Smart System for Automated Irrigation Using Internet of Things Devices

摘要

Precision agriculture involves applying artificial intelligence, computers, sensors, and automation to improve crop field productivity while monitoring environmental conditions to conserve soil, water, and other natural resources focusing on agricultural sustainability. Despite many applications in agriculture, data monitoring and recording technologies have limited use due to the price. Low-cost open-source systems, like the ones available with the Internet of things (IoT) world, can potentially be developed as a universal-fit and cloud-connected technology for multiple applications. We designed and built a basic data collecting system using a commercial standalone embedded computer with Python programming language, serial data interface (SDI)-12/analog sensor adaptor, and digital sensors to monitor soil moisture and transmit the data remotely. SDI-12 is a standard communication protocol that transfers digital sensor measurements to a data recorder. We set up a pilot study that automatically collected and uploaded the data into the Internet to allow remote data transfer and access. The system performed reliably over 1 week with potting soil under field conditions without maintenance and successfully recorded data in real-time. The volumetric water content ranged from 0.03 to 0.23 m(3).m(-3), dielectric permittivity from 3.3 to 18.9 (unitless), EC from 0.0 to 0.3 dS.m(-1), and soil temperature from 20.7 to 44.8 degrees C. All the data were successfully collected and uploaded to the cloud every 20 min, allowing users to remotely monitor the data using a free online application. However, heavy rainfall and high insolation could damage the system through excessive moisture or overheating, requiring a waterproof and heavy-duty protection case. The ThingSpeak channel allows customizing to suit a user's specific requirements or adding more features for further development, such as automated irrigation, which can improve irrigation and fertilization efficiency by applying water and fertilizers at the right time based on sensor readings.