Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors
The agricultural robots limit complicated manoeuvring jobs, reducing the need for human intervention. Automation and control of these robots are performed by observing and practising the crop type, regulations, and external weather conditions. This article proposes and discusses a Manoeuvering Adapt...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682401086X |
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| author | Majed Abdullah Alrowaily Omar Alruwaili Mansoor Alghamdi Menwa Alshammeri Muteeb Alahmari Ghulam Abbas |
| author_facet | Majed Abdullah Alrowaily Omar Alruwaili Mansoor Alghamdi Menwa Alshammeri Muteeb Alahmari Ghulam Abbas |
| author_sort | Majed Abdullah Alrowaily |
| collection | DOAJ |
| description | The agricultural robots limit complicated manoeuvring jobs, reducing the need for human intervention. Automation and control of these robots are performed by observing and practising the crop type, regulations, and external weather conditions. This article proposes and discusses a Manoeuvering Adaptable Task Processing Model (MATPM). This model is designed to improve the adaptability and precision of agricultural robots coinciding with farming and climatic conditions. For this purpose, extreme machine learning is employed to learn, align, and respond to external conditions to improve precise manoeuvring. The external impacting features and their adaptability to the crop and climatic conditions are valued using the learning process. In this process, maximum adaptability is considered to improve precision agriculture. The tasks are then classified based on adaptability and executed sequentially. If an unpredictable impacting factor hinders a task, then the adaptability is paused from training and it improves the chances of training by using different completed and paused tasks from the previous manoeuvring processes. Therefore, precision in smart farming and robot system adaptability is ensured with fewer adaptability errors. From the comparative assessment, the proposed model improves adaptability by 8.71 % and precision by 11.44 %, with 8.96 % less adaptability error for various tasks/ day. |
| format | Article |
| id | doaj-art-0d4c34086fb94411b4fa306eab50072c |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-0d4c34086fb94411b4fa306eab50072c2024-12-21T04:27:54ZengElsevierAlexandria Engineering Journal1110-01682024-12-01109655668Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errorsMajed Abdullah Alrowaily0Omar Alruwaili1Mansoor Alghamdi2Menwa Alshammeri3Muteeb Alahmari4Ghulam Abbas5Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia; Corresponding authors.Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi ArabiaDepartment of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi ArabiaDepartment of Computer Science, Imam Abdulrahman Bin Faisal University (IAU), Dammam 34212, Saudi ArabiaSchool of Electrical Engineering, Southeast University, Nanjing 210096, China; Corresponding authors.The agricultural robots limit complicated manoeuvring jobs, reducing the need for human intervention. Automation and control of these robots are performed by observing and practising the crop type, regulations, and external weather conditions. This article proposes and discusses a Manoeuvering Adaptable Task Processing Model (MATPM). This model is designed to improve the adaptability and precision of agricultural robots coinciding with farming and climatic conditions. For this purpose, extreme machine learning is employed to learn, align, and respond to external conditions to improve precise manoeuvring. The external impacting features and their adaptability to the crop and climatic conditions are valued using the learning process. In this process, maximum adaptability is considered to improve precision agriculture. The tasks are then classified based on adaptability and executed sequentially. If an unpredictable impacting factor hinders a task, then the adaptability is paused from training and it improves the chances of training by using different completed and paused tasks from the previous manoeuvring processes. Therefore, precision in smart farming and robot system adaptability is ensured with fewer adaptability errors. From the comparative assessment, the proposed model improves adaptability by 8.71 % and precision by 11.44 %, with 8.96 % less adaptability error for various tasks/ day.http://www.sciencedirect.com/science/article/pii/S111001682401086XAdaptability FactorMachine LearningManoeuvering ControlSmart AgricultureTask Error |
| spellingShingle | Majed Abdullah Alrowaily Omar Alruwaili Mansoor Alghamdi Menwa Alshammeri Muteeb Alahmari Ghulam Abbas Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors Alexandria Engineering Journal Adaptability Factor Machine Learning Manoeuvering Control Smart Agriculture Task Error |
| title | Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors |
| title_full | Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors |
| title_fullStr | Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors |
| title_full_unstemmed | Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors |
| title_short | Application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors |
| title_sort | application of extreme machine learning for smart agricultural robots to reduce manoeuvering adaptability errors |
| topic | Adaptability Factor Machine Learning Manoeuvering Control Smart Agriculture Task Error |
| url | http://www.sciencedirect.com/science/article/pii/S111001682401086X |
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