Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios

Abstract

The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which represents a difficult task. In this study, four different YOLO (You Only Look Once) object detectors version, along with all their various scales, were trained on a public ‘Weeds’ dataset with 4203 digital images of weeds growing in lawns with a total of 11,385 annotations and tested for weed detection in turfgrasses. Different weed species were considered as one class (‘Weeds’). Trained models were tested on the test subset of the ‘Weeds’ dataset and three additional test datasets. Precision (P), recall (R), and mean average precision (mAP_0.5 and mAP_0.5:0.95) were used to evaluate the different model scales. YOLOv8l obtained the overall highest performance in the ‘Weeds’ test subset resulting in a P (0.9476), mAP_0.5 (0.9795), and mAP_0.5:0.95 (0.8123), while best R was obtained from YOLOv5m (0.9663). Despite YOLOv8l high performances, the outcomes obtained on the additional test datasets have underscored the necessity for further enhancements to address the challenges impeding accurate weed detection.

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks

“…In automated weed control systems, actuators are constrained by the limited time available to process images and execute treatments. 55 Integrating these high-speed systems into robotic platforms facilitates real-time detection and action, even while the machinery is in motion. In this study, GoogLeNet and ResNet achieved notable speeds of 33.33 and 29.95 fps, respectively.…”

Implementasi Algoritma EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite untuk Sistem Deteksi Gulma

“…Pada penelitian membandingkan beberapa metode YOLO seperti YOLOv5m, YOLOv6l, YOLOv7, YOLOv8l, dengan EfficientDet. Hasil penelitian tersebut menunjukkan bahwa YOLOv8 memiliki performansi paling tinggi dengan nilai precision mencapai 0,9476 [17].…”

Research on Automatic Counting of Drill Pipes for Underground Gas Drainage in Coal Mines Based on YOLOv7-GFCA Model

“…After matrix multiplication of the attention branch and the depth convolution branch, they are added to the original residual branch x to obtain the final output F 3 in Equation (10).…”

“…In the context of coal mine drilling sites, these algorithms are unsuitable for real-time deployment of industrial equipment. On the other hand, first-stage algorithms like Single Shot MultiBox Detector(SSD) [9] and You Only Look Once(YOLO) series [10] exhibit fast detection speeds. In fact, in certain, specific domains, comparable detection accuracy can be achieved by these algorithms to that of two-stage algorithms.…”