Intra-row weed control in organic or low-input cropping systems is more difficult than in conventional agriculture. The various mechanical and thermal devices available for intra-row weed control are reported in this review. Low-tech mechanical devices such as cultivators, finger-weeders, brush weeders, and torsionweeders tend to be used in low density crops, while spring-tine harrows are mainly applied in narrow-row high-density crops. Flame weeding can be used for both narrow and wide-row sown crops, provided that the crop is heat-tolerant. Robotic weeders are the most recent addition to agricultural engineering, and only a few are available on the market. Nowadays, robotic weeders are not yet used in small and medium sized farms. In Europe, highincome niche crops are often cultivated in small farms and farmers cannot invest in high-tech solutions. Irrespectively of the choice of low- or high-tech machines, there are several weeders that can be used to reduce the use of herbicides, making of them a judicious use, or decide to avoid them.

The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which represents a difficult task. In this study, four different YOLO (You Only Look Once) object detectors version, along with all their various scales, were trained on a public ‘Weeds’ dataset with 4203 digital images of weeds growing in lawns with a total of 11,385 annotations and tested for weed detection in turfgrasses. Different weed species were considered as one class (‘Weeds’). Trained models were tested on the test subset of the ‘Weeds’ dataset and three additional test datasets. Precision (P), recall (R), and mean average precision (mAP_0.5 and mAP_0.5:0.95) were used to evaluate the different model scales. YOLOv8l obtained the overall highest performance in the ‘Weeds’ test subset resulting in a P (0.9476), mAP_0.5 (0.9795), and mAP_0.5:0.95 (0.8123), while best R was obtained from YOLOv5m (0.9663). Despite YOLOv8l high performances, the outcomes obtained on the additional test datasets have underscored the necessity for further enhancements to address the challenges impeding accurate weed detection.

Autonomous mowers are battery-powered machines designed for lawn mowing that require very low human labour. Autonomous mowers can increase turf quality and reduce local noise and pollution compared with gasoline-powered rotary mowers. However, very little is known about the effects of autonomous mowing on encroaching weeds. The aim of this research was to compare the effects of an autonomous mower and an ordinary gasoline-powered mower on weed development in an artificially infested tall fescue (Festuca arundinacea Schreb.) turf with different nitrogen (N) rates. A three-way factor experimental design with three replications was adopted. Factor A consisted of three N rates (0, 75, and 150 kg ha−1), factor B consisted of two mowing systems (autonomous mower vs. walk-behind gasoline rotary mower equipped for mulching), and factor C which consisted of four different transplanted weed species: (a) Bellis perennis L., (b) Trifolium repens L.; (c) Trifolium subterraneum L.; and (d) Lotus corniculatus L. Of these, B. perennis is a rosette-type plant, while the other three species are creeping-type plants. The interaction between mowing system and transplanted weed species showed that the four transplanted weed species were larger when mowed by the autonomous mower than by the rotary mower. The autonomous mower yielded larger weeds probably because the constant mowing height caused the creeping weed species to grow sideways, since the turfgrass offered no competition for light. N fertilization increased turf quality and mowing quality, and also reduced spontaneous weed infestation. Autonomous mowing increased turf quality, mowing quality, but also the percentage of spontaneous weed cover.

Battery-powered autonomous mowers are designed to reduce the need of labor for lawn mowing compared with traditional endothermic engine mowers and at the same time to abate local emissions and noise. The aim of this research was to compare autonomous mower with traditional rotary mower on a tall fescue ( Festuca arundinacea ) lawn under different nitrogen (N) rates. A two-way factor experimental design with three replications was adopted. In the study, four N rates (0, 50, 100, and 150 kg·ha ⁻¹ ) and two mowing systems (autonomous mower vs. gasoline-powered walk-behind rotary mower equipped for mulching) were used. As expected, N fertilization increased turf quality. At the end of the trial, the autonomous mower increased turf density (3.2 shoots/cm ² ) compared with the rotary mower (2.1 shoots/cm ² ) and decreased average leaf width (2.1 mm) compared with the rotary mower (2.7 mm). Increased density and decreased leaf width with autonomous mowing yielded higher quality turf (7.3) compared with the rotary mower (6.4) and a lower weed incidence (6% and 9% cover for autonomous mower and rotary mower, respectively). Disease incidence and mowing quality were unaffected by the mowing system. The autonomous mower working time was set to 10 hours per day (≈7.8 hours for mowing and 2.2 hours for recharging) for a surface of 1296 m ² . The traditional rotary mower working time for the same surface was 1.02 hours per week. The estimated primary energy consumption for autonomous mower was about 4.80 kWh/week compared with 12.60 kWh/week for gasoline-powered rotary mowing. Based on turf quality aspects and energy consumption, the use of autonomous mowers could be a promising alternative to traditional mowers.

The termination of cover crops in conventional no-till systems is mostly conducted mechanically in combination with herbicides. Combining flaming and roller crimpers could be a viable solution to avoid using herbicides for cover crop termination in farming systems where herbicides are banned, or at least to reduce their use in an integrated management approach. This research tested the effects of flaming used in combination with three different types of roller crimpers to terminate a fall-sown cover crop mixture of winter pea and barley. The cover crop termination rate was visually assessed in terms of percentage of green cover provided by cover crop plants at different intervals from the termination date, and estimated using a log-logistic non-linear regression model with four parameters. Machine performance data are also reported. The results show that, irrespective of the roller type, flaming significantly boosted the effect of the roller crimpers. In fact, an economic threshold for cover crop suppression of 85% was reached only when the rollers were used in combination with flaming. Nevertheless, none of the methods were able to reach the 100% of cover crop suppression. In some case, the combined use of flaming and roller crimpers allowed reaching the 90% of cover crop devitalisation, which happened six weeks after the termination date. More importantly, the use of flaming in combination with rollers shortened the time needed to achieve the estimated levels of devitalisation, compared with the rollers used alone. We conclude that flaming is an effective tool to increase the effectiveness of roller crimpers. Nevertheless, further research is needed to identify solutions to overcome the barrier of the high operational costs of flaming, which is constraining its wider adoption by farmers. Future studies could focus, for instance, on the development of a new prototype of combined machine for crimping and flaming the cover crops simultaneously, which could potentially reduce the operational costs.

Climate change and labour shortage are re-shaping farming methods. Agricultural tasks are often hard, tedious and repetitive for operators, and farms struggle to find specialized operators for such works. For this and other reasons (i.e., the increasing costs of agricultural labour) more and more farmers have decided to switch to autonomous (or semi-autonomous) field robots. In the past decade, an increasing number of robots has filled the market of agricultural machines all over the world. These machines can easily cover long and repetitive tasks, while operators can be employed in other jobs inside the farms. This paper reviews the current state-of-the-art of autonomous robots for agricultural operations, dividing them into categories based on main tasks, to analyze their main characteristics and their fields of applications. Seven main tasks were identified: multi-purpose, harvesting, mechanical weeding, pest control and chemical weeding, scouting and monitoring, transplanting and tilling-sowing. Field robots were divided into these categories, and different characteristics were analyzed, such as engine type, traction system, application field, safety sensors, navigation system, country of provenience and presence on the market. The aim of this review is to provide a global view on agricultural platforms developed in the past decade, analyzing their characteristics and providing future perspectives for next robotic platforms. The analysis conducted on 59 field robots, those already available on the market and not, revealed that one fifth of the platforms comes from Asia, and 63% of all of them are powered by electricity (rechargeable batteries, not solar powered) and that numerous platforms base their navigation system on RTK-GPS signal, 28 out of 59, and safety on LiDAR sensor (12 out of 59). This review considered machines of different size, highlighting different possible choices for field operations and tasks. It is difficult to predict market trends as several possibilities exist, like fleets of small robots or bigger size platforms. Future research and policies should focus on improving navigation and safety systems, reducing emissions and improving level of autonomy of robotic platforms.