Results 221 to 230 of about 91,296 (293)
Spectral network analysis illuminates coordinated plant traits across a climate gradient. [PDF]
Ray R +6 more
europepmc +1 more source
Abstract Monitoring spatial variations in plant growth and forecasting yield before harvest provides valuable insights for optimizing agronomic decision‐making in potato (Solanum tuberosum L.) cultivation. Although unmanned aerial vehicle (UAV)‐based remote sensing has recently enabled the development of tuber fresh weight (TW) estimation models, their
Yuto Imachi +7 more
wiley +1 more source
Feasibility of Hyperspectral Imaging and Machine Learning for Rapid Prescreening of Aflatoxin B<sub>1</sub> in Maize Kernels. [PDF]
Jiang Y +5 more
europepmc +1 more source
Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials
Abstract Improving selection for multiple disease resistance (MDR) and yield in maize (Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB ...
Danilo E. Moreta +7 more
wiley +1 more source
Phenology-Dependent Sex Identification in Mature <i>Ginkgo biloba</i> Using Hyperspectral Imaging. [PDF]
Zhao Z +8 more
europepmc +1 more source
Abstract Fruit size, shape, color, and percent fruit rot are important quality traits for breeding cranberry (Vaccinium macrocarpon Ait.). Image analysis can be used to measure these traits, but affordable hardware for standardized image capture and integrated user‐friendly software pipelines are lacking.
Jeffrey L. Neyhart +8 more
wiley +1 more source
Phasor-Based Spatio-Spectral Segmentation for Hyperspectral Fluorescence Microscopy. [PDF]
Barna M +4 more
europepmc +1 more source
Philosophy of phenomic prediction and its incompatibility with causal inference
Abstract Breeding programs need to make decisions frequently to improve populations and develop varieties efficiently. These needs led to the development of genomic prediction in the early 2000s and phenomic prediction in the mid‐2010s. In practice, phenomic prediction techniques rely on the same statistical tools and computational frameworks as other ...
Mitchell J. Feldmann +2 more
wiley +1 more source
Spectral-Structural Collaborative Learning for Fine-Grained Hyperspectral Mineral Classification. [PDF]
Qiu Y, Zhang Y, Cao S, Wu W, Xie S.
europepmc +1 more source

