Document Type : Research Paper
Authors
1 Department of Biosystems Engineering, Faculty of Agriculture, Tarbiat Madari University, Tehran, Iran
2 Department of Biosystems Engineering. Faculty of Agriculture, University of Ilam. Ilam. Iran
Abstract
Keywords
Main Subjects
Pests, plant diseases, and weeds are among the most significant challenges in crop production, accounting for approximately 32% of annual global crop losses, according to data from the Food and Agriculture Organization (FAO) (Lan et al., 2017; Guo et al., 2019). Herbicides are the most effective method for pest and disease control. However, conventional ground-based spraying methods, such as knapsack or tractor-mounted sprayers, are inefficient on sloped terrain, leading to higher spraying costs. Research indicates that using appropriate sprayers tailored to specific crops and pest species can reduce pesticide consumption by nearly 20-70% (Namvar & Heidari, 2014). Therefore, it is crucial to compare sprayers utilizing different technologies from various perspectives to identify the most effective option for pest control. Doing so can help to minimize environmental pollution, enhance production system efficiency, prevent unnecessary costs associated with pesticide overuse, and improve both crop quality and yield.
Agricultural drones are unmanned aerial vehicles designed to collect crop data quickly and cost-effectively, as well as to perform spraying and sowing operations. With recent advancements in drone technology and the recognition of their agricultural potential, they are increasingly being used for farm and orchard spraying. Farmers worldwide have been encouraged to adopt spraying drones due to their advantages, including low-altitude flight, reduced herbicide consumption, lower energy usage, decreased spraying costs, improved uniformity, and higher returns (Abdolkarim et al., 2019). Naseri et al. (2021) evaluated the application of drones for weed control in sugarcane farms. They generally recommended further research into drone-based spraying in sugarcane farms, especially pre-vegetative spraying, to promote sustainable agriculture and reduce herbicide consumption. Biglia et al. (2022) investigated the impact of flight modes and spray system adjustments on canopy deposition, coverage, and off-target losses in drone spraying. Their findings revealed that flight modes significantly affected spraying efficiency. Traditional airblast sprayers, which have a low spraying rate, provided better canopy coverage and minimized off-target losses compared to the most optimized drone spraying system configuration.
Given the significance of herbicide consumption rates in agriculture, their environmental impact, and the potential role of drones as a modern spraying technology, this study evaluated a sprayer drone using water-sensitive cards. In addition, image processing was used to determine the number, diameter, and volume of droplets, aiming to determine spraying indices for including volumetric median diameter, numerical median diameter, spraying quality, coverage, and uniformity.
The studied sprayer drone was an eight-engine (octocopter) MG-1P model with variable-sweep wings, manufactured by DJI in China. This octocopter, capable of carrying a 23 Kg payload, featured dual motors on each of its four sturdy arms. The spraying system consisted of four specialized nozzles mounted beneath its propellers for precision application. Its tank had a maximum capacity of 10 liters and has been designed to distribute both solid and liquid materials through the nozzles.
The sprayer drone was tested in farms covering an area of 5,000 m² in Darreh Shahr County, Ilam, in April 2023 (Figure 2). During the air experiment, the temperature ranged from 17°C to 22°C. While no wind was perceptible, data from the AccWeather meteorological website sgowed a wind speed of 4 Km/h.
In each experiment, four pre-coded water-sensitive cards were placed on the ground, spaced 1 m apart in both width and length. The drone then performed the spraying operation in autopilot mode. Each treatment was replicated three times to capture variations in spraying conditions. The drone was evaluated based on three factors: spraying rate, flight speed, and spraying height. According to the manufacturer’s recommendations, the optimal spraying rate for grain farms in the region was 8 L/ha, with a flight speed of 5 m/s and spraying height of 2 m. Therefore, the spraying rate was selected at three levels (6, 8, and 10 L/ha), the flight speed at three levels (4, 5, and 6 m/s), and the spraying height at three levels (1.5, 2, and 2.5 m above ground level), considering that the drone was unable to operate at height of 1 m.
After the experiments were completed, the water-sensitive cards were collected and scanned using a 300 dpi HP Scanjet G2410. The output images were then processed in MATLAB software using command codes. First, the images were retrieved in MATLAB for cropping. They were then separated from the RGB channel and converted into binary images. At this stage, pixels with values equal to or below a set threshold were displayed in black, while those with values above the threshold appeared in white. The binary images were then inverted, resulting in white spots on a black background.
Equation (1) was used to determine the primary and actual diameter of droplets based on water-sensitive card analysis:
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(1) |
Next, the actual diameter of droplets was calculated by dividing their measured diameter by a constant factor, as represented in Equation (1).
The volumetric median diameter and numerical median diameter were determined through coding. Equations (2), (3), and (4) were used to calculate spraying quality, coverage area, and the relative span factor. Since spraying quality is derived by dividing the volumetric median diameter by the numerical median diameter, this parameter serves to compare droplet volume with droplet count.
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(2) |
in which SQ represents spraying quality, VMD denotes volumetric median diameter, and NMD is the numerical median diameter (Daneshmand Vaziri, 2015).
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(3) |
in which SC represents the coverage area of the droplets, Aat is the total area of all droplets, and At is the total area.
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(4) |
in which RSF represents the relative spraying factor as a criterion of spraying uniformity, d0.9 represents the droplet diameter at 90% volumetric, d0.1 represents the droplet diameter at 10% volumetric, and d0.5 represents the droplet diameter at 50% volumetric (Daneshmand Vaziri, 2015).
Data were analyzed using analysis of variance (ANOVA) and comparison of means in the SPSS software package. The graphs were drawn in MS Excel software.
The present research tested spraying rate at three levels (6, 8, and 10 L/ha), flight speed at three levels (4, 5, and 6 m/s), and spraying height at three levels (1.5, 2, and 2.5 m above ground level). Based on the results, the volumetric median diameter decreased as spraying altitude increased at a spraying rate of 10 L/ha. However, the trend was nearly reversed at spraying rates of 8 and 6 L/ha. Furthermore, at an altitude of 2.5 m and a spraying rate of 10 L/ha, the volumetric median diameter of herbicide droplets decreased as flight speed increased, whereas it increased at spraying rates of 8 and 6 L/ha. No distinct ascending or descending trend was observed at altitudes of 2 and 1.5 m. In the interaction between spraying rate and flight speed, the volumetric median diameter decreased as the spraying rate was reduced, while it increased as the flight speed was reduced. In the interaction between spraying rate and flight altitude, a decrease in volumetric median diameter was observed with a lower spraying rate, whereas it increased with a lower flight altitude. No specific trend was observed in the interaction between flight speed and altitude.
The numerical median diameters were calculated for different treatments of spraying rate, flight speed, and spraying height in µm. The results showed that the numerical median diameter increased as flight speed increased at a spraying rate of 10 L/ha, whereas it exhibited the opposite trend at spraying rates of 8 and 6 L/ha. This suggests that spraying altitude significantly influences numerical median diameter. In the interaction between spraying rate and flight speed, variations in these factors had no consistent effect on numerical median diameter. In the interaction between spraying rate and flight altitude, a slight increase in numerical median diameter was observed as the spraying rate decreased, while a mild increase occurred as flight altitude decreased. A similar trend was noted in the interaction between flight speed and altitude.
The spraying quality of droplets was examined across different treatments involving spraying rate, flight speed, and spraying height. At a spraying rate of 10 L/ha, spraying quality decreased as drone speed increased. However, at spraying rates of 8 and 6 L/ha, spraying quality improved at higher speeds. The interaction between spraying rate and drone speed showed that spraying quality remained nearly the same between spraying rates of 6 and 8 L/ha. However, when the spraying rate increased to 10 L/ha, spraying quality improved at higher speeds. A similar trend was observed in the interaction between spraying rate and flight altitude. Conversely, no specific trend was found in the interaction between flight speed and altitude, as spraying quality remained nearly constant.
The values of coverage area, as influenced by different treatments of spraying rate, drone speed, and spraying height, showed inconsistent trends. In the interaction between spraying rate and drone speed, the coverage area of droplets was greater at a spraying rate of 10 L/ha compared to the other two rates. However, variations in drone speed did not cause significant changes in coverage area. In the interaction between spraying rate and flight altitude, increasing altitude reduced coverage area at a spraying rate of 10 L/ha, though no clear trends were observed at other spraying rates. In the interaction between flight speed and altitude, the highest coverage area was observed at medium speed and altitude, although the difference was minimal.
The spraying uniformity values obtained under different treatments of spraying rate, flight speed, and spraying height did not follow a single trend. In fact, the interactive effects of various factors on spraying uniformity varied across cases. However, all results fell within a certain classification. Therefore, spraying uniformity remained constant regardless of the influencing factors and variables.
The studied drone was an icta-copter MG-1P model with variable-sweep wings, manufactured by DJI in China. Assessments were conducted on a 5,000-m³ plot of land. After its specifications were recorded, the drone performed spraying operations in autopilot mode at various spraying rates (6, 8, and 10 L/ha), flight speeds (4, 5, and 6 m/s), and spraying heights (1.5, 2, and 2.5 m), with samples collected accordingly. According to ANOVA results, spraying rate significantly influenced all parameters, including volumetric median diameter, numerical median diameter, spraying quality, coverage area, and spraying uniformity of herbicide droplets. Flight speed and altitude significantly (P < 0.05) affected numerical median diameter, while flight speed significantly (P < 0.05) influenced coverage area. The comparison of means for the main effects on the parameters revealed that volumetric median diameter, spraying quality, and coverage area decreased at lower spraying rates. However, the highest median diameter showed its peak value at the manufacturer-recommended speed and altitude used in grain farms in the region. As speed and altitude decreased, numerical median diameter increased. Based on the comparison of means for the main effects on spraying uniformity, the drone maintained consistent spraying uniformity under all tested conditions. Thus, depending on flight conditions, the drone will change parameters that influence spraying. Consequently, the measured factors will not follow a fixed pattern, with spraying uniformity remaining at a stable level.
Conceptualization: Ala Kamel Abd (Master’s student, first author). Methodology: Ala Kamel Abd. Software and Data Analysis: Dr. Kobra Heidar Beigi. Validation: Ala Kamel Abd, Dr. Amir Azizpanah, Dr. Kobra Heidar Beigi. Formal Analysis: Dr. Kobra Heidar Beigi. Investigation: Ala Kamel Abd. Resources: Ala Kamel Abd. Data Curation: Somayeh Koohi. Writing—Original Draft Preparation: Ala Kamel Abd. Writing—Review & Final Editing: Somayeh Koohi, Dr. Amir Azizpanah.
All authors have read and agreed to the published version of the manuscript.
If the study did not report any data, you might add “Not applicable” here.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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