1. Introduction
The surface on which a sport is played makes a huge difference, not only in the way it is played but on player health, maintenance and even the likability of the local environment. There are many benefits to choosing a turfgrass yard or field over other options. Operating preventative fungicide applications may help keep your sward healthy and disease-free. Precision agriculture aims to apply a precise and appropriate amount of inputs of water, pesticides, fertilizers etc., to the crop at the right time for improving productivity and quality [
1], which may considerably reduce the quantity of pesticides and savings in input costs [
2]. The second benefit concerns environmental impacts [
3]. Consequently, concerning crops, soils and farmers, precision agriculture has become a key element of sustainable agriculture [
4,
5] by reducing pressure on the environment through increasing machinery efficiency. For example, the use of GNSS (Global Navigation Satellite System) reduces agriculture fuel consumption, as when satellite imagery supports variable rate technology application of pesticides including sprayer machines and can eventually reduce the total amount used [
3,
6].
Current technological advancements allow the use of real-time sensors in the soil to collect and transmit data instantly without the need for human presence [
7,
8]. Precision agriculture has further been enabled by RGB (Red, Green, Blue) or multispectral cameras to capture multiple field images that can then be combined via photogrammetric methods to build orthophotos covering large areas. The multispectral images include several values per pixel besides the traditional red, green and blue values to process and analyze spectral vegetation indices that can provide detailed information on plant health, including fungal infections and treatment needs [
9]. In phytosanitary applications, the majority of fungicide products are used in the form of a spray to protect turfgrass fields and optimize their quality [
10]. The precise distribution of agrochemicals is essential to ensure effective intervention with a significant impact on both production costs and the environment.
Spray operation should use drop shadows to deliver the active component to the target area. Fungicide treatment effectiveness as a function of drop sizes and velocity has been the subject of an expansive disquisition and, despite the process complexity, some trends are well established [
1,
11]. As a rule of thumb, drops whose sizes exceed 300 µm in diameter tend to splash on the target shell. Among others, the parameters determining the splash characteristics are the drop kinetic energy and the area characteristics [
12,
13]. On the other hand, small driblets under 200 µm in diameter are prone to interact with the wind, which may beget their drift downwards from the target [
1,
11]. Still, nozzles are characterized by a wide distribution of drop size (span) involving implicit drifting or effectiveness losses due to splashing marvels. Therefore, the spray should contain optimal driblets regarding their droplet sizes and velocity. A better spray uniformity may ameliorate treatment effectiveness and reduce drift hazards. On this basis, reducing the extent of the drop size distribution is still a challenge in the field of precision phytosanitary treatment.
Spray pressure, nozzle size, and tractor speed for a boom sprayer are the main parameters determining boom flow rate, drop size, drift, and subsequently treatment application efficiency [
10]. To solve this problem, remote sensing has become an essential toolset in the modernization of ground-based high throughput plant phenotyping (HTPP), ultimately including advances in yield, but including adaptation to abiotic stressors, biotic limiting conditions as vulnerability to diseases and pests, and indeed quality [
14,
15,
16,
17]. As a classical approach of remote sensing using satellites, unmanned aerial vehicle UAVs, visible and near-infrared (VNIR) imaging spectroscopy has proven a reasonably reliable ability in biophysical crop evaluations in agriculture [
18,
19] such as the normalized difference vegetative index (NDVI) [
20] resulting from visible and near-infrared (NIR) reflectance that is strictly related to vegetation presence or vigor [
21,
22]. The main difficulties regarding the use of NDVI include its atmospheric impact, ease of saturation, and sensor quality [
23].
Various RGB vegetation indices (RGB VIs), estimated from commercial RGB cameras, have proven their capability to predict yield, evaluate nutrient deficits, and measure disease impacts [
24,
25] as a less expensive alternative to scientific multispectral VNIR or thermal infrared (TIR) sensors [
26]. RGB images can be treated using comparisons between red, green, and blue light as broadband reflectance values or through the use of alternative color spaces, as with the Breedpix code [
27]. The normalized green–red difference index (NGRDI), which is closely related to vegetation presence or vigor, and the triangular greenness index (TGI), which estimates chlorophyll concentration in leaves and canopies, are both calculated from the treatment of R, G, and B as separate spectral bands [
25]. In the hue–saturation–intensity (HSI) color space, where the hue (H) element describes color chroma crossing the visible spectrum in the form of an angle between 0° and 360° [
14]. The percentage of pixels in the image in the hue range from 60° to 180° presents the green index area (GA), but the percentage of pixels in the image in the hue range from 80° to 180° presents the greener green area (GGA) excluding yellowish-green tones that might be partially stressed or senescent. The crop senescence index (CSI) is calculated from GA and GGA, providing strong segregation between resistant and sensitive genotypes in different treatments [
14,
20,
28,
29]. In the Commission Internationale de I’Edairage (CIE), CIELab color space model, dimension L* represents lightness, the a* factor presents green to red, and the b* factor expresses blue to yellow. Dimensions u* and v* in the CIELuv color space model are perceptibly homogeneous coordinates and symbolize axes like a* and b* in separating the color spectrum [
30]. The CIElab and CIEluv color spaces can concurrently contrast the green vegetation amount with the reddish/brown soil background and yellowing caused by the foliar chlorophyll loss, both communal symptoms of nitrogen deficit. Already, for improving crop performance, RGB VIs have been used at both the canopy and leaf status [
16,
31,
32].
In this current study, the defined remote sensing indices, hue, a*, u*, NGRDI, and NDVI are examined for their potential to ensure effective fungicide intervention with significant impact. Then, we evaluate the performance of a set of remote sensing RGB VIs from natural color images acquired at the ground level on a playing turf. Additionally, we evaluated how these different data contribute to improving multivariate model estimations of grass phytosanitary conditions in combination with the operating parameters of a boom pressure sprayer, such as spray pressure and nozzle size, to provide some improvements over traditional practices. This study aims to assess if defined remote sensing indices can be used to ensure effective fungicide intervention with significant impact and provide support in golf turf maintenance.
4. Conclusions
Assessing golf course turfgrass quality using image processing methods and depending on different spray parameters has been highlighted here using simple and economical modern remote sensing information acquisition and monitoring technologies. Multispectral and RGB image-based vegetation indices showed good correlations, with NGRDI selected as the most relevant indicator to monitor the treatment efficiency and, therefore, the turf quality as a function of spray pressure, nozzle size, and forward speed. So, these different intertwined application mechanical properties were employed to predict NGRDI, as the best indicator of turfgrass health, by MLR to derive further insights. In fact, the treatment uniformity coefficient decreased with increasing working pressure and ranged from 86.64% at 2.6 bars to 80.71% at 5.6 bars for the size 0.5. The nozzle size also affected the treatment distribution quality. Using the nozzle size 0.6 increased the spray volume and the droplet size and therefore decreased the distribution quality and consequently the treatment efficiency. Regarding the GA index, the average value increased at the low pressure 2.6 bars and the size 0.5. The CSI values further increased to 57.127, following the low-pressure use. Moreover, the higher pressure of 5.6 bars decreased the grass chlorophyll from 4500 and 2500 through the TGI index. For the two nozzle sizes 05 and 06, NGRDI varied from 0.117 to 0.089, respectively. NDVI results equally tended to decrease until 0.3 using the size 0.6. The MLR model had an agreeable NGRDI prediction performance with an RMSE of 0.023 and a high correlation coefficient (R2 = 0.88). In summary, good results were obtained for estimating turfgrass vigor as a function of various treatment management parameters with the support of remote sensing technologies at different scales.