Non-destructive Phenotyping of Lettuce Plants in Early Stages of Development with Optical Sensors
By: Ivan Simko, Ryan J. Hayes, Robert T. Furbank
Abstract
The objective of the present study was to test the feasibility of using non-destructive phenotyping with optical sensors for the evaluations of lettuce plants in early stages of development. We performed the series of experiments to determine if hyperspectral imaging and chlorophyll fluorescence imaging can determine phenotypic changes manifested on lettuce plants subjected to the extreme temperature and salinity stress treatments.
Non-Invasive Survey of Old Paintings using VNIR Hyperspectral Sensor
By: E. Matouskova, K. Pavelka, Z. Svadlenkova
Abstract
This paper shows first results of the project on painting documentation field as well as used instrument. Hyperspec VNIR by Headwall Photonics was used for this analysis. It operates in the spectral range between 400 and 1000 nm. Comparison with infrared photography is discussed. The goal of this contribution is a non-destructive deep exploration of specific paintings.
Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging
By: Anisur Rahman, Lalit Mohan Kandpal, Santosh Lohumi, Moon S. Kim, Hoonsoo Lee, Changyeun Mo, Byoung-Kwan Cho
Abstract
The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000–1550 nm.
The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and multivariate calibration models were built by using partial least squares (PLS) regression with different preprocessing spectra. The results showed that the regression model developed by PLS regression based on Savitzky–Golay (S–G) first-derivative preprocessed spectra resulted in better performance for MC, pH, and the smoothing preprocessed spectra-based model resulted in better performance for SSC in intact tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (rpred) of 0.81, 0.69, and 0.74 with root mean square error of prediction (RMSEP) of 0.63%, 0.06, and 0.33% Brix respectively. The full wavelengths were used to create chemical images by applying regression coefficients resulting from the best PLS regression model.
These results obtained from this study clearly revealed that hyperspectral imaging, together with suitable analysis model, is a promising technology for the nondestructive prediction of chemical components in intact tomatoes.
Pedogenic hematitic concretions from the Triassic New Haven Arkose, Connecticut, Implications for understanding Martian diagenetic processes
By: J.H. Wilson, S.M. McLennan, T.D. Glotch, E.T. Rasbury, E.H. Gierlowski-Kordesch, R.V. Tappero
Abstract
We examine pedogenic sedimentary concretions from the New Haven Arkose. We use spectroscopic and geochemical methods to characterize the concretions. New Haven concretions consist of ~ 20% hematite, quartz, goethite and montmorillonite. Differences from other concretions are a negative Ce anomaly and lack of abundant Mn. New Haven concretions possess a pattern of Ni enrichment, similar to the Martian “blueberries.”
Performance & Feasibility of Drone-Mounted Imaging Spectroscopy for Invasive Aquatic Vegetation Detection
By: Erik A. Bolch, Erin L. Hestir, Shruti Khanna
Abstract
Invasive plants are non-native species that can spread rapidly, leading to detrimental economic, ecological, or environmental impact. In aquatic systems such as the Sacramento-San Joaquin River Delta in California, USA, management agencies use manned aerial vehicles (MAV) imaging spectroscopy missions to map and track annual changes in invasive aquatic plants. Advances in unmanned aerial vehicles (UAV) and sensor miniaturization are enabling higher spatial resolution species mapping, which is promising for early detection of invasions before they spread over larger areas.
Photon migration of Raman signal in bone as measured with spatially offset Raman spectroscopy
By: Kay Sowoidnich, John H. Churchwell, Kevin Buckley, Allen E. Goodship, Anthony W. Parker, Pavel Matousek
Abstract
Spatially offset Raman spectroscopy (SORS) is currently being developed as an in vivo tool for bone disease detection, but to date, information about the interrogated volume as influenced by the light propagation and scattering characteristics of the bone matrix is still limited. This paper seeks to develop our general understanding of the sampling depths of SORS in bone specimens as a function of the applied spatial offset.
Potential of in-field Vis/NIR hyperspectral imaging to monitor quality parameters of alfalfa
By: Chiara Cevol, Luca Di Cecilia, Luca Ferrari, Angelo Fabbri, Giovanni Molari
Abstract
The aim of this study was to evaluate the potentiality of the in-field Vis/NIR hyperspectral imaging combined with chemometric to predict moisture content of alfalfa after conditioning. Several combinations of conditioning level, time of day (morning and afternoon), and time after the conditioning (0, 15 and 120 min) were considered to carry out hyperspectral acquisitions.
Predicting intramuscular fat content of pork using hyperspectral imaging
By: L. Liu, M.O. Ngadi
Abstract
Intramuscular fat (IMF) content is an important quality trait of pork. It influences taste, juiciness and tenderness of the meat. The aim of this study was to develop an objective, rapid, and non-destructive method for predicting the IMF content of pork using hyperspectral imaging technology.
Predicting plants in the wild: Mapping arctic and boreal plants with UAS-based visible and near infrared reflectance spectra
By: Peter R. Nelson, Kenneth Bundy, Kevaughn Smith, Matt Macander, Catherine Chan
Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale.
Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy
By: Catherine Chan, Peter R. Nelson, Daniel J. Hayes, Yong-Jiang Zhang, Bruce Hall
Abstract
We assessed airborne spectral data accompanied by ground sampled water potential over three developmental stages of wild blueberries collected throughout the 2019 summer on two adjacent fields, one irrigated and one non-irrigated. Ground sampled leaves were collected in tandem to the hyperspectral image collection with an unoccupied aerial vehicle (UAV) and then measured for leaf water potential.
