  Selective weed treatment is a critical step in autonomous crop management as
related to crop health and yield. However, a key challenge is reliable, and
accurate weed detection to minimize damage to surrounding plants. In this
paper, we present an approach for dense semantic weed classification with
multispectral images collected by a micro aerial vehicle (MAV). We use the
recently developed encoder-decoder cascaded Convolutional Neural Network (CNN),
Segnet, that infers dense semantic classes while allowing any number of input
image channels and class balancing with our sugar beet and weed datasets. To
obtain training datasets, we established an experimental field with varying
herbicide levels resulting in field plots containing only either crop or weed,
enabling us to use the Normalized Difference Vegetation Index (NDVI) as a
distinguishable feature for automatic ground truth generation. We train 6
models with different numbers of input channels and condition (fine-tune) it to
achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification
metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested
for MAV integration. Dataset used in this paper is released to support the
community and future work.
