Prediction of Short-Distance Aerial Movement of Phakopsora pachyrhizi Urediniospores Using Machine Learning
| Autor: | Liwei Wen, Glen L. Hartman, C. R. Bowen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
010504 meteorology & atmospheric sciences Correlation coefficient Wind Plant Science Poisson distribution 01 natural sciences Wind speed Machine Learning symbols.namesake Statistics Plant Diseases 0105 earth and related environmental sciences Urediniospore Phakopsora pachyrhizi biology Ecology Temperature Humidity Models Theoretical Spores Fungal Wind direction biology.organism_classification Random forest symbols Soybeans Soybean rust Agronomy and Crop Science 010606 plant biology & botany |
| Zdroj: | Phytopathology®. 107:1187-1198 |
| ISSN: | 1943-7684 0031-949X |
| Popis: | Dispersal of urediniospores by wind is the primary means of spread for Phakopsora pachyrhizi, the cause of soybean rust. Our research focused on the short-distance movement of urediniospores from within the soybean canopy and up to 61 m from field-grown rust-infected soybean plants. Environmental variables were used to develop and compare models including the least absolute shrinkage and selection operator regression, zero-inflated Poisson/regular Poisson regression, random forest, and neural network to describe deposition of urediniospores collected in passive and active traps. All four models identified distance of trap from source, humidity, temperature, wind direction, and wind speed as the five most important variables influencing short-distance movement of urediniospores. The random forest model provided the best predictions, explaining 76.1 and 86.8% of the total variation in the passive- and active-trap datasets, respectively. The prediction accuracy based on the correlation coefficient (r) between predicted values and the true values were 0.83 (P < 0.0001) and 0.94 (P < 0.0001) for the passive and active trap datasets, respectively. Overall, multiple machine learning techniques identified the most important variables to make the most accurate predictions of movement of P. pachyrhizi urediniospores short-distance. |
| Databáze: | OpenAIRE |
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