Boosting Intelligent Farming: Federated Learning for Dispersed AI

Abstract

Additionally, the internet of things (IoT) sensors used in agriculture is accom- panied by a lot of data that can be used for optimization purposes (practice). This chapter explores how such concerns are addressed, and shows how smart farming can be strengthened through federated learning which is an exceptional form of decentralized machine learning. Farming-on-device, a distributed artificial intelligence (AI) promotes privacy for users and enhances it on-field. With this technique, the models are trained cooperatively using information from all nodes of the network without needing any transfer of raw data. The paper probes several cases where this approach has been applied to smart agriculture, such as improving crop yield prediction or detection of pests and diseases including in pre-cision farming. Through such instances, the chapter seeks to exhibit the impact that federated learning may have on smart agriculture and set up a cooperative future trend based on data.