We propose a novel energy-aware federated learning (FL)-based system, namely SusFL, for sustainable smart farming to address the challenge of inconsistent health monitoring due to fluctuating energy levels of solar sensors. This system equips animals, such as cattle, with solar sensors with computational capabilities, including Raspberry Pis, to train a local deep-learning model on health data. These sensors periodically update Long Range (LoRa) gateways, forming a wireless sensor network (WSN) to detect diseases like mastitis. Our proposed SusFL system incorporates mechanism design, a game theory concept, for intelligent client selection to optimize monitoring quality while minimizing energy use. This strategy ensures the system's sustainability and resilience against adversarial attacks, including data poisoning and privacy threats, that could disrupt FL operations. Through extensive comparative analysis using real-time datasets, we demonstrate that our FL-based monitoring system significantly outperforms existing methods in prediction accuracy, operational efficiency, system reliability (i.e., mean time between failures or MTBF), and social welfare maximization by the mechanism designer. Our findings validate the superiority of our system for effective and sustainable animal health monitoring in smart farms. The experimental results show that SusFL significantly improves system performance, including a $10\%$ reduction in energy consumption, a $15\%$ increase in social welfare, and a $34\%$ rise in Mean Time Between Failures (MTBF), alongside a marginal increase in the global model's prediction accuracy.

Federated learning has shown enormous promise as a way of training ML models in distributed environments while reducing communication costs and protecting data privacy. However, the rise of complex cyber-physical systems, such as the Internet-of-Things, presents new challenges that are not met with traditional FL methods. Hierarchical Federated Learning extends the traditional FL process to enable more efficient model aggregation based on application needs or characteristics of the deployment environment (e.g., resource capabilities and/or network connectivity). It illustrates the benefits of balancing processing across the cloud-edge continuum. Hierarchical Federated Learning is likely to be a key enabler for a wide range of applications, such as smart farming and smart energy management, as it can improve performance and reduce costs, whilst also enabling FL workflows to be deployed in environments that are not well-suited to traditional FL. Model aggregation algorithms, software frameworks, and infrastructures will need to be designed and implemented to make such solutions accessible to researchers and engineers across a growing set of domains. H-FL also introduces a number of new challenges. For instance, there are implicit infrastructural challenges. There is also a trade-off between having generalised models and personalised models. If there exist geographical patterns for data (e.g., soil conditions in a smart farm likely are related to the geography of the region itself), then it is crucial that models used locally can consider their own locality in addition to a globally-learned model. H-FL will be crucial to future FL solutions as it can aggregate and distribute models at multiple levels to optimally serve the trade-off between locality dependence and global anomaly robustness.

First of all, when growing agricultural products, which is the first step of food production, in the future, consumers are expected to grow and use plants directly for cooking without using pesticides at home. There are already many companies that have introduced growers of plants that make this possible. Samsung Electronics and LG Electronics are examples. Vegetables are automatically grown by placing the seeds in the inner shelf of the planter, which is similar in size to a household refrigerator. Temperature, humidity and nutrients are automatically controlled by AI (artificial intelligence). Heliponics, a start-up from Purdue University in the United States, has also introduced the'Gropot' indoor plant grower. Artificial intelligence automatically adjusts the temperature and humidity of agricultural products... The entire process of distribution and transportation is tracked seamlessly using blockchain technology.

Researchers working on smart irrigation systems have developed a way to choose the most accurate weather forecast, out of those offered in the week leading up to a given day. Dr Eric Wang, an Internet of Things researcher at James Cook University (JCU) in Cairns, works on technology that allows farmers to make data-driven decisions. "Every farmer would love to have a perfect weather forecast, but accurate forecasts are even more important to those who are embracing technology, and in particular the Internet of Things (IoT)," Wang said. In farming, the Internet of Things involved smart devices that talked to each other, to make recommendations such as when, where and how much to irrigate, Wang said. "That decision requires a lot of information, such as the needs of the particular crop, the current stage of its development, soil moisture and of course the weather."

Australia's Charles Sturt University (CSU) has announced plans to create a "hands-free" smart farm where robots will do all the work -- no human laborers required. The challenge: The majority of the food we eat comes from farms, and as the population grows, so does the amount of food needed to feed it. However, there's a lot of work that needs to be done around a farm, and many farmers are having trouble finding people to do it. Labor shortages have been a chronic problem in farms throughout the developed world. The idea: Robots and AI could help close the labor gap, literally doing the jobs people used to do.

For the uninitiated, Hydroponics is a technique for growing a smart farm within a greenhouse. Here, a'smart farm' refers to a soilless, vertical setup that can house a thousand plants and more. A Hydroponics farm may just be our best bet for churning out chemical-free produce in less than half the space of actual farmland. Technology doesn't let innovation rest and always pushes for advancement. The same goes for Hydroponics, which has gone a step further to evolve into what's already being referred to as'Smart Hydroponics'.

Cities around the world are getting smarter. Already, street lights in places like San Diego are turning off, and conserving energy, when vehicles and pedestrians aren't around. Soon, connected garbage cans will tell waste haulers when they need to be emptied, optimizing collection routes. Smart buildings will notify maintenance staff of impending repair needs. And parking spots will find you, instead of the other way around.