Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years to increase weed detection speed and accuracy, resolving the contradiction between the goals of enhancing soil health and achieving sufficient weed control for profitable farming. In recent years, a variety of platforms, such as satellites, airplanes, unmanned aerial vehicles (UAVs), and close-range platforms, have become more commonly available for gathering hyperspectral images with varying spatial, temporal, and spectral resolutions. Plants must be divided into crops and weeds based on their species for successful weed detection. Therefore, hyperspectral image categorization also has become popular since the development of hyperspectral image technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for weed detection and species separation. Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed for weeds discrimination analysis.

Urban sprawl literature review: Definition and driving force

Urbanisation-environment interactions are increasingly non-linear, yet traditional Coupling Coordination Degree (CCD) frameworks often rely on static, linear assumptions that fail to capture complex feedback loops. This study proposes an integrated framework combining CRITIC-weighted CCD assessment with interpretable machine learning (Random Forest and XGBoost) to decode the co-evolution of urbanisation and ecological integrity. Taking Malaysia, a country characterised by rapid expansion and high spatial disparity-as a critical case study, we utilized multi-source remote sensing and longitudinal statistics across 16 states. Our XGBoost model (Test R² ≈ 0.87) outperformed traditional regressions, confirming significant non-linear and dynamic coupling effects. By applying SHAP (SHapley Additive exPlanations), we moved beyond mere prediction to identify the sustainability "tipping points" within the system. Key findings reveal: (1) a distinct spatial decoupling in northern and east-coast regions; (2) a "hump-shaped" threshold effect for built-up expansion where coordination peaks before declining; and (3) the critical role of forest/water assets as non-linear ecological buffers. These results demonstrate that sustainable transitions depend more on spatial structure and asset management than on income growth alone. This interpretable AI-CCD framework provides a scalable, evidence-based toolkit for low- and middle-income countries to navigate the trade-offs between development and ecological preservation.

Monitoring urban expansion in small and medium-sized cities is essential for assessing sustainable development. Nighttime light (NTL) data, widely available and consistently capturing human activity intensity, provide a useful proxy for delineating built-up areas. However, accurately mapping urban extents from NTL remains challenging due to radiance blooming effect and limited spatial resolution. This study introduces the vegetation–water–building–thermal nighttime urban index (VWBTNUI), a multi-dimensional fusion framework that integrates NTL with spectral and thermal information to reduce spillover effects and enhance physical consistency in urban extent mapping. Using three representative inland cities in Sichuan Province, western China, VWBTNUI was compared with raw NTL data and four widely used composite indices. Results demonstrate that VWBTNUI consistently outperforms existing approaches, achieving overall accuracy (OA) values above 0.88, F1 scores above 0.80 and Kappa coefficients exceeding 0.72. Furthermore, the urban area estimates derived by VWBTNUI maintained relative area errors below 10%. The extracted urban extents also exhibit strong agreement with benchmark products. Relying on globally accessible datasets and simple pixel-level operations, VWBTNUI offers a scalable and reproducible solution for urban monitoring in data-scarce regions. By offering reliable baseline information for regional planning, the approach supports evidence-based governance and contributes to advancing Sustainable Development Goal (SDG) 11 on inclusive, safe, resilient, and sustainable cities.

Broadleaf weed (BLW) infestation is a major challenge in rice cultivation, particularly during the early vegetative stages when competition for resources is most critical. This study aims to enhance early-stage detection and classification of three prevalent BLW species— Monochoria vaginalis (MV) , Limnocharis flava (LF), and Sphenoclea zeylanica (SZ)—in rice fields using unmanned aerial vehicle (UAV)-based hyperspectral imaging integrated with machine learning techniques. The research was conducted in a 1-hectare rice plot (Block L5A, Plot 121) near Pusat Benih Padi Felcra Sdn Bhd, Perak, Malaysia, a site characterized by high weed density. Hyperspectral data were acquired using a DJI Matrice 600 UAV equipped with a Resonon Pika L hyperspectral camera flown at 40 meters altitude. ENVI Classic 5.3 software was used to perform supervised classification based on selected regions of interest (ROIs) for training. Three classification algorithms—Support Vector Machine (SVM), Minimum Distance (MD), and Parallelepiped (PP)—were compared at 15, 25, and 30 days after sowing (DAS). Among them, SVM consistently achieved the highest classification accuracy, exceeding 99% for all weed species across all growth stages, with minimal omission and commission errors. Vegetation cover analysis showed an increasing trend in BLW expansion over time, while rice cover fluctuated and soil cover declined, indicating the competitive dominance of weeds. The findings underscore the effectiveness of UAV hyperspectral imaging combined with machine learning—especially SVM—as a scalable, accurate, and efficient approach for early weed detection. This methodology can support precision agriculture by enabling timely and targeted weed management strategies, ultimately improving rice yield and sustainability.