To boost production and efficiency, new technology needs to be implemented. In the United Arab Emirates (UAE) and other Gulf Cooperation Council (GCC) countries started a green revolution in the desert by using vertical farming and clay techniques to hold more water. Drones have been hovering above fields in the United Arab Emirates to monitor crops and collect data on water levels and soil quality, among other technologies being used to improve food production. Oman has employed aerial vehicles to boost the growth of its agricultural industry as well.
Recognizing this reality and the benefits of enhancing the CMS (crop management system), research team at NU are intending to develop an all-encompassing agricultural solution. A comprehensive solution to agricultural problems such as irrigation and pesticide spraying to protect crops from locusts and other insects is devised. Our research also will include the development of an automated date harvesting gadget that can climb the date tree and utilize image processing algorithms to identify the dates before collecting them using a cutting machine without any human intervention.
The Research Team at the National University of Science and Technology proposes developing an artificial intelligence-based (AI-based) UAV (unmanned aerial vehicle) system for agricultural applications to support the Irrigation Sector. The automated date harvesting gadget will ascend the date’s tree, identify the dates using AI-based image processing techniques, and harvest the dates using a cutting mechanism.
The main objectives are:
- To save time crop scouting (validate treatment/actions taken).
- To enable farmers to construct VRPs (variable-rate prescriptions) for crop protection and manage products in a way that improves ROI (return on investment) in real-time.
- To develop a dates plucking mechanism to eliminate/minimize human effort and risk for inexperienced climbers.
- To determine the most optimal and cost-effective date-tree harvesting mechanism. Moreover, to boost efficiency by automating the harvesting process.
- With the developed AI model using deep learning features, farmers can also estimate yield from afield, one of the project’s primary goals.
Research Team
Principal Investigator: Dr K.Suresh Manic
Co-Principal Investigator: Dr Ali Al Bimani
Co-Investigators:
Dr Hamood Darwish Al Hasni
Mr Ali Abdullah Al Mahruqi
Mr Imad Saud Al Naimi,
Other Researchers:
Mr Dhandapani Ragavesh
Mr Joy Varghese
External Collaborators:
Dr. Seyedali Mirjalini
Director and Professor AI research Centre
Torrens University, Australia
https://seyedalimirjalili.com/
Dr V.Rajinikanth
Professor, Department of Computer Science and Engineering
Division of Research and Innovation
Saveetha School of Engineering
SIMATS, Chennai, Tamilnadu, India