Smart Pest Monitoring and Management System with Integrated Deep Learning and Unmanned Aerial Vehicle (UAV) Technologies

Infestation of pest is one of the leading agricultural problems which have led to a lot of losses in the yield and risks food security. The use of conventional forms of pest control is usually inefficient, consumes a lot of chemicals and requires response time. The paper describes a smart pest monitoring and management system, combining the deep learning, Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) technologies to detect pests in real-time and give decision support. The annotated mixed data consisting of primary pest images gathered in the Federal College of Agriculture, Ishiagu, and secondary data in the Kaggle repository was used to generate a model based on YOLOv10. The model had a precision of 0.84, a recall of 0.82 and a mean Average Precision (mAP@50) of 0.83 indicating that the model was very effective in detecting and classifying a variety of pest species at an acceptable level of accuracy. A recommendation algorithm based on rules was installed to offer specific pesticide recommendations depending on the identified type of pest and the IoT-based email notification module provided the real-time notifications to the farmers to take immediate action. To realise remote sensing and aerial pest surveillance, the UAV was simulated and designed in the Simulink environment to ensure the efficient coverage and the reliable data capture. The integrated system offers a smart and long-term solution to the pest management process by eliminating false alarms, reducing pesticide waste, and enhancing the reaction time. The limitation of the study lies on the condition that the system was only being implemented as a simulation and lacks real-world validation, hence, it is recommended that future studies should adopt the real-world implementation approach for the identification of pests.

Smart Agriculture; Pest Detection; YOLOv10; Internet of Things (IoT)

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