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Article

Comparison of the Spray Effects of Air Induction Nozzles and Flat Fan Nozzles Installed on Agricultural Drones

by
Seung-Hwa Yu
1,
Yeongho Kang
2 and
Chun-Gu Lee
1,*
1
National Academy of Agricultural Science, Rural Development Administration, Jeonju 54875, Republic of Korea
2
Jeollabuk-do Agricultural Research & Extension Services, Iksan 54591, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(20), 11552; https://doi.org/10.3390/app132011552
Submission received: 15 September 2023 / Revised: 18 October 2023 / Accepted: 20 October 2023 / Published: 22 October 2023
(This article belongs to the Special Issue Agriculture 4.0 – the Future of Farming Technology)
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Abstract

:
Pest control is essential for increasing agricultural production. Agricultural drones with spraying systems for pest control have generated great interest among farmers. However, spraying systems installed on unmanned aerial vehicles, like any other sprayer, can cause damage to the environment due to drift of the agent. Air induction (AI) nozzles are known to produce less drift (e.g., larger spray drops) than other nozzles, but there is a lack of research analyzing their effectiveness in combination with drones. In this study, AI and flat fan nozzles were installed on drones to evaluate their spray and pest control performance. Aerial spraying was conducted on rice and soybeans to measure the coverage and penetration ratio and analyze the crop production as well as the crop damage due to pests and diseases. The drone flight was conducted at an altitude of 3 m and a velocity of 2 m/s. Spray droplets were collected using water-sensitive paper at two heights above the soil surface. The experiments showed that the crop coverage with the AI nozzle was 130% higher than that with the flat fan nozzle. The drift reduction of AI nozzles increased the coverage of spray droplets. But the difference in the penetration ratios, which is the ratio of agents to be delivered inside the crop, was not significant between the nozzles. Also, there was no significant difference in crop yield and pest control efficacy. Consequently, the performance of the AI nozzle did not show differences from that of the XR nozzle, except for coverage. However, the AI nozzle raised less drift, so it should be considered for use in aerial control.

1. Introduction

The increasing global population reached 8 billion in November 2022; it is projected to reach 9.7 billion by 2050 [1]. Thus, the demand for food has also increased with the population; therefore, food production will also need to increase by up to 70% [2]. To achieve this food production goal, pests must be controlled because crop yield would substantially decrease without pest control [3]. In particular, pests and insects are becoming increasingly common as a result of global warming, necessitating pest-prevention measures [4]. However, the number of farmers is continuously decreasing, causing a farm labor shortage [5]. In the past, the main method of control was pesticide spraying through knapsack sprayers and boom sprayers, which was laborious and exposed workers to agents. To solve this problem, many researchers have studied various sprayers for effective and efficient pest control. Moreover, recently, considerable attention has been focused on the use of agricultural spraying drones [6,7,8,9].
Agricultural spraying drones comprise a system that sprays plant protection products using a remote or automatic control tool [10,11,12]. Plant protection products are sprayed from unmanned aerial vehicles and delivered to crops using gravity and downwash airflow from the rotor. Agricultural drones facilitate convenient operations, high work efficiency for targeted application, and high worker safety; therefore, their use by farmers is gradually increasing [13,14,15]. In particular, agricultural drones are widely used in East Asia [16]. For example, China has 106,000 drones for pest control, and their work area is estimated to be 64 million hectares [17]. Xiongkui et al. [18] analyzed the current pest control status using drones in China, Japan, and South Korea and presented the challenges of aerial spraying. In Europe, a few studies of spray drones in mountainous terrain have been conducted in spite of regulation [19]. Several studies focused on improving the usability of agricultural drones have been also conducted [20,21].
With the growth of the agricultural spraying drone market, there is a huge concern about the drift of pesticides and drop bounce from agricultural drone spray systems [22]. In Europe, aerial spraying is generally not permitted due to the risk of drifts [23]. In South Korea, aerial spraying has also become a problem due to revised pesticide use standards in 2019. The quantity of drift is generally associated with the ratio of fine spray droplets [24]. Depending on the size of the spray droplets, more than 70% of the volume can be drifted [25].
Many factors including flight altitude, additives, and downwash can affect the spraying performance of agricultural drones [26,27,28]. Various studies have been conducted to analyze droplet downwash from drones [29]. Yang et al. [30] conducted a study to build a CFD model to analyze the effects of downwinds and crosswinds on the behavior of sprayed droplets. Zhan et al. [31] researched influence of the downwash airflow of UAVs and found that the strength of the airflow is positively correlated with deposition and penetration and negatively correlated with uniformity and drift. The type of nozzle determines the diameter of the sprayed droplets and the spray angle and shape, thereby substantially impacting spraying performance [32,33]. Chen et al. [25] used four flat fan nozzles with different orifice sizes to analyze the effect of droplet size on droplet deposition and drift. They verified that the deposition, penetration of droplets, and drift distribution were affected by the droplet size. Guo et al. [34] used machine learning methods to analyze the droplet diameter and distribution pattern of the nozzles. Among the various nozzles, flat fan nozzles are widely used for unmanned aerial sprayers [35] because of a high spray efficiency [36]. In contrast to flat fan nozzles, AI nozzles produce large droplets by including air bubbles inside the droplets and reduce the drift of droplets [37,38,39]. Dafsari et al. [40] developed a new AI nozzle for aerial spraying, tested it, and found that it worked as designed and had a smaller spray diameter than a conventional AI nozzle. However, there is a lack of research that actually uses drones equipped with AI nozzles to conduct control and analyze their effectiveness. Therefore, research is needed to actually perform aerial spraying with AI nozzles and compare performance differences with conventional nozzles when using drones.
When comparing the performance of two nozzles, the effect of pest reduction when pest control is conducted using agricultural drones must also be investigated [41]. The control efficacy is one of the most important criteria of the spraying system. However, most previous studies have only examined the deposition of spray droplets using collectors to evaluate the physical spraying performance of agricultural drones. Furthermore, even when pest control is conducted using the same system, the penetration rate of plant protection products may vary depending on the size and shape of the crops, affecting pest control efficacy [25,42,43]. However, only a few studies have been conducted to compare the differences in the pest control efficacy of crops when using agricultural drones.
The main objective of this study was to compare the spraying performance of a flat fan nozzle and an AI nozzle when using a drone. To compare spraying performance, the deposition, penetration rate, and control efficacy have to be analyzed using water-sensitive paper. In order to compare the different results between crops, we planned to perform the experiment on soybeans and rice. Crops have to be collected after pest control to compare control efficacy by investigating the yield and damage caused by pests.

2. Materials and Methods

2.1. Experimental Site

The experiments were conducted at a farmland in Gimje, South Korea (Figure 1). The experimental field was where rice and soybeans were cultivated, and the areas for rice and soybean cultivation were 4000 m2, respectively. Rice and soybeans are the main food crops in South Korea, and, recently, soybean cultivation in paddy fields has been increasing, so both were selected as target crops. The rice variety was Sindongjin, which was planted on 2 June with a planting density of 15 plants/m2. The soybean variety was Daechan, which was planted on 17 May with a planting density of 8.3 plants/m2. The spraying was conducted around 9 am to minimize the influence of wind. The weather conditions during the experiment were measured using a kestrel 5500 weather meter (KestrelMeters, Boothwyn, PA, USA). The temperature during the experiment was 20.9~25.3 °C, and the wind speed was 1~2 m/s. There were no significant obstacles that affected pest control around the experimental site.

2.2. Equipment

The agricultural drone used in the experiment was a multicopter (SG-10p, Hankooksamgong, Seoul, Repubic of Korea) with eight rotors (Figure 2). An agricultural drone was equipped with a flight controller (K++, JIYI, Shanghai, China). Maximum thrust of rotor is 5.1 kg. The detailed specifications of the agricultural spraying drone are listed in Table 1. The pump installed on the drone had a rated pressure of 0.8 MPa and a rated flow rate of 5.5 L/min. During the experiment, the chemical solution tank was filled with 10 L of the plant protection product solution before the flight. Only the two front nozzles were used to eliminate the overlapping effect of spraying the front and back nozzles. The nozzles were installed in the pipe located just below the rotor. The horizontal distance between the nozzles was 158 cm and the spray width of the drone was 4 m. The drone was operated by a professional using a remote control (T12 Data link Remote Controller, Skydroid, Quanzhou, China).
Conventional flat fan nozzles (XR110015VS, Teejet Technologies, Glendale Heights, IL, USA) and AI nozzles were used with drones. The XR110015VS has a flat fan-shaped spray form, and the droplet size is classified as fine on a general spraying pressure. In the AI nozzle, the sprayed droplet diameter is enlarged via air induction. The AI nozzle is less affected by wind compared with other nozzles. To develop a new AI nozzle for aerial spraying, a research on nozzle design factors was conducted and a new AI nozzle was developed based on it [40]. The developed AI nozzle has a spray rate of 0.6 LPM, an air to liquid mass ratio (ALR) of 0.0005, and a spray angle of 110 degrees. The volume median diameter (VMD) was measured based on the standardized method to identify the characteristics of the droplets sprayed from the two nozzles [44]. The VMD is an important factor in determining the amount of drift because it determines how far the droplets travel horizontally in the wind. If the VMD of the spray droplets is smaller, spray droplets can move farther away depending upon the wind speed and air inversion characteristics at the time of spraying [45]. The measurement results showed that the VMDs of the flat fan and AI nozzles were 189 and 450 μm, respectively. The specifications of the nozzles are listed in Table 2.

2.3. Evaluation of Spray Coverage and Penetration

To analyze the effect of nozzle type on coverage and penetration, the agricultural spraying drone was equipped with a flat fan nozzle and an AI nozzle and sprayed with water on 6 September and 5 October 2021. The drone flew at a height of 3 m above the ground and a speed of 2 m/s during the experiment. The flight distance of single spraying was 40 m. The pressure of pump was set to 0.28 Mpa based on the nozzle recommendation [44]. The flow rate of the pump at this pressure was 1.2 L/min. The sprayed droplets were collected using water-sensitive paper (76 mm × 52 mm, Teejet Technologies, Glendale Heights, IL, USA) [46]. Since the water-sensitive paper is sensitive to moisture, the humidity was controlled below 80% by draining the water in the rice field the day before the experiment. To minimize the effects of humidity, the paper was put out immediately prior to spraying and collected a few seconds after the drone sprayed the plant protection product. Three sampling points were symmetrically arranged along the perpendicular direction of the agricultural drone flight route with three repetitions (Figure 3). The interval distance between sampling points was 2 m. The total length of the sampling points was designed for the effective spray width. The distance between the flight center line was 10 m and, while the drone was returning, the spraying stopped. The water-sensitive paper was attached to the angle-control label stand (Figure 4). The water-sensitive paper was installed at two levels: the canopy (h1 = 80 cm) and middle point (h2 = 40 cm) of the crop at each sampling point. The collectors were installed at three points on the flight path. After the experiment, each water-sensitive paper was sealed in a zipper bag to block contact with moisture and delivered to the laboratory. The images of the water-sensitive paper were obtained using a red, green, and blue camera and analyzed via binarization.
The coverage and penetration ratio (PR) were measured to evaluate the nozzle spray performance because the amount of agent delivered to the surface or inside the crop affects control effectiveness. Coverage refers to the ratio of the droplet deposition area on the collector to the entire collector area. The amount of deposition can be compared based on coverage (Equation (1)) [47]. The collector located at the canopy was used to analyze the coverage. The PR was measured to identify the ratio of agents to be delivered inside the crop. PR is important, especially for leafy plants, because it affects the delivery of the agent [48]. The PR was calculated using the ratio of droplet areas attached to the canopy and middle point of crops (Equation (2)). R (Version 4.2.2, The R Foundation, Vienna, Austria), a statistical analysis software, was employed to analyze the data. A two-way analysis of variance was conducted to analyze sensitive factors affecting the spray performance.
C =   A D A W
PR = C 2 C 1
where C is coverage, AD is total area of droplets deposited, AW is total area of water-sensitive paper, PR is penetration ratio, C1 is coverage of water-sensitive paper at h1, and C2 is coverage of water-sensitive paper at h2.

2.4. Efficacy Analysis

The plant protection product was sprayed three times on rice and soybeans to compare the pest control efficacy based on the nozzle type in July and August 2021. Insecticides and fungicides are shown in Table 3. The plant protection product was diluted with water in a ratio of 1:8. The amount of plant protection product sprayed in each pest control operation for each plot was 0.44 L.
The rice and soybean fields were divided into three areas: the area of pest control using an AI nozzle, the area of pest control using a flat fan nozzle, and the nontreated area (Figure 5). The total area of 4000 m2 for each crop was divided into areas of 1600, 1600, and 800 m2. To reduce experimental error, the seed variety, soil condition, and cultivation method were the same between treatments. The drone worked across the plot and flew perpendicular to the boundary to minimize the impact on neighboring areas. Based on the treatment, the yield and damaged crop ratio caused by pests and diseases were investigated. In the case of rice, we targeted rice blast and rice stem borer and sheath blight, and in the case of soybeans, we targeted anthracnose, litura, and clavatus. For sheath blight, rice stem borer, anthracnose, and litura, the percentage of affected stems were examined. For rice blast, the percentage of affected ears were examined. For anthracnose and clavatus, the percentage of affected soybeans were examined. Once the plant reached full maturity, a harvest was conducted for pest inspection and production investigation. The three subplots per treatment, area of 3 m × 1.2 m, were selected and investigated by collecting crops for yield and damaged crop ratio data. Duncan’s test was conducted to analyze the difference between the experimental results.

3. Results and Discussion

3.1. Coverage and Penetration Ratio of Spray

The analysis of the coverage of droplet deposition by plants and nozzles showed that the coverage of the experiment with the AI nozzle (5.05 and 3.35%) was 2.5 times higher than that of the experiment with the flat fan nozzle (2.11 and 1.44%) in both plants (Figure 6). When the same amount of spray is applied using both nozzles, an increase in deposition indicates a decrease in drift. This means that in drone aerial sprayers, just like in ground-based sprayers, the use of AI nozzles reduces drift compared with the use of flat fan nozzles. The larger diameter of the droplets sprayed from the AI nozzle resulted in less drift, consistent with previous research [25]. However, Hunter et al. [49] showed that coverage decreases as the size of the droplet increases. This appears to be caused by downwash from the drone affecting the behavior of the spray droplets. For more accurate analysis, a downwash analysis of the drone should also be performed. According to two-way analysis of variance, there is significant difference only in the effect of the nozzle type (p < 0.05) (Table 4). This means that it is useful to use AI nozzles to deliver more spray droplets to the crop during spraying operations. Although the average deposition of rice was higher than that of soybeans, there was no significant difference by plant [50,51]. The deposition of droplets is affected by the height of the crop, size of the spray drops, and wind speed, but in this study the crops were of similar height, so there does not seem to be a difference in coverage [52].
The C2 of the AI nozzle was 1.68% in rice and 1.12% in soybean. And the C2 of the flat fan nozzle was 0.27% in rice and 0.73% in soybean. The PR averages of the AI nozzle were (27.4 and 33.3%) and those of the flat fan nozzle were (19.0 and 31.5%), respectively (Figure 7). To analyze the effect of plant and nozzle on the PR, a two-way analysis of variance was performed, and it was found that there was no significant difference in the treatment (Table 5). According to previous research, it was expected that larger size spray droplets have a greater PR [25]. However, in this study, spray droplets applied to soybeans using the flat fan nozzle resulted in higher-than-expected C2. After checking the experimental data, the C2 value was measured to be higher in the downwind direction in two out of three iterations. This may have been caused by a temporary strong wind at the time of the experiment, and we believe that it needs to be improved through additional experiments in the future.

3.2. Control Efficacy

The crop productions at sprayed areas were higher than that of non-sprayed areas for both crops (Table 6). To compare the difference caused by the nozzle, Duncan’s test was conducted to determine whether there was a difference in crop production. As a result, no significant difference was found between the experimental groups using AI and flat fan nozzles for both crops (p < 0.05). Based on the coverage measurements in Section 3.1, the amount of spray droplets delivered to the crop canopy was higher with the AI nozzle, but there was no difference in the PR, so there was no difference in yield. And it is considered that this is the result of trying to minimize the impact of various factors on crop yields, but not being able to fully control them. Also, the increased size of the spray droplets resulted in increased deposition with less drift, but the decreased surface area seems to have reduced the effectiveness. Further research is needed to determine the optimal spray droplet size.
To compare direct pest control efficacy, the damage ratio of rice caused by pests and diseases was investigated (Figure 8). The investigation results showed that in the case of AI nozzle treatment, the damage ratios of rice blast, rice stem borer, and sheath blight were 39.3%, 1.0%, and 6.5%, respectively; in the case of flat fan nozzle treatment, these values were 42.0%, 2.9%, and 8.0%, respectively; and for the control group, these values were 71.1%, 7.6%, and 35.0%, respectively. The damage ratios caused by three pests and diseases were reduced by 6.4%, 65.5%, and 18.7% when using AI nozzles compared with those when using flat fan nozzles. Therefore, the AI nozzle is shown to have better control efficacy, giving some indication of smaller drop drift using the conventional nozzles.
The damage ratios of soybeans caused by pests and diseases were also investigated (Table 7). The pest that causes damage to the leaves was anthracnose and the insect was litura. And the pest that damages grain was anthracnose and the insect was clavatus. It was shown that the damage caused by pests outweighed the damage caused by diseases and that the leaf damage was more severe than grain damage. For more detailed analysis, Duncan’s test was conducted to determine whether there was a difference in the damage ratios. However, no significant difference was found between the experimental groups using AI and flat fan nozzles for both crops (p < 0.05). Similar to the results of the production investigation, there was no significant difference in the PR values between treatments, so it seems that there is no difference in control efficacy. When analyzing the effects of aerial control in the future, it is considered more accurate to examine PR values rather than canopy coverage. Wang et al. [53] conducted a study using four different types of sprayers and found that control efficacy varied depending on the type of sprayer. Further analysis is needed to determine if the no significant difference is caused by a small difference from the nozzle or other factors (e.g., wind speed).

4. Conclusions

The number of farmers using spraying drones is increasing owing to its various advantages. However, spray drift has been a challenge in aerial spraying systems. To address this issue, an AI nozzle with an effective drift reduction capacity was used. However, there are few research works that installed AI nozzles to drones and analyzed their effectiveness. In this study, AI and flat fan nozzles were installed on drones to evaluate their spray performance. Moreover, the pest control performance was also evaluated. The deposition and penetration ratios were measured using a flat fan nozzle and an AI nozzle. Pest control efficacy was analyzed based on the type of target crops (rice and soybean). The crops used were rice and soybean. The study results are listed as follows:
  • For both crops, coverage was more than 2.3 times higher when using AI nozzles compared with that when using flat fan nozzles. The larger size droplets of the AI nozzle apparently reduced drift, which was attributed to increased deposition of the sprayed droplets. A two-way analysis of variance showed that the effect of the nozzle on the coverage was significant (p < 0.05). For the effect of the plant on coverages, there was no significant difference.
  • The penetration ratios of both nozzles were analyzed. A two-way analysis of variance showed no significant difference due to the treatment method. It seems that weather conditions may have affected the results of the experiment. Previous studies of vertically growing maize, which has a similar morphology to rice, have yielded different results than this study, and additional analysis is required.
  • The crop production rate was investigated. Duncan’s test showed no significant difference between the experimental groups. Environmental and genetic factors seem to have influenced production. Also, it seemed that the decreased surface area reduced the effectiveness.
  • For the control efficacy, Duncan’s test showed no significant difference between the experimental groups. As the results of the production investigation, similar PR values between treatments made no difference in control efficacy.
  • When measuring aerial spray performance, penetration rates must be measured at the same time.
The analysis of the aforementioned results showed that using AI nozzles instead of flat fan nozzles for aerial pest control increased the droplet deposition of the sprayed droplets. However, there was no significant difference in production and control efficacy. Thus, this study showed that aerial control with AI nozzles delivers more spray droplets to the field and the effect on control efficacy needs further study.
In this study, a single flight condition (2 m/s forward speed at 3 m height) was used to compare the effect of nozzle type on control performance. In the future, it is necessary to conduct experiments under various flight conditions and/or to compare UAV loadings over time to drift. However, since it is difficult to control various conditions in the field, the primary analysis is performed through Computational Fluid Dynamic (CFD) simulations and supplemented with field experiments.

Author Contributions

Conceptualization, C.-G.L. and S.-H.Y.; methodology, C.-G.L. and S.-H.Y.; software, C.-G.L.; validation, C.-G.L., Y.K. and S.-H.Y.; formal analysis, C.-G.L. and Y.K.; investigation, C.-G.L. and Y.K.; resources, S.-H.Y.; data curation, C.-G.L.; writing—original draft preparation, C.-G.L.; writing—review and editing, C.-G.L. and S.-H.Y.; visualization, C.-G.L.; supervision, S.-H.Y.; project administration, S.-H.Y.; funding acquisition, S.-H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was carried out with the support of “Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01557501)” Rural Development Administration, Republic of Korea.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. View of experimental site. On the left, the field with rice cultivar ‘Sindongjin’, and right, Soyabean cultivar ‘Daechan’.
Figure 1. View of experimental site. On the left, the field with rice cultivar ‘Sindongjin’, and right, Soyabean cultivar ‘Daechan’.
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Figure 2. Agricultural spraying drone with quad rotor (SG-10P).
Figure 2. Agricultural spraying drone with quad rotor (SG-10P).
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Figure 3. Layout of flight path and collectors. The distance between groups of collectors is 10 m and the drones fly perpendicular to the wind direction.
Figure 3. Layout of flight path and collectors. The distance between groups of collectors is 10 m and the drones fly perpendicular to the wind direction.
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Figure 4. (left) Arrangement of water-sensitive paper at two height levels (40, 80 cm) and (right) appearance of angle-control label stand.
Figure 4. (left) Arrangement of water-sensitive paper at two height levels (40, 80 cm) and (right) appearance of angle-control label stand.
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Figure 5. Layout of field for experiment treatment, sampling zone, and flying path.
Figure 5. Layout of field for experiment treatment, sampling zone, and flying path.
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Figure 6. Coverage of droplet deposition with error bar representing standard deviation by plants and nozzles.
Figure 6. Coverage of droplet deposition with error bar representing standard deviation by plants and nozzles.
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Figure 7. Penetration ratio of droplet deposition with error bar representing standard deviation by plants and nozzles.
Figure 7. Penetration ratio of droplet deposition with error bar representing standard deviation by plants and nozzles.
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Figure 8. Control efficacy of rice after spraying by pests and diseases.
Figure 8. Control efficacy of rice after spraying by pests and diseases.
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Table 1. Specifications of agricultural spraying drone.
Table 1. Specifications of agricultural spraying drone.
ParameterValue
Dimension (mm)2075 (W) × 2075 (L) × 700 (H)
Total weight (kg)14.5
Flight controllerK++
Maximum thrust (kg/rotor)5.1
Maximum takeoff weight (kg)24.9
Rated pressure (MPa)0.8
Rated flow (L/min)5.5
Capacity of solution tank (L)10
Spray width(m)4
Number of used nozzles2
Table 2. Characteristics of flat fan and AI nozzles.
Table 2. Characteristics of flat fan and AI nozzles.
NozzlePressure (MPa)Spray AngleVMD (μm)
XR1100150.28110189
AI0.28110450
Table 3. Ingredients of plant protection products.
Table 3. Ingredients of plant protection products.
SprayingDatePlant Protection ProductDosage
Rice
116 July 20% Ferimzone + 1.5% Fluxapyroxad + 8% Etofenprox + 3% Metaflumizone0.44 L
25 August6% Azoxystrobin + 5% Validamycin A + 10% Etofenprox0.44 L
312 August8% Etofenprox + 3% Metaflumizone + 30% Ferimzone + 3% Thifluzamide0.44 L
Soybean
15 July8% Etofenprox + 3% Metaflumizone0.44 L
25 August6% Azoxystrobin + 5% Validamycin A + 10% Etofenprox0.44 L
312 August8% Etofenprox + 3% Metaflumizone + 13.6% Boscalid + 8% Pyraclostrobin0.44 L
Table 4. Two-way analysis of variance of droplet deposition.
Table 4. Two-way analysis of variance of droplet deposition.
FactorDegree of
Freedom		Sum of
Squares		Fp-Value
Plant16.150.7640.3904
Nozzle143.95.4500.0279
Plant × Nozzle11.920.2380.6300
Table 5. Two-way analysis of variance of penetration ratio.
Table 5. Two-way analysis of variance of penetration ratio.
FactorDegree of
Freedom		Sum of
Squares		Fp-Value
Plant111042.6680.117
Nozzle13980.9620.337
Plant × Nozzle120.0050.947
Table 6. Crop production rate by treatment.
Table 6. Crop production rate by treatment.
Rice (g/m2)Soybean (g/m2)
AI nozzle465a324a
Flat fan nozzle449a309a
Control322b240b
a,b: the same letter note indicates no significant difference at the p = 0.05 significant level, different letters indicate significant differences at p = 0.05 significant level.
Table 7. Control efficacy of soybean after spraying.
Table 7. Control efficacy of soybean after spraying.
Damaged Leaf RatioDamaged Grain Ratio
Disease (%)Pest (%)Disease (%)Pest (%)
AI nozzle0.5a11.8a2.2a3.6a
Flat fan nozzle0.7a15.9a2.4a4.7a
Control2.3b49.7b8.0b18.4b
a,b: the same letter note indicates no significant difference at the p = 0.05 significant level, different letters indicate significant differences at p = 0.05 significant level.
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Yu, S.-H.; Kang, Y.; Lee, C.-G. Comparison of the Spray Effects of Air Induction Nozzles and Flat Fan Nozzles Installed on Agricultural Drones. Appl. Sci. 2023, 13, 11552. https://doi.org/10.3390/app132011552

AMA Style

Yu S-H, Kang Y, Lee C-G. Comparison of the Spray Effects of Air Induction Nozzles and Flat Fan Nozzles Installed on Agricultural Drones. Applied Sciences. 2023; 13(20):11552. https://doi.org/10.3390/app132011552

Chicago/Turabian Style

Yu, Seung-Hwa, Yeongho Kang, and Chun-Gu Lee. 2023. "Comparison of the Spray Effects of Air Induction Nozzles and Flat Fan Nozzles Installed on Agricultural Drones" Applied Sciences 13, no. 20: 11552. https://doi.org/10.3390/app132011552

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