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.

本研究设计了一个基于卷积神经网络的实用系统，通过计算机视觉和深度学习方法对咖啡树叶上生物胁迫病原体进行早期和准确的识别和评估，实验结果表明该系统可能是一个适合专家和农民识别和量化咖啡种植区生物胁迫的工具。

咖啡叶生物逆境分类和严重程度评估的深度学习