Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability

Abstract

Many important ecological processes play out over large geographic ranges, and accurate large-scale monitoring of populations is a requirement for their effective management. Of particular interest are agricultural weeds, which cause widespread economic and ecological damage. However, the scale of weed population data collection is limited by an inevitable trade-off between quantity and quality. Remote sensing offers a promising route to the large-scale collection of population state data. However, a key challenge is to collect high enough resolution data and account for between-site variability in environmental (i.e., radiometric) conditions that may make prediction of population states in new data challenging. Here, we use a multi-site hyperspectral image dataset in conjunction with ensemble learning techniques in an attempt to predict densities of an arable weed (Alopecurus myosuroides, Huds) across an agricultural landscape. We demonstrate reasonable predictive performance (using the geometric mean score-GMS) when classifiers are used to predict new data from the same site (GMS = 0.74-low density, GMS = 0.74-medium density, GMS = 0.7-High density). However, even using flexible ensemble techniques to account for variability in spectral data, we show that out-of-field predictive performance is poor (GMS = 0.06-low density, GMS = 0.13-medium density, GMS = 0.08-High density). This study highlights the difficulties in identifying weeds in situ, even using high quality image data from remote sensing.

Keywords

• black-grass
• machine learning
• weeds
• hyperspectral imagery