Imaging spectroscopy can help predict water stress in wild blueberry barrens, according to a University of Maine-led study. The technology involves measuring the light reflected off objects depicted in images captured by drones, satellites, and other remote sensing technology to classify and gather pertinent information about the objects.
According to researchers, it can precisely measure light across dozens, if not hundreds, of bands of colors. The reflectance spectra can depict nutrient levels, chlorophyll content, and other indicators of health for various crops.
Scientists from UMaine, the Schoodic Institute, and Wyman’s, one of the world’s largest purveyors of wild blueberries, found in their research that when incorporated into models, imaging spectroscopy can help predict whether wild blueberry fields will lack sufficient water for growing. The technology helps growers evaluate irrigation routines and manage water resources in a way that avoids damaging the crop.
The team collected imaging spectroscopy data by deploying a drone equipped with a spectrometer for capturing visible and near-infrared light to photograph wild blueberry fields owned by Wyman’s in Debois, Maine. Researchers then processed the images to measure reflected light spectra from the plants for indications of chlorophyll levels and other properties that would help estimate their water potential, which, they say, is the primary force driving water flow and an indicator of water stress. At the same time, the group collected small branches with leaves from wild blueberry plants in the plots to assess their water potential and validate the spectra-based estimation. Pictures and samples were collected in the spring and summer when the plants experienced peak bloom, green fruit, and color break.
The data from both drone images and ground samples were incorporated into models, which they developed using machine learning and statistical analysis, to estimate water potential, and thereby predict water stress, of the plants in the barrens. Models from the ground sample data were used to help guide the development of and validate the model created with data from the images. The results of both sets of models were comparable, demonstrating that imaging spectroscopy can accurately predict water stress in wild blueberry barrens at different times of the growing season. With the efficacy of the technology confirmed, researchers say scientists can capitalize on the benefits of it, such as conducting repeated measurements on small objects such as blueberry leaves with ease.
