Object-level benchmark for deep learning-based detection and classification of weed species

Abstract

Weeds can decrease yields and the quality of crops. Detection, localisation, and classification of weeds in crops are crucial for developing efficient weed control and management systems. Deep learning (DL) based object detection techniques have been applied in various applications. However, such techniques generally need appropriate datasets. Most available weed datasets only offer image-level annotation, i.e., each image is labelled with one weed species. However, in practice, one image can have multiple weed (and crop) species and/or multiple instances of one species. Consequently, the lack of instance-level annotations of the weed datasets puts a constraint on the applicability of powerful DL techniques. In the current research, we construct an instance-level labelled weed dataset. The images are sourced from a publicly available weed dataset, namely the Corn weed dataset. It has 5997 images of Corn plants and four types of weeds. We annotated the dataset using a bounding box around each instance and labelled them with the appropriate species of the crop or weed. Overall, the images contain about three bounding box annotations on average, while some images have over fifty bounding boxes. To establish the benchmark dataset, we evaluated the dataset using several DL models, including YOLOv7, YOLOv8 and Faster-RCNN, to locate and classify weeds in crops. The performance of the models was compared based on inference time and detection accuracy. YOLOv7 and its variant YOLOv7-tiny models both achieved the highest mean average precision (mAP) of 88.50% and 88.29% and took 2.7 and 1.43 ms, respectively, to classify crop and weed species in an image. YOLOv8m, a variant of YOLOv8, detected the plants in 2.2 ms with the mAP of 87.75%. Data augmentation to address the class imbalance in the dataset improves the mAP results to 89.93% for YOLOv7 and 89.39% for YOLOv8. The detection accuracy and inference time performed by YOLOv7 and YOLOv8 models in this research indicate that these techniques can be used to develop an automatic field-level weed detection system.