Prediction of Sorghum Biomass Using Time Series Uav-Based Hyperspectral and Lidar Data

Recent advances in remote sensing technology and algorithms for data analysis potentially provide significant utility for agricultural applications. This paper focuses on development of a data analytics based predictive modeling strategy that incorporates multi-temporal data acquired by multi-sensor data acquisition systems, and accommodates environmental inputs. Predictive models based on Recurrent Neural Networks (RNNs) are developed to incorporate high dimensional, multi-modal, multi-temporal input data. Remote Sensing (RS) features derived from Light Detection and Ranging (LiDAR) and hyperspectral inputs, as well as weather-related data are incorporated in RNN models to predict yield. Results from multiple experiments focused on high throughput phenotyping of sorghum for biomass predictions are provided and evaluated for agricultural test fields at Purdue University.