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 article proposes a solution to improve irrigation efficiency, which is called DRLIC (deep reinforcement learning for irrigation control). 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 in which the output of our DRL control agent is unsafe, such as irrigating too much or too little. 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 develop 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 test-bed consisting of six almond trees. During a 15-day in-field experiment, we compare the water consumption of DRLIC with a widely used irrigation scheme. Our results indicate that DRLIC outperforms the traditional irrigation method by achieving water savings of up to 9.52%.

The Northeast Passage (NEP) holds immense potential as a link for maritime transport activities between Europe and Asia, primarily due to the extended sailing season resulting from global warming. However, the economic viability of the Arctic shipping route remains disputed. This study aims to comprehensively evaluate the feasibility of container transportation along the NEP compared to that along the Suez Canal Route (SCR) by using current (2021-2023) and future (2025-2065) scenarios. The results reveal that larger vessels have lower CO2 emissions and costs than small vessels in the NEP, but the costs for larger vessels in the NEP are still higher than those in the SCR throughout both summer and winter seasons under the current scenario. The outcomes also show that a progressive carbon tax scheme will increase the unit shipping costs for all routes in the future scenario, with the NEP being most economically viable during summer. Furthermore, the extended navigable period (NP) bolsters the NEP's economic cost advantage during a seasonal period. Nevertheless, from a year-round operations standpoint, the NEP remains less competitive than the SCR before 2065. The conclusions drawn from this research serve as a significant resource for decision-makers when formulating operational plans.