A Deep Learning-Based Sensor Modeling for Smart Irrigation System

Agronomy

Saddam Aziz¹  Rizwan Qureshi²  Muhammad Umar Farooq³  Maira Sami⁴  Saad Qasim Khan⁵  Rukhshanda Anjum⁶  Ferhat Sadak⁷  Muhammad Khurram⁸ 

[1] College of Mechatronics Engineering, Shenzhen University, Shenzhen 518061, China;College of Science and Engineering, Hammad Bin Khalifa University, Doha 34410, Qatar;Department of Business Studies, Namal Institute Mianwali, Mianwali 42200, Pakistan;Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Karachi 75600, Pakistan;Department of Computer and Information Systems, NED University of Engineering and Technology, Karachi 75270, Pakistan;Department of Mathematics and Statistics, University of Lahore, Lahore 54590, Pakistan;Department of Mechanical Engineering, Bartin University, Bartin 74100, Turkey;National Center of Artificial Intelligence, NED University of Engineering and Technology, Karachi 75270, Pakistan;

关键词: neural networks;    artificial intelligence;    sensor reliability;    agritech;    precision agriculture;    Recurrent Neural Networks;   

DOI  :  10.3390/agronomy12010212

来源: DOAJ

【 摘 要 】

The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.

【 授权许可】

Unknown