Agustan Latif, Handaru Jati, Herman Dwi Surjono, Mani Yusuf
Effective weed detection plays a crucial role in sustainable agriculture, boosting crop productivity and supporting environmental conservation. This study compares three deep learning models—YOLOv5, YOLO-NAS, and mask region-based convolutional neural network (Mask R-CNN)-against traditional methods in terms of accuracy, processing speed, and adaptability in tropical agricultural conditions, with Merauke, Indonesia, as the case study. The results show that YOLO-NAS delivers the highest accuracy at 96% with a processing time of 25 ms per image, making it suitable for high-precision applications. YOLOv5 balances strong accuracy (94%) with faster processing at 12 ms per image, establishing it as the most effective for real-time scenarios. Mask R-CNN also achieves 94% accuracy and provides advanced segmentation capabilities, but its slower processing speed of 31 ms limits large-scale implementation. Traditional methods perform poorly in comparison, with only 85% accuracy and processing time above 50 ms per image. These findings highlight the transformative potential of artificial intelligence (AI)-based weed detection for precision agriculture, particularly in tropical regions like Merauke. Adoption of models such as YOLOv5 reduces manual labor dependence while advancing efficient, eco-friendly weed management. Future research should expand datasets and explore newer models like YOLOv8, YOLO-NAS, vision transformers (ViTs), and hybrid approaches. © This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/
Faculty of Engineering, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Department of Information Systems, Faculty of Engineering, Universitas Musamus, Merauke, Indonesia; Department of Electronics Engineering and Informatics Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Department of Agrotechnology, Universitas Musamus, Merauke, Indonesia
