| Abstract |
Digital image processing for the early detection of plant pests as insects in vegetable crops is essential for plant's yield and quality. In recent years, deep learning has made strides in the digital image processing, opening up new possibilities for pest monitoring. In this paper, state-of-the-art deep learning models are presented to detect common insect pests in vegetable cultivation named whiteflies and black aphids. Due to the absence of data sources addressing the aforementioned insect pests, adhesive traps for catching the target insects were used for the creation of an annotated image dataset. In total 225 images were collected, and 5904 insect instances were labelled by expert agronomists. This dataset faces many challenges such as the tiny size of objects, occlusions and resemblance. Object detection models were used like YOLOv3, YOLOv5, Faster R-CNN, Mask R-CNN, and RetinaNet as baseline algorithms for benchmark experiments. For achieving accurate results, data augmentation was used. This study has addressed these challenges by applying deep learning models which are able to deal with tiny object detection ascribed to very small insect size. The experiment results exhibit a mean Average Precision (mAP) of 75%. Dataset is available for download at https://zenodo.org/record/7139220 |
