Agricultural Spray Research Articles

Agricultural spray drone deposition, Part 1: Methods for high throughput spray pattern analysis

Abstract Effective pesticide application is dependent on precise and sufficient delivery of active ingredients to targeted pests. Water-sensitive papers (WSP) have been used to estimate the stain coverage, droplet density, droplet size, total spray volume, and other spray quality metrics by analyzing deposit stains using image analysis software. However, due to their expensive cost, WSPs are typically distributed along unidimensional transects at intervals of 0.5 m or more, which comprises 0.5% or less of the total treated area. This might limit the ability to accurately represent the deposition of agricultural sprayers with irregular patterns, such as agricultural drone sprayers in their early development stage. This study introduces a novel approach utilizing white Kraft paper and a blue colorant proxy for assessing spray deposition. A custom Python-based image analysis tool, SprayDAT (Spray Droplet Analysis Tool), was developed and compared to traditional image analysis software, DepositScan. Both models showed increased accuracy in detecting larger objects, with SprayDAT generally performing better for smaller droplets. DepositScan underestimated the total deposited spray volume by up to 2.7 times less compared to the colorant extraction assessed via spectrophotometry and the predicted output based on flow rate, coverage, and speed. Accuracy of software-estimated spray volume declined with increasing total stain coverage, likely due to overlapping stain objects. Droplet density exhibited a Gaussian trend with peak density at approximately 22% stain cover, offering evidence for overlapped stains for both DepositScan and SprayDAT as stain cover increased. Both models showed exponential growth in volumetric median diameter (VMD) with increasing stain cover. SprayDAT is freely accessible through an online repository. It features a user-friendly interface for batch-processing large sets of scanned images and offers versatility for customization to meet individual needs, such as adjusting spread factor, updating the standard curve for spray volume estimation, or modifying the stain detection threshold.

Reinforcement Learning and PD Control Based Trajectory Tracking for a Quadcopter UAV

Nowadays, quadcopter unmanned aerial vehicles (UAV) are used in a wide variety of areas, such as reconnaissance and surveillance, firefighting, search and rescue, agricultural spraying, cargo transportation, photography, and mapping. The use of quadcopters in a very wide area makes their trajectory tracking control important. In order for quadcopters to perform their duties successfully, they must be able to follow the given trajectory with the least error. In this study, the quadcopter’s trajectory tracking under random noise is provided by an algorithm based on reinforcement learning and a proportional derivative (PD) controller. Modeling, simulations, and reinforcement learning algorithms were carried out using the MATLAB program. Simulations were made under noise for the x, y, z trajectories and roll, pitch, and yaw angles of the quadcopter. A detailed time response analysis was performed by obtaining rise time, overshoot, and settling time data. It has been observed that the references given were successfully followed thanks to the algorithm based on reinforcement learning.

A method to spatially assess multipass spray deposition patterns via UV fluorescence and weed population shifts

AbstractSpray deposition patterns from agricultural sprayers are traditionally sampled discretely along a field transect accounting for 0.5% or less of the treated area. Such methods may not fully capture the dimensional variability inherent in large‐scale, multiple‐pass spray applications, especially evident from an agricultural spray drone (ASD). This study investigated the utilization of UV‐fluorescent dye and nighttime aerial imaging techniques to assess large‐scale, multipass spray deposition patterns. Accuracy of digital hue from UV‐fluorescent photography to predict deposition of proxy dye was confirmed via fluorometry assessed intensity levels of extracted UV‐fluorescent dye from 384 Petri dishes placed prior to treatment. Results showed that ASD applications, regardless of nozzle type, exhibited greater spatial variability within the target area compared to ride‐on sprayer applications, primarily due to overapplication. Additionally, the ASD generated spray drift to adjacent nontarget areas that was at least three times more than that of ride‐on and spray‐gun sprayers. Multipass deposition was further assessed via in situ smooth crabgrass infestation following treatment with quinclorac or topramezone by multipass ASD or hand‐held, four‐nozzle spray boom. Weed infestation annotated from overlaid grids with 9.3‐dm2 ground resolution inconsistently detected spatial heterogeneity between transects assessed along the center and edge of each sprayer pass. The ASD controlled smooth crabgrass 11% more than the hand‐held sprayer, albeit with an 18% increase in spray drift to nontarget areas, similar to the UV‐fluorescence study. Digitally assessed average hue of fluorescence photography appears to be a viable method to assess multidimensional and continuous spatial relationships of spray deposition.

Ai] Explore!: An Educational Game for Developing Programming Skills and Algorithmic Thinking

The game [ai] explore! was developed to introduce the algorithm behind the popular science exhibition. The idea behind the [ai] explore! exhibition was to show the application of mathematics and computer science in solving real engineering problems by creating the exhibit using the mathematical algorithm Heat Equation Driven Area Coverage (HEDAC). The algorithm is based on scientific research and has many applications: exploration and search in unknown space, painting, planning agricultural spraying, scanning 3D objects, etc. The exhibits show the process by which the algorithm paints objects with a virtual brush and explores their shape. By surveying the high school students, it was shown that new mathematical and engineering results can be explained and turned into a playable game. After playing the game, students respond positively to the game presented and state that they have a good understanding of the HEDAC algorithm presented and how it works. This motivated us to investigate [ai] explore! as an educational game for teaching mathematics and computer science. The aim of the research described in this paper is to investigate the potential of the developed game as an educational game. The game-based learning (GBL) approach is increasingly used to increase student motivation and engagement in STEM lessons. The main research question is: How do primary school teachers and students perceive the usefulness of the game [ai] explore! in supporting teaching and learning of computer science concepts? We have developed and implemented teaching scenarios on how the game [ai] explore! can be used as an educational game in computer science lessons. We show that the game has great potential to teach primary school students programming. In addition, the game promotes algorithmic thinking and computational thinking skills in general, which are valuable not only in computer science but also in other fields such as mathematics, where a systematic and analytical approach to problem-solving is required.

Efficient Real-Time Droplet Tracking in Crop-Spraying Systems

Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices.

Fostering rehydration and facilitating bioactive release through cellulose-assisted leaf surface treatment

Glyphosate is a widely used herbicide in weed control and crop protection. However, its low bioavailability on leaf surfaces of weeds led to excessive use of glyphosate, inducing herbicide-resistant development and major sustainable agricultural and environmental concerns. This study addresses these challenges by developing cellulose-assisted glyphosate formulations using superior rehydration and sustainable release capability of nanocelluloses. We prepared glyphosate-loaded nanocellulose particles (CNP) and cellulose nanofibers (CNF) to enhance the rehydration and sustained release of glyphosate on leaf surfaces. Our results have demonstrated that nanocelluloses significantly improved water capture on the leaf surface and gradual release of glyphosate, with CNP and CNF formulations showing an 8.75-fold increase in water adsorption on cotton leaves compared to the control group over 12 h. Furthermore, incorporating an inorganic salt improved moisture adsorption efficiency. The formulations exhibited high compatibility with existing spray technologies, offering substantial economic and environmental benefits for agriculture practices. This approach highlights the potential application of polysaccharides in revolutionizing agrochemical applications and environmental sustainability, providing great potential in agricultural spraying practices.

Pesticide Exposure and Neurobehavioral Performance across various Agricultural Spray Seasons in Ecuador, and implementation of Agroecological Production Alternatives

Pesticide Exposure and Neurobehavioral Performance across various Agricultural Spray Seasons in Ecuador, and implementation of Agroecological Production Alternatives

Optimizing Vane Number for Enhanced Performance of Mist Blower Nozzle in Agricultural Spraying

The objective of this study is to optimize the number of vanes equipped in the propeller of a mist blower's nozzle. Performance tests were conducted on the approved sprayer test bed, also known as a patternator, to measure several parameters, including effective spraying discharge, effective spraying width, spraying angle, effective spraying height, effective spraying range, droplet diameter, and droplet density. The vane number was optimized using the weighting method. The results indicate that increasing the number of vanes used is directly correlated with higher values of effective spraying width (ESW), spraying angle, effective spraying range (ESR), and droplet density. Conversely, it is inversely correlated with the value of effective spraying discharge (ESD), effective spraying height (ESH), and droplet diameter. The vane number was optimized using the weighting method. The most effective results in the mist blower performance test are achieved by using 12 vanes. This configuration produces droplets with a diameter of 195.44 ±9.68 μm and a density of 320 ± 17.44 droplets/cm2. The mist blower also has ESW of 136 ± 1.73 cm, ESH of 68.14 ± 4.19 cm, ESD of 4.41 ± 0.14 L/min, and ESR of 5.76 ± 0.04 m. Keywords: Agricultural spraying, Mist blower, Nozzle optimization, Performance evaluation, Vane number.

Comparison between Liquid Immersion, Laser Diffraction, PDPA, and Shadowgraphy in Assessing Droplet Size from Agricultural Nozzles

Spray droplet diameters play a key role in the field of liquid plant protection product (PPP) application technology. However, the availability of various measurement techniques, each with its unique operating principles for evaluating droplet size spectra, can lead to different interpretations of spray characteristics. Therefore, in this study, four measurement techniques—Liquid Immersion (LI), Laser Diffraction (LD), Phase Doppler Particle Analysis (PDPA), and Shadowgraphy (SG)—were utilized to evaluate the droplet size distribution of agricultural spray nozzles. Additionally, PDPA and SG were used to assess the average velocity of spray droplets. Experiments were conducted in three different laboratories with the main aim of comparing results obtained with various types of equipment utilized under ordinary practical conditions. Spraying tests were carried out using three flat fan nozzles and an air-induction flat fan nozzle. As a general trend, the lowest values for droplet diameters were measured using the Laser Diffraction technique, followed by Shadowgraphy. The PDPA technique provided the highest values for mean diameters (D10, D20, and D30) and the numeric median diameter (Dn0.5), whereas the Liquid Immersion method yielded the highest values for the Sauter mean diameter (D32) and volumetric diameters (Dv0.1, Dv0.5, and Dv0.9). Importantly, all measurement techniques were able to discriminate the four nozzles based on their Dv0.5 diameter. Average droplet velocities showed a similar pattern across the four nozzles with the PDPA and the SG measurement techniques. The differences in diameter values observed with the four measurement techniques underline the necessity of always including reference nozzles in spray quality assessments to base classifications on relative rather than absolute values.

An Autonomous Unmanned Ground Vehicle: A Technology Driven Approach for Spraying Agrochemicals in Agricultural Crops

The application of chemicals through spraying is an essential component of agricultural management systems. However, the use of these chemical products results in the accumulation of residues in the soil, leading to a decline in soil fertility and plant diversity. Additionally, farmers who manually spray pesticides onto their crop fields are at risk of developing health issues such as tumors, hypersensitivity, allergies, and skin diseases. As the global population continues to grow and climate change impacts farmland ecosystems, the occurrence of pest epidemics in crops is becoming more frequent. Consequently, there is a greater demand for enhanced tools and methods to improve crop production. Unmanned ground vehicles (UGVs) have the potential to revolutionize agricultural spraying by offering efficient and effective applications of crop protection agrochemicals. This article provides an overview and consolidation of the present research and advancements in UGV application technologies and different sensors that are specifically designed for spray applications of agrichemicals. It examines the features of both hardware and software architecture for implementing control strategies, obstacle detection, and avoidance systems with real-time integrated systems, and the ability to perform variable-rate agrochemical applications.

Numerical simulation of agricultural unmanned helicopter corn canopy spraying

Plant protection by unmanned aerial vehicle (UAV) is a highly complicated process, especially considering the vast and diversified crop canopy. Furthermore, the droplet dispersal mechanism above and inside the crop canopy remains unclear. Owing to this, the authors used the lattice Boltzmann method (LBM) to simulate the application of plant protection UAV. The LBM is suitable not only to simulate the rotor tip vortex of UAV but also to model the crop canopy with detailed geometry. It is found that the streamwise and spanwise speed of the downwash flow achieve an extreme value both on top of the corn canopy and near the ground. When the droplet diameter decreased from 400 to 50 μm, its deposit region width doubled and its floating time increased from 3 s to more than 12 s. Small droplets are more likely to be lifted by the secondary flow near the ground and drift for a longer distance, while larger droplets are deposited directly. The increase in application height weakened the secondary spanwise flow near the ground and caused less droplet re-lifting and spanwise movement. The best point to initiate and terminate spraying is 2.5 m after passing the target corn canopy region. The droplet penetration rate in the canopy decreased with an increase in droplet size. This study used the LBM to model the unmanned helicopter application on a full corn canopy. Droplet deposition and distribution correspond to droplet size and canopy structure. The simulation results establish a foundation to optimize agricultural aerial spraying in an efficient and methodical manner.

Deposition of Water and Emulsion Hollow Droplets on Hydrophilic and Hydrophobic Surfaces

The deposition of spray droplets is a hot topic in the field of plant protection. The air-induction nozzle, which is commonly used in agricultural spray, can produce droplets containing bubbles. However, few studies have addressed the deposition of hollow droplets. In the present study, we used experimental and numerical methods to investigate the deposition of hollow droplets. Three kinds of liquid—water, oil-based emulsion and organosilicon—were used to produce hollow droplets, and the diameter of droplets varied from 3 to 4.5 mm. Both hydrophilic and hydrophobic surfaces were selected as deposition targets. The results show that the deposition of hollow droplets can generate a central jet, which is similar to the Wortington jet. High deposition velocity and the large bubble volume were responsible for the large attainable height of the central jet. On the hydrophilic surface, for water hollow droplets with Weber number (We) ranging from 350 to 391, the central jet began to break up as the bubble fraction of the hollow droplet reached 0.15. Based on the numerical results, it was found that the internal pressure difference between the bottom liquid and the air cavity leads to the formation of the central jet. The bubble volume and impact velocity were both positively correlated with the internal pressure difference. The oil-based emulsion promoted the adherence of the hollow droplet by lubricating the hydrophobic surface. The oil-based emulsion hollow droplets shifted from rebounding to adhering on the hydrophobic surface as the emulsion concentration reached 0.4%.

Active ingredient identification and purification of organosilicon spray adjuvant and its uptake and translocation in rice (Oryza sativa L.).

The assessment of residue, absorption, conduction, and degradation of agricultural organosilicon surfactants in the environment is hindered by the lack of information on active ingredients and corresponding quantitative standards for organosilicon spray adjuvants. The spray adjuvant 'Jiexiaoli,' a primary organosilicon spray agent in China, was identified as hydroxy (polyethylene) propyl-heptamethyl trisiloxane (TSS-H) with 3-15 ethoxy (EO) groups. Purification of TSS-H was achieved through semi-preparative separation using high-performance liquid chromatography (HPLC), resulting in TSS-H purity exceeding 96%. An accurate residual detection method for nine oligomers (4-12 EO) of TSS-H in rice roots, stems, leaves, and culture solution samples was developed using HPLC tandem high-resolution mass spectrometry (HPLC-HRMS). Recoveries for nine oligomers of TSS-H in the four matrices ranged from 80.22% to 104.01%. Foliar application experiments demonstrated that TSS-H did not transfer from the upper to the lower parts of the rice plant. The half-lives of each oligomer (4-12 EO) in leaves were less than 3.21 days. Root application experiments revealed a root concentration factor (RCF) ranging from 0.20 to 0.56, a biological enrichment factor (BCF) ranging from 0.36 to 0.68, a transpiration factor (TSCF) ranging from 0.069 to 0.086, and a transport factor (TF) ranging from 0.08 to 0.43. These results indicated that TSS-H could be absorbed by rice roots and conducted to the above-ground parts of rice plants. This study fills the data gap in the environmental risk and food safety assessment of agricultural silicone spray adjuvants. © 2024 Society of Chemical Industry.

How does an emulsion drop's viscosity influence its impact on various meshes?

The impact of emulsion drops on a mesh is studied. These drops find applications in various fields, e.g., agricultural sprays, where the drop passes through a mesh for atomization. Both penetration (full or partial) and lateral spreading are observed in most cases. The maximum spread's variation with Weber number (We) shows liquid independence for drops impacting a given mesh. The variations can be concluded into one. However, when both lateral spread and penetration are concerned, the emulsion drop with the highest water content tested stands out as different. It shows dissimilar characteristics due to its higher viscosity. The drop's lateral spread Reynolds number is lower than the others, indicating a significant dependence on the liquid viscosity. Rising viscosity resists the lateral inertia. Surface tension (ST) and density do not have much influence. The balance between the downward and after-impact lateral inertia and their resistance makes the lateral spread on a given mesh independent of liquid. Three regimes, full, partial, and no penetration, can be defined. A dissimilarity in the after-penetration jet length is detected. The length is inversely proportional to the emulsion's rising water content. The drop with the highest viscosity, together with ST, provides the highest resistance to penetration. Thus, the length reduces abruptly. When the after-impact penetration cone angle is studied, the average angle reduces with We for the highest viscosity emulsions. An abrupt rise in resistance is detected. The study's novelty lies in bringing out the viscosity's influence on the emulsion drop's impact on a mesh.

Sampling, quantification and mathematical modeling in agricultural spray drift: A review

An effective spray of agrochemicals is inevitable for crop production for viable agriculture. Spraying inherently suffers from drift, which has always been one of the major concerns in agriculture, affecting the intent of agrochemical spraying and posing serious environmental hazards. Complete elimination of spray drift is impractical under field conditions but can be minimized using precision spraying techniques. Agricultural spray drift has several detrimental effects, such as environmental damage, polluting water bodies, human and animal health risks, chemical exposure, and economic losses, and may also lead to conflicts between neighboring farmers. Hence, the assessment of spray drift is a salient part of the design process of plant protection equipment to achieve maximum deposition in both chemical and biological pesticide applications. The different methods used to study the drift of a sprayer include test bench, wind tunnel and phase Doppler particle analyzer (PDPA) methods. In the field-level assessment, the fluorometric tracer sampling method conforming to ISO-22866:2005 was used. Plume dispersion, particle tracking and computational fluid dynamics (CFD) are the major mathematical modeling approaches for spray drift simulation studies. Among various methodologies and techniques, an appropriate method for spray drift assessment should be adopted in accordance with factors such as crop parameters, mode of application, and environmental conditions.

A Computational Framework for Precise Aerial Agricultural Spray Delivery Processes

A Computational Framework for Precise Aerial Agricultural Spray Delivery Processes

NAVIGATOR PREPARATION DURING SPRAYING BY UAVS

The article presents the results of research on the implementation of navigator preparation duringagricultural spraying, conditioning the safe and precise performance of the task. This assumptionwas confirmed during spraying with unmanned aerial vehicles carried out in 2021–2023. Theexperience gained and conclusions enabled the development of a concept of initial and directnavigational preparation for unmanned aircraft systems performing spraying. It is also possibleto use this algorithm to perform deactivation by drones. The results obtained from the conductedaerial tests (within two scenarios: agricultural spraying and decontamination) have also shown thatthe risk analysis methodology recommended by EASA (SORA) entails a number of limitations andcannot be applied during spraying as it causes gaps in safety management. Therefore, a proprietaryEPEP risk analysis method has been developed specifically for spraying, which allows the pilot,the operator of an unmanned aircraft, to perform a risk analysis and manage it accordinglyimmediately before performing the task, compensating or minimizing the hazards that should beat an acceptable level before spraying.

Development Of Agriculture Spraying Drone

The primary objective of the AGRICULTURE SPRAYING DRONE is to enhance the efficiency of agricultural spraying through the utilisation of unmanned aerial vehicles (UAVs) and also to minimise waste and adverse effects on the environment while optimising conventional agricultural spraying techniques. With the help of a 22000 mAh battery, the system combines cutting-edge parts, including the Hobby wing X6plus motors, K++ V2 flight controller, Sky Droid T12 receiver and transmitter, Hobby wing pump, flowmeter, Can hub, obstacle avoidance system, and PMU. The drone, with a total weight of 29 kg and a maximum payload capacity of 12 kg, guarantees accurate and productive spraying operations across large agricultural areas. Key characteristics include adaptive spraying patterns tailored to specific field conditions, targeted pesticide application, and real-time crop health monitoring. An additional mobile application enhances the system by offering farmers the ability to remotely operate, schedule spraying, and examine field data. In order to show how the suggested drone system may be implemented practically and its effectiveness assessed in actual agricultural settings, the article describes the system architecture and provides a use-case scenario. By enabling more effective and sustainable agricultural methods, this research advances precision agriculture technologies. Key Words: Agriculture Spraying Drone, Precision Spraying, Sustainability.

New insights into head-on bouncing of unequal-size droplets on a wetting surface

The impact of a liquid droplet with another droplet or onto a solid surface are important basic processes that occur in many applications such as agricultural sprays and inkjet printing, and in nature such as pathogens transport by raindrops. We investigated the head-on collision of unequal-size droplets of the same liquid on wetting surfaces using the direct numerical simulations technique at different size ratios. The unsteady Navier–Stokes equations are solved and the liquid–gas interface is tracked using the geometric volume-of-fluid method. The numerical model is validated by comparing simulation results of two extreme cases of droplets bouncing with the experimental data from previous studies and the agreement is quite accurate. The validated model is employed to simulate droplets bouncing at several size ratios at different Weber numbers and Ohnesorge number. Two distinct regimes are identified, namely, the inertial regime, where the restitution coefficient is a constant value close to 0.3, the viscous regime, where the restitution coefficient declines. To understand the bouncing behaviour, the velocity field is analysed and an energy budget calculation is performed. The distribution of the sessile droplet energy is found to be important and the sessile droplet surface energy is calculated by its deformation characteristics such as crater depth. Finally, a scaling analysis is performed to rationalize the insensitivity of the coefficient of restitution in the inertial regime, and its decline in the viscous regime, at large size ratios.

Analyzing the synergistic impact of UAV-based technology and knapsack sprayer on weed management, yield-contributing traits, and yield in wheat (Triticum aestivum L.) for enhanced agricultural operations

Modern agriculture uses agrochemicals to boost crop yield and quality. Using knapsack sprayers, Indian farmers apply herbicides, insecticides, and other chemicals. Herbicides are applied by manually operated knapsack sprayers, which are laborious, uneven, and prone to human mistakes. Unmanned aerial vehicles (UAVs) are a promising agricultural spraying alternative to manual operations. Future agricultural and field scouting technology aids weed management, pest control, and automated irrigation systems. Drone herbicide spraying was compared to traditional and mechanical knapsack sprayers in this study. The study also tested knapsack (manual) and UAV herbicide pre- and post-emergence and spraying on wheat crops. Herbicide application by knapsack and drones was examined for weed control, yield-contributing characters, and wheat yield. The experiments were performed at Vasantrao Naik Marathwada Krishi Vidyapeeth's Department of Agronomy in Parbhani, Maharashtra. A randomized block design (RBD) with eight treatments, including drones and knapsack sprayers for weed management in wheat crops with different herbicide application times and repeated hoeings (cultural method), was compared to absolute weed-free, where no weeds were allowed to grow in wheat by frequent weeding and weedy inspection. The experimental field measured 7.2 m x 6 m gross land area and 6.3 m x 5.6 m net land area. When winds were light, the drone was flown at 3 m to avoid herbicide droplets drifting. The study found that unweeded wheat yields dropped 38 % owing to weed insects. The pre-emergence (PE) application using a knapsack sprayer and post-emergence (POE) herbicide application via a drone synergistically resulted in the highest growth and yield for T3, attaining values of 11.02 gm/plant, an average of 284.97 grains/plant, and a per-hectare grain yield of 4256.59 kg/ha. The cultural approach of weed management (T4) reduced yield by 21.2 %; however, treatment T3 improved yield contributing characteristics and losses by just 2.8 %. UAV-based herbicide spraying is a fast and easy method that contributes to achieving sustainable development goal (SDG) 2 for Zero Hunger by promoting sustainable plant protection and crop production.