, which are connected to the widely used embedded system (ESP32). The system uses the ThingSpeak cloud and ThingView app to enable wireless communication between the device and the farm owner, enhancing their interaction. The automated control of the two water pumps is based on the readings of various environmental factors. Moreover, this will also present a mathematical-driven function known as linear interpolation to calibrate the water level sensor in percentage. This system was created using the V-model software development approach. Results and conclusion: Farmers can access comprehensive farm data from anywhere i

关键词： Cloud computing Embedded system Food security Intelligent sensors Internet-of-things (IoT) Irrigation system Smart agriculture ThingSpeak platform V model

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Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

arXiv

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arXiv 2025年

作者： Hou, Qingshan Zhou, Yukun Goh, Jocelyn Hui Lin Zou, Ke Yew, Samantha Min Er Srinivasan, Sahana Wang, Meng Lo, Thaddaeus Lei, Xiaofeng Wagner, Siegfried K. Chia, Mark A. Yang, Dawei Jiang, Hongyang Ran, AnRan Santos, Rui Somfai, Gabor Mark Zhou, Juan Helen Chen, Haoyu Chen, Qingyu Cheung, Carol Yim-Lui Keane, Pearse A. Tham, Yih Chung Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore School of Computer Science and Engineering Northeastern University Shenyang China Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore Centre for Medical Image Computing University College London London United Kingdom NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Singapore Institute of Ophthalmology University College London London United Kingdom Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Hong Kong Department of Ophthalmology Stadtspital Zürich Zurich Switzerland Spross Research Institute Zurich Switzerland Department of Ophthalmology Semmelweis University Budapest Hungary Centre for Sleep and Cognition Centre for Translational MR Research Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Joint Shantou International Eye Center Shantou University the Chinese University of Hong Kong Guangdong Shantou China Shantou University Medical College Guangdong Shantou China Department of Biomedical Informatics and Data Science Yale School of Medicine Yale University New Haven United States Duke-NUS Medical School Singapore

The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM pre-trained on 142 million natural images, has shown promise in non-medical domains. However, its applicability to clinical tasks remains underexplored. To address this, we conducted head-to-head evaluations by finetuning RETFound and three DINOv2 models (large, base, small) for ocular disease detection and systemic disease prediction tasks, across eight standardized open-source ocular datasets, as well as the Moorfields AlzEye and the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0.850–0.952 vs. 0.823-0.944, across three datasets, all P≤0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

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Redesigning dispenser component to enhance performance crop-dusting agriculture drones

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Materials Today: Proceedings 2023年 81卷 166-172页

作者： Rajvin Subramaniam Sami Salama Hussen Hajjaj Kisheen Rao Gsangaya Mohamed Thariq Hameed Sultan Mohd Fazly Mail Lee Seng Hua Department of Mechanical Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Centre for Advanced Mechatronics and Robotics (CaMaRo) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Laboratory of Biocomposite Technology Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Department of Aerospace Engineering Faculty of Engineering Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Aerospace Malaysia Innovation Centre (944741-A) Prime Minister’s Department MIGHT Partnership Hub Jalan Impact 63000 Cyberjaya Selangor Darul Ehsan Malaysia Farm Mechanization Program Engineering Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang Selangor Darul Ehsan Malaysia Laboratory of Biopolymer and Derivatives Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia

Agricultural drones are Unmanned Aerial Vehicles (UAVs) applied to farming in order to help increase crop production and improve farm efficiency. Crop-dusting drones are able to spray powdered fertilizer, pesticide, or insecticide on crops from the air, resulting in reduced costs and increased efficiency compared to traditional methods. The objective of this research work was to improve and enhance the performance of the crop-dusting process using agricultural drones. This research paper details the development of an improved storage container design and stabilized release mechanism for crop-dusting. To stabilise the release of payload when the drone is moving or swaying in the air, a gimbal mechanism was added to the release mechanism in which weights were used to produce constant downward force which balances the mechanism. Tests conducted on the prototype system showed that it performs as required. The stabilization tests show an improvement in accuracy of granule spread when using the stabilized release mechanism developed in this research work. As such, the wastage of fertilizers and pesticides can be reduced. The prototype system was also found to much more capable at carrying heavier loads for a longer period of time compared to current designs. Thus, using the prototype system developed for this research work, the size of storage container as well as the weight of fertilizer or pesticide granule payloads can be increased.

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Autonomous fertilizer mixer through the Internet of Things (IoT)

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Materials Today: Proceedings 2023年 81卷 295-301页

作者： Abdul Hadi Ishak Sami Salama Hussen Hajjaj Kisheen Rao Gsangaya Mohamed Thariq Hameed Sultan Mohd Fazly Mail Lee Seng Hua Department of Mechanical Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Centre for Advanced Mechatronics and Robotics (CaMaRo) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Laboratory of Biocomposite Technology Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Department of Aerospace Engineering Faculty of Engineering Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Aerospace Malaysia Innovation Centre (944741-A) Prime Minister’s Department MIGHT Partnership Hub Jalan Impact 63000 Cyberjaya Selangor Darul Ehsan Malaysia Farm Mechanization Program Engineering Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang Selangor Darul Ehsan Malaysia Laboratory of Biopolymer and Derivatives Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia

Organic fertilizers have been used since the dawn of mankind to improve soil fertility and promote crop growth. However, the process of preparing organic fertilizers for use in agricultural activities is a labour-intensive and time-consuming procedure. Thus, in order to reduce labour costs and improve the efficiency of organic fertilizer preparation, automated organic fertilizer mixers were developed. For this research work, an improved organic fertilizer mixer based on the Internet of Things (IoT) technology capable of monitoring the status of fertilizer production remotely and providing updates and alerts to workers was developed. The aim of this work was to further reduce labour workload and costs, thus increasing efficiency of the organic fertilizer mixing process. In order to accommodate the IoT monitoring system, several modifications and design changes were carried out on the prototype of an automated organic fertilizer mixer. Tests conducted on the prototype unit showed that it performs as required. The IoT monitoring system successfully captures and uploads information on the weight of agricultural waste mixture in the storage drum to the IoT platform. This information can then be used to alert workers on when to add more agricultural waste material to the mixture or remove the processed organic fertilizer. Furthermore, an overall operational cost comparison of different organic fertilizer production methods showed that the automated organic fertilizer prototype provides operational cost savings of over 5 times when compared to current automated systems. Future work includes further automation to the agricultural waste filling and organic fertilizer removal process.

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Design and development of a robot for spraying fertilizers and pesticides for agriculture

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Materials Today: Proceedings 2023年 81卷 242-248页

作者： Afif Shazwan Abdul Ghafar Sami Salama Hussen Hajjaj Kisheen Rao Gsangaya Mohamed Thariq Hameed Sultan Mohd Fazly Mail Lee Seng Hua Department of Mechanical Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Centre for Advanced Mechatronics and Robotics (CaMaRo) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Laboratory of Biocomposite Technology Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Department of Aerospace Engineering Faculty of Engineering Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Aerospace Malaysia Innovation Centre (944741-A) Prime Minister’s Department MIGHT Partnership Hub Jalan Impact 63000 Cyberjaya Selangor Darul Ehsan Malaysia Farm Mechanization Program Engineering Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang Selangor Darul Ehsan Malaysia Laboratory of Biopolymer and Derivatives Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia

The agriculture industry is one that is highly resource- and labour-intensive. As such, farmers are increasingly turning to technology and automation to address this issue. However, agricultural robots are far too complicated, slow, and costly to be made publicly available. As a result, the agriculture sector still lags behind in integrating modern technologies. This research paper details the development of a low-cost agricultural robot for spraying fertilizers and pesticides in agriculture fields as well as for general crop monitoring. The prototype system is a two-wheeled robot that consists of a mobile base, a spraying mechanism, a wireless controller for controlling the robot movement, and a camera for crop health and growth monitoring as well as detecting the presence of pests in the agriculture field. Tests conducted on the prototype system show that while the productivity of the robot in terms of crop coverage is slightly lower than a human worker, the labour cost savings afforded by the agricultural robot prototype is much greater as it functions completely in an autonomous mode and only requires the operator to control the robot when placing it at the start of the crop path. Furthermore, the prototype system also provides greater resource savings and reduction in the contamination of underground water sources due to leeching process, thus achieving precision agriculture goals. Lastly, the excellent battery life of the prototype system ensures that there will be no increase in the operation times and reduction in the efficiency of the fertilizer and pesticide spraying process due to the recharging times when replacing human workers. Future recommendations include making the agricultural robot fully autonomous, using either a rail- or line-following system, to further reduce the labour requirements and costs.

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