A deep learning approach for automatic counting of bedbugs and grape moth

Abstract

The bedbug and the grape moth are the most significant pests affecting rice and vineyards, causing great damage. However, these pests are only two examples of the many insect pests that exist with great potential to cause significant crop damage. Insect traps are among the most appropriate solution for monitoring and counting, influencing the selection and dosage of the pesticide to be applied for pest control. However, the counting and monitoring operations are based on the frequent visit of technicians to the site and are supported by inefficient counting methods, which is a challenging and time-consuming task. This study proposes the automatic counting of bedbugs and grape moths in traps using deep learning algorithms. We use three different databases, Pest24, Bedbug and Grape moth. Pest24 is a public dataset with a great diversity of insects. The Bedbugs and the Grape moth datasets are private datasets provided by mySense, a precision agriculture platform developed and managed by researchers from the University of Trás-os-Montes e Alto Douro (UTAD). First, we trained the Pest24 dataset with YOLOv5, and we got an mAP of 69.3%. Then, using the weights obtained from the Pest24 dataset, we trained the Bedbug and Grape moth datasets. The best results for the bedbug dataset were obtained with the YOLOv5 with transfer learning with an AP of 96.5% and a counting error of 63.3%. The best result was obtained with YOLOv5 without transfer learning of Pest24 with an AP of 90.9% and a counting error of 6.7 for the Grape moth.