\u201cHyperspectral information \u2013 beyond the visible range \u2013 can provide insights and deepen our understanding about many biological processes between leaf tissues and pathogens,\u201d said\u00a0Yu Jiang<\/a>, assistant professor and systems engineer in the School of Integrative Plant Science (SIPS), Horticulture Section, at Cornell AgriTech in CALS, who is also a senior scientist on the papers and project.<\/p>\n The project also solved a major problem limiting hyperspectral imaging at microscopic scales, Jiang said. That issue concerns spatial resolution, which makes it possible to detect and distinguish a very tiny spot on a leaf as a symptom of disease in relation to the surrounding pixels that represent healthy cells.<\/p>\n \u201cImagine trying to find a Coke can in a photograph of a large room,\u201d Jiang said. \u201cIf the image isn\u2019t detailed enough to separate the can from the background, the light reflected by the can gets mixed with light from everything around it. That makes its spectral signature \u2013 the distinctive pattern of light it reflects \u2013 harder to recognize.\u201d<\/p>\n HyperBird is a hyperspectral extension of Blackbird, a prototype technology developed by Jiang and\u00a0Lance Cadle-Davidson<\/a>, Ph.D. \u201803, a research plant pathologist at the USDA\u2019s Agricultural Research Service (ARS), an adjunct associate professor in SIPS, and one of the senior project scientists and authors.<\/p>\n In 2021, the team announced the release of Blackbird, designed to speed up the assessment of thousands of grape leaf samples for evidence of infection, which had been a bottleneck in Cadle-Davidson\u2019s lab\u2019s research to develop powdery mildew resistant grape varieties. Blackbird, a robotic high-throughput phenotyping camera that records in red, blue and green wavelengths, was a first step towards HyperBird\u2019s capabilities.<\/p>\n While Blackbird provided the spatial resolution of an optical microscope, HyperBird supplies roughly 200 times more spectral resolution per pixel. This level of hyperspectral detail allows HyperBird to collect data in three dimensions per pixel. Technicians still need to physically collect dime-sized leaf cutouts for HyperBird, the most time-consuming task in analyzing the samples and which the robotics students are trying to automate.<\/p>\n A panel of grape leaf samples is put under the microscope of the HyperBird spectroscopy machine.<\/p>\n A study published May 20 in the journal Plant Disease<\/a>, led by Saeed Hosseinzadeh, Ph.D. \u201923, a former postdoctoral researcher in Gold\u2019s lab, took a first step to evaluate the use of hyperspectral imaging for automated high-throughput phenotyping for plant pathology and breeding programs. To that effect, the technology collected nine terabytes of high-quality hyperspectral images in a day to show proof-of-principle for detecting disease before it becomes visible. It also detected fungicide residues, to discern which treatment was used to fight mildew and its effectiveness. Finally, it revealed grapevine lineages being bred for disease resistance by identifying subtle optical signatures and relating those cues to a breeding line\u2019s unique genetic makeup, which influences physical \u2013 and observable \u2013 traits such as the leaf\u2019s pigments, wax structure, water content and internal anatomy.\u00a0<\/p>\n HyperBird was then formally developed by Jinhong Yu, a doctoral student in Jiang\u2019s lab and first author of a paper describing the process,\u00a0published Sept. 8 in the journal Plant Phenomics<\/a>. It shows how HyperBird offers high-throughput hyperpectral imaging at the microscopic level. The robot is capable of identifying tiny diseased areas and comparing them with the average spectra across the rest of the grape leaf to determine the progression of downy and powder milder diseases. By drilling down to minute detail, HyperBird can detect disease much faster than previous methods.<\/p>\n \u201cNow, we can get the equivalent accuracy at day three that we used to get at day six or day nine,\u201d Gold said. \u201cWe\u2019ve just increased the throughput by two to three times.\u201d<\/p>\n![\"Cornell](\"https:\/\/media.univcomm.cornell.edu\/chronicle\/initiatives\/nysimpact\/nys-impact.png\")
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