Regarding problems like reduced precipitation and an increase in population, water resource scarcity has become one of the most critical problems in modern-day societies, as a consequence, there is a shortage of available water resources for irrigation in arid and semi-arid countries. On the other hand, it is possible to utilize modern technologies to control irrigation and reduce water loss. One of these technologies is the Internet of Things (IoT). Despite the possibility of using the IoT in irrigation control systems, there are complexities in designing such systems. Considering this issue, it is possible to use agent-oriented software engineering (AOSE) methodologies to design complex cyber-physical systems such as IoT-based systems. In this research, a smart irrigation system is designed based on Prometheus AOSE methodology, to reduce water loss by maintaining soil moisture in a suitable interval. The designed system comprises sensors, a central agent, and irrigation nodes. These agents follow defined rules to maintain soil moisture at a desired level cooperatively. For system simulation, a hybrid agent-based and system dynamics model was designed. In this hybrid model, soil moisture dynamics were modeled based on the system dynamics approach. The proposed model, was implemented in AnyLogic computer simulation software. Utilizing the simulation model, irrigation rules were examined. The system's functionality in automatic irrigation mode was tested based on a 256-run, fractional factorial design, and the effects of important factors such as soil properties on total irrigated water and total operation time were analyzed. Based on the tests, the system consistently irrigated nearly optimal water amounts in all tests. Moreover, the results were also used to minimize the system's energy consumption by reducing the system's operational time.

Current state-of-the-art residential irrigation systems, such as WaterMyYard, rely on rainfall data from nearby weather stations to adjust irrigation amounts. However, the accuracy of rainfall data is compromised by the limited spatial resolution of rain gauges and the significant variability of hyperlocal rainfall, leading to substantial water waste. To improve irrigation efficiency, we developed a cost-effective irrigation system, dubbed ERIC, which employs machine learning models to estimate rainfall from commodity doorbell camera footage and optimizes irrigation schedules without human intervention. Specifically, we: a) designed novel visual and audio features with lightweight neural network models to infer rainfall from the camera at the edge, preserving user privacy; b) built a complete end-to-end irrigation system on Raspberry Pi 4, costing only \$75. We deployed the system across five locations (collecting over 750 hours of video) with varying backgrounds and light conditions. Comprehensive evaluation validates that ERIC achieves state-of-the-art rainfall estimation performance ($\sim$ 5mm/day), saving 9,112 gallons/month of water, translating to \$28.56/month in utility savings. Data and code are available at https://github.com/LENSS/ERIC-BuildSys2024.git

Urban environments face significant challenges due to climate change, including extreme heat, drought, and water scarcity, which impact public health, community well-being, and local economies. Effective management of these issues is crucial, particularly in areas like Sydney Olympic Park, which relies on one of Australia's largest irrigation systems. The Smart Irrigation Management for Parks and Cool Towns (SIMPaCT) project, initiated in 2021, leverages advanced technologies and machine learning models to optimize irrigation and induce physical cooling. This paper introduces two novel methods to enhance the efficiency of the SIMPaCT system's extensive sensor network and applied machine learning models. The first method employs clustering of sensor time series data using K-shape and K-means algorithms to estimate readings from missing sensors, ensuring continuous and reliable data. This approach can detect anomalies, correct data sources, and identify and remove redundant sensors to reduce maintenance costs. The second method involves sequential data collection from different sensor locations using robotic systems, significantly reducing the need for high numbers of stationary sensors. Together, these methods aim to maintain accurate soil moisture predictions while optimizing sensor deployment and reducing maintenance costs, thereby enhancing the efficiency and effectiveness of the smart irrigation system. Our evaluations demonstrate significant improvements in the efficiency and cost-effectiveness of soil moisture monitoring networks. The cluster-based replacement of missing sensors provides up to 5.4% decrease in average error. The sequential sensor data collection as a robotic emulation shows 17.2% and 2.1% decrease in average error for circular and linear paths respectively.

Residential outdoor water use in North America accounts for nearly 9 billion gallons daily, with approximately 50\% of this water wasted due to over-watering, particularly in lawns and gardens. This inefficiency highlights the need for smart, data-driven irrigation systems. Traditional approaches to reducing water wastage have focused on centralized data collection and processing, but such methods can raise privacy concerns and may not account for the diverse environmental conditions across different regions. In this paper, we propose a federated learning-based approach to optimize water usage in residential and agricultural settings. By integrating moisture sensors and actuators with a distributed network of edge devices, our system allows each user to locally train a model on their specific environmental data while sharing only model updates with a central server. This preserves user privacy and enables the creation of a global model that can adapt to varying conditions. Our implementation leverages low-cost hardware, including an Arduino Uno microcontroller and soil moisture sensors, to demonstrate how federated learning can be applied to reduce water wastage while maintaining efficient crop production. The proposed system not only addresses the need for water conservation but also provides a scalable, privacy-preserving solution adaptable to diverse environments.

Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers, but do not account for future soil moisture loss. Predicting soil moisture loss is challenging because it is influenced by numerous factors, including soil texture, weather conditions, and plant characteristics. This paper proposes a solution to improve irrigation efficiency, which is called DRLIC. DRLIC is a sophisticated irrigation system that uses deep reinforcement learning (DRL) to optimize its performance. The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss. We introduce an irrigation reward function that enables our control agent to learn from previous experiences. However, there may be instances where the output of our DRL control agent is unsafe, such as irrigating too much or too little water. To avoid damaging the health of the plants, we implement a safety mechanism that employs a soil moisture predictor to estimate the performance of each action. If the predicted outcome is deemed unsafe, we perform a relatively-conservative action instead. To demonstrate the real-world application of our approach, we developed an irrigation system that comprises sprinklers, sensing and control nodes, and a wireless network. We evaluate the performance of DRLIC by deploying it in a testbed consisting of six almond trees. During a 15-day in-field experiment, we compared the water consumption of DRLIC with a widely-used irrigation scheme. Our results indicate that DRLIC outperformed the traditional irrigation method by achieving a water savings of up to 9.52%.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper health care, education, living conditions, wages, and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

One of the challenges of aerial imagery, whether from an airplane or a satellite, is making sense of what you see. What is that image telling you? Ceres Imaging, a California startup with offices in Nebraska and Washington, is using artificial intelligence to answer that question. The company is entering its ninth crop season of providing high-resolution crop imagery for customers. However, John Bourne, vice president of marketing, Ceres Imaging, says the company wanted to work on ways to "productize" the good science it was developing, so three years ago it brought artificial intelligence technology to irrigation issue identification.

It is not hard to see that the need for clean water is growing by considering the decrease of the water sources day by day in the world. Potable fresh water is also used for irrigation, so it should be planned to decrease freshwater wastage. With the development of technology and the availability of cheaper and more effective solutions, the efficiency of irrigation increased and the water loss can be reduced. In particular, Internet of things (IoT) devices has begun to be used in all areas. We can easily and precisely collect temperature, humidity and mineral values from the irrigation field with the IoT devices and sensors. Most of the operations and decisions about irrigation are carried out by people. For people, it is hard to have all the real-time data such as temperature, moisture and mineral levels in the decision-making process and make decisions by considering them. People usually make decisions with their experience. In this study, a wide range of information from the irrigation field was obtained by using IoT devices and sensors. Data collected from IoT devices and sensors sent via communication channels and stored on MongoDB. With the help of Weka software, the data was normalized and the normalized data was used as a learning set. As a result of the examinations, a decision tree (J48) algorithm with the highest accuracy was chosen and an artificial intelligence model was created. Decisions are used to manage operations such as starting, maintaining and stopping the irrigation. The accuracy of the decisions was evaluated and the irrigation system was tested with the results. There are options to manage, view the system remotely and manually and also see the system s decisions with the created mobile application.

It is always fun to look at the widening expansion of sectors that are being helped by artificial intelligence (AI). Farming has regularly used technology to improve yields. In recent years, global warming has made it more important to manage water resources through improved irrigation. Now the agriculture industry is looking at adopting AI in many ways. One of those methods is to analyze crops to better manage yield.

The kings of Angkor Wat may have inadvertently caused the downfall of their own vast empire by seizing land from local farmers, a new study claims. Researchers studying the ancient Khmer civilisation, which thrived in modern-day Cambodia for 600 years, wanted to discover the reason for its 15th-century decline. The abandonment of Angkor has long puzzled historians, with many attributing it to the 1431 AD invasion by Thai forces, though this is hotly debated. Angkor was the capital city of this now-extinct culture, and the iconic Angkor Wat temple was built in the early 12th century by King Suryavarman II. But later, in the 1400s, kings sitting on the throne once occupied by the great Suryavarman II saw their empire crumble and eventually disappear.