Dr. Mitchell Neilsen Profile

Dr. Mitchell Neilsen

at Kansas State Univ

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Proceedings Article | 13 June 2023 Poster + Paper

Enhancing grain facility management with AI-based insect detection and identification system

Querriel Arvy Mendoza, Lester Pordesimo, Mitchell Neilsen

Proceedings Volume 12545, 125450I (2023) https://doi.org/10.1117/12.2672253

KEYWORDS: Data modeling, Cameras, Education and training, Machine learning, Machine vision, Imaging systems, Agriculture, Systems modeling, Object detection, Image analysis

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This research paper presents an AI-based insect detection system that uses an affordable and power-saving selfcontained computer - the Jetson Nano, a manual focus camera, and a trained Convolutional Neural Network (CNN). The system addresses the need for real-time monitoring and detection of insect pests in grain storage and food facilities, which is crucial for effective insect control and decision-making. The camera-based monitoring system employs CNN to detect and identify small-scale stored grain insect pests. The Jetson Nano processes insect images captured by the camera using the trained machine learning model. The system's effectiveness is evaluated by computing F1 scores, and the accuracy is analyzed under varying illumination settings, including white LED light, yellow LED light, and the absence of any light source. Taking adult warehouse beetles (Trogoderma variabile) and cigarette beetles (Lasioderma serricorne (F.)) as test cases, the system was found to accurately detect the presence and type of insects, making it an affordable and efficient solution for identifying and monitoring insect infestations in stored product facilities. This automated insect detection system can reduce pest control costs, save producers time and energy, and maintain product quality. The proposed system offers a practical solution for automated insect detection in grain storage and food facilities. The low-cost and low-power Jetson Nano makes the system affordable and accessible for system developers and ultimately for a wide range of producers. The system's ability to detect and identify insect pests in real time enables quick decision-making and effective pest control management. The results demonstrate that the proposed system is a promising approach for automated insect detection and monitoring in stored product facilities.

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