Weeds are significant contributors to the decline in crop yield and quality [1]. Weeds compete with crops in terms of nutrients, water, and sunlight. Weed losses are expected to reach 11 billion USD per year in India, ranging from 13.8% in transplanted rice to 76% in soybean; in which, weeds contribute the highest potential loss, accounting for 34% of all biotic stressors, followed by insects of 18% and diseases of 16% [2]. The high morphological, physiological, and anatomical plasticity of wild species such as weeds makes them more resistant to environmental stressors than crop species [3].

The interaction of weeds with other biological components is; it can damage nearby crops [4]. Due to this reason, weed containing herbicide residuals can cause the accumulation of off-flavour products [5], or in some cases, making them harmful to humans and animal health when they enter the food chain [6]. If consumed, the detrimental health ingredients could cause hepatic failure in humans and farm animals [7]. Herbicides move away in various ways, from the target plants, triggering pollution in the environment. The sorption process binds herbicides to soil particles, resulting in severe soil pollution [8]. Then, herbicides seeping to deeper layers of the soil surface or carried directly to field drains could enhance losses of herbicides in target crops and contaminate the surface and groundwater. This potentially leads to soil and water pollution, putting the above and belowground wildlife biodiversity at risk, including flora, fauna, and microorganism [9]. On the other hand, herbicides applied in farming activities spray drift in the air, and the volatilised, dispersed, and transported of its residues over a long distance facilitates the process of environmental recycling between the atmospheric and terrestrial environments. However, this process creates air pollution in the local environment and adversely impacts the global environment [10]. Thus, alternative weed mitigation strategies must be designed and promoted to mitigate and eliminate the ecological, environmental, and potential social problems with the intensive use of herbicides.

Spraying herbicides is the most common approach to weeding worldwide [11]. Weeding is typically conducted by uniformly spraying herbicides over the field, irrespective of weed density, which results in over-spraying in weed-free areas. This approach of weeding generates herbicide waste and pollutes the agricultural ecological environment. The site-specific weed management (SSWM) approach was suggested to tackle these problems [12]. SSWM is a strategy that consists of varying management of weed within a crop field to suit the variation in density, location, and composition of the weed population [13]. Weed populations are often dispersed irregularly inside crop fields. Therefore, the basis of this control strategy is to provide a guideline of weed spatial information to apply the herbicides with a minimum consumption by adapting it according to actual needs and utilised other techniques, including any use of plant derivatives that comprises of allelopathy effect, i.e., natural herbicides to minimize agrochemical pollution [14], thereby helping to lessen soil, water, and air pollution. By realising these benefits, detailed and resource-efficient approach of herbicide spraying with SSWM in smart farming decreased herbicide consumption by 40% to 60% [15], thus providing better environmental protection, sustainable agricultural production, and increasing economic profits.

The first step in implementing a SSWM strategy is weed detection and mapping (Figure 1). This task includes creating a weed map by integrating the sensor, processing procedures, and the actuation system. On-the-ground or remote sensing technologies can be used to capture weed images or non-imaging data. Previous research has shown that ground-based approaches (also known as proximal sensing) can capture high-resolution images, allowing for the early detection of substantially lower weed densities, and the discrimination of primary plant species [16,17]. Alternatively, traditional remote sensing platforms such as piloted airborne and satellite may investigate wider areas but have lower image spatial resolution [18].

The emerging priority of remote sensing in the precise management of weed is to facilitate extracting information relevant for data-driven decisions [19]. A remote sensing technique must meet three requirements: (i) supply cost-effective data, (ii) ability of capturing and providing information promptly, and (iii) have user-defined spectral characteristics to enable crop indication adjustment. Satellites, manned planes, and ground-based platform can be used with remote sensing sensors. Satellite image analysis offers some solutions that could cover the entire fields and solving problems of the applications of herbicides by sampling, but it has a lower resolution and depends on high weed infestation in the absence of clouds to obtain good results [20]. Furthermore, different types of satellites offer some advantages and disadvantages of its features (Table 1). Contrarily, manned aircraft can cover broad areas but are prohibitively expensive. Handheld sensors are very accurate; yet, when compared to aerial remote sensing, their coverage area is incredibly limited [21].

,27]. Current UAVs have higher image spatial resolutions, whereby technological breakthroughs in miniaturisation sensors are embedded. The most recent generation of multispectral (i.e., sensors offer from 3 to 7 bands), superspectral (i.e., sensors offer from 7 to 20 bands), and hyperspectral (i.e., sensors offer more than 20 bands) provides an opportunity to create very precise weed maps [28]. Advances in two-dimensional and three-dimensional sensor and camera images, as well as more powerful and efficient computers processing data streams in near real-time, could provide the tools required for real-time SSWM [29]. Nonetheless, those spectral cameras, 3D cameras, and LiDARs are costly. Generally, they are used on broad land- and time scales. Since it is smaller and lighter than other sensors, an RGB camera is a more cost-effective sensor.

2. Current Trend of UAV Applications for Detection of Weed

Research on the application of UAV for weed detection and mapping mainly highlight four issues: (i) spectral differences of weeds detection, (ii) types of aerial images from several sensors and platforms on weed detection, (iii) effect of spatial and spectral resolutions on weed detection, and (iv) algorithms and classification techniques for weed mapping. UAVs have primarily been assessed in different crops such as maize, wheat, sugarcane, cultivar, chilli, onion, vineyard, pistachio, baby-leaf red lettuce, barley, and mixed agricultural field such as pea and strawberry (Table 62). Those are among the world’s most widely cultivated crops, and they are highly vulnerable to weed competition, particularly during the seedling stage of the growing cycle. Our systematic review found that the seedling stages of crop contribute the highest, i.e., 27.42% in weed detection. One study [53] proposed that crop images could be taken precisely in the early season, so that specifically color-dependent segmentation can be applied to segment weed patches to achieve the higher accuracy of an algorithm.

UAVs have shown the remarkable potential of low altitude applications in agriculture since they are more cost-effective and easier to use [26 This review has figured out few types of UAV that being used for weed detection which includes single-rotor, multi-rotor, and fixed-wing (Figure 52). Ahmad et al. [40] used single-rotor as a spraying unit in the target and off-target zones for outer field weed control application. On the other hand, there are two studies that used the multi-rotor on the cultivated rice in China, in which Huang [38] captured imagery on few patches of Cyperus iric while Huang [52] captured the Chinensis, Cyperus iric, Digitaria sanguinalis, Scop, and Barnyard grass. In another study, Khan et al. [52] used multi-rotor to obtain imagery for two different crops which is pea and strawberry and Eleusine indica on infested weeds in Pakistan. In terms of fixed-wing, Zisi et al. [62] used this type of UAV to capture the images of S. marianum and patches of other weeds such as Solanum elaeagnifolium, Avena sterilis L., Bromus sterilis L., Cav, Cardaria draba L., Conium maculatum L., and Rumex sp. L. at the field that previously cultivated with cereals in Greece. Also, for fixed-wing that detect other weed types, Barrero and Perdomo [63] has detected Gramineae at the rice field in Columbia, whereas Tamouridou et al. [64] has identified S. marianum, and other weed types that consist the mixing of Avena sterilis, Rumexsp. L., Bromus sterilis L., Conium maculatum L., Cardaria draba L., and Solanum elaeagnifolium Cav at the field that previously cultivated with cereals in Greece.