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.

Leaves act as a primary organ for the interception of solar radiation and their spatial arrangement determines how the plant canopy interacts with light. Many studies have been carried out on the penetration of radiation into crops however to date, few results are available on turfgrasses, mainly due to the difficulties of introducing sensors into the turf without disturbing the natural position of the leaves. In the present research two warm season turfgrasses, hybrid bermudagrass (Cynodon dactylon × transvaalensis) 'Patriot' and manilagrass (Zoysia matrella) 'Zeon', were studied. The aim was to describe their canopy architecture grown with minimal disturbance to the natural arrangement of the leaves and stems, and to determine the potential effects of canopy architecture on light penetration and reflectance. Radiometric measurements were carried out at eight different profile levels of turfgrasses that were up to 12 cm tall. A LI-COR 1800 spectroradiometer with an optical fiber cable and a 7 mm diameter sensor was used. Measurements were carried out in the 390–1100 nm region at 5 nm intervals. The LAI value was higher for the manilagrass (9.0) than for the hybrid bermudagrass (5.6). The transmitted radiation was found to be closely dependent on downward cumulative LAI. Despite a more upright habit (mean insertion angle of 22.4° ±3.4), Zoysia matrella showed a higher NIR reflectance compared to Cdxt, which has a horizontal leaf arrangement (mean insertion angle 62.1° ± 9.6). The species studied showed substantial differences both in terms of phytometric characteristics and in the capacity to attenuate solar radiation.

Enhancing vineyards sustainability and reducing herbicides usage is a crucial theme, thus alternative weed management methods are starting to be studied. Cover crops have been shown to provide for several environmental services such as performing an efficient weed control and promoting biodiversity, thus improving the sustainability of the overall management system. However, the use of cover crops is usually confined to the interrow area in order to avoid competition with vines. Under-trellis weed management in vineyards is an important challenge, conventionally fulfilled by cultivation or repeated herbicides applications. Autonomous mowers are small autonomous machines that have shown a great efficiency when employed in agricultural contexts. Due to their reduced size, they can easily prevent the excessive vertical growth of weeds both in the interrows and under trellis without the use of chemical applications. The aim of this trial was to evaluate if the combination of cover crop species and autonomous mowers management could improve vineyard sustainability. Vineyard floor cover that was managed with an autonomous mower had a shorter canopy height and a lower weed dry biomass compared to the conventionally managed vineyard floor cover. Vineyard floor management providing cover crops and autonomous mowing had a significantly lower weed cover percentage compared to conventional floor management, especially under-trellis. The results of this trial suggest that the combination of cover crops and autonomous mowers may be a sustainable and reliable technique to include in vineyards floor management.

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.

Tall fescue (Schedonorus arundinaceus (Schreb.) Dumort.) is often managed with a cutting height ranging from 70 to 100 mm in ornamental lawns. Some autonomous mowers have been specifically designed to maintain mowing height in the same range. Generally, autonomous mowers operate by following random trajectories, and substantial overlapping is needed to obtain full coverage of the working area. In the case of tall grass, this may cause lodging of grass plants, which in turn may reduce turf quality. The introduction of a navigation system based on systematic trajectories has the potential to improve the performances of autonomous mowers with respect to machine efficiency and turf quality. With the aim of determining the effects of reduced mowing frequency and systematic navigation systems on turf quality and mower performances in terms of working time, energy consumption and overlapping, the performances of two autonomous mowers working with random and systematic trajectories were tested on a mature tall fescue lawn at 90 mm cutting height. The working efficiency was approximately 80% for the systematic trajectories and approximately 35% for the random trajectories; this was mainly due to the lower overlapping associated with systematic trajectories. Turf quality was slightly higher for the mower working systematically (a score of 8 using a 1–9 score with 1 = poor, 6 = acceptable and 9 = best) compared to the one working randomly (quality of 7 and 6 on a 1–9 scale with 1 = poor and 9 = best). No appreciable lodging was observed in either case. For tall, managed lawns, systematic trajectories may improve autonomous mowers’ overall performances.