Fresh Fruit Bunch Ripeness Classification Methods: A Review
<p dir="ltr">The escalating demand for palm oil necessitates enhanced production strategies. As the trend shifts towards automated harvesting to meet the demand, precise ripeness classification has become pivotal. Manual methods are inefficient and error-prone because of workforce co...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513541623513088 |
|---|---|
| author | Jin Yu Goh (22046393) |
| author2 | Yusri Md Yunos (22046396) Mohamed Sultan Mohamed Ali (17317003) |
| author2_role | author author |
| author_facet | Jin Yu Goh (22046393) Yusri Md Yunos (22046396) Mohamed Sultan Mohamed Ali (17317003) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jin Yu Goh (22046393) Yusri Md Yunos (22046396) Mohamed Sultan Mohamed Ali (17317003) |
| dc.date.none.fl_str_mv | 2024-06-28T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11947-024-03483-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Fresh_Fruit_Bunch_Ripeness_Classification_Methods_A_Review/29899529 |
| 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 Agricultural biotechnology Engineering Environmental engineering Computer vision in agriculture Agriculture automation Non-destructive assessment Oil palm technology |
| dc.title.none.fl_str_mv | Fresh Fruit Bunch Ripeness Classification Methods: A Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The escalating demand for palm oil necessitates enhanced production strategies. As the trend shifts towards automated harvesting to meet the demand, precise ripeness classification has become pivotal. Manual methods are inefficient and error-prone because of workforce constraints. The present review scrutinizes the following non-destructive ripeness classification methods: spectroscopy, inductive sensing, thermal imaging, light detection and ranging, laser-light backscattering imaging, and computer vision. The review focuses on identifying reliable techniques capable of real-time and accurate classification in dynamic and unstructured environments. All aforementioned techniques are discussed in intricate detail, accompanied by thorough critiques. This review then presents a performance comparison and benchmarking process, providing comprehensive insights into the strengths and weaknesses of each technique. A compelling solution emerges in the fusion of light detection and ranging and computer vision techniques. This synergy capitalizes on their strengths to offset individual limitations, offering a potent approach. Furthermore, this fusion yields added value in terms of localization and mapping, rendering it exceptionally suitable for real-time classification in complex environments. This review provides insights into bridging the gap between automated harvesting needs and ripeness assessment precision, thereby fostering advancements in the palm oil industry.</p><h2>Other Information</h2><p dir="ltr">Published in: Food and Bioprocess Technology<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11947-024-03483-0" target="_blank">https://dx.doi.org/10.1007/s11947-024-03483-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4b9ec8c07101a69afdbf6f9bf71c9365 |
| identifier_str_mv | 10.1007/s11947-024-03483-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29899529 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Fresh Fruit Bunch Ripeness Classification Methods: A ReviewJin Yu Goh (22046393)Yusri Md Yunos (22046396)Mohamed Sultan Mohamed Ali (17317003)Agricultural, veterinary and food sciencesAgricultural biotechnologyEngineeringEnvironmental engineeringComputer vision in agricultureAgriculture automationNon-destructive assessmentOil palm technology<p dir="ltr">The escalating demand for palm oil necessitates enhanced production strategies. As the trend shifts towards automated harvesting to meet the demand, precise ripeness classification has become pivotal. Manual methods are inefficient and error-prone because of workforce constraints. The present review scrutinizes the following non-destructive ripeness classification methods: spectroscopy, inductive sensing, thermal imaging, light detection and ranging, laser-light backscattering imaging, and computer vision. The review focuses on identifying reliable techniques capable of real-time and accurate classification in dynamic and unstructured environments. All aforementioned techniques are discussed in intricate detail, accompanied by thorough critiques. This review then presents a performance comparison and benchmarking process, providing comprehensive insights into the strengths and weaknesses of each technique. A compelling solution emerges in the fusion of light detection and ranging and computer vision techniques. This synergy capitalizes on their strengths to offset individual limitations, offering a potent approach. Furthermore, this fusion yields added value in terms of localization and mapping, rendering it exceptionally suitable for real-time classification in complex environments. This review provides insights into bridging the gap between automated harvesting needs and ripeness assessment precision, thereby fostering advancements in the palm oil industry.</p><h2>Other Information</h2><p dir="ltr">Published in: Food and Bioprocess Technology<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11947-024-03483-0" target="_blank">https://dx.doi.org/10.1007/s11947-024-03483-0</a></p>2024-06-28T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11947-024-03483-0https://figshare.com/articles/journal_contribution/Fresh_Fruit_Bunch_Ripeness_Classification_Methods_A_Review/29899529CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298995292024-06-28T09:00:00Z |
| spellingShingle | Fresh Fruit Bunch Ripeness Classification Methods: A Review Jin Yu Goh (22046393) Agricultural, veterinary and food sciences Agricultural biotechnology Engineering Environmental engineering Computer vision in agriculture Agriculture automation Non-destructive assessment Oil palm technology |
| status_str | publishedVersion |
| title | Fresh Fruit Bunch Ripeness Classification Methods: A Review |
| title_full | Fresh Fruit Bunch Ripeness Classification Methods: A Review |
| title_fullStr | Fresh Fruit Bunch Ripeness Classification Methods: A Review |
| title_full_unstemmed | Fresh Fruit Bunch Ripeness Classification Methods: A Review |
| title_short | Fresh Fruit Bunch Ripeness Classification Methods: A Review |
| title_sort | Fresh Fruit Bunch Ripeness Classification Methods: A Review |
| topic | Agricultural, veterinary and food sciences Agricultural biotechnology Engineering Environmental engineering Computer vision in agriculture Agriculture automation Non-destructive assessment Oil palm technology |