Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress
This work addresses the need for early and accurate detection of pests and pathogens in coffee plantations to support agricultural sustainability, though it appears incremental in applying existing deep learning methods to this specific domain.
The paper tackled the problem of identifying and estimating the severity of biotic stress on coffee leaves using a multi-task convolutional neural network system, achieving results that indicate it is a suitable tool for assisting experts and farmers.
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.