Скачать с ютуб Predicting field-level cane and sugar yields through satellite imagery and environmental data в хорошем качестве

Predicting field-level cane and sugar yields through satellite imagery and environmental data

An accurate model for predicting sugarcane yield will benefit many aspects of managing growth and harvest of sugarcane crops. This talk uses Sentinel-1 and Sentinel-2 satellite imagery in combination with climate, soil and elevation data to predict field-level sugarcane yield across the multiple sugar mill areas in the Wet Tropics of Australia at different time steps over four consecutive growing seasons (2016–2019). A total of ≈1400 field-level measurements is used to train predictive machine learning models of cane yield (t/ha), commercial cane sugar (CCS, %), sugar yield (t/ha), crop varieties and ration numbers. About the speaker Dr Yuri Shendryk is a GIS and Remote Sensing Specialist. Previously Yuri was a Postdoctoral Fellow at CSIRO specializing in remote sensing and GIS applications. Yuri develops high-tech algorithms to process terabytes of satellite and airborne data – work that enables others to make decisions for a sustainable future. After earning a master’s degree in geophysics, Yuri spent more than three years working and studying geospatial engineering in Ukraine, Sweden and Germany. In 2017 Yuri received his PhD from the University of New South Wales (UNSW). Yuri's research is centred around the integration of remote sensing, GIS and spatial statistics to explore interactions between species, environment and land use – in particular, forest health monitoring and precision agriculture.