Claim · model-written summary
The authors conclude that the method can be deployed in edge devices for greenhouse monitoring or fruit-picking robots.
- Taxon
- tomato
Solanum lycopersicumt0 - Outcome
- can be deployed in edge devicese4
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“tomato ripeness”
e4“the results of this research can be deployed in edge devices”
Title
Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge
Wang Yuanqiao; Gou Wenbo; Wang Chuanyu; et al. International journal of agricultural and biological engineering 2024. doi:10.25165/j.ijabe.20241702.8403
Other claims from this paper (4)
- Method / resource The method achieved a real-time processing speed of 90.1 fps on an RTX 3070Ti.
- Method / resource The method achieved a real-time processing speed of 65.5 fps on an RTX 2060 S.
- Method / resource The method reached an average detection accuracy of 97% compared with manually measured ripeness.
- Method / resource The method was more accurate and efficient than other instance segmentation models in actual greenhouse testing.