Results 231 to 240 of about 5,818,272 (282)

Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery

open access: yesThe Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract An agronomic trait such as stand count is important for cultivar development and crop management practices. Manually counting the number of plants is time consuming, labor‐intensive, and prone to error. The use of unoccupied aerial systems (UAS)‐collected red, green, blue (RGB) imagery in conjunction with advanced deep learning and image ...
Aliasghar Bazrafkan   +1 more
wiley   +1 more source

NIRS Coupled With Machine Learning Algorithms for the Authentication and Quality Assessment of Indigenous Cattle and Buffalo Meat

open access: yeseFood, Volume 7, Issue 5, October 2026.
Portable NIRS combined with machine learning enabled rapid and accurate authentication of cattle and buffalo meat. Optimized models (XGBoost, CNN) achieved > 98% accuracy, while SHAP revealed key spectral regions. This dual approach offers a field‐ready, reagent‐free tool for fraud prevention and traceability. ABSTRACT Ensuring the authenticity of high‐
Dip Ghosh, Raad Al Deen, Md. Abul Hashem
wiley   +1 more source

Ultrasonic Vibration Assisted Laser Processing: A Hybrid Paradigm for Extreme Surface Enhancement

open access: yesRare Metals, Volume 45, Issue 10, October 2026.
ABSTRACT Traditional laser processing techniques offer many advantages, but they often encounter limitations such as heat‐affected zones, recast layers, residual tensile stresses, and poor surface finish. The integration of ultrasonic vibration (UV) presents a promising hybrid approach to overcome these challenges by introducing beneficial effects like
Aziz Ul Hassan Mohsan   +6 more
wiley   +1 more source

Spatial productivity disparities in Great Britain

open access: yesEconomica, Volume 93, Issue 372, Page 1533-1570, October 2026.
We study productivity disparities across metropolitan and non‐metropolitan areas in Great Britain. Spatial disparities in productivity are large and persistent. Using a development accounting framework, we show that differences in area size and in the spatial distribution of human and business capital are key explanatory factors.
Mimosa Distefano   +3 more
wiley   +1 more source

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