Early season weed mapping in rice crops using multi-spectral UAV data

ABSTRACTIn this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral ortho-mosaic, derived from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into ‘weed’/‘no weed’ classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an ‘alarmist’ approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are pro...