Skip to main content
ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » Stored Product Insect and Engineering Research » Research » Publications at this Location » Publication #406904
Research Project: Advancing Technologies for Grain Trait Measurement and Storage Preservation

Location: Stored Product Insect and Engineering Research

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

Author
item MENDOZA, QUERRIEL - Kansas State University
item Pordesimo, Lester
item NIELSEN, MITCHELL - Kansas State University

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 5/13/2023
Publication Date: 6/13/2023
Citation: Mendoza, Q.A., Pordesimo, L.O., Nielsen, M.L. 2023. Enhancing grain facility management with AI-based insect detection and identification system. Proceedings of SPIE. 12545. Article 1254501. https://doi.org/10.1117/12.2672253.
DOI: https://doi.org/10.1117/12.2672253

Interpretive Summary: x

Technical Abstract: This research paper presents an AI-based insect detection system that uses an affordable and power-saving self-contained 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.