Computer vision-based plants phenotyping: A comprehensive survey
Summary: The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the ind...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223027864 |
| _version_ | 1797371349725872128 |
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| author | Talha Meraj Muhammad Imran Sharif Mudassar Raza Amerah Alabrah Seifedine Kadry Amir H. Gandomi |
| author_facet | Talha Meraj Muhammad Imran Sharif Mudassar Raza Amerah Alabrah Seifedine Kadry Amir H. Gandomi |
| author_sort | Talha Meraj |
| collection | DOAJ |
| description | Summary: The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps. |
| first_indexed | 2024-03-08T18:18:31Z |
| format | Article |
| id | doaj.art-6215719e39534ffe9c4d1512c3c7c219 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-08T18:18:31Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-6215719e39534ffe9c4d1512c3c7c2192023-12-31T04:26:32ZengElsevieriScience2589-00422024-01-01271108709Computer vision-based plants phenotyping: A comprehensive surveyTalha Meraj0Muhammad Imran Sharif1Mudassar Raza2Amerah Alabrah3Seifedine Kadry4Amir H. Gandomi5Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, PakistanDepartment of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, PakistanDepartment of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, PakistanDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Applied Data Science, Noroff University College, Kristiansand, Norway; MEU Research Unit, Middle East University, Amman 11831, Jordan; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, LebanonFaculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia; University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary; Corresponding authorSummary: The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps.http://www.sciencedirect.com/science/article/pii/S2589004223027864PhenotypingPlant BiologyMachine learning |
| spellingShingle | Talha Meraj Muhammad Imran Sharif Mudassar Raza Amerah Alabrah Seifedine Kadry Amir H. Gandomi Computer vision-based plants phenotyping: A comprehensive survey iScience Phenotyping Plant Biology Machine learning |
| title | Computer vision-based plants phenotyping: A comprehensive survey |
| title_full | Computer vision-based plants phenotyping: A comprehensive survey |
| title_fullStr | Computer vision-based plants phenotyping: A comprehensive survey |
| title_full_unstemmed | Computer vision-based plants phenotyping: A comprehensive survey |
| title_short | Computer vision-based plants phenotyping: A comprehensive survey |
| title_sort | computer vision based plants phenotyping a comprehensive survey |
| topic | Phenotyping Plant Biology Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223027864 |
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