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.

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.

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.

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.

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.

Satellite remote sensing of leaf nitrogen (N) content is an interesting technique for agricultural crops for both economic and environmental reasons since it allows the monitoring of fertilization, and hence can potentially reduce the application of N according to real plant needs. The objective of this trial was to compare the N status in different turfgrasses using both remote multispectral data acquired by GeoEye-1 satellite and two ground-based instruments. The study focused on creating a N content gradient on three warm-season turfgrasses, (Cynodon dactylon × transvaalensis ‘Patriot’, Paspalum vaginatum ‘Salam’, Zoysia matrella ‘Zeon’), and two cool-season (Festuca arundinacea ‘Grande’, Lolium perenne ‘Regal 5’). The linear gradient of applied N ranged from 0 to 342 kg ha⁻¹ for the warm-season and from 0 to 190 kg ha⁻¹ for the cool-season turfgrasses. Proximity and remote-sensed reflectance measurements were acquired and used to determine the normalized difference vegetation index (NDVI). Our results proved that proximity-sensed NDVI is highly correlated with data acquired from satellite imagery. The correlation coefficients between data from the satellite and the other sensors ranged from 0.90 to 0.99 for the warm-season and from 0.83 to 0.97 for the cool-season species. ‘Patriot’ had a clippings N content ranging from 1.20% to 4.1%, thus emerging as the most reactive species to N fertilization. As such, the GeoEye-1 satellite can adequately assess the N status of different turfgrass species and its spatial variability within a field, depending on the N rates applied. In future, information obtained from satellites could allow precision fertilizer management on sports fields, golf courses, or other extended green areas.