Federated Learning: Crop classification in a smart farm decentralised network

Abstract

In this paper, the application of federated learning to smart farming has been investigated. The Federated averaging model has been used to carry out crop classification using climatic parameters as independent variables and crop types as labels. The decentralised machine learning models have been used to predict chickpea crops. Through experimentation, it has been observed the model converges when learning rates of 0.001 and 0.01 are considered using the Stochastic gradient descent (SGD) and the Adam optimizers. The model using the Adam optimizer converged faster than the SGD optimizer, this was achieved after 100 epochs. Analysis from the farm dataset has shown that the decentralised models achieve faster convergence and higher accuracy than the centralised network models.

Keywords

• Federated Learning
• Classifier chain Gaussian (CCGNB)
• Binary Relevance Gaussian (BRGNB)
• Label powerset Gaussian Naïve Bayes (LPGNB)