In recent years digital sensors have been successfully integrated on board Unmanned Aerial Vehicles (UAV) to assess crop vigour, vegetation coverage, and to quantify the ‘greenness’ of foliage as indirect measurements of crop nitrogen status. The classical approach of precision agriculture has involved the use of multispectral sensors onboard UAV and the development of numerous vegetation indices associated with vegetation parameters, such as the mostly used Normalized Difference Vegetation Index (NDVI). However, the main negative issue when dealing with multi and hyper-spectral reflectance measuring tools is their high cost and complexity from the operational point of view. As a low-cost alternative, vegetation indices derived from Red Green Blue (RGB) cameras have been employed for remote-sensing assessment, providing data on different stress conditions and species. Digital images record information as amounts of RGB light emitted for each pixel of the image; however, the intensity of red and blue will often alter how green an image appears. To simplify the interpretation of digital colour data, recent studies have suggested converting RGB values to the more intuitive Hue, Saturation, and Brightness (HSB) colour spectrum, and then into a single measure of dark green colour, the Dark Green Color Index (DGCI). In this study, NDVI acquired by a ground-based handheld crop sensor and by a multispectral camera mounted on board a UAV has been compared with DGCI calculated from images taken with a commercial digital camera on board a UAV, trying to quantify the colour of turfgrass that had received different nitrogen (N) rates. The objectives of the trial were to study an affordable easy-to-use tool evaluating the relationship among NDVI, DGCI and leaf nitrogen content on turfgrass.

Autonomous mowers are becoming increasingly common in public and private greenspaces. Autonomous mowers can provide several advantages since these machines help to save time and energy and prevent operators from possible injuries. Current autonomous mowers operate by following random trajectories within areas defined by a shallow-buried boundary wire that has the purpose to generate an electro-magnetic field. Once the electro-magnetic field is perceived by the autonomous mower, the machine will stop and change direction. Mowing along random trajectories is considered an efficient solution to manage areas with a variable number of obstacles. In agriculture, autonomous technologies are becoming increasingly popular since they can help to increase both the quantity and quality of agricultural products by reducing productive cost and improving the production process. Thus, even autonomous mowers may be useful to carry out some of the agricultural operations that are highly time consuming. In fact, some autonomous mowers designed and realized to work in vineyards and home vegetable gardens are already available on the market. The aim of this study was to compare the work capacity of six autonomous mowers that move along random trajectories in areas with a high number of obstacles to assess if these machines may be employed in some agricultural contexts. The six autonomous mowers were split in three groups based on their size (large, medium, and small) and were left to work in two areas with equal number of obstacles but different layouts. The first area (Site A) had a square shape and an extension of 23.04 m², in order to keep the autonomous mowers enclosed inside it. The second area (Site B) had a square shape and an extension of 84.64 m², in order to have a part of the area with no obstacles. The layout and the size of the two areas affected the autonomous mowers performances in different ways. The six autonomous mowers working on Site A obtained similar results and higher performances compared to the same mowers working on Site B. All the autonomous mowers proved to be able to mow more than 89% of Site A after 2 h and more than 98% of Site A after 5 h. On Site B small size autonomous mowers obtained the best results mowing more than 83% of the area with obstacles after 2 h and more than 98% of the area with obstacles after 5 h. However, specific work settings allowed larger autonomous mowers to improve their efficiency, obtaining similar results compared to smaller autonomous mowers.

Before the introduction of positioning technologies in agriculture practices such as global navigation satellite systems (GNSS), data collection and management were time-consuming and labor-intensive tasks. Today, due to the introduction of advanced technologies, precise information on the performance of agricultural machines, and smaller autonomous vehicles such as robot mowers, can be collected in a relatively short time. The aim of this work was to track the performance of a robot mower in various turfgrass areas of an equal number of square meters but with four different shapes by using real-time kinematic (RTK)-GNSS devices, and to easily extract data by a custom built software capable of calculating the distance travelled by the robot mower, the forward speed, the cutting area, and the number of intersections of the trajectories. These data were then analyzed in order to provide useful functioning information for manufacturers, entrepreneurs, and practitioners. The path planning of the robot mower was random and the turfgrass area for each of the four shapes was 135 m² without obstacles. The distance travelled by the robot mower, the mean forward speed, and the intersections of the trajectories were affected by the interaction between the time of cutting and the shape of the turfgrass. For all the different shapes, the whole turfgrass area was completely cut after two hours of mowing. The cutting efficiency decreased by increasing the time, as a consequence of the increase in overlaps. After 75 minutes of cutting, the efficiency was about 35% in all the turfgrass areas shapes, thus indicating a high level of overlapping.

Turfgrass mowing is one of the most important operations concerning turfgrass maintenance. Over time, different mowing machines have been developed, such as reel mowers, rotary mowers, and flail mowers. Rotary mowers have become the most widespread mowers for their great versatility and easy maintenance. Modern rotary mowers can be equipped with battery-powered electric motors and precise settings, such as blade rpm. The aim of this trial was to evaluate the differences in power consumption of a gasoline-powered rotary mower and a battery-powered rotary mower. Each mower worked on two different turfgrass species (bermudagrass and tall fescue) fertilized with two different nitrogen rates (100 and 200 kg ha⁻¹). The battery-powered mower was set at its lowest and highest blade rpm value, while the gasoline-powered mower was set at full throttle. From the data acquired, it was possible to see that the gasoline-powered mower had a much higher primary energy requirement, independent of the turf species. Moreover, comparing the electricity consumption of the battery-powered mower over time, it was possible to see that the power consumption varied according to the growth rate of both turf species. These results show that there is a partial waste of energy when using a gasoline-powered mower compared to a battery-powered mower.

The establishment of permanent cover crops is becoming a common practice in vineyard floor management. Turfgrass science may provide species and techniques with a high potential for improving the sustainability of vineyard floor management. Based on this assumption, an experiment was carried out during 2018 and 2019 at the Donna Olimpia Vineyard, Bolgheri, Italy. The trial aimed at comparing an innovative floor management system based on a turf-type cultivar of bermudagrass mown with an autonomous mower with a conventional floor management system. Ground cover percentage, energy consumption, CO₂ emissions, grapevine water status, leaf nitrogen content, fruit yield and must composition have been assessed in order to perform the comparison. The innovative vineyard floor management produced an almost complete ground cover (98%) at the end of the second growing season, with the resident species reduced to a small percentage (4%). Resident species growing under-trellis were efficiently controlled without herbicide applications. A lower primary energy consumption and a reduction in CO₂ emissions were observed for the innovative management system compared to the conventional management system. Grapevine water status, leaf chlorophyll content, soil–plant analyses development (SPAD), fruit yields and must composition were similar between the different soil management systems. Based on results obtained in this trial, turf-type bermudagrass and innovative mowing machines may contribute to enhance the sustainability of vineyard floor management.

Poor quality in turfgrass mowing is highlighted by the shredded leaf tips with necrotic tissues that give an unsightly brownish colour to the turf and may also lead to turf disease. Mowing quality is also typically assessed by visual rating, thus the score depends on the person doing the assessment. To make the evaluation of mowing quality not subjective, an innovative method was developed. The aim of the trial was to examine the effects of different mowing systems and two different nitrogen rates (100 and 200 kg ha⁻¹) on two turfgrass species in order to test the new mowing quality calculation. Three different mowing systems were used: a battery-powered rotary mower set at 3000 rpm and 5000 rpm respectively and a gasoline-powered rotary mower set at full throttle. The battery-powered mower at low blade rpm produced a poorer mowing quality and turf quality than the gasoline-powered mower and battery-powered mower at high rpm, which produced a similar mowing quality and turf quality. Leaf tip damage level values showed a significant correlation with the results of the visual mowing quality assessment. Lower leaf tip damage level values (slightly above 1) corresponded to higher visual mowing quality scores (around 8).