Design and development of smart farm automated organic irrigation system for water resource optimization - Dela Cruz, John R. (Animo Repository, 2020)
The design of an irrigation system is important in agriculture. Facing the challenges of climate change and the scarcity of freshwater, a pursuit of designing an efficient irrigation system is necessary to sustain smart farming. As such, automation is needed to address the water usage efficiency and limited power available for a farm not connected to the electric grid. In the advent of precision agriculture and artificial intelligence, these concepts shall now be considered in making the automation possible. Numerous studies have been done on the techniques and technologies to be used, and the decision support system. These include Web-based and IoT-based technologies. This study focused on the design and development of an automated organic irrigation system for a smart farm for water resource optimization. The system was divided into two subsystems - Water Supply Monitoring Subsystem (WSMS) and the Chamber Irrigation and Control Subsystem (CICS). These subsystems were connected to a centralized controller employing Fuzzy Inference System (FIS). Environmental parameters such as soil moisture, temperature, and humidity have been considered in the design of the irrigation system. The sensors and actuators used in the system were connected to the Wireless Sensor Network (WSN). The irrigation system was deployed in a closed chamber with two plant beds, where each plant bed employs both ordinary drip and micro-drip surface irrigation. Tomato plant was used in this experiment. Environmental parameters were measured and analyzed, and were inputted on the FIS to control the flow of water through the solenoid valve. The results of the experiment shows that the designed and developed irrigation system has measured the environmental parameters, and controlled the flow of the water by controlling the opening time of the actuator. The FIS was helpful in order to decide the opening time of the actuator. This controlled the amount of water needed to water the plants and prevented overwatering and flooding on the plant box. Further studies on precision agriculture, particularly the application of deep learning and other artificial intelligence techniques was recommended.