Method / resourcemodel-written summary

Labeling a whole crack with a single bounding box gave particularly promising YOLO detection performance for cherry tomato cracks, compared with subdividing the crack into smaller boxes.

A claim from Development of an AI-Based Detection System for Cherry Tomato Skin Splitting and Cracking · 2024

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Labeling a whole crack with a single bounding box gave particularly promising YOLO detection performance for cherry tomato cracks, compared with subdividing the crack into smaller boxes.

Outcome
performancee1
Direction
particularly promisinge1
Comparator
subdivided the crack into smaller boxese0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Solanum lycopersicum var. cerasiforme
Solanum lycopersicum var. cerasiforme · TaxonJS · CC BY-SA 3.0 · Wikimedia Commons ↗

Verbatim

t0

“Cherry Tomato”

✓ located verbatim in the source · title, characters 48–61
e0

“the other subdivided the crack into smaller boxes”

✓ located verbatim in the source · abstract, characters 380–429
e1

“the approach of enclosing the entire crack with a single bounding box demonstrated particularly promising performance”

✓ located verbatim in the source · abstract, characters 516–633

Title

Development of an AI-Based Detection System for Cherry Tomato Skin Splitting and Cracking

Masanori Sato; Zhaohui Tan; Takahiro Naruse; et al. 2024 13th International Conference on Renewable Energy Research and Applications (ICRERA) 2024. doi:10.1109/icrera62673.2024.10815464

Other claims from this paper (1)

Record rev-00f2b3ebb302046f50c120b9f7f1981a120da0678f41d74fae6ed6e5584b755b · schema claims-v0.9 · position 1 in the model’s answer