GENERAL STUDY OF CLASSICAL AND NONCLASSICAL CONTRIBUTIONS OF TWO PHOTON ABSORPTION PROCESS IN ORGANIC MOLECULES

Freiman Estiven Triana Arango (2023, [Tesis de doctorado])

"Two-photon absorption (TPA), a nonlinear optical phenomenon, is gaining attention for applications like laser scanning, microscopy, and therapy. Recent research explores entangled two photon absorption (ETPA) using correlated photons but faces debates regarding its magnitude and detection. This study introduces a novel method using changes in Hong-Ou-Mandel (HOM) interferogram visibility to probe ETPA's presence. It employs Rhodamine B dye and entangled photons at around 800nm to investigate conditions conducive to observing ETPA-induced changes. This innovative approach distinguishes genuine ETPA signals from linear optical losses often masquerading as ETPA effects, addressing a significant field challenge."

Two-photon absorption Entangled two-photon absorption Hong-Ou-Mandel HOM dip visibility Joint Spectral Intensity Entangled photons CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA FÍSICA ÓPTICA OPTICA NO LINEAL OPTICA NO LINEAL

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Gerald Blasch David Hodson Francelino Rodrigues (2023, [Artículo])

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Very High Resolution Imagery Disease Detection Methods Early Growth Stages CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA UNMANNED AERIAL VEHICLES STEM RUST PHENOTYPING HIGH-THROUGHPUT PHENOTYPING WHEAT