Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.

Secondary metabolite contents (total phenolic, flavonoid, tocopherol, and tocotrienol) and antioxidant activities of Hashemi rice bran extracts obtained by ultrasound-assisted and traditional solvent (ethanol and 50:50 (v/v) ethanol-water) extraction techniques were compared. Phenolic and, flavonoid compounds were identified using ultra-high performance liquid chromatography and method validation was performed. Significant differences (p < 0.05) were observed among the different extraction techniques upon comparison of phytochemical contents and antioxidant activities. The extracts obtained using the ethanol-water (50:50 v/v) ultrasonic technique showed the highest amounts of total phenolics (288.40 mg/100 g dry material (DM)), total flavonoids (156.20 mg/100 g DM), and total tocotrienols (56.23 mg/100 g DM), and the highest antioxidant activity (84.21% 1,1-diphenyl-2-picrylhydrazyl (DPPH), 65.27% β-carotene-linoleic bleaching and 82.20% nitric oxide scavenging activity). Secondary metabolite contents and antioxidant activities of the rice bran extracts varied depending of the extraction method used, and according to their effectiveness, these were organized in a decreasing order as follows: ethanol-water (50:50 v/v) ultrasonic, ethanol-water (50:50 v/v) maceration, ethanol ultrasonic and ethanol maceration methods. Ferulic, gallic and chlorogenic acids were the most abundant phenolic compounds in rice bran extracts. The phytochemical constituents of Hashemi rice bran and its antioxidant properties provides insights into its potential application to promote health.

Crop production is a constant battle with weeds, in which weeds, generally, are victorious. Therefore, rather than channeling our efforts into the development of a “silver bullet” to control weeds, the focus should be on sustainable weed management in both natural- and agro-ecosystems. However, sustainable weed management can be a challenge in the context of global climate change. Over the past few decades, global climate change, mostly indicated by phenomena such as increased atmospheric temperature and elevated CO₂ levels, is evident due to human activities and natural events. These phenomena also affect regional/local climate, resulting in significant influences on the agricultural systems of a particular region. Rising CO₂ levels may give comparative advantages to C₃ plants through increased photosynthesis, biomass production and yield, compared to C₄ plants. Plants with C₄ photosynthetic pathways, on the other hand, are likely to benefit more from rising global temperatures than C₃ plants. Thus, the differential responses of C₃ and C₄ plants to climate change may alter crop–weed interactions and competition outcomes, most likely at the expense of the crop. Climate change will likely cause shifts in weed community compositions, their population dynamics, life cycle, phenology, and infestation pressure. Some weed species may go extinct, while some others may become more aggressive invaders. Weeds are, generally, colonizers and have some unique biological traits and ecological amplitudes that enable them to successfully dominate crops in a habitat with changed environmental conditions. Moreover, climate shifts, especially erratic rainfall and drought, may affect herbicide selectivity and efficacy or the success of bio-control agents resulting in an establishment of a mixed and complex population of C₃ and C₄ weed species adding to the complexity of weed management. Although elevated CO₂ levels will stimulate the productivity of major C₃ crops, most troublesome agricultural weeds will likely be more responsive to a rise in CO₂ than crops, and thus may dominate the agro-ecosystem. It is predicted that, as temperature rises, the majority of the C₄ weeds will flourish and will pose serious crop yield losses. Understanding and assessment of the impact of simultaneous changes in multiple climate factors and their complex interactions on crops and weeds are therefore necessary to formulate an adaptive weed management approach and build resilience. Moreover, strategic policies and strong actions need to be taken to reduce the root causes of CO₂ and other greenhouse gas emissions to minimize the impact of climate change on weed biology and management.

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.

Parthenium hysterophorus is an invasive weed species that competes aggressively with other plants and is also allelopathic. It poses a significant risk to human health, livestock, the environment, soil, and agriculture. However, given some clinical studies, its potential for antidiabetic, antioxidant, antitumor, herbicidal, pesticidal, and antimalarial therapies should be researched further in attempts to discover more relevant applications. It can be used as a nutrient-dense, readily available, and cheap fertilizer. Parthenium can also be used as an herbicide, an insecticide, and a phyto-remedial mediator to extract metals and dyes from agricultural waste. Here we provide basic information on the morphology, reproduction, environmental impacts, and management of this species. Effects of methanol, ethanol, hexane, acetone, and aqueous (water) Parthenium extracts are described. Because P. hysterophorus is said to be one of the world’s seven worst weeds, some control measures, including mechanical, chemical, cultural, and biological control, are discussed. The allelopathy of this weed is difficult to regulate, and there are both positive and negative interactions between Parthenium and other species due to allelochemical action. Several toxic phenolic compounds produced by P. hysterophorus are responsible for weed suppression, and we discuss details of their mode of action and potential applications.