  Crop and weed monitoring is an important challenge for agriculture and food
production nowadays. Thanks to recent advances in data acquisition and
computation technologies, agriculture is evolving to a more smart and precision
farming to meet with the high yield and high quality crop production.
Classification and recognition in Unmanned Aerial Vehicles (UAV) images are
important phases for crop monitoring. Advances in deep learning models relying
on Convolutional Neural Network (CNN) have achieved high performances in image
classification in the agricultural domain. Despite the success of this
architecture, CNN still faces many challenges such as high computation cost,
the need of large labelled datasets, ... Natural language processing's
transformer architecture can be an alternative approach to deal with CNN's
limitations. Making use of the self-attention paradigm, Vision Transformer
(ViT) models can achieve competitive or better results without applying any
convolution operations. In this paper, we adopt the self-attention mechanism
via the ViT models for plant classification of weeds and crops: red beet,
off-type beet (green leaves), parsley and spinach. Our experiments show that
with small set of labelled training data, ViT models perform better compared to
state-of-the-art CNN-based models EfficientNet and ResNet, with a top accuracy
of 99.8\% achieved by the ViT model.
