  The rapid advances in Deep Learning (DL) techniques have enabled rapid
detection, localisation, and recognition of objects from images or videos. DL
techniques are now being used in many applications related to agriculture and
farming. Automatic detection and classification of weeds can play an important
role in weed management and so contribute to higher yields. Weed detection in
crops from imagery is inherently a challenging problem because both weeds and
crops have similar colours ('green-on-green'), and their shapes and texture can
be very similar at the growth phase. Also, a crop in one setting can be
considered a weed in another. In addition to their detection, the recognition
of specific weed species is essential so that targeted controlling mechanisms
(e.g. appropriate herbicides and correct doses) can be applied. In this paper,
we review existing deep learning-based weed detection and classification
techniques. We cover the detailed literature on four main procedures, i.e.,
data acquisition, dataset preparation, DL techniques employed for detection,
location and classification of weeds in crops, and evaluation metrics
approaches. We found that most studies applied supervised learning techniques,
they achieved high classification accuracy by fine-tuning pre-trained models on
any plant dataset, and past experiments have already achieved high accuracy
when a large amount of labelled data is available.
