Application of the random forest machine algorithm in forecasting diseased panicle rate of wheat scab in Jiangsu province

The identification of meteorological and biotic factors that have significant impacts on wheat scab and the development of models for predicting diseased panicle rates at different stages are of remarkable significance for improving the ability to predict scab seriousness and protecting ecological environment of farmlands. On the basis of observations of diseased panicle rate and winter wheat phenology as well as daily meteorological elements in 13 cities in Jiangsu Province of China during the period from 2002 to 2008, the dominant meteorological elements that affect diseased panicle rate are identified, and the contributions of individual elements to diseased panicle rate are assessed for different phonological stages in various regions. Models that are initialized at different times for predicting diseased panicle rates are developed using the random forest（RF）regression algorithm. The reliability of the models is verified against observations of diseased panicle rates. Meteorological and biotic factors during the heading and flowering stage have the largest contribution to final diseased panicle rates, followed by that in the jointing stage and overwintering period. The dominant factors that determine final diseased panicle rates are relative humidity, the total number of consecutive rainy days larger than 3 d, and sunshine during the heading and flowering stages. Sunshine duration, precipitation, relative humidity and rainy days during the jointing stage have significant influences on final diseased panicle rates. Temperature and snowfall during the overwintering period have large precursor impact on final diseased panicle rates. The identified relative importance of key variables in each growth period is consistent with the theory on the development, release, infection, and epidemic of scab. The accuracy of models predicting diseased panicle rates based on RF algorithm varies with the number of critical characteristic variables, regions, the value of parameter M_try, and the growth period. The earliest time when the models can be used to yield useable prediction of diseased panicle rates is the beginning of March. The longest valid forecast time of the models is about 3 months. With the time approaching the maturity period and increases in the number of important characteristic variables as inputs, the accuracy of the modes increases and the discrepancy between predicted and observed diseased panicle rates is significantly reduced. Models have better skills in predicting medium and serious categories of scab. This study indicates that the RF algorithm is able to provide reliable prediction of scab and thus has a great application potential.