Claim · model-written summary
The authors report MaizeNet robustness under diverse field conditions and scalability to larger datasets.
- Outcome
- robustness under the diverse field conditions and scalability to the larger agricultural datasetse0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Verbatim
e0“The robustness under the diverse field conditions and scalability to the larger agricultural datasets brought out the utility of MaizeNet as a useful tool for precision agriculture.”
t0“Maize Cob”
Title
MaizeNet: High-Performance Image-Based Maize Cob Detection using Lightweight CNNs
Nidhi Mishra; Banti Lalnunthari Springer Link (Chiba Institute of Technology) 2025. doi:10.1051/shsconf/202521601063/pdf
Other claims from this paper (4)
- Method / resource MaizeNet detected maize cobs with 91.4% mean average precision.
- Method / resource MaizeNet processed 25 frames per second on standard mobile hardware.
- Method / resource Comparative evaluations found MaizeNet outperformed other deep learning models.
- Method / resource An ablation study found architectural choices affected model performance.