Weed Detection in Agriculture using End-to-End Object Detection with Transformers in ResNet-50

Weed infestations significantly expose agricultural productivity to danger, leading to substantial yield losses in large-scale farming operations. Accurate identification and targeted treatment of various weed species are critical for effective weed control in contemporary farming practices. This research explores an innovative approach for weed detection utilizing the DETR (End-to-End Object Detection) model with the ResNet-50 backbone in a multi-label scenario, leveraging a custom-built dataset containing annotations for four distinct weed species present in 3956 images. This study diverges from prior methodologies and focuses on comprehensive weed detection using advanced deep learning architectures. Unlike previous approaches, the investigation specifically assesses the DETR-ResNet-50 model's efficacy in the precise identification and localization of multiple weed species within a single image. The research methodology encompasses annotations for multiple weed species in single images, covering diverse scenarios encountered in practical agricultural settings. Although primarily utilizing loss values for model evaluation, additional metrics such as mAP (mean average precision), IoU (intersection over union), precision, and recall were employed to assess the model's performance. The study concludes that the DETR-ResNet-50 model showcases promising potential for effective weed detection, signifying its viability for practical deployment in agricultural settings.