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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.

Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits. The objectives of the trial were i) to compare the spectral reflectance of 3 turfgrasses acquired via UAV and by a ground-based instrument; ii) to test the sensitivity of the 2 data acquisition sources in detecting induced variation in N levels. N application gradients from 0 to 250 kg ha-1 were created on 3 different turfgrass species: Cynodon dactylon x transvaalensis (Cdxt) 'Patriot', Zoysia matrella (Zm) 'Zeon' and Paspalum vaginatum (Pv) 'Salam'. Proximity and remote-sensed reflectance measurements were acquired using a GreenSeeker handheld crop sensor and a UAV with onboard a multispectral sensor, to determine Normalized Difference Vegetation Index (NDVI). Proximity-sensed NDVI is highly correlated with data acquired from UAV with r values ranging from 0.83 (Zm) to 0.97 (Cdxt). Relating NDVI-UAV with clippings N, the highest r is for Cdxt (0.95). The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%. UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management. For relatively small green areas, a hand-held crop sensor can be a less expensive and more practical option.

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.

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.

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.

The openers are the planter components that interact with soil and several researchers studied openers characteristics and behaviour in different conditions, but few explored the effects on crop emergence, growth and yield. The aim of this study is to evaluate and quantify any effects of openers on crop de-velopment and yield. The performance of three planters equipped with five different openers were compared on maize in a field test: double disc (DD), punch planter (PP), horizontal furrow with winged opener (HW), vertical furrow with winged opener (VW), vertical furrow with shank opener (SO). Seed spacing, depth, penetration resistance and plant emergences, root dry mass and yield were measured respectively on seeding slots and during crop development to evaluate openers effects. The results showed low variability in seed depth and spacing when DD and PP openers were used despite higher level of compaction on DD slot. High variability was found on maize plants when VW and HW openers were used. SO obtained relevantly lower yield in absolute value -35% (1.7 Mg ha-1) compared to other openers. However, the high variability observed in the different replicates and plant adaptability to stress conditions could explain the absence of significant differences in crop yield.

Mechanical under-row weed control in the vineyard emerges as a sustainable choice compared to chemical control, with tillage-based approaches proving especially efficient. A rollhacke, finger weeder, and blade weeder are valid alternatives to commonly used implements that cause excessive soil disruption and display suboptimal efficiency. The trial aimed to compare different under-row weed control strategies in terms of weed control efficacy and operational performance. Among these, in ST1, a tool-holder equipped with both a rollhacke and finger weeder was used at the first and second intervention; in ST2, a rollhacke was used at the first intervention and blade weeder at the second one; in ST3, firstly the tool-holder equipped with a rollhacke and finger weeder was used, then the blade weeder; in ST4, a rollhacke was used first and then the tool-holder equipped with a rollhacke and finger weeder. Weed height, weed cover, and weed biomass were evaluated before the first and after the second intervention. Total field time, fuel consumption, and CO2 emissions of each strategy were assessed. ST1 proved to be the best compromise in terms of weed control effectiveness and operational performance compared to the other strategies. Indeed, ST1 tendentially achieved a lower weed height (20.42 cm) and weed biomass around vine trunks (105.33 g d.m. m−2) compared to the other strategies. In terms of total field time, fuel consumption and CO2 emissions, ST1 recorded intermediate values equal to 3.85 h ha−1, 15.29 kg ha−1, and 48.72 kg ha−1, respectively. Further studies are needed to evaluate these strategies in different vineyard conditions, considering their effect on weed flora composition. Furthermore, exploring automation technology for real-time implement adjustments based on weed infestation levels could further improve the intervention effectiveness and efficiency.

The need to reduce the use of agrochemicals in order to work towards sustainable farming systems has influenced scientific research on weeds in recent years [...]

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety.

Organic vegetable production is particularly affected by weed pressure and mechanical weeding is the major tactic implemented by growers to keep weeds under economic thresholds. Living mulch (LM) has been shown to provide several environmental services; however, LM management is required to avoid competition between service crops and cash crops. The aim of this trial was to evaluate two innovative LM-based management systems: a system that provided LM growth regulation by means of flaming (LM-FL) and a system where the LM was regularly mowed by an autonomous mower (LM-AM), both compared with a control without LM and based on standard tillage operations (TILL). The three management systems were evaluated in terms of crop production, weed control, and energy consumption on a 2 yr organic crop rotation of cauliflower (Brassica oleracea L. var botrytis) and eggplant (Solanum melongena L.). LM-AM produced an acceptable fresh marketable yield for both vegetable crops. Moreover, the weed dry biomass obtained in LM-AM-managed plots was lower compared to the LM-FL plots and ranged approximately from 200 to 300 kg ha−1. Furthermore, LM-AM management resulted in lower energy consumption (−2330 kWh ha−1 with respect to the TILL system and −7225 kWh ha−1 with respect to the LM-FL system). The results of this trial suggest that autonomous mowers have a great potential to improve LM management and help with implementing sustainable organic vegetable systems.