Is herbicide applied using drones as efficient as when applied using terrestrial systems?

Abstract

Background  drones for pesticide application have become more popular due to their practicality and reduction in cost. Accessing drone efficacy in herbicide application is needed.

Objective  This work aimed to determine the efficiency of drones in applying glyphosate and glufosinate to control ryegrass and oilseed radish compared to electrostatic and conventional ground herbicide applications.

Methods  The experiments were conducted in a factorial arrangement with five replications and repeated in time. Factor A consisted of spraying methods [drone, electrostatic ground spraying (electrostatic), and conventional ground spraying (conventional)], and factor B consisted of eight doses of glyphosate and glufosinate. A CO₂ pressurized backpack was equipped with XR TeeJet^® 11001VS spray nozzles for electrostatic and conventional applications, adjusted to a spray volume of 100 L ha^-1. The drone, Pelicano^® 2022 model (Skydrones), was equipped with the same spray nozzles but was regulated to a spray volume of 10 L ha^-1. Results: The results demonstrated that glyphosate application via drones provided superior control of ryegrass and oilseed radish compared to terrestrial methods, whereas glufosinate’s effectiveness was comparable to that of ground-based applications.

Conclusions  drone application of glyphosate and glufosinate was efficient in controlling ryegrass and oilseed radish, being as good as ground spraying, even demonstrating that low spray volume does not significantly interfere with weed control in this study.

Digital Agriculture; Efficiency; UAV; Spray output; Weed

1.Introduction

Herbicide application is a routine yet critical practice in agriculture, essential to preserving modern agriculture’s yield potential. The success of this method critically depends on the herbicide application technology, which is an interdisciplinary field that encompasses biology, chemistry, ecology, economics, environmental science, electronics, and artificial intelligence (Contiero et al., 2018). Efficient herbicide application on target enhances herbicide efficacy, reduces environmental and operational risks, and prevents economic losses (Matuo, 1990; Giles et al., 2008).

The herbicide spraying process is intricate, relying on the technology used for droplet production, the inclusion of adjuvants in the spray solution, physiological aspects of the target weed or crop, environmental conditions, and equipment configuration. Off-target herbicide movement poses a significant challenge, and effective herbicide application minimizes such off-target movement (Ebert, Downer, 2008; Creech et al., 2015a, 2015b).

Several technologies have been developed to enhance herbicide application and reduce off-target movement. Electrostatic spray systems have been explored within this technological framework. These systems charge droplets positively, creating an electrified cloud that attracts to negatively charged plant surfaces, thus enhancing droplet adhesion (Chaim, 2006; Sasaki et al., 2015), altering the droplets course, increasing the application efficiency, reducing the losses of the application solution into the air and soil (Maynagh et al., 2009; Sasaki et al., 2015; Urkan et al., 2016; Yamane, Miyazaki, 2017; Appah et al., 2019; Jamadar, Ashta, 2019). The size of the droplets significantly influences the electrostatic system, with the smallest being the most efficient (Law, 2001). Despite these advancements, some studies have reported negligible improvements in application efficiency and reduced impact on weed deposition, and drift using electrostatic spraying (Law, 2001; Bayer et al., 2011; Magno Júnior et al., 2011; Campos et al., 2020).

Aerial application optimization is one of the most advanced technologies studied within application technology (Wilson, 2003). Currently, the use of drones for spraying agricultural products has been expanding, gaining popularity in the context of smart agriculture (Subramanian et al., 2021; Biglia et al., 2022) due to their known benefits. drones can apply pesticides on tall perennial crops, uneven terrain, and small areas (Lan, Chen, 2018; Chen et al., 2022). Notably, smaller drones can navigate smaller crop areas without the need for a runway, and the downward airflow from their rotors enhances droplet deposition and reduces drift (Zhang et al., 2018; Shouji et al., 2021; Wang et al., 2021).

The future of drone technology in agriculture looks promising, with increasing adoption driven by factors such as farm size, the age and education level of farm workers, and supportive public sector policies (Michels et al., 2021; Adekoya et al., 2022, Bai et al., 2022). However, drones have limitations, and because they are a relatively new technology, several unknown factors may affect herbicide efficacy. One of the most important limitations is flight autonomy constrained by battery capacity, though efforts are ongoing to extend operational durations (Dorling et al., 2017).

drones enable low-volume spraying, which may influence herbicide performance based on spray distribution and deposition; in our hypothesis, the low-volume application may be beneficial for systemic herbicides, such as glyphosate, but it might be detrimental for contact herbicides, such as glufosinate. This study aims to evaluate the efficiency of drone-applied glyphosate and glufosinate in controlling ryegrass and oilseed radish compared to traditional ground spraying methods, exploring whether drone technology can surpass earlier technological advancements like electrostatic systems.

2.Materials and methods

2.1 Plant Material

The plants used in the experiments were grown in a greenhouse located in Capão do Leão City, RS, Brazil, in 2021 and 2022. The test plants used were ryegrass (Lolium multiflorum L.) and oilseed radish (Raphanus sativus L.), which are susceptible to glyphosate and glufosinate herbicides. Each species was sown in 2.8 L pots filled with soil (consisting of 65.42% sand, 25.18% silt, and 9.40% clay), and plants were thinned to five plants pot^-1after emergence. When oilseed radish plants reached the six to eight-leaf stage and ryegrass plants began tillering, they were transferred to the field to perform the applications. Following herbicide application, the plants were returned to the same greenhouse for subsequent analysis.

2.2 Experimental design and evaluations

The experiment was arranged a completely randomized design in a factorial arrangement with five replications, and it as replicated in time (twice). Factor A consisted of the spraying technologies being: 1) drone, 2) electrostatic ground spraying (electrostatic), and 3) conventional ground spraying (conventional). Factor B included the doses of glyphosate (0.00, 11.72, 23.44, 46.87, 93.75, 187.50, 375.00, and 750.00 g ae ha^-1) or glufosinate (0.00, 6.25, 12.50, 25.00, 50.00, 100.00, 200.00, 400.00, 800.00, and 1600.00 g ai ha^-1). The commercial herbicide used in the experiment was ZAPP QI 620^® glyphosate (rate – 1.5 L ha^-1) and OFF- ROAD^® glufosinate (rate – 2.0 L ha^-1) which no vegetable oil was added for application.

The “electrostatic” and “conventional” treatments were applied using a CO₂-pressurized backpack sprayer regulated to a spray volume of 100 L ha^-1. The application speed was 3.6 ms^-1 at a height of 50 cm above the plants. The Pelicano^® 2022 (Skydrones) drone with a four-rotor propeller size of 90 cm was used. The weight of the drone is 11 kg without batteries, and it has a maximum load of 25 kg. The volumetric capacity of the equipment is 10 L, with an application swath of 4 to 5 meters. The iOS system tablet application automatically operated the drone. The drone was calibrated to a spray volume of 10 L ha^-1, at a speed of 5 ms^-1 at a height of 3 m above the plants. All application technologies were equipped with XR-type spray nozzles 11001VS (TeeJet® Spraying Systems Co.), with meteorological conditions of 26±7°C, relative humidity of 70±10%, and wind speed of 3±1 ms^-1 in the treatment areas. To use the same strategy used by the other application method, the application was done with one pass per replication.

Visual control assessments were performed using a percentage rating scale, where 0 indicates no damage, and 100 indicates plant death (Asociation Latinoamericana de Malezas, 1974). The evaluations were carried out 7, 14, 21, and 28 days after application (DAA), assigning a score for the control of each weed species. Additionally, weeds were cut at the soil surface level at 28 DAA, placed in paper bags, and dried on a forced-air oven at a temperature of 65 ± 2 °C until reaching a constant weight to determine the dry aboveground dry weight.

2.3 Data analysis

Before data analysis date was checked for normality, and homoscedasticity and then submitted to the F test. Non-linear logarithmic regressions were constructed as proposed by Knezevic et al. (2007). For the control variable, the three-parameter Weibull model was used (Equation 1), whereas, for the dry matter, the three-parameter log-logistic model was used (Equation 2). The models are represented in the following equations:

$y=d^{\#}+\mathrm{ex}\{-\exp [b(\log x-\tilde{e})\left]\right\}$ Eq. 1

$y=\frac{d^{\#}}{(1+\exp \exp [b(\log x-\log e)]}$ Eq. 1

where y is the percentage of control, x is the herbicide dose (g ai or ae ha^-1); “b,” “d”, and “e” are parameters of the curve, so “b” is the slope of the curve, “d# is the upper limit of the

curve when the lower limit is e=0, and “e” is the inflection point of the curve (ED₅₀ - the dose that provides 50% response of the variable for the three-parameter log-logistic model), “~e” is the logarithm of the inflection point of the curve (logarithm of ED₅₀). The lower limit of the curve was considered zero. The estimated doses of the herbicides used to achieve control and reduction of plant growth by 50% (ED₅₀) with the technologies used were calculated using the DRC package, with the ED function (Ritz et al., 2015) of the statistical program R (R Core Team, 2022). The curves’ lower limit (parameter c) was constrained for the model to perform biological estimations (Kniss, Streibig, 2018).

3.Results and Discussion

Statistical analysis indicated no significant effect of growing seasons; therefore, data were combined for a more robust analysis. Ryegrass control at 28 DAA for glyphosate was better when the application was administered by drone (Figure 1A). For glufosinate, there was no difference between the application technologies with ED₅₀ of 87.4 and 91.8 g ai ha^-1for drone and electrostatic, respectively (Table 1). For aboveground dry weight, glyphosate application on ryegrass showed no difference between spraying performed via drone and terrestrial spraying (conventional and electrostatic) (Figure 2A). conventional resulted in better control than electrostatic, with ED₅₀ of 21.6 and 41.9 g ae ha^-1, respectively (Table 2). The glufosinate herbicide did not show significance when applied via conventional and electrostatic (Table 2), whereas when applied via drone, it showed an ED₅₀ of 48.8 g ai ha^-1 (Figure 2B).

Figure 1
Control of ryegrass (A, C) and oilseed radish (B, D) at 28 days after application of glyphosate (A, B) and glufosinate (C, D) through conventional ground sprayer (conventional), electrostatic ground sprayer (electrostatic) and via drone (drone)

Table 1
Parameters of the log-logistic dose-response curves for glyphosate and glufosinate on ryegrass and oilseed radish control at 28 DAA when sprayed via conventional ground sprayer (conventional), electrostatic ground sprayer (electrostatic) and via drone (drone)

Herbicide	Parameters	Application technology
		conventional	electrostatic	drone
Ryegrass
Glyphosate (Figure 1A)	b	1.7	1.4	1.1
	d	97.9	99.6	94.1
	ED50	37.6 (±3.2)*	45.8 (±3.8)	26.8 (±3.0)
	p-value	3.9910e-16	<2.2000e-16	6.6130e-11
Glufosinate (Figure 1C)	b	0.6	0.7	0.6
	d	108.8	102.0	104.6
	ED50	385.8 (±317.5)	91.8 (±16.8)	87.4 (±15.9)
	p-value	0.2850	6.2640e-06	4.8000e-06
Oilseed radish
Glyphosate (Figure 1B)	b	1.8	1.7	1.6
	d	93.2	97.2	92.6
	ED50	119.6 (±8.2)	151.8 (±11.5)	50.0 (±3.4)
	p-value	<2.2e-16	<2.2e-16	<2.2e-16
Glufosinate (Figure 1D)	b	0.8	0.9	0.7
	d	98.7	104.4	96.6
	ED50	279.0 (±41.9)	331.1 (±50.4)	117.2 (±13.4)
	p-value	4.469e-08	1.405e-12	8.944e-08

• * Values in parenthesis represent the standard error coefficient

Figure 2
Dry weight (g plant-1) of ryegrass (A, C) and oilseed radish (B, D) at 28 days after application of glyphosate (A, B) and glufosinate (C, D) through conventional ground sprayer (conventional), electrostatic ground sprayer (electrostatic) and via drone (drone)

Table 2
Parameters of the log-logistic model for glyphosate and glufosinate for aboveground dry weight of ryegrass and oilseed radish at 28 DAA when sprayed via conventional ground sprayer (conventional), electrostatic ground sprayer (electrostatic) and via drone (drone)

Herbicide	Parameters	Application Technology
		conventional	electrostatic	drone
Ryegrass
Glyphosate (Figure 2A)	b	0.7	0.9	0.7
	d	4.0	4.0	4.0
	ED50	21.6 (±7.5)*	41.9 (±11.0)	29.6 (±8.7)
	p-value	0.0042	0.0002	0.0008
Glufosinate (Figure 2C)	b	0.6	0.8	0.7
	d	4.0	3.9	3.7
	ED50	112.6 (±58.4)	45.0 (±25.7)	48.8 (±7.5)
	p-value	0.0552	0.0815	0.0057
Oilseed radish
Glyphosate (Figure 2B)	b	1.2	1.1	0.9
	d	3.0	2.9	2.9
	ED50	134.6 (±32.6)	147.8 (±40.6)	73.2 (±23.5)
	p-value	5.2360e-05	0.0003	0.0020
Glufosinate (Figure 2D)	b	0.5	0.6	0.6
	d	2.9	2.8	2.9
	ED50	87.8 (±51.0)	117.5 (±48.1)	54.3 (±18.8)
	p-value	0.0860	0.0151	0.0042

• * Values in parenthesis represent the standard error coefficient

Oilseed radish control with glyphosate performed best when applied by drone, whereas application via electrostatic resulted in the worst performance (Figure 1C). The glufosinate also performed better when applied by drone, obtaining an ED₅₀ of 117.2 g ai ha^-1(Table 1). Glufosinate efficacy did not differ between terrestrial spray forms (Figure 1D).

When applied by drone, the aboveground dry weight results for oilseed radish showed that the glyphosate performed better, obtaining an ED₅₀ of 73.2 g ae ha^-1(Figure 2C). There was no difference between conventional and electrostatic on ryegrass with glyphosate (Table 2). The application of glufosinate via conventional was not significant, and there was no difference between spraying via electrostatic and drone on the oilseed radish (Figure 2D).

In the experiments, the difference between the spray volume used by terrestrial spraying was 10 times greater than that used by the drone; this could influence herbicide effectiveness on weeds due to the lower deposition/spray coverage, but both herbicides showed better or equal control when applied by drone.

Studies indicate that glyphosate efficacy is not compromised by reducing the volume of the spray solution and may even be enhanced when applied at lower volumes (Cranmer, Linscott, 1991; Ramsdale et al., 2003; Bueno et al., 2013). This improvement is attributed to the characteristics of the spray solution, as reducing the spray volume increases the concentration of the herbicide’s active ingredient in each droplet, which favors the diffusion process into the leaf. Additionally, glyphosate is a mobile herbicide within the plant, meaning its effectiveness is not directly tied to application coverage (Hua Liu et al., 1996). Moreover, applying glyphosate with drones has been shown to be more efficient on the tested weeds compared to terrestrial spraying. On the other hand, glufosinate could have reduced efficacy due to its limited translocation (Takano et al., 2020). Against this hypothesis, our results showed that glufosinate did not suffer a loss of efficiency, which can be explained by the downward movement and the turbulence caused by the drones’ propellers. This phenomenon may have led to higher deposition in the abaxial part of the leaves (Jiang et al., 2022). The downward airflow generated by the drone rotors significantly improves droplet deposition, aiding in higher deposition on both the adaxial and abaxial surfaces of the leaves. This improvement is mediated by the pressure differential between the upper and lower surfaces of the leaf, resulting in a leaf reversal effect (Yang et al., 2018). A study by Martin et al. (2020) found that deposition on the abaxial surface of the leaves was four times greater with a drone than when applied via a backpack sprayer.

Applying glyphosate through electrostatic on ryegrass led to faster ryegrass control (Martin, Latheef, 2017). However, the results found glyphosate that application via electrostatic did not perform better than conventional (Figures 1 and 2). The drone and the electrostatic performed similarly when applying glufosinate, resulting in greater ryegrass control than conventional (Figure 1B).The electrostatic could also lead to better deposition of the herbicide because the spray mixture is charged (positively and negatively), causing greater attraction of the drop to the leaf surface by creating an electrostatic field, improving the deposition of the spray mixture on the target (Law, 2001; Sánchez-Hermosilla et al., 2022). Furthermore, electrostatic leads to better spray deposition on the adaxial and abaxial leaf surface (Maski, Durairaj, 2010).

4.Conclusions

This study demonstrates that glyphosate application via drones provides control of ryegrass and oilseed radish comparable to traditional ground methods. Similarly, glufosinate applied using drones was equally effective as terrestrial applications, confirming that drone technology is a viable and efficient method for weed management, even for contact herbicides.

It is important to note, however, that this experiment did not consider the potential variability caused by spray overlap. Future research should focus on assessing the distribution of spray coverage across larger areas and include considerations for overlapping applications. This will be crucial for optimizing the precision and effectiveness of drone technology in pesticide application, enhancing its utility in modern agricultural practices.

Acknowledgments

The authors thank the Crop Protection Graduate Program/UFPel and Schroder Consultoria Agro for supporting this research and Fábio Vianna Agronegócios for collaboration on drone applications.

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• Funding:
  We thank the Coordination for the Improvement of Higher Education Personnel (CAPES) and the National Council for Scientific and Technological Development (CNPq) for their financial support for academic development. Mr. Roque Palacios-Zuñiga, Rubens Polito and Bruno Araújo received a PhD assistantship from the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) Finance code 001. Luis Avila received funding National Council for Scientific and Technological Development (CNPq) Grant number 310830/2019-2; and Edinalvo Camargo received Research Fellowship from the National Council for Scientific and Technological Development (CNPq) Proc. 311449/2022-0

Edited by

• Editor in Chief:
  Carol Ann Mallory-Smith

• Associate Editor:
  Leonardo B. de Carvalho

Publication Dates

• Publication in this collection
  11 Nov 2024
• Date of issue
  2024

History

• Received
  16 Feb 2024
• Accepted
  1 Sept 2024