The hazard of fungal and bacterial crop syndrome can be predicted using risk models with exact ecological parameters such as temperature, relative humidity, solar radiation, wind speed, and leaf wetness duration. The ecological Parameter has recognized as key in the management of crop disease. Air temperature and moisture pressure the preponderance of fungal place diseases. In ecological factors mainly condensation also impacts pest populations, as well as contamination deposits. a lot of parameters are well unspoken, readily defined, and effortlessly measured. The trouble and vagueness connected with monitoring ecological aspects at the local leaf balance and the complication of up scaling to the crop stage stop obtainable disease risk models from life form used with consistency. One nonparametric arithmetical move toward in receipt of scant notice for the modeling of crop paddy syndrome forecast is that of artificial intelligence. In this project AIs estimate this key environmental variable at local crop scales, using local and regional weather station data and site-specific sensing data. The ultimate goal is to embed the AI into a highly-portable tool, designed to predict leaf wetness duration in conjunction with local weather stations, and as input to real-time decision support systems.
artificial intelligence, highly-portable tool, site-specific sensing data
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