Abstract:One of the most important factors for successful agricultural production is the irrigation system in place. In this study, a precision irrigation system, which takes advantage of the various phases of plant growth, was developed and implemented using the sensor network technology integrated with IOS/Android. The amount of water in the soil was measured via sensors that were placed on certain points of the area to be irrigated. These sensors were placed near the root of the product. Data from sensors was transmitted via Wi-Fi in real-time to a mobile phone based on IOS/Android. In the light of obtained data, the seasonal precision irrigation system was created depending on the amount of water required by the plants at each stage of their growth stage. The required energy of the system was provided by solar energy. The system can be controlled by smart phones, which increases the usability of the system. When design performance was analyzed, it was observed that some important advantages such as obtaining high efficiency with water, time and energy saving and reducing the workforce were ensured. Five separate laterals were used for the irrigation system. There were valves on each lateral, which realized the opening and closing process depending on the water need. A total of 16 humidity sensors were used in the irrigation system and the data from these sensors was transferred to the IOS/Android server via the programmable controller (PLC). The basic electrical equipment in the irrigation system was monitored and controlled via mobile devices. Control parameters were obtained by comparing the real values and reference values by a closed-loop system and determine the new working status of the irrigation system.

Determining the risk priorities for the building stock in highly seismic-prone regions and making the final decisions about the buildings is one of the essential precautionary measures that needs to be taken before the earthquake. This study aims to develop an Artificial Neural Network (ANN)-based model to predict risk priorities for reinforced-concrete (RC) buildings that constitute a large part of the existing building stock. For this purpose, the network parameters in the network structure have been optimized by establishing a hybrid structure with the Genetic Algorithm (GA). As a result, the ANN model can make accurate predictions with maximum efficiency. The suggested ANN model is a feedforward back-propagation network model. It aims to predict the risk priorities for 329 RC buildings in the most successful way, for which the performance score was calculated using the Turkey Rapid Evaluation Method (2013). In this paper, a GA-ANN hybrid model was implemented in which the ANN, using the most successful gene revealed by the model, produced successful results in calculating the performance score. In addition, the required input parameters for obtaining more efficient results in solving such a problem and the parameters that need to be used in establishing such an ANN network structure have been optimized. With the help of such a model, the operation process will be eliminated. The created hybrid model was 98% successful in determining the risk priority in RC buildings.

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Plants’ need for water has become a topic of research for the agriculture industry. The fact that plant species are very diverse and each plant’s need for water varies makes it difficult to plan programs with conventional irrigation methods. Plants exhibit different stages from their seed time to harvest season. Each stage is defined within as days, and the amount of water needed by the plant throughout these stages varies. In this study, optimization of the irrigation schedule for each stage of a plant is provided. The amount of water needed by the plant was first figured out by using climatic data, and then, these values were recalculated in relation to the plant type. The amount of water needed at each stage was related to the plant type by using particle swarm optimization. The obtained results revealed the optimal irrigation schedule for each stage with the obtained data.