Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction
This work addresses data privacy concerns for agricultural stakeholders using crop disease prediction, but it is incremental as it applies an existing FL method to a new domain.
The paper tackled the problem of data privacy in machine learning for maize leaf disease prediction by evaluating Federated Learning (FL) as a distributed training alternative, finding that FL enhances data privacy in heterogeneous domains.
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.