Fastlink to record 333381
Showing items 1 to 1 of 1.
| Full TGIF Record # 333381 Item 1 of 1 |
|
|---|---|
| Web URL(s): | https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/152314 Last checked: 11/30/2023 |
| Publication Type: | Report |
| Content Type: | Abstract or Summary only |
| Author(s): | Kitchin, Elisabeth Clover Artemis; McCall, David S.; Henderson, Caleb A. |
| Author Affiliation: | Kitchin: Presenting Author and School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA; McCall and Henderson: School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA |
| Title: | From pixels to pathogens: Developing a machine learning dollar spot detection model |
| Section: | Turf pest management oral I (includes student competition) Other records with the "Turf pest management oral I (includes student competition)" Section C05 turfgrass science Other records with the "C05 turfgrass science" Section |
| Meeting Info.: | St. Louis, Missouri: October 29-November 1, 2023 |
| Source: | ASA, CSSA, SSSA International Annual Meeting. 2023, p. 152314. |
| Publishing Information: | [Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America] |
| # of Pages: | 1 |
| Abstract/Contents: | "Dollar spot (Clarireedia jacksonii) is one of the most economically significant diseases to amenity turfgrasses, with frequent fungicides often applied for acceptable suppression. The disease is one of the most widely studied diseases among academic and industry researchers. Accurate and efficient quantification of dollar spot incidence is vital for research but requires extensive time requirements for routine infection center counts. This project presents the development of a machine learning model for objective quantification of dollar spot incidence and estimated disease coverage from digital images. Through extensive training on a diverse and comprehensive dataset, our machine learning model has achieved an accuracy and precision rate of 93% and 76%, respectively, in identifying and quantifying dollar spot on creeping bentgrass. These metrics underscore the model's efficacy in recognizing and quantifying dollar spot manifestation across various conditions. Leveraging deep learning techniques, the model identifies and delineates the spatial extent of dollar spots in turfgrass images, thus eliminating the inherent subjectivity in manual assessments. This model aids researchers by reducing the reliance on labor-intensive, manual assessments susceptible to observer bias. The automation of this machine-learning model fosters the standardization of dollar spot quantification. It offers a tool for turf pathologists to make well-informed decisions regarding disease suppression in various chemical, cultural, and cultivar evaluations." |
| Language: | English |
| References: | 0 |
| See Also: | See also related item, From Pixels to Pathogens: Developing a Machine Learning Dollar Spot Detection Model, 2023, R=342660.R=342660 |
| Note: | "42-8" This item is an abstract only! |
| ASA/CSSA/SSSA Citation (Crop Science-like – may be incomplete): | Kitchin, E. C. A., D. S. McCall, and C. A. Henderson. 2023. From pixels to pathogens: Developing a machine learning dollar spot detection model. Agron. Abr. p. 152314. |
| Fastlink to access this record outside TGIF: | http://tic.msu.edu/tgif/flink/RECNO/333381 |
| Choices for finding the above item: | |
| Web URL(s) : | https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/152314 Last checked: 11/30/2023 |
| InterLibrary Loan: | Request through your local library's inter-library loan service (bring or send a copy of this TGIF record) |
| If there are problems with this record, send us feedback |