Application of Machine Learning for Insect Monitoring in Grain Facilities

In this study, a basic insect detection system consisting of manual-focus camera, Jetson Nano—a low-cost, low-power single-board computer, and trained deep learning model was developed. The validated through live visual feed. Detecting, classifying, monitoring pests in grain storage or food facility real time is vital to making control decisions. camera captures the image passes it Nano for processing. runs deep-learning detect presence species insects. With three different lighting situations: white LED light, yellow no condition, results are displayed on monitor. Validating using F1 scores comparing accuracy based light sources, tested with variety stored able classify adult cigarette beetles warehouse acceptable accuracy. demonstrate that an effective affordable automated solution detection. Such can help reduce pest costs save producers energy while safeguarding quality products.