Saad Qasim Khan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Saad Qasim Khan has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Ferhat Sadak
Department of Mechanical Engineering, Bartin University, Bartin 74100, Turkey
1 shared publication - Maira Sami
Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Karachi 75600, Pakistan
1 shared publication - Muhammad Khurram
Department of Computer and Information System Engineering, NEDUET, Karachi 75270, Pakistan
1 shared publication - Muhammad Umar Farooq
Department of Business Studies Namal University Mianwali Pakistan
1 shared publication - Rizwan Qureshi
Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA
1 shared publication - Rukhshanda Anjum
Department of Mathematics and Statistics, University of Lahore, Lahore, Pakistan
1 shared publication
Latest Publications
- Open Access
A Deep Learning-Based Sensor Modeling for Smart Irrigation System
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.
Open AccessA Deep Learning-Based Sensor Modeling for Smart Irrigation System
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.