Presenter : Penchala Vineeth Kurapati

Type: Oral

This study depicts developing an algorithm for optimizing harvest schedule of sugarcane crop in different scenarios i.e. optimizing harvest schedule for maximum yield; grouping farms based on distance and optimizing harvest schedule; grouping farms based on distance, planting date and optimizing harvest schedule by using genetic algorithm, for optimizing harvest schedule DSSAT-CANEGRO model is chosen as a medium for which it requires inputs such as management, soil, weather data here weather data i.e. temperature has been selected for estimating using remote sensing techniques because, it varies spatially if we collect from meteorological department it provides data for the station not for the field, so with the help of remote sensing techniques the maximum and minimum temperatures for different seasons were estimated by creating a regression model and the data obtained by remote sensing techniques is used as an input. This algorithms are essential and beneficial to both the farmers and sugar industry for managing the farm for different cases.

Keywords—harvest schedule; optimizing; genetic algorithm; temperature; remote sensing