Futuristic technologies are transforming the agricultural landscape, enabling farmers to optimize crop yields, reduce environmental impact, and improve the efficiency of their operations. From precision farming and machine learning to genetic engineering and blockchain, these technologies offer new opportunities for farmers to address long-standing challenges and drive sustainable growth. This chapter addresses the potential upsides and downsides of using cutting-edge technology in agriculture. Specifically, it focuses on the benefits of federated learning collaborated with machine learning, deep learning, IoT, etc. in precision farming, smart agriculture, disease diagnosis, UAVs, yield prediction, and sustainable agriculture. This analysis is the first of its kind in this field, covering all available papers in WoS, Scopus, and Research Gate up to 2023. The development of specialized solutions requires a cooperative strategy in order to fully reap the advantages of these advances.
Top1 Introduction
Federated learning, a machine learning technique, enables numerous individuals to collaborate on developing a common model without sharing any of their personal data.
This is a rapidly evolving field with immense potential for application in various domains health care, cloud computing, edge computing, security, data security, cyber security, IoT and agriculture etc.. This paper is focused on the benefits of federated learning in agriculture domain including precision farming, disease diagnosis, UAVs, and the Internet of Agriculture Things, there is still much to explore in terms of its full potential in these areas. This strategy has enormous promise in agriculture, where privacy concerns might restrict stakeholders' sharing of important information. Federated Learning enables farmers, academics, and other stakeholders to collaborate on the development of more precise crop production, waste reduction, and sustainable agriculture models. The ability to compile and analyze data from numerous sources without violating privacy is only one of its many advantages. Farmers may keep their data secure while gaining from the knowledge and experience of others by using federated learning. Also, researchers have access to a greater variety of data, which results in models that are more precise and reliable (Kang, Wang, Li, 2022; H. Liu et al., 2022; Khan, Fawad, & Salam, 2022; Aggarwal et al., 2022; SuriyaKrishnaan et al., 2022). Federated Learning has the ability to fundamentally alter how we think about agriculture by allowing us to develop more precise and long-lasting models without jeopardizing privacy. With the ability to build shared models without compromising privacy, Federated Learning opens up new possibilities for collaboration between farmers, researchers, and other stakeholders. By allowing parties to train models on their private data and share only the model parameters, Federated Learning enables the creation of more precise and long-lasting models (Schoenke et al., 2021; Ray, 2017).
With the ability to analyze data from different sources and identify trends and patterns that might not be visible from individual data sets, Making data-driven decisions on the best times to sow, water, and harvest crops may aid farmers. Increased output, less waste, and more environmentally friendly agricultural methods can result from this. The areas where federated learning can help in innovate the new model is shown in Figure 1.
Figure 1. Federated learning in agriculture