Weed management is always a challenge in crop production, exacerbated by the issue of herbicide resistance. Excessive herbicide application not only leads to the development of herbicide resistance weeds but also causes environmental problems. In precision agriculture, innovative weed management methods, especially advanced remote sensing and computer vision technologies for targeted herbicide applications, i.e., site-specific weed management (SSWM), have recently drawn a lot of attention. Challenges exist in accurately and reliably detecting diverse weed species under varying field conditions. Significant efforts have been made to advance computer vision technologies for weed detection. This comprehensive review provides an in-depth examination of various methodologies used in developing weed detection systems. These methodologies encompass a spectrum ranging from traditional image processing techniques to state-of-the-art machine and deep learning models. The review further discusses the potential of these methods for real-time applications, highlighting recent innovations, and identifying future research hotspots in SSWM. These advancements hold great promise for further enhancing and innovating weed management practices in precision agriculture.

Roadside vegetation composition assessment is essential to maintain ecological stability, control invasive species, and ensure the adoption of environmental regulations in areas surrounding active roadside construction zones. Traditional monitoring methods involving visual inspections are time-consuming, labor-intensive, and not scalable. Remote sensing offers a valuable alternative to automating large-scale vegetation assessment tasks efficiently. The study compares the performance of proximal RGB imagery processed using deep learning (DL) techniques against the vegetation indices (VIs) extracted at higher altitudes, establishing a foundation to use the prior in performing vegetation analysis using unmanned aerial vehicles (UAVs) for broader scalability. A pixel-wise annotated dataset for eight roadside vegetation species was curated to evaluate the performance of multiple semantic segmentation models in this context. The best-performing MAnet DL achieved a mean intersection over union of 0.90, highlighting the model’s capability in composition assessment tasks. Additionally, in predicting the vegetation cover—the DL model achieved an R² of 0.996, an MAE of 1.225, an RMSE of 1.761, and an MAPE of 3.003% and outperformed the top VI method of SAVI, which achieved an R² of 0.491, an MAE of 20.830, an RMSE of 23.473, and an MAPE of 59.057%. The strong performance of DL models on proximal RGB imagery underscores the potential of UAV-mounted high-resolution RGB sensors for automated roadside vegetation monitoring and management tasks at construction sites.