SmartWeed: An Autonomous Rover System for Real‐Time Weed Detection and Classification in Agricultural Fields

Weeds are a significant challenge to crop quality and quantity and therefore there is a need to adopt effective weed control and management systems. Nowadays, object detection has found extensive applications in the agricultural field such as the detection of weeds through deep learning, machine learning, image processing and IoT. The idea in this paper is to present the proposal of an autonomous rover that can identify and classify weeds in real time using the YOLO object detection method. The dataset that will be utilised in the current research is a collection of 5997 images of weed instances, allowing even more accurate detection and classification of weeds. We also combined the Convolutional Block Attention Module (CBAM) with YOLO to enable the model to pay attention to the useful spatial and channel‐wise features, as an evaluation of the performance of various YOLO models is based on inference time and weed detection accuracy. Based on the experiment, YOLOv8 and its variant YOLOv8‐X demonstrated the best mean average precision (mAP) of 93.6% with that inference times of 3.4 and 2.2 ms per image, respectively. YOLOv9‐E (an extension of YOLOv9) using CBAM, on the other hand, had better mAP of 99.5% with inference times of 10.6 and 2.5 ms, respectively. These findings indicate that YOLOv8 and YOLOv9 hold a good prospective of automatic field‐level weed detection and emphasise the significance of high‐quality datasets, efficient model architectures and attention mechanisms to the efficient and correct autonomous weed management.