An IoT-Based System for Efficient Detection of Cotton Pest
Source abstract
Abstract
Considering the importance of cotton products, timely identification of pests (flying moths—being a significant threat to cotton crops) helps to protect cotton crops and improve their production and quality. This study proposes real-time detection of Cotton Flying Moths (CFMs) with the assistance of an Internet of Things (IoT)-based system in the agricultural field. The proposed prototype contains a group of sharp infrared sensors, a Zigbee-based communication module, an Arduino 2560 Mega board, a lithium polymer battery (to power the mote), a gateway device, and an unmanned aerial vehicle (UAV) to respond as a pesticide-sprayer against the detected pest. The proposed pest detection algorithm detects the flying insects’ presence by monitoring variations in the reflected light. Based on this, it sends a detection alert to the gateway device. The gateway device sends detection coordinates to the drone/UAV to respond by spraying pesticide in the detection region. A real testbed and simulation scenarios were implemented to evaluate the effectiveness of the proposed detection system. The results of the testbed implementation suggest the effectiveness of the sensor design and CFM detection. Initial results from the simulation study indicate the suitability of the proposed prototype deployment in the agricultural field. The proposed prototype would not only help minimize the use of pesticides but also maintain the quality and quantity of cotton products. The originality of this study is the custom-made and cost-effective IoT prototype for CFM detection in the agricultural field.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Mosquito-borne diseases and control · Insect and Arachnid Ecology and Behavior
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, Medina 42351, Saudi Arabia
Department of Computer Science and IT, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan
Department of Computer Science, University of Karachi, Karachi 75270, Pakistan
Funding
the Deanship of Scientific Research, Islamic University of Madinah, Madinah, Kingdom of Saudi Arabia
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