Leaf blast forecasting models are not accessible to the growers due to operational difficulties in data processing and input flow. Availability of short-range weather forecast, blast prediction has a promising future as most models are weather-data driven. AI-driven assessment of quick and accurate estimates of leaf wetness duration (LWD) during the vulnerable crop growth period is likely to ensure leaf blast monitoring at regional scale for tactical management decision. Using machine learning tools to identify geo-spatial disease pattern for specific weather and remote sensing data will help to develop a generic expert system software for many diseases. The project aims to develop an operational expert system software for seamless use. Development of expert software system ensures automation dispelling operational difficulties and has potential to attract industries demanding automation of agricultural systems.
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