Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera
Microgreens are the first leafy seedlings of edible plants. Microgreen farming is yet to be automated; the main challenge for automation is the lack of a sensory mechanism to detect and quantify microgreen phenotypes. This paper presents a novel automated microgreen phenotyping method targeting yiel...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375523002113 |
| _version_ | 1797246785929871360 |
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| author | Bhanu Watawana Mats Isaksson |
| author_facet | Bhanu Watawana Mats Isaksson |
| author_sort | Bhanu Watawana |
| collection | DOAJ |
| description | Microgreens are the first leafy seedlings of edible plants. Microgreen farming is yet to be automated; the main challenge for automation is the lack of a sensory mechanism to detect and quantify microgreen phenotypes. This paper presents a novel automated microgreen phenotyping method targeting yield estimation. The paper demonstrates that phenotyping can be effectively performed using a consumer-grade RGB-D camera. First, the depth and RGB images are captured. Thereafter, the plant segments are filtered and the canopy is identified. Using image processing, the canopy height and density are calculated. Both yield prediction regression analysis and a TensorFlow learning algorithm are evaluated to estimate the yield as a function of height and canopy density. The authors believe the algorithm discussed in this paper is the first phenotyping algorithm combining RGB and depth data for microgreen yield estimation. |
| first_indexed | 2024-03-08T19:57:56Z |
| format | Article |
| id | doaj.art-3928e2dcc81547f9bb60b0772dfcf104 |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-04-24T19:48:19Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-3928e2dcc81547f9bb60b0772dfcf1042024-03-25T04:18:10ZengElsevierSmart Agricultural Technology2772-37552024-03-017100384Automated microgreen phenotyping for yield estimation using a consumer-grade depth cameraBhanu Watawana0Mats Isaksson1Corresponding author.; Department of Mechanical Engineering and Product Design Engineering, School of Engineering, Swinburne University of Technology, John St, Hawthorn, VIC 3122, AustraliaDepartment of Mechanical Engineering and Product Design Engineering, School of Engineering, Swinburne University of Technology, John St, Hawthorn, VIC 3122, AustraliaMicrogreens are the first leafy seedlings of edible plants. Microgreen farming is yet to be automated; the main challenge for automation is the lack of a sensory mechanism to detect and quantify microgreen phenotypes. This paper presents a novel automated microgreen phenotyping method targeting yield estimation. The paper demonstrates that phenotyping can be effectively performed using a consumer-grade RGB-D camera. First, the depth and RGB images are captured. Thereafter, the plant segments are filtered and the canopy is identified. Using image processing, the canopy height and density are calculated. Both yield prediction regression analysis and a TensorFlow learning algorithm are evaluated to estimate the yield as a function of height and canopy density. The authors believe the algorithm discussed in this paper is the first phenotyping algorithm combining RGB and depth data for microgreen yield estimation.http://www.sciencedirect.com/science/article/pii/S2772375523002113MicrogreenPhenotypePlant phenotypeDepth cameraRGB-DTensorFlow |
| spellingShingle | Bhanu Watawana Mats Isaksson Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera Smart Agricultural Technology Microgreen Phenotype Plant phenotype Depth camera RGB-D TensorFlow |
| title | Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera |
| title_full | Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera |
| title_fullStr | Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera |
| title_full_unstemmed | Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera |
| title_short | Automated microgreen phenotyping for yield estimation using a consumer-grade depth camera |
| title_sort | automated microgreen phenotyping for yield estimation using a consumer grade depth camera |
| topic | Microgreen Phenotype Plant phenotype Depth camera RGB-D TensorFlow |
| url | http://www.sciencedirect.com/science/article/pii/S2772375523002113 |
| work_keys_str_mv | AT bhanuwatawana automatedmicrogreenphenotypingforyieldestimationusingaconsumergradedepthcamera AT matsisaksson automatedmicrogreenphenotypingforyieldestimationusingaconsumergradedepthcamera |