Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction

TL;DR

This paper explores the application of federated learning to maize leaf disease prediction, evaluating its effectiveness in improving data privacy while maintaining classification accuracy with CNN models.

Contribution

It is the first to evaluate federated learning for maize leaf disease prediction, analyzing its performance, training time, and privacy benefits in a distributed setting.

Findings

Federated learning can improve data privacy in crop disease diagnosis.

Distributed CNN training shows comparable accuracy to centralized models.

Network traffic and model complexity influence federated learning efficiency.

Abstract

The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine learning techniques fall short in data privacy, as they require sharing the data in the training process with a central server, disregarding competitive or regulatory concerns. Thus, Federated Learning (FL) aims to support distributed training to address recognized gaps in centralized training. As far as we know, this paper inaugurates the use and evaluation of FL applied in maize leaf diseases. We evaluated the performance of five CNNs trained under the distributed paradigm and measured their training time compared to the classification performance. In addition, we consider the suitability of distributed training considering the volume of network traffic and the number of parameters of each CNN. Our results indicate that FL potentially enhances data privacy in heterogeneous domains.