Abstract
Background and goals New pesticide spray technologies are needed to replace inefficient conventional air-assisted practices for protecting grapes from diseases and insect pests.
Methods and key findings A laser-guided intelligent sprayer was evaluated in an experimental vineyard for three consecutive growing seasons. Treatments included the intelligent sprayer with low (0.065 L/m3) and high (0.13 L/m3) base spray deposition rates, and the conventional constant-rate application using the same sprayer but with the intelligent functions deactivated (935.4 L/ha). Evaluations included comparisons of spray coverage and deposition uniformity within vines, spray volume consumption, chemical cost savings, control of fungal diseases and Japanese beetles, and yields among the three treatments and nontreated plots as control. The conventional treatment consistently produced excessively higher spray coverage within vines than the low- and high-rate intelligent sprayer treatments, while spray deposition uniformity on grapevines did not differ significantly among treatments. Even though foliar disease severity was significantly higher in the intelligent low-rate treatment, marketable yields were not significantly different from either the intelligent high-rate or conventional constant-rate treatments; however, marketable yields in the intelligent low-rate treatment were 1.7 times higher than in nontreated plots. Japanese beetle incidence and herbivory varied significantly among treatments depending on sampling date, however, intelligent low- and high-rate treatments had significantly fewer beetles in the grapevine canopy than nontreated control plots for the majority of sampling periods each year. Furthermore, intelligent high-rate treatments suppressed Japanese beetles just as well as conventional air-assisted treatments. The intelligent spray treatments reduced spray volume by 29 to 83% compared to the conventional spray treatment, resulting in annual chemical savings of US$469 (high rate) and US$712 (low rate) per hectare.
Conclusions and significance Intelligent sprayer technology could offer economically sustainable management of fungal and oomycete diseases and Japanese beetles for grape production.
Introduction
Axial fan air-blast spray technology has been used since the 1940s in the United States (Fox et al. 2008) and is the primary method of applying agrochemicals to fruit crops and nursery plants for disease and insect control in regions east of the Rocky Mountains (Chen et al. 2021). However, the application efficiency of air-blast sprayers is very low because sprays are continuously discharged at constant rates, regardless of crop canopy structures and empty spaces in the field. In fruit tree orchards, spray losses to airborne drift and ground settlement range from as little as 3% to more than 63%, depending on the crop architecture and phenological stage, wind speed, and spray droplet size (Herrington et al. 1981, Buisman et al. 1989, Vercruysse et al. 1999). Although few studies have been conducted to quantify spray losses from air-blast sprayers in vineyards, Pergher and Gubiani (1995) determined that losses to the ground and spray drift ranged from 34.5 to 48.9% and from 6.5 to 19.8%, respectively, depending on the spray rate and airflow rate.
Fungicide and insecticide applications can cost grape producers up to $1042/ha/season in an established vineyard (Davis et al. 2020). In addition to reduced income due to chemical waste, off-target drift and ground drop-off of pesticides have negative consequences to human and animal health and the environment (Alavanja et al. 2004, Lee et al. 2011, Pimentel and Burgess 2014, Garcia-Garcia et al. 2016, Bonvallot et al. 2018). Drift can also lead to lawsuits that could cost applicators as much as $775,000 (Simmons 2005), and can be a factor in the banning of certain pesticides. The economic costs to public health and the environment, including pesticide resistance development, yield reduction, bird depletion, and contamination of groundwater, total nearly $10 billion annually in the U.S. alone (Pimentel et al. 1992, Pimentel 2005, Pimentel and Burgess 2014). However, pesticides are critical to protecting crops, including grapes, from diseases and insect pests; thus, spray technology that can reduce losses due to drift or ground drop-off is needed.
Major modifications to axial fan air-assisted technology (referred to as conventional air-assisted technology) to improve efficiency or reduce spray loss through off-target losses have been made only recently. Laser-guided intelligent sprayer technology developed by the U.S. Department of Agriculture (USDA)–Agricultural Research Service (ARS) and The Ohio State University (Wooster, OH) has taken conventional air-assisted spray technology into the 21st century (Shen et al. 2017). The intelligent sprayer (also referred to as the iSprayer or laser-guided, variablerate sprayer) uses a light detection and ranging (LiDAR) laser scanning sensor to measure canopy architecture and density, while simultaneously accounting for tractor speed, to make plant-targeted spray applications using pulse-width modulated solenoid valves (Shen et al. 2017, Zhu et al. 2017). The technology can be retrofitted onto existing axial air-assisted sprayers as well as quantum mist sprayers or pneumatic airshear sprayers and is independent of light conditions.
Diseases including downy mildew (Plasmopara viticola), black rot (Guignardia bidwellii), Phomopsis cane and leaf spot (Diaporthe ampelina previously Phomopsis viticola), and powdery mildew (Erysiphe necator) are a major limiting factor in the production of grapes in the Midwest. Although management requires an integrated approach, fungicides applied from prebloom until the grapes reach pea size (four to five weeks postbloom, depending on the cultivar) are necessary for adequate disease control and maximum yield and fruit quality. Japanese beetles (Popillia japonica) are a potentially damaging insect pest of grape (Potter and Held 2002, Nabity et al. 2009). Adults attack only the foliage of grapevines, skeletonizing leaves by eating around major leaf veins (Hawley and Metzger 1940). Japanese beetle herbivory can reduce photosynthesis capacity and increase water loss (Aldea et al. 2005). In addition, fruit quality and yield can be reduced due to too much sun exposure (Kliewer and Lider 1968, Steel and Greer 2008, Lee et al. 2007, Tarara et al. 2008). The objective of this study was to validate the use of the intelligent sprayer as an effective and sustainable approach to managing foliar diseases and insect pests such as Japanese beetles in grape.
Materials and Methods
Intelligent sprayer technology and experimental design
An existing vineyard planted with Traminette (Joannes Seyve 23-416 × Gewürztraminer) on Wooster silt loam in the early 2000s at The Ohio State University, College of Food, Agricultural, and Environmental Sciences—Wooster (40°44′N; 81°54′W) was used for this study. In 2019, 2020, and 2021, vines were sprayed with fungicides and insecticides (collectively referred to as pesticides) according to a commercial spray program (Supplemental Tables 1 to 3, respectively) with a modified axial turbine fan air-assisted sprayer (model ZENIT B11, Hardi International A/S) (Figure 1). The sprayer was modified with eight five-port nozzles then retrofitted with the intelligent spray control system consisting of a LiDAR laser scanning sensor and a radar speed sensor mounted onto the sprayer as described (Shen et al. 2017). A computer tablet that managed sensor functions and sprayer operations was mounted inside the tractor cabin along with a control switch panel that allowed the sprayer to be manually switched back and forth between intelligent and conventional modes (Shen et al. 2017). The sprayer was equipped with 40 flat-fan pattern nozzles (XR8004VS, Teejet Technologies), 20 on each side. Each nozzle was coupled with a pulse-width modulated solenoid valve to produce variable flow rates.
Vineyard air-assisted sprayer retrofitted with the intelligent spray control system consisting of a laser scanning sensor and pulse-width modulated nozzles for field tests.
Treatments included pesticides applied using: 1) the intelligent sprayer mode at a base spray deposition rate of 0.065 L/m3 (referred to as intelligent low-rate treatment); 2) the intelligent sprayer mode at a base spray deposition rate of 0.13 L/m3 (2020 and 2021 only, referred to as intelligent high-rate treatment); 3) the conventional mode to continuously discharge sprays at a constant spray application rate of 935.4 L/ha, which was operated at 413 kPa pressure and 4.8 km/hr travel speed (referred to as conventional treatment); and 4) no pesticide sprays (nontreated control). The base spray deposition rate was the amount of spray volume required to cover 1 m3 of crop foliage volume, required as an input for intelligent spray applications. The intelligent sprayer automatically adjusts the spray output based on the real-time travel speed, and thus travel speed of the intelligent sprayer can be variable during applications (Shen et al. 2017). However, the 4.8 km/hr travel speed was still used for all intelligent sprayer mode treatments to simplify the tests. The treatments were arranged in a randomized complete block design with six replicated blocks. Each treatment plot consisted of three (2020 and 2021) or four to six (2019) vines separated by two to three buffer vines. Pesticides were applied to both sides of the vines. The spray volume applied to each plot was measured and displayed on the cabin computer touch screen in real-time and recorded manually after every treatment plot. In all three years, volume data were collected from 25 to 30 cm shoot growth (Eichhorn-Lorenz [E-L] stages 15 to 18) to preharvest (E-L stage 37) phenological stages. The spray volume per vine for each test run was calculated from the recorded total spray volume divided by the number of vines sprayed.
Determination of spray coverage
Foliar spray coverage and spray deposition uniformity across target vines for each spray treatment were measured with rectangular 26 × 76 mm water-sensitive papers (WSPs) (Syngenta Crop Protection AG). Prior to spraying, WSPs were placed at five locations (bottom, top, and center of the leading trunk, and along the left and right sides of the cordons) (Figure 2) within the center vine of each treatment plot. The WSPs were collected and stored in paper bags after they were sprayed and dried (five to six hours, depending on relative humidity). To place the WSPs on vines, a 167 mm zip tie was attached to the cordon or trunk and each WSP was clipped onto the zip tie with an 85 mm twin alligator clip. Spray coverage was assessed only when relative humidity was 80% or lower. In 2019, the coverage was assessed on 5, 24, and 31 May; 7, 14, 19, and 27 June; and 5 and 12 July, for a total of nine assessments. In 2020, coverage was assessed three times (18 and 25 June and 9 July) based on the experience and information obtained from the 2019 tests. Each WSP was scanned at 600 dpi in gray scale using a CanoScan LiDE 300 flatbed photo scanner (Canon USA Inc.), and percent spray coverage was determined using DepositScan software following the procedure described (Zhu et al. 2011). Spray coverage was not measured in the nontreated control blocks because they were not sprayed.
Placement of water-sensitive papers (WSP) on the center vine within each replicated plot. The WSPs were placed at five locations: 1) left cordon, 2) top center of the leading trunk, 3) right cordon, 4) center of the leading trunk, and 5) bottom of the leading trunk. Image created by M. L. Lewis Ivey using canva.com.
Foliar disease severity
Diseases (i.e., downy mildew, black rot, Phomopsis cane and leaf spot) were not rated individually in this study. Total foliar disease on the center vine (both sides of the vine) in each replicated plot was rated on a continuous scale from 0 to 100% disease severity. In 2019, percent foliar disease was rated on 6 June, 23 July, 12 Aug, and 10 Sept. In 2020, assessments were made on 7 and 17 July, 25 Aug, and 9 and 29 Sept. In 2021, assessments were made on 17 June, 6 and 20 July, 16 Sept, and 8 Oct. Area under the disease progress curve (AUDPC) was calculated to assess foliar disease severity over time using the following formula (Madden et al. 2007):
Eq. 1
where t is the time, and yi is the disease severity (percent foliage affected) at t = i (sample time points in a sequence).
Fruit clusters were harvested from all vines within each plot at 21 to 22 Brix and weighed, and marketable yield was calculated using the following equation:
Eq. 2
where Y is the marketable yield (kg/ha), W is the fruit weight (kg), A is the plot area sprayed (m2), and 10000 is the number of meters squared (m2) per hectare. Marketable yield was also calculated with the fruit weight divided by the total number of vines used for collecting the fruits.
Cost of pesticides
In 2019, 2020, and 2021, the cost of the total amount of pesticides (liters or grams) used during each treatment was determined using the 2019 wholesale price of the pesticides (Green Star Cooperative, Inc.) and converted to cost of product used per hectare with the following equation:
Eq. 3
where C is the cost of pesticides used per hectare ($/ha), V is the spray volume per vine (L/vine), N is the number of vines in the sprayed plot, E is the pesticide concentration in the tank mixture (kg/L) (Supplemental Tables 1 to 3), U is the cost of pesticides used per unit ($/kg), and A is the plot area sprayed (m2).
Japanese beetle incidence and herbivory
The incidence of Japanese beetles and associated herbivory was assessed seven times across four experimental replicates in 2019, from 10 July to 2 Aug, and nine times across six experimental replicates in 2020, from 10 July to 6 Aug. Japanese beetle incidence was measured by walking through experimental plots and counting all visible beetles on the canopy of all three grapevines in each treatment plot. Japanese beetle herbivory was measured by randomly selecting five grapevine shoots in each experimental plot and evaluating the five terminal leaves on those shoots for characteristic symptoms of Japanese beetle feeding damage (i.e., skeletonization). Percent herbivory in each treatment was calculated by dividing the number of skeletonized terminal leaves by the total number of leaves evaluated in each plot (n = 25). Four foliar applications of the insecticide Carbaryl 4L (42.6% carbaryl) were made to experimental plots in 2019 (Supplemental Table 1) according to three treatments: 1) nontreated control, 2) conventional air-assisted, and 3) intelligent low-rate. In 2020, three foliar applications of Carbaryl 4L were made to experimental plots (Supplemental Table 2) according to four treatments: 1) intelligent low-rate, 2) intelligent high-rate, 3) conventional air-assisted, and 4) nontreated control. Insecticide applications were made seven days apart in both years of the study.
Data analysis
All data were analyzed using a linear mixed model with the GLIMMIX procedure of SAS (ver. 9.4) (SAS Institute, Inc.) unless otherwise specified.
Disease severity and AUDPC
The three years of data were combined, giving the equivalent of 12 replicates for 2019 and 18 replicates per treatment for 2020 and 2021. Equal variance across fixed effects was evaluated using studentized residual plots. The fixed-effect factor was treatment. Random factors were year, block, and treatment time. Least squares means (LSMEANS) (Stroup et al. 2018) were estimated for the main effects, and pairwise contrasts were calculated to determine significant differences for treatments.
Japanese beetle incidence and herbivory
The number of Japanese beetles and percent herbivory observed on grapevines over time was analyzed using repeated measures analysis of variance (ANOVA) in the MIXED procedure, with treatment and date as predictor variables and number of Japanese beetles and percent herbivory as response variables, respectively. LSMEANS were estimated for the main effects of treatment and date, as well as the interaction between the main effects. Tukey-Kramer posthoc tests were used to determine significant differences between treatments over time.
Yield
The three years of yield data were combined, giving the equivalent of 12 replicates for 2019 and 18 replicates per treatment for 2020 and 2021. Equal variance across fixed effects was evaluated using studentized residual plots. The fixed-effect factor was treatment. Random factors were year and block within year. LSMEANS were estimated for the main effects, and pairwise contrasts were calculated to determine significant differences for treatments.
Volume
Volume data were analyzed with fixed-effect factors of treatment, date of spray application, and the interaction between treatment and date of spray applications. Date was considered a repeated measure. Random-effect factors were year, block, and the interaction of treatment and block. Compound symmetry structure was used for the correlation of observations within plots over time (Stroup et al. 2018). LSMEANS were estimated for the main effects and interaction of the fixed-effects factors, and pairwise contrasts were calculated to determine significant differences for treatments and date. Slices of LSMEANS were used to estimate treatment differences at each date.
Percent spray coverage
Percent spray coverage as measured using WSP was analyzed for each year because there was no equal variance detected. Using the 2019 data, fixed-effect factors were treatment, WSP position in the canopy, and the interaction of treatment and position. Random effects in the 2019 model were date of spray application, block within date, and treatment within date and block. Using the 2020 data, fixed-effect factors were treatment, position in the canopy, date, and the interaction of treatment and position. Random effects in the 2020 model were block, block within date, and treatment within date and block. Position was considered as a spatially repeated measure in both years (Stroup et al. 2018). For both years of data, compound symmetry was used for the correlation structure of coverage values at distinct positions in the canopy at each date. LSMEANS were estimated for the main effects and interaction of the fixed-effect factors, and pairwise contrasts were calculated to determine significant differences for treatments and positions.
In addition, spray deposition uniformity across the single vine canopy for each treatment on each sprayed day in 2019 and 2020 was evaluated with the uniformity index (Chen et al. 2013a) from measured spray coverage values at five WSP locations on each target vine (Figure 2), using the following equation:
Eq. 4
where I is the uniformity index, which ranges from 0 to 1 (lower uniformity when I is closer to 0 and greater uniformity when I closer to 1), i is the order of comparisons (
), n is the number of WSP positions on the same vine canopy to be compared (n = 5 in this case), 
is the total combinations of two WSP positions on the vine (
), and ui is the outcome of significant differences in spray coverage between two WSP positions, and is assigned as 0 or 1 if they are significantly different or not significantly different, respectively. The same WSP positions on target vines of the six replicated blocks were used as replications of each treatment for the comparisons of significant differences, and ANOVA multiple comparisons with Fisher’s least significant difference test at the 95% confidence level were used for the statistical analyses.
Results
Foliar disease severity and disease progression
Percent foliar disease severity at harvest was highest in the nontreated control plots in 2019, 2020, and 2021 with a combined disease severity value of 89% ± 2.8% (p < 0.0001) (Table 1). Disease severity was lowest in the plots with the intelligent high-rate treatment (40% ± 4.5%) and conventional treatment (33% ± 2.7%). Vines with the intelligent low-rate treatment had significantly more disease (53% ± 2.7%) than the other two treatments. Foliar disease progressed the fastest in the nontreated control plots with an average three-year combined AUDPC value of 2781 ± 133 (p < 0.0001) (Table 1). Mean AUDPC calculated from disease severity in vines that were sprayed with fungicides using the conventional treatment was 342 ± 129, statistically the same as vines sprayed with the intelligent high-rate treatment (340 ± 214). Mean AUDPC calculated from disease severity in vines that were sprayed with the intelligent low-rate treatment (924 ± 129) was significantly lower than the mean AUDPC value of nontreated control plots, but significantly higher than the AUDPC values from the intelligent high-rate or conventional treatment.
Mean percent foliar fungal disease severity and area under the disease progress curve (AUDPC) at the final disease assessment immediately prior to harvest.
Japanese beetle incidence and herbivory
During both years, a general increase was observed in the incidence of Japanese beetles and their associated herbivory from the beginning of the monitoring period in early July to the final sampling date in August.
In 2019, there was a significant date × treatment interaction in both the incidence of Japanese beetles (F12,9 = 15.93, p = 0.0001) and percent herbivory (F12,9 = 8.72, p = 0.001) in grapevine plots. Overall, significant differences in Japanese beetle incidence were observed between the nontreated control and the conventional air-assisted and intelligent-low treatments on five out of seven sampling dates (Figure 3). There were no differences in Japanese beetle incidence between treatments during the first two dates of the sampling period (10 and 15 July; nontreated versus air-assisted versus intelligent-low: p ≥ 0.46). However, by the third sampling date (18 July) conventional air-assisted and intelligent-low treatment plots had significantly fewer beetles in the grapevine canopy than the nontreated control plots (18 July: nontreated versus air-assisted: p = 0.002; nontreated versus intelligent-low: p = 0.01), and this pattern remained consistent for the remaining four sampling dates in 2019 (Figure 3). Furthermore, Japanese beetle incidence did not differ between conventional air-assisted and intelligent-low treatment plots from the third sampling date through the seventh and final sampling date (18 July through 2 Aug: air-assisted versus intelligent-low: p ≥ 0.61); both treatments suppressed Japanese beetles equally and significantly better than the nontreated control (Figure 3). When evaluating percent herbivory, there were no significant differences between treatments on any of the seven sampling dates (10 July through 2 Aug: p ≥ 0.06); rather, percent herbivory varied by date, ranging between 5 and 68%, and generally increasing over the sampling period (Figure 4).
Cumulative mean number (incidence) of Japanese beetles on three grapevines in nontreated control, conventional, and intelligent low-rate insecticide treatment plots across seven sampling dates in 2019. Means ± standard error means are shown. Means with different letters denote significant differences between treatments within sampling dates at α = 0.05.
Percent Japanese beetle herbivory on grapevines in nontreated control, conventional, and intelligent low-rate insecticide treatment plots across seven sampling dates in 2019. Means ± standard error means are shown. Percent herbivory was calculated by examining five terminal leaves on each of five random grapevine shoots in each plot (n = 25) for skeletonization.
In 2020, there was again a significant date × treatment interaction in both the incidence of Japanese beetles (F24,20 = 12.01, p < 0.0001) and percent herbivory (F24,20 = 4.41, p = 0.0007) in grapevine plots. Japanese beetle incidence increased over the course of the sampling period, but more beetles were observed in 2020 than in 2019. Overall, the lowest numbers of Japanese beetles occurred in conventional air-assisted and intelligent-high treatment plots, the highest numbers in nontreated control plots, and intermediate numbers of beetles in intelligent-low treatment plots (Figure 5). Japanese beetle incidence was significantly higher in nontreated control plots than in any other treatment plots during the first two sampling dates (10 and 14 July: nontreated versus air-assisted versus intelligent-low versus intelligent-high: p ≤ 0.007). By the third sampling date (15 July), conventional air-assisted and intelligent-high treatment plots had significantly fewer Japanese beetles in the canopy than nontreated control plots, and this pattern remained consistent for five of the remaining six sampling dates (15 through 30 July: nontreated versus air-assisted versus intelligent-high: p ≤ 0.02). However, Japanese beetle incidence did not differ between nontreated and intelligent-low treatment plots for the majority of the season (15 July through 6 Aug: nontreated versus intelligent-low: p ≥ 0.34). On the final sampling date in 2020 (6 Aug), only conventional air-assisted treatment plots had significantly fewer Japanese beetles than the nontreated control (p = 0.01).
Cumulative mean number (incidence) of Japanese beetles on three grapevines in nontreated control, conventional, intelligent low-, and intelligent high-rate insecticide treatment plots across nine sampling dates in 2020. Means ± standard error means are shown. Means with different letters denote significant differences between treatments within sampling dates at α = 0.05.
When evaluating percent herbivory in 2020, there were differences between treatments on four out of nine sampling dates, but there was no consistent pattern that any single treatment reduced herbivory better than another during the sampling period (Figure 6). Across treatments and dates, Japanese beetle feeding damage ranged between 13 and 20%. There were significantly fewer skeletonized leaves in conventional air-assisted and intelligent-high treatment plots than in nontreated control plots on 14 and 17 July (nontreated versus air-assisted versus intelligent-high: p ≤ 0.05). Furthermore, the intelligent-high treatment had significantly less herbivory than the nontreated control on the fifth sampling date (20 July; nontreated versus intelligent-high: p = 0.005), but by the sixth sampling date, only conventional air-assisted treatment plots exhibited reduced herbivory relative to the nontreated control (24 July; nontreated versus air-assisted: p = 0.03). After 24 July, there was no difference between treatments in levels of Japanese beetle herbivory (28 July through 6 Aug: p ≥ 0.35). The phenological stage of grapevines did not influence percent herbivory (p = 0.99) (data not shown).
Percent Japanese beetle herbivory on grapevines in nontreated control, conventional (airblast), intelligent-low, and intelligent-high rate insecticide treatment plots across nine sampling dates in 2020. Means ± SEM are shown. Percent herbivory was calculated by examining five terminal leaves on each of five random grapevine shoots in each plot (n=25) for skeletonization. Asterisks denote significant differences between treatments within sampling dates at α = 0.05.
Yield
On average, marketable yield (kg/vine and kg/ha) was significantly lower from the nontreated vines than the vines that were sprayed with pesticides using intelligent or conventional treatment (p = 0.0016; p = 0.0017, respectively) (Table 2). There were no significant differences in yield from vines treated with the intelligent low-rate (6.8 kg/vine; 10,123 kg/ha), intelligent high-rate (6.6 kg/vine; 8681 kg/ha), or conventional (5.9 kg/vine; 8507 kg/ka) treatments, even though the foliar disease severity (Table 1) with the intelligent sprayer at the low-rate treatment was significantly higher than the other two treatments.
Marketable yield following a season-long foliar fungal disease and Japanese beetle spray program using intelligent sprayer and conventional air-assisted technologies.
Pesticide spray output
Pesticide spray output varied significantly with phenological stage and sprayer technology (p < 0.0001) (Table 3). Although not always significant, the volume applied by the intelligent treatments (both rates) gradually increased as the grape canopy expanded, whereas the volume applied by the conventional treatment remained nearly constant. Spray output from the conventional treatment was significantly higher than outputs from the two intelligent treatments (low and high rates) at each phenological stage (p < 0.0001). The output from the intelligent low-rate and intelligent high-rate treatments ranged from 0.21 to 0.41 L/vine and 0.16 to 0.71 L/vine, respectively. Significant differences were not detected in the spray outputs between the two intelligent treatments until the second berry touch (E-L 32) application, where the high-rate treatment dispensed on average 0.14 L/vine or 1.4 times more than that of the low-rate treatment. This trend continued for the final two spray applications (veraison and preharvest applications), with 1.7 to 1.8 times the volume dispensed from the intelligent high-rate treatment compared to the low-rate treatment. Output from the conventional treatment ranged from 0.77 to 1.03 L/vine. Across all phenological stages, the conventional treatment applied 2.3 to 4.5 times more pesticide than the intelligent low-rate treatment and 1.4 to 5.9 times more than the high-rate treatment.
Mean pesticide spray output (volume) per vine at different phenological stages using low- and high-rate intelligent sprayer and air-assisted technologies during the 2019, 2020, and 2021 production seasons. E-L, Eichhorn-Lorenz.
Spray coverage
In 2019, percent spray coverage varied significantly with the location of WSPs within the vine, and the treatment with a significant interaction (Table 4; p = 0.0161) and spray coverage at all positions on the vines for both treatments was greater than 40%. Mean percent coverage was significantly lower at every location within the vine when spraying with the intelligent low-rate treatment (0.065 L/m3 rate) compared to the conventional treatment. Mean percent coverage using the intelligent low-rate ranged from 48% at the bottom of the leading trunk to 65% in the middle of the leading trunk. Percent coverage was the lowest at the bottom of the leading trunk (48%) using the low-rate treatment compared to all the other locations within the low-rate treatment and conventional air-assisted treatment. No differences were observed in percent spray coverage at all the two cordon locations and top center leading trunk using intelligent low-rate sprayer technology. With the conventional treatment, mean percent coverage ranged from 70% on the left cordon to 77% on the right cordon and center of the leading trunk. Percent coverage was significantly lower on the left cordon (70%) compared to the right cordon or center of the leading trunk (77% each). Percent coverage at the top (73%) and bottom (72%) of the leading trunk was similar.
Percent coverage of pesticides applied using intelligent sprayer and conventional air-assisted technology, assessed using water-sensitive paper in 2019.
In 2020, percent spray coverage was also affected by the treatment (p = 0.0001), the date (p < 0.0001), and the treatment by date (p < 0.0001), but not by the position (p = 0.1662), treatment by position (p = 0.8423), or treatment by date by position (p = 0.4504) (Table 5). Across all positions, mean percent coverage (70% ± 4.4%) was highest when pesticides were applied with the conventional treatment (Table 6). Mean percent coverage across all positions was 50% ± 4.1% when pesticides were applied with the intelligent high-rate treatment, which was significantly higher than percent coverage with the low-rate treatment (30% ± 4.6%).
Type III tests of fixed effects for percent spray coverage assessed using water-sensitive paper (WSP) in 2020. DF, degrees of freedom.
Percent coverage of pesticides applied using low- and high-rate intelligent sprayers and conventional air-assisted technology assessed using water-sensitive paper (WSP) in 2020.
Figure 7 shows mean spray deposition uniformity across target vine canopies for conventional, intelligent low-rate, and intelligent high-rate treatments, in the 2019 and 2020 growing seasons. The mean uniformity index of nine sprays in 2019 was 0.93 ± 0.11 for the conventional treatment and 0.96 ± 0.10 for the intelligent low-rate treatment. Similarly in 2020, the index was 0.80 ± 0.35 for the conventional treatment, 0.90 ± 0.17 for the intelligent low-rate treatment, and 1.00 for the intelligent high-rate treatment. Statistical analyses (p < 0.05) indicated there was no significant difference in the uniformity index between conventional and intelligent low-rate treatments in 2019, and among the three treatments in 2020. There was also no significant difference in the uniformity index for the same treatment between 2019 and 2020. Thus, the intelligent sprayer provided uniform spray coverage across the entire vine canopy in either low- or high-rate treatment, regardless of the changes in the canopy growth during the growing season.
Comparisons of spray deposition uniformities (see Equation 4) across target vine canopies for conventional (yellow), intelligent low-rate (blue), and intelligent high-rate (red) treatments in 2019 and 2020 growing seasons. Means ± standard deviations are shown for the conventional and intelligent low-rate treatments.
Cost of pesticides
The total cost per hectare of the pesticides applied using intelligent sprayer technology or conventional air-assisted technology was compared for the pesticide applications in 2019, 2020, and 2021 beginning at 25 to 30 cm shoot growth (E-L stage 16 to 17) to preharvest (E-L stage 36 to 37) phenological stages, for a combined total of 10 applications. In all three years, the total cost of pesticides was highest for the conventional treatments (Table 7). In 2019, the total cost of pesticides was $496/ha higher using the conventional treatment ($2505/ha) than the intelligent low-rate treatment ($2009/ha). In 2020, the total cost of pesticides was $713/ha and $596/ha higher using the conventional treatment ($965/ha) than the intelligent low-rate ($252/ha) and high-rate treatments ($369/ha), respectively. Likewise, in 2021, pesticide costs using the conventional treatment ($716/ha) were higher than the costs using the intelligent low-rate ($156/ha) and high-rate ($247/ha) treatments.
Total cost of pesticides per hectare used to treat grapevines using conventional air-assisted and intelligent sprayer technologies from 25 to 30 cm shoot growth (Eichhorn-Lorenz [E-L] stage 16 to 17) to preharvest (E-L stage 36 to 37) phenological stages in 2019, 2020, and 2021.
Discussion
Laser-guided intelligent sprayer technology for the application of pesticides is revolutionizing the specialty crop industry as a sustainable and cost-effective approach to manage insect pests and diseases. In this study, intelligent sprayer technology was validated as an effective tool for controlling foliar fungal and oomycete grape diseases and Japanese beetle populations and herbivory over three seasons. In the first year of the study, the spray deposition rate of 0.065 L/m3 used for the intelligent sprayer was selected based on previous testing with apple plants (Chen et al. 2012). Although total yield (per vine and per hectare) was the same between vines sprayed with fungicides using the intelligent sprayer at this low spray rate and conventional air-assisted sprayer technology, foliar disease severity was lower in the plots treated with conventional air-assisted technology.
We hypothesized that the reduced efficacy was attributed to not enough fungicide being dispensed from the sprayer at the low spray rate, so in 2020 and 2021, a higher spray rate (0.13 L/m3) was added to the study. By doubling the spray rate of the intelligent sprayer, we achieved foliar disease and Japanese beetle control and yields comparable to the conventional air-assisted sprayer, which was dispensing pesticides at the application rate of 935.4 L/ha. Also, the intelligent high-rate treatment improved the spray coverage uniformity compared to the low-rate and conventional treatments (Figure 7). The uniformity index of the intelligent high-rate treatment reached 1.0 because there were no significant differences in spray coverage among the five sample positions on each tested plant during the growing seasons. That is, the intelligent high-rate mode provided uniform spray coverage across the entire plant by automatically adjusting individual nozzle outputs to match changes of architectures within the plants and during the growing season.
A similar study comparing intelligent technology to conventional air-assisted technology on winegrapes in Oregon found that powdery mildew disease was more effectively controlled when the spray deposition rate of the intelligent sprayer was increased from 0.0625 to 0.125 L/m3, and that powdery mildew control was comparable to conventional air-assisted technology (Warneke et al. 2022). Likewise, Boatwright et al. (2020) and Chen et al. (2021) found no difference in disease and insect incidence when intelligent sprayer technology, compared with air-assisted technology, was used in peach and apple orchards, respectively.
Intelligent sprayer technology uses a laser scanning sensor to measure canopy architecture and density to make plant-targeted spray applications (Shen et al. 2017, Zhu et al. 2017). Through targeted spraying, the amount of pesticide dispensed onto the vine is significantly reduced compared to the conventional spray technology, which constantly discharges chemicals into the field by ignoring crop growth and empty space between vines. In this study, the volume of pesticide applied to each vine using the intelligent sprayer varied depending on the phenological stage of the vine but was always significantly less than the volume applied using the conventional air-assisted sprayer. As was expected, the largest reduction in spray volume using the intelligent sprayer occurred early in the season, when canopy density was lowest. Similar results in the vineyard and apple orchard have been reported (Nackley et al. 2021).
Overall, there was a 29 to 83% reduction in the amount of pesticide output with the intelligent sprayer compared to the conventional air-assisted sprayer. This level of reduction in pesticide use is comparable to reductions reported in other small fruit crops such as blueberry and black raspberry, and several tree fruit crops (Boatwright et al. 2020, Fessler et al. 2020, Chen et al. 2021, Nackley et al. 2021). By using less pesticides with the intelligent sprayer, the pesticide cost per hectare was substantially lower. Depending on the spray rate and based on 2019 pricing, it saved between $469 and $712/ha between 2019 and 2021. Considering that there was not a loss in efficacy with reduced pesticide output, and product costs per hectare were much lower with the intelligent sprayer, the conventional air-assisted sprayer was inefficient to dispense pesticides. Further evidence of conventional air-assisted sprayer inefficiency is its excessive spray coverage on vines.
Although significantly less pesticide was applied to the vines with the intelligent sprayer technology compared to air-assisted technology, near optimal spray coverage was still achieved. Here, optimal pesticide spray coverage for adequate disease and insect pest control is defined as 20 to 30% coverage in the canopy, where percent coverage <20% will not effectively control disease and insect pests, while percent coverage >30% is excessive (Holownicki et al. 2000, Chen et al. 2013a, Mangado et al. 2013). In the two years (2019 and 2020) of percent spray coverage evaluations, percent coverage was always higher using the conventional air-assisted sprayer than the intelligent sprayer, and at nearly every location within the vine coverage exceeded 60% with the conventional air-assisted sprayer. Although percent spray coverage using the intelligent sprayer exceeded 60% with the low-rate treatment in 2019, in 2020, average percent coverage was 30%. With the intelligent high-rate treatment, percent coverage was 69% or higher at the second prebloom spray, but the average percent coverage over the entire season was 50%, still within the optimal range.
Because foliar disease severity was lower when pesticides were applied using the intelligent sprayer at the high rate compared to the low rate, but percent pesticide coverage was approaching being excessive, adjusting the spray rate so that coverage is consistently between 30 and 50% could further improve the efficiency of the intelligent sprayer. When applying pesticide with the laser-guided intelligent sprayer, Salcedo et al. (2020) found that excessive coverage was applied to the target row, and the next row of apple trees received adequate to excessive coverage as well. For both conventional and intelligent technologies, spray deposition was uniform within the vine based on the uniformity index assessments; however, Salcedo et al. (2020) found greater uniformity in spray coverage applications applied using the intelligent technology. This was expected because the intelligent spray technology automatically adjusted spray outputs to match sectional canopy volumes (Shen et al. 2017). Also, a reduction in spray volume is not correlated with coverage (Wise et al. 2010), and both technologies used the same spray nozzles to deliver the same pesticides within a season.
Although drift potential was not measured in this study, previous reports have demonstrated that the amount of drift and ground drop-off from the intelligent sprayer is significantly reduced compared to conventional air-assisted sprayers (Chen et al. 2013b, Silva et al. 2018, Fessler et al. 2020, Nackley et al. 2021, Salcedo et al. 2021). For example, Fessler et al. (2020) reported reductions in off-target drift by 83 and 87% in apple orchards when using the intelligent technology at various spray deposition rates. Salcedo et al. (2021) reported that airborne drift and ground loss were reduced by 90 and 84%, respectively, when young apple trees were sprayed with the intelligent sprayer compared to a conventional air-assisted sprayer. Most recently, Nackley et al. (2021) observed a reduction in off-target losses of up to 33% in the vineyard and 40% in the orchard. The reduction in spray drift is attributed to the laser sensor detection and the computations that rapidly determine when and which nozzles should turn on or off. Unlike conventional air-assisted technology, intelligent sprayer technology does not constantly dispense pesticides across the entire field. Thus, there is less pesticide dispensed and less potential for off-target drift. As a result, there is increased potential for greater profits, as there is less pesticide waste. Although the potential benefits to the environment and worker health are not easily quantified, decreased use of pesticides and reduced off-target exposure in a production system that relies almost exclusively on pesticides for disease and insect control is a giant leap forward in sustainability.
Conclusions
Intelligent sprayer technology is a new and effective tool for sustainable management of fungal and oomycete diseases and Japanese beetles. It offers the potential to reduce the volume of pesticides used in the vineyard by 29 to 83%, compared to conventional air-assisted technology, without compromising foliar disease and Japanese beetle control or yield. The direct and indirect benefits of using less volume of pesticides are numerous, including 1) spraying more area of the vineyard on a single tank, thereby reducing the amount of time required to refill spray tanks; 2) conserving water, which is especially important in drought-stricken areas of the U.S.; 3) pesticide input cost savings; and 4) enhanced pesticide stewardship. With the intelligent sprayer, less pesticide is applied to the vines and grape clusters and less pesticide is entering the environment, which enhances crop and public health safety. The laser-guided intelligent spray technology delivers a uniform, targeted application to the vines that can result in less drift and off-target applications. Lastly, there is the potential for increased profits due to the reduction in pesticide and water use.
Supplemental Data
The following supplemental materials are available for this article in the Supplemental tab above:
Supplemental Table 1 Pesticide spray program, including product and application rate, phenological stage, and application date, used to control foliar fungal diseases and Japanese beetle in grape (cv. Traminette) in 2019.
Supplemental Table 2 Pesticide spray program, including product and application rate, phenological stage, and application date, used to control foliar fungal diseases and Japanese beetle in grape (cv. Traminette) in 2020.
Supplemental Table 3 Pesticide spray program, including product and application rate, phenological stage, and application date, used to control foliar fungal diseases and Japanese beetle in grape (cv. Traminette) in 2021.
Footnotes
This research was supported by the United States Department of Agriculture—Agricultural Research Service Cooperative Agreement No. 58-5082-8-006. The authors declare no conflict of interest. Technical contributions are acknowledged for Adam Clark at USDA-ARS Application Technology Research Unit and Becky Colon at The Ohio State University, College of Food, Agricultural, and Environmental Sciences (CFAES) - Wooster, Research Services-Agricultural Operations, Wooster OH.
Wodzicki LM, Madden LV, Long EY, Zhu H and Lewis Ivey ML. 2023. Evaluation of a laser-guided intelligent sprayer for disease and insect management on grapes. Am J Enol Vitic 74:0740024. DOI: 10.5344/ajev.2023.23013
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- Received February 2023.
- Accepted May 2023.
- Published online August 2023
This is an open access article distributed under the CC BY 4.0 license.
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