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    Development of a cloud-based automatic irrigation system: A case study on strawberry cultivation
    (Institute of Electrical and Electronics Engineers Inc., 2018) Avşar, Ercan; Buluş, Kurtuluş; Saridaş, Mehmet Ali; Kapur, Burçak
    High increment rate of human population brings about the necessity of efficient utilization of world resources. One way of achieving this is providing the plants with the optimum amount of water at the right time in agricultural applications. In this paper, a cloud-based drip irrigation system, which determines the amount of irrigation water and performs the irrigation process automatically, is presented. Basically, water level in a Class A pan is continuously measured via a water level sensor and duration of irrigation is calculated using total amount of level decrement in a given time interval. The irrigation process is initialized by powering solenoid valves through a microcontroller board. To measure the environmental quantities such as temperature, humidity and pressure, an extra sensor is included in the system. A GSM/GPRS module enables internet connection of the system and all the sensor data as well as system status data are recorded in a cloud server. Furthermore, an accompanying Android application is developed to monitor the instantaneous status of the system. The system is tested on a strawberry field in a greenhouse. Currently, it is active for about four months and first observations imply that the system is capable of successfully perform the irrigation task. © 2018 IEEE.
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    Öğe
    Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations
    (Murat Yakar, 2021) Turan, Veysel; Avşar, Ercan; Asadihendoustani, Davood; Aydın, Emine Avşar
    Flight safety and reliability improvement is an important research issue in aerial applications. Multi-rotor drones are vulnerable to motor failures leading to potentially unsafe operations or collisions. Therefore, researchers are working on autonomous landing systems to safely recover and land the faulty drone in on a desired landing area. In such a case, a suitable landing zone should be detected rapidly in for emergency landing. Majority of the works related with autonomous landing utilize a marker and GPS signals to detect landing site. In this work, we propose a landing system framework that involves only the processing of images taken from the onboard camera of the vehicle. First, the objects in the image are determined by filtering and edge detection algorithm, then the most suitable landing zone is searched. The area that is free from obstacles and closest to the center of the image is defined as the most immediate and suitable landing zone. The method has been tested on 25 images taken from different heights and its performance has been evaluated in terms runtime on a single board computer and detection precision and recall values. The average measured runtime is 2.4923 seconds and 100% of precision and recall values are achieved for the images taken from 1m and 2m. The smallest precision and recall values are 79.1% and 81.2%, respectively. © Author(s) 2021.