  Biotic stress consists of damage to plants through other living organisms.
Efficient control of biotic agents such as pests and pathogens (viruses, fungi,
bacteria, etc.) is closely related to the concept of agricultural
sustainability. Agricultural sustainability promotes the development of new
technologies that allow the reduction of environmental impacts, greater
accessibility to farmers and, consequently, increase on productivity. The use
of computer vision with deep learning methods allows the early and correct
identification of the stress-causing agent. So, corrective measures can be
applied as soon as possible to mitigate the problem. The objective of this work
is to design an effective and practical system capable of identifying and
estimating the stress severity caused by biotic agents on coffee leaves. The
proposed approach consists of a multi-task system based on convolutional neural
networks. In addition, we have explored the use of data augmentation techniques
to make the system more robust and accurate. The experimental results obtained
for classification as well as for severity estimation indicate that the
proposed system might be a suitable tool to assist both experts and farmers in
the identification and quantification of biotic stresses in coffee plantations.
