A Smart Trap for Counting Olive Moths Based on the Internet of Things and Deep Learning

We present a novel smart trap for counting olive moths using Internet of Things principles and deep learning algorithms. The smart trap takes a picture of the captured insects once per day and processes it using a deep convolutional neural network to detect “Prays oleae” insects and count their number. Then, the results are transferred via a wireless connection to a backend cloud server. The proposed smart trap is designed to reduce power consumption as much as possible. Two deep neural network models (i.e., YOLO V5 and YOLO V7) are employed to detect and count Prays oleae insects, using our newly created Prays oleae dataset, PraysDB. Our experimental results demonstrate the detection quality, energy efficiency, and computational performance of our smart trap.