Monitoring Wheat Midge Populations using CNNs on White Sticky Cards of Pheromone Traps in Field Settings

One of the most common insects that attack wheat crops in North America is the orange wheat blossom midge (WMs) Sitodiplosis mosellana (Diptera: Cecidomyiidae). WMs larvae cause significant feeding damage to wheat kernels, decreasing yield/productivity. To determine when WM adults emerge and to help determine population size and threat level, manual counts of male WM attracted to pheromone-baited sticky traps can be used. This method is labour-intensive due to the often large numbers of WM males stuck to traps (1500-3000), which can take around one hour to count properly. If multiple traps per field are used, the time to count is magnified. A machine vision system that monitors the traps with high frequency (48 times a day) is more convenient because it can continuously collect and analyze large amounts of data quickly and accurately. This research utilizes a state-of-the-art object detection network, You Only Look Once version 8 (YOLOv8), to detect and count WMs in the images taken from white sticky cards under natural field settings. It achieves a mean average precision (mAP at 0.5 IoU) of 87.11% and mAP at 0.5-0.95 IoU of 43.55% in detecting WMs with 98.7% precision and 99.03% recall values. These results represent an improvement over the performance of the previously top-performing object detection model, YOLOv5, which achieved mAP at IoU 0.5 of 77.37%, mAP at IoU 0.5-0.95 of 41.07%, a precision of 86.07%, and a recall value of 88.46%.