Luntian: An automated, high-throughput phenotyping system for the greeness and biomass of C4 rice
Plant phenotyping is a vital process that helps farmers and researchers assess the growth, health, and development of a plant. In the Philippines, phenotyping is done manually, with each plant specimen measured and assessed one by one. However, this process is laborious, time-consuming, and prone to...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2015
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/9957 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Plant phenotyping is a vital process that helps farmers and researchers assess the growth, health, and development of a plant. In the Philippines, phenotyping is done manually, with each plant specimen measured and assessed one by one. However, this process is laborious, time-consuming, and prone to human error. Automated phenotyping systems have attempted to address this problem through the use of cameras and image processing, but these systems are proprietary and designed for plants and crops which are not commonly found in the Philippines. In order to alleviate this problem, research was conducted to develop a high through put rice phenotyping system that automates the identification of plant greenness and plant biomass of C4 Rice. The system was developed in order to provide an efficient way of phenotyping C4 rice by automating the process. It implements various image processing techniques and was tested in the screen house setup used by International Rice Research Institute (IRRI). The systems design was finalized in consultation with and tested by the C4 researchers from IRRI. The respondent was pleased with the systems usability and remarked that it would be beneficial to their current process if used. To evaluate the systems accuracy, the generated greenness and biomass values were compared with the values obtained through the manual process. The greenness module registered a 21.9792% mean percent error in comparison to using the Leaf Color Chart. On the other hand, the biomass module yielded 206.0700% mean percent error using compressed girth measurements. |
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