Results 251 to 260 of about 112,356 (331)
Recent Advancements in Hyperspectral Image Reconstruction from a Compressive Measurement. [PDF]
Han XH, Wang J, Jiang H.
europepmc +1 more source
Detection of Hardening in Mangosteens Using near-Infrared Hyperspectral Imaging [PDF]
Saranya Workhwa +4 more
openalex +1 more source
Abstract Input management practices within precision agriculture systems have enabled grain and oilseed producers to apply agricultural inputs variably, facilitating resource, yield, and profit optimization. Despite significant progress in variable rate technologies, research comparing different methodologies and comprehensive guidelines to assist ...
Karen Joané Truter +3 more
wiley +1 more source
Enhanced Hyperspectral Image Classification Technique Using PCA-2D-CNN Algorithm and Null Spectrum Hyperpixel Features. [PDF]
Liu H, Bi W, Mughees N.
europepmc +1 more source
ABSTRACT Mung bean (Vigna radiata (L.) Wilczek) is emerging as a valuable ingredient in modern food systems due to its nutritional benefits, functional versatility, and alignment with plant‐based, clean‐label, and sustainable food trends. This review highlights recent technological advancements in mung bean processing, covering primary processing steps
Suresh Sakhare +4 more
wiley +1 more source
A new band selection approach integrated with physical reflectance autoencoders and albedo recovery for hyperspectral image classification. [PDF]
Sangeetha V, Agilandeeswari L.
europepmc +1 more source
Water loss is a key factor affecting the postharvest quality and shelf life of blueberries, and storage conditions (humidity and time) play an important role in regulating water retention capacity of stored berries. This study aims to explore the variation of moisture content (MC) in blueberries under different storage humidity and storage time ...
RunKai Wang +3 more
wiley +1 more source
Multiscale superpixel depth feature extraction for hyperspectral image classification. [PDF]
Yan Q, Zhang S, Chen X, Zheng Z.
europepmc +1 more source
Physics‐informed multimodal learning for snapshot dental spectral reflectance prediction
Abstract Accurate color matching is essential to achieving aesthetically realistic outcomes in dental crown and bridge restorations. Traditional visual methods, however, are often affected by lighting variations and observer subjectivity. These limitations can lead to metamerism and inconsistent clinical outcomes.
Yujun Feng +5 more
wiley +1 more source

