Flowering Onset Detection: Traditional Learning vs. Deep Learning Performance in a Sparse Label Context (Papers Track)
Mauricio Soroco (University of British Columbia); Joel Hempel (University of British Columbia); Xinze Xiong (University of British Columbia); Mathias Lécuyer (University of British Columbia); Joséphine Gantois (University of British Columbia)
Detecting temporal shifts in plant flowering times is of increasing importance in a context of climate change, with applications in plant ecology, but also health, agriculture, and ecosystem management. However, scaling up plant-level monitoring is cost prohibitive, and flowering transitions are complex and difficult to model. We develop two sets of approaches to detect the onset of flowering at large-scale and high-resolution. Using fine grain temperature data with domain knowledge based features and traditional machine learning models provides the best performance. Using satellite data, with deep learning to deal with high dimensionality and transfer learning to overcome ground truth label sparsity, is a challenging but promising approach, as it reaches good performance with more systematically available data.