Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress

TL;DR

This paper presents a deep learning-based multi-task system using convolutional neural networks to classify and estimate the severity of biotic stress on coffee leaves, aiding sustainable agriculture and early intervention.

Contribution

It introduces a novel multi-task CNN approach combined with data augmentation for accurate biotic stress classification and severity estimation on coffee leaves.

Findings

High accuracy in stress classification

Effective severity estimation results

Robustness improved by data augmentation

Abstract

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.