  In modern agriculture, usually weeds control consists in spraying herbicides
all over the agricultural field. This practice involves significant waste and
cost of herbicide for farmers and environmental pollution. One way to reduce
the cost and environmental impact is to allocate the right doses of herbicide
at the right place and at the right time (Precision Agriculture). Nowadays,
Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for
weeds localization and management due to its ability to obtain the images of
the entire agricultural field with a very high spatial resolution and at low
cost. Despite the important advances in UAV acquisition systems, automatic
weeds detection remains a challenging problem because of its strong similarity
with the crops. Recently Deep Learning approach has shown impressive results in
different complex classification problem. However, this approach needs a
certain amount of training data but, creating large agricultural datasets with
pixel-level annotations by expert is an extremely time consuming task. In this
paper, we propose a novel fully automatic learning method using Convolutional
Neuronal Networks (CNNs) with unsupervised training dataset collection for
weeds detection from UAV images. The proposed method consists in three main
phases. First we automatically detect the crop lines and using them to identify
the interline weeds. In the second phase, interline weeds are used to
constitute the training dataset. Finally, we performed CNNs on this dataset to
build a model able to detect the crop and weeds in the images. The results
obtained are comparable to the traditional supervised training data labeling.
The accuracy gaps are 1.5% in the spinach field and 6% in the bean field.
