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A novel nondestructive detection approach for seed cotton lint percentage using deep learning

Lijie Geng, Pengji Yan, Zhikun Ji, Chunyu Song et al. • Journal of Cotton Research (2024)

Abstract Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price. The traditional measuring method of lint percentage is labor-intensive and time-consumi...

DOAJ Open Access (DOAJ) DOI: 10.1186/s42397-024-00178-x

Geo-Temporal Selective Approach for Dynamic Depth Estimation in Outdoor Object Detection and Distance Measurement

Muhammad Faseeh, Misbah Bibi, Murad Ali Khan, Atif Rizwan et al. • IEEE Access (2024)

Accurate depth information is crucial for various computer vision applications such as augmented reality, 3D modeling, and autonomous vehicles. Recent advancements have significantly improved both supervised and self-supervised methods for ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3485089