# Evaluation of Autonomous Mowers Weed Control Effect in Globe Artichoke Field
## 1 Introduction  
The adoption of autonomous technologies is becoming increasingly popular in agriculture as they are able to improve the quality and quantity of agricultural products [1, 2] as well as reduce productive costs [3]. These new technologies allow for the innovation of production processes and can also favor a more sustainable economic development [4]. For these reasons, since 2012, investments relating to advanced agricultural technologies have increased annually by 80% [5], and a significant amount has been used for automation [6]. Robots can be used to conduct autonomously repetitive and hazardous agricultural practices such as harvesting, spraying and weed management [7], some of the most important challenges in the agricultural industry [8]. Vegetables are high-value crops and total production value can reach USD 19,385 ha\({}^{-1}\); therefore, effective weed control is crucial to achieve an adequate yield [9]. Indeed, weeds represent a major issue for vegetable production causing yield losses worldwide. Several previous studies have reported yield loss caused by weeds in vegetables. For example, [PERSON] et al. found that weed competition for the entire growing season reduced the yield of lettuce by 56% [10], whereas in broccoli, the presence of ryegrass (_Lolium_ spp.) at 600 plants m\({}^{-2}\) within the row has resulted in the loss of the entire production [11]. In addition to yield losses, weeds can also reduce products quality and their commercial value [12]. To date, the availability of herbicides for vegetable producers is limited and the production of new herbicides for crops such as vegetables is not a priority for the industry [13; 14]. To compensate the lack of effective herbicides, vegetables producers rely on manual weeding. However, problems related to labor shortage and high labor cost have led to an increasing interest for the development of autonomous robotic weeders [15]. Within the last decade, new technologies for weed control have been developed, some of which have been commercialized. Robovator (F. Poulsen Engineering ApS, Hvalso, Denmark), for example, is a commercially available tractor-mounted intrarowed water that relies on hoeing tools controlled by cameras to control weeds [16]. [PERSON] et al. [17] tested this machine in broccoli and lettuce and found that Robovator was able to remove 18 to 41% more weeds than the standard cultivator. Nevertheless, Robovator, like other commercially available robotic weeders, such as IC-Cultivator (Machinefabriek Stekete, Netherlands) and Ferrari Removed (Costruzioni machine Ferrari, Italy) are not able to differentiate crop and weeds, but the technology is based on crop recognition by row pattern [13]. Consequently, if weeds reach the same size of the crop, or the weed density is high enough to hide the row pattern, those machines did not show acceptable performances [18]. Nowadays, the development of a fully automated robotic weeder is mainly hindered by the lack of an automated and reliable technique for distinguishing crops and weeds [19]. Over the years, researchers have developed and evaluated various techniques to differentiate crop and weeds. Machine vision is one of the most used techniques for this purpose [20]. Many researches have been conducted on the 2-D approach, but to date its application is limited to field with low or moderate weed densities [21]. The 3-D approach, being able to use much richer features that can be extracted from 3D data, presents the potential to overcome many limitations in 2D imaging. However, its slow operation speed respect to a 2-D approach, and its computational complexity limits its wider adoption [19]. Weed and crop differentiation should be fast enough to operate at normal field speeds above 0.5 m\(\cdot\)s\({}^{-1}\) in order to be useful in agriculture [18]. Machine learning techniques have also been used to distinguish crops and weeds [22].