# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 1 Introduction  
Weed encroachment within turfgrass swards strictly depends on the turfgrass management regime and may lead to a loss of functional quality and aesthetic perception. To date, the best weed control on turfgrasses is achieved by broadcast application of synthetic herbicides [1]. Synthetic herbicides in the European Union have been subjected to strict bans due to herbicide exposure's health and environmental risks [2, 3]. According to the European Commission [4], approximately 100 different synthetic herbicides are allowed for turfgrass and landscape management. However, there are slight discrepancies between what is allowed in various European countries. Many endeavors are underway to replace synthetic herbicides and find appropriate products, tools, or management techniques that effectively control weeds in turfgrasses and urban environments. Currently, the most effective weed removal methods in turfgrasses or urban hard surfaces involve localized applications of nonselective biological products (i.e., acetic acid) [5] or thermal treatments [6], however, adequate efficacy has yet to be achieved. Robotic machines that can autonomously detect and remove weeds show great promise for more sustainable weed control in turfgrasses [7, 8, 9]. Weed detection is fulfilled using various methods such as image processing, machine learning, and computer vision techniques, and it's an area of active research and development. Indeed, various works have been published investigating the feasibility of using machine vision technology for weed detection in turfgrass and grassland systems using Bayes classifier, morphology operator [10], weed shapes and texture features [11, 12, 13], color [12, 13] andvarious filters and aggregation techniques [14]. Recently deep learning (DL) has emerged as an effective application in various scientific domains, including computer vision [15]. Deep convolutional neural networks (DCNN) have an extraordinary ability to extract features from digital images, thus classifying images and detecting objects [16]. These promising results promoted the production and publication of more than 16 open-access datasets for algorithm training in customized contexts [17]. DL algorithms for object detection can be classified into two categories: single-shot detectors (SSD) and two-stage detectors. Two-stage detectors (i.e., R-CNN, Mask R-CNN) first generate regions within the input image that may contain the objects; then regions are classified into objects by a neural network. [PERSON] et al. [18] compared different two-stage detectors based on DCNNs to detect annual bluegrass (_Poa annuua_ L.) and various broadleaf weeds (_Hydrocstyle_ spp., _Hedytis cornybosa_ L. [PERSON], _Richardia scabra_ L.) in dormant Dermudagrass and actively growing Dermudagrass. The authors obtained excellent performance with F1 scores > 0.95. Similarly, [PERSON] et al. [19] reported that DetectNet reliably detected _Oenothean laciniata_ Hill) in baiagrass (_Paspulum notatum_ Flugge) with an overall accuracy of >0.99 and a recall value of 1.00. [PERSON] et al. [20] assessed the feasibility of DCNNs two-stage object detectors to detect broadleaves weeds (_Taraxacum officinal_ [PERSON], _Glechoma laderacea_ L. and _Euphorobia maculata_ L.) in a cool-season turfgrass system of perennial ryegrass (_Lolium perenne_ L.). The authors compare four different DCNN architectures, and the best performances in weed detection were achieved by DetectNet (F1 scores > 0.98 and recall values > 0.99. In general, two-stages detectors result in higher accuracy than single-stage detectors. [PERSON] et al. [21] evaluate DenseNet, EfficientNetV2, ResNet, RegNet, and VGGNet to detect and discriminate multiple weed species growing in turfgrass. Results showed an F1 score of 0.950 for VGG-Net detecting _T. officinal_ and an F1 score of 0.983 when detecting and discriminating _Paspulum dilatation_ Poir. and _Coperus rotundus_ L. and _Trifolium repens_ L. in Dermudagrass turf.