Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor
<p dir="ltr">There are many problems related to the use of machine learning and machine vision technology on a commercial scale for cutting sugarcane seeds. These obstacles are related to complex systems and the way the farmers operate them, the possibility of damage to the buds duri...
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2024
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| _version_ | 1864513543199522816 |
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| author | Abdallah Elshawadfy Elwakeel (19852263) |
| author2 | Loai S. Nasrat (18271596) Mohamed Elshahat Badawy (19864859) I. M. Elzein (19852272) Mohamed Metwally Mahmoud (15213516) Kitmo (13389936) Mahmoud M. Hussein (16965014) Hany S. Hussein (17854653) Tamer M. El-Messery (18271602) Claude Nyambe (19864862) Salah Elsayed (6429233) Manar A. Ourapi (18271599) |
| author2_role | author author author author author author author author author author author |
| author_facet | Abdallah Elshawadfy Elwakeel (19852263) Loai S. Nasrat (18271596) Mohamed Elshahat Badawy (19864859) I. M. Elzein (19852272) Mohamed Metwally Mahmoud (15213516) Kitmo (13389936) Mahmoud M. Hussein (16965014) Hany S. Hussein (17854653) Tamer M. El-Messery (18271602) Claude Nyambe (19864862) Salah Elsayed (6429233) Manar A. Ourapi (18271599) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdallah Elshawadfy Elwakeel (19852263) Loai S. Nasrat (18271596) Mohamed Elshahat Badawy (19864859) I. M. Elzein (19852272) Mohamed Metwally Mahmoud (15213516) Kitmo (13389936) Mahmoud M. Hussein (16965014) Hany S. Hussein (17854653) Tamer M. El-Messery (18271602) Claude Nyambe (19864862) Salah Elsayed (6429233) Manar A. Ourapi (18271599) |
| dc.date.none.fl_str_mv | 2024-10-17T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0306584 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Advanced_design_and_Engi-economical_evaluation_of_an_automatic_sugarcane_seed_cutting_machine_based_RGB_color_sensor/29654918 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Agriculture, land and farm management Crop and pasture production Economics Applied economics Engineering Communications engineering Control engineering, mechatronics and robotics Mechanical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Sugarcane Seed Economic Analysis Buds Machine vision Signal processing Economics Machine Learning |
| dc.title.none.fl_str_mv | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">There are many problems related to the use of machine learning and machine vision technology on a commercial scale for cutting sugarcane seeds. These obstacles are related to complex systems and the way the farmers operate them, the possibility of damage to the buds during the cleaning process, and the high cost of such technology. In order to address these issues, a set of RGB color sensors was used to develop an automated sugarcane seed cutting machine (ASSCM) capable of identifying the buds that had been manually marked with a unique color and then cutting them mechanically, and the sugarcane seed exit chute was provided with a sugarcane seed monitoring unit. The machine’s performance was evaluated by measuring the damage index at sugarcane stalk diameters of 2.03, 2.72, 3.42, and 3.94 cm. where two different types of rotary saw knives had the same diameter of 7.0 in/180 mm the two knives had 30 and 80 teeth, also we used five cutting times of 1000, 1500, 2000, 2500, and 3000 ms. All tests were done at a fixed cutting speed of 12000 rpm. In addition, the machine’s performance was evaluated by conducting an economic analysis. The obtained results showed that the most damage index values were less than 0.00 for all cutting times and sugarcane stalk diameters under testing, while the DI values were equal zero (partial damage) for sugarcane stalk diameter of 3.42 cm at cutting times of 2000 ms and 2500 ms, in addition to the DI values being equal zero (extreme damage) for sugarcane stalk diameter of 3.94 cm at cutting times of 1500 ms and 2000 ms. The economic analysis showed that the total cost of sugarcane seeds per hectare is 70.865 USD. In addition, the ASSCM can pay for itself in a short period of time. The payback time is 0.536 years, which means that the ASSCM will save enough money to pay for itself in about 6.43 months. Finally, we suggest using a rotary saw knife with 80 teeth and a cutting time of 2000 ms to cut sugarcane stacks with an average diameter of 2.72 cm. This will result in higher performance and lower operating costs for the ASSCM.</p><p dir="ltr">Correction: Correction: Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor: <a href="https://doi.org/10.1371/journal.pone.0324915" target="_blank">https://doi.org/10.1371/journal.pone.0324915</a>, published online 19 May 2025.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0306584" target="_blank">https://dx.doi.org/10.1371/journal.pone.0306584</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_97b109312ba6b34b688dbabd9d2ea63f |
| identifier_str_mv | 10.1371/journal.pone.0306584 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29654918 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensorAbdallah Elshawadfy Elwakeel (19852263)Loai S. Nasrat (18271596)Mohamed Elshahat Badawy (19864859)I. M. Elzein (19852272)Mohamed Metwally Mahmoud (15213516)Kitmo (13389936)Mahmoud M. Hussein (16965014)Hany S. Hussein (17854653)Tamer M. El-Messery (18271602)Claude Nyambe (19864862)Salah Elsayed (6429233)Manar A. Ourapi (18271599)Agricultural, veterinary and food sciencesAgriculture, land and farm managementCrop and pasture productionEconomicsApplied economicsEngineeringCommunications engineeringControl engineering, mechatronics and roboticsMechanical engineeringInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningSugarcaneSeedEconomic AnalysisBudsMachine visionSignal processingEconomicsMachine Learning<p dir="ltr">There are many problems related to the use of machine learning and machine vision technology on a commercial scale for cutting sugarcane seeds. These obstacles are related to complex systems and the way the farmers operate them, the possibility of damage to the buds during the cleaning process, and the high cost of such technology. In order to address these issues, a set of RGB color sensors was used to develop an automated sugarcane seed cutting machine (ASSCM) capable of identifying the buds that had been manually marked with a unique color and then cutting them mechanically, and the sugarcane seed exit chute was provided with a sugarcane seed monitoring unit. The machine’s performance was evaluated by measuring the damage index at sugarcane stalk diameters of 2.03, 2.72, 3.42, and 3.94 cm. where two different types of rotary saw knives had the same diameter of 7.0 in/180 mm the two knives had 30 and 80 teeth, also we used five cutting times of 1000, 1500, 2000, 2500, and 3000 ms. All tests were done at a fixed cutting speed of 12000 rpm. In addition, the machine’s performance was evaluated by conducting an economic analysis. The obtained results showed that the most damage index values were less than 0.00 for all cutting times and sugarcane stalk diameters under testing, while the DI values were equal zero (partial damage) for sugarcane stalk diameter of 3.42 cm at cutting times of 2000 ms and 2500 ms, in addition to the DI values being equal zero (extreme damage) for sugarcane stalk diameter of 3.94 cm at cutting times of 1500 ms and 2000 ms. The economic analysis showed that the total cost of sugarcane seeds per hectare is 70.865 USD. In addition, the ASSCM can pay for itself in a short period of time. The payback time is 0.536 years, which means that the ASSCM will save enough money to pay for itself in about 6.43 months. Finally, we suggest using a rotary saw knife with 80 teeth and a cutting time of 2000 ms to cut sugarcane stacks with an average diameter of 2.72 cm. This will result in higher performance and lower operating costs for the ASSCM.</p><p dir="ltr">Correction: Correction: Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor: <a href="https://doi.org/10.1371/journal.pone.0324915" target="_blank">https://doi.org/10.1371/journal.pone.0324915</a>, published online 19 May 2025.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0306584" target="_blank">https://dx.doi.org/10.1371/journal.pone.0306584</a></p>2024-10-17T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1371/journal.pone.0306584https://figshare.com/articles/journal_contribution/Advanced_design_and_Engi-economical_evaluation_of_an_automatic_sugarcane_seed_cutting_machine_based_RGB_color_sensor/29654918CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296549182024-10-17T03:00:00Z |
| spellingShingle | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor Abdallah Elshawadfy Elwakeel (19852263) Agricultural, veterinary and food sciences Agriculture, land and farm management Crop and pasture production Economics Applied economics Engineering Communications engineering Control engineering, mechatronics and robotics Mechanical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Sugarcane Seed Economic Analysis Buds Machine vision Signal processing Economics Machine Learning |
| status_str | publishedVersion |
| title | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor |
| title_full | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor |
| title_fullStr | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor |
| title_full_unstemmed | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor |
| title_short | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor |
| title_sort | Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor |
| topic | Agricultural, veterinary and food sciences Agriculture, land and farm management Crop and pasture production Economics Applied economics Engineering Communications engineering Control engineering, mechatronics and robotics Mechanical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Sugarcane Seed Economic Analysis Buds Machine vision Signal processing Economics Machine Learning |