IoT-Based Cotton Plant Pest Detection and Smart-Response System
Source abstract
Abstract
IoT technology and drones are indeed a step towards modernization. Everything from field monitoring to pest identification is being conducted through these technologies. In this paper, we consider the issue of smart pest detection and management of cotton plants which is an important crop for an agricultural country. We proposed an IoT framework to detect insects through motion detection sensors and then receive an automatic response using drones based targeted spray. In our proposed method, we also explored the use of drones to improve field surveillance and then proposed a predictive algorithm for a pest detection response system using a decision-making theory. To validate the working behavior of our framework, we have included the simulation results of the tested scenarios in the cup-carbon IoT simulator. The purpose of our work is to modernize pest management so that farmers can not only attain higher profits but can also increase the quantity and quality of their crops.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · IoT and Edge/Fog Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Karachi 75300, Pakistan
Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia
Department of Computer Science and IT, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan
Funding
Deanship of Scientific Research, Islamic University of Madinah, Madinah, Saudi Arabia
References (40)
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