Puddled transplanted rice (PTR) is the pre-dominant rice establishment system in irrigated regions of South and Southeast Asia, contributing over 75% of global rice production. Its widespread adoption is attributed to better crop establishment and stable yields. However, weed infestation remains a major constraint, as weeds compete with rice for essential resources during critical growth stages, reducing crop productivity (Chauhan, 2012; Oerke, 2006) and (Perumal et al., 2025).
       
To overcome weed-induced losses, herbicides have become indispensable in rice production, particularly under conditions of labour scarcity and increasing labour costs. Post-emergence herbicides such as florpyrauxifen-benzyl and cyhalofop-butyl are widely used due to their broad-spectrum weed control and suitability for transplanted rice (Rao et al., 2017). Nevertheless, their effectiveness largely depends on the application method. Conventional knapsack spraying often results in uneven spray coverage, lower efficiency and greater operator exposure to agrochemicals, especially in flooded fields (Lan and Chen, 2021; Zhang et al., 2013) and (Ashraf et al., 2024b).
       
Addressing these limitations, unmanned aerial vehicles (UAVs) have emerged as a promising alternative for herbicide application. In transplanted rice, where flooded field conditions hinder ground-based spraying and increase labour requirements, UAVs provide an efficient means of ensuring timely and uniform herbicide application while minimizing operator exposure (Jeevan et al., 2024; Paul et al., 2025). UAV spraying offers rapid field coverage, reduced labour and water requirements, improved accessibility in waterlogged fields and enhanced operator safety (Chen et al., 2021; Brankov et al., 2023; Zhang et al., 2013). However, spray drift and inadequate droplet deposition remain major challenges affecting herbicide performance. Rotor-generated airflow can alter droplet behaviour, increasing drift and evaporation losses, thereby reducing herbicide retention and efficacy (Hewitt, 2018; Chen et al., 2022; Wang et al., 2019).
       
The use of spray adjuvants has been proposed as an effective strategy to overcome these constraints. By improving droplet spreading, adhesion, retention and penetration, adjuvants enhance herbicide deposition on target weeds while reducing drift and evaporation losses (Brankov et al., 2023) and (Ashraf et al., 2024a). Consequently, adjuvant-assisted aerial applications have been reported to improve herbicide efficacy and weed control (Green and Beestman, 2007; Gaskin and Steele, 2009).
       

Despite growing interest in UAV spraying, information on the comparative performance of different adjuvants under transplanted rice conditions remains limited. Therefore, the present study was undertaken to evaluate the effect of different adjuvants on UAV-based herbicide application in transplanted rice, with emphasis on weed control efficiency, herbicide efficiency indices and crop productivity.

Experimental site
 
A field experiment was conducted during the Navarai season of 2025-26 (January-May) at the wetland farm of SRM College of Agricultural Sciences, Baburayanpettai, Tamil Nadu. The site, located in the north-eastern agro-climatic zone (12.38°N, 79.73°E; 50 m above mean sea level), had sandy loam soil with a slightly alkaline reaction (pH 7.6) and electrical conductivity of 0.265 dS m^-1. The soil was low in available nitrogen (0.4 kg ha^-1), phosphorus (0.19 kg ha^-1) and medium in potassium (42 kg ha^-1).
       
Meteorological data recorded during the crop period showed maximum and minimum temperature ranging from 29.2-38.5°C and 18.2-28.0°C, respectively. The season received 33.5 mm rainfall over four rainy days, while wind velocity ranged from 3.9 to 4.8 km h^-1 and relative humidity from 61.4 to 73.8%.
 
Crop husbandry
 
Rice variety CO (R) 55, with a crop duration of 110-115 days, was used for the study. Seeds obtained from the Paddy Breeding Station, Tamil Nadu Agricultural University, Coimbatore, were raised in a wet nursery and 15 days old seedlings were transplanted in the puddled field at 20 × 10 cm spacing with 2-3 seedlings hill^-1.
       
The field was cultivated, puddled, levelled and provided with irrigation and drainage channels. Recommended agronomic practices, except weed management treatments, were followed as per the crop production guide (CPG, 2020).
       
Fertilizers were applied at 150:50:50 kg ha^-1 NPK through urea, diammonium phosphate and muriate of potash. Phosphorus was applied basally, while nitrogen and potassium were applied in splits at active tillering, panicle initiation and heading stages. A shallow water layer was maintained during establishment, followed by 5 cm standing water until ten days before harvest.
 
Experimental design and treatments
 
The experiment was laid out in a randomized block design (RBD) with seven treatments replicated thrice. The experimental plot measured 11 × 8 m (60 cents). A buffer zone of 3 m was maintained between adjacent plots to minimize spray drift, seepage and treatment interference during UAV herbicide application. The treatments consisted of post-emergence application of florpyrauxifen-benzyl + Cyhalofop-butyl @ 150 g ha^-1 using UAV with different adjuvants, along with UAV spraying without adjuvant, knapsack spraying, weed-free check and weedy check. The post-emergence herbicide used in the study was Novlect containing florpyrauxifen-benzyl (2.13%) + cyhalofop-butyl (10.64%) formulated as EC, manufactured by Corteva. The herbicide was applied at a total dose of 150 g a.i. ha^-1 at 20 DAT.
The treatment details were as follows:
T₁: Spray of Florpyrauxifen-benzyl + Cyhalofop-butyl @ 150 g ha^-1 with silicon-based adjuvant using UAV.
T₂: Spray of Florpyrauxifen-benzyl + Cyhalofop-butyl @ 150 g ha^-1 with All-Purpose Spray Adjuvant (APSA) using UAV.
T₃: Spray of Florpyrauxifen-benzyl + Cyhalofop-butyl @ 150 g ha^-1 with methylated seed oil adjuvant using UAV.
T₄: Spray of Florpyrauxifen-benzyl + Cyhalofop-butyl @ 150 g ha^-1 using UAV.
T₅: Spray of Florpyrauxifen-benzyl + Cyhalofop-butyl @ 150 g ha^-1 using knapsack sprayer
T₆: Weed-free check.
T₇: Weedy check (unweeded control).
       
*The post-emergence herbicide was applied at 20 days after transplanting (DAT).
 
UAV spraying and adjuvant application
 
Herbicide application was carried out using a hexacopter UAV spraying system (Fig 1), with specifications and spray parameters presented in Table 1 and 2. Spraying was performed during early morning hours (07:00-09:00 h) under favourable weather conditions to minimize drift and evaporation. The UAV was operated at a flight height of 1.5 m, forward speed of 5 m s^-1 and spray volume of 40 L ha^-1 (Fig 2). Silicon-based, methylated seed oil and all-purpose spray adjuvants (Table 3) were individually mixed with florpyrauxifen-benzyl + cyhalofop-butyl before application.










       
In T5, the herbicide was applied using a knapsack sprayer fitted with a flat-fan nozzle (11002 XR) at 2.5 bar pressure and 500 L ha^-1 spray volume. Weed-free plots (T6) were maintained by manual weeding at 20 and 40 DAT, whereas no weed control measures were adopted in the weedy check.
 
Weed observations
 
Weed flora
 
The experimental field contained mixed weed flora comprising grasses, sedges and broad-leaved weeds. Weed species present in the experimental plots were identified and grouped accordingly.

Weed density
 
Weed density was recorded at 40, 60 DAT using a quadrat of 0.25 m² placed randomly at four locations in each plot. Weed species within the quadrat were counted separately and categorized into grasses, sedges and broad-leaved weeds. Weed density was expressed as number m^-2.
 
Weed dry weight
 
Weed dry weight was estimated at 40, 60 DAT. Weeds collected from quadrat samples were sun dried and later oven dried at 70°C until constant weight was obtained. The dry weight was recorded and expressed as g m^-2.
 
Weed control efficiency (WCE)
 
Weed control efficiency was calculated using the formula suggested by Mani et al., (1973).

Where,
Wpc= Weed population in control plot.
Wpt= Weed population in treated plot.
 
Herbicide efficiency index (HEI)
 
Herbicide efficiency index (HEI) was calculated to evaluate the effectiveness of weed management treatments based on crop yield and weed dry matter production, as suggested by Rana and Kumar (2014), using the following formula:

 
Where,
Y_t= Grain yield of treated plot (kg ha^-1).
Y_c= Grain yield of unweeded control plot (kg ha^-1).
W_t= Weed dry matter in treated plot (g m^-2).
 
Crop resistance index (CRI)
 
Crop resistance index (CRI) was calculated to evaluate the ability of the crop to withstand weed competition under different weed management practices as proposed by Rana and Kumar (2014), using the formula:

 
Where,
Y_t= Grain yield of treated plot (kg ha^-1).
Y_c= Grain yield of unweeded control plot (kg ha^-1).
W_t= Weed dry matter in treated plot (g m^-2).
W_c= Weed dry matter in unweeded control plot (g m^-2).
 
Weed persistence index (WPI)
 
Weed persistence index (WPI) was calculated to estimate the persistence of weeds under different weed management practices, as described by Rana and Kumar (2014), using the following expression:

 
Where,
W_t= Weed dry matter in treated plot (g m^-2).
W_c= Weed dry matter in unweeded control plot (g m^-2).
 
Yield attributes and yield
 
Yield attributes including productive tillers m^-2, filled grains panicle^-1, total grains panicle^-1 and test weight were recorded from randomly selected plants in each plot. Grain yield was recorded from the net plot area after threshing, drying and cleaning and adjusted to 14 per cent moisture content before expressing in kg ha^-1. Straw yield was recorded after proper drying and expressed in kg ha^-1.
 
Statistical analysis
 
The data were analyzed statistically using analysis of variance (ANOVA) appropriate for randomized complete block design (RCBD) as suggested by Gomez and Gomez (1984). Weed count data were subjected to square root transformation √(x + 0.5), while percentage data were angularly transformed before analysis. Statistical analysis was performed using R software and treatment means were compared using Critical Difference (CD) at 5% probability level (P = 0.05).

Effect of adjuvant-based UAV spraying on weed density and dry weight
 
The experimental field was infested with a mixed weed flora comprising grasses [Echinochloa crus-galli (L.) P. Beauv., Echinochloa colona (L.) and Leptochloa chinensis (L.) Nees], sedges (Cyperus difformis L. and Cyperus iria L.) and broad-leaved weeds [Marsilea quadrifolia L., Bergia capensis L., Ludwigia parviflora (Roxb.) H. Hara, Monochoria vaginalis (Burm. f.) C. Presl, Ammannia baccifera L. and Eclipta alba (L.) Hassk].
       
Weed density and dry weight differed significantly among the weed management treatments at both 40 and 60 DAT (Table 4 and 5). Among the herbicide-treated plots, UAV application of florpyrauxifen-benzyl + cyhalofop-butyl with a silicon-based adjuvant (T₁) recorded the lowest total weed density (42.68 and 52.86 plants m^-2) and total weed dry weight (30.42 and 43.06 g m^-2) at 40 and 60 DAT, respectively, representing reductions of 76.7% and 75.5% in weed density over the weedy check. APSA (T₂) and methylated seed oil (T₃) also reduced weed density and dry weight compared with UAV application without adjuvant (T₄), but remained significantly inferior to the silicon-based adjuvant.




       
The superior weed suppression achieved with the silicon-based adjuvant may be attributed to its lower surface tension, which enhanced droplet spreading, retention and herbicide uptake on weed foliage. Compared with APSA and methylated seed oil, organosilicon adjuvants provide greater leaf coverage, improving herbicide deposition under UAV application. In addition, rotor-generated downwash facilitated deeper canopy penetration and its interaction with the organosilicon adjuvant likely enhanced herbicide interception and efficacy, resulting in significantly lower weed density and dry weight. Similar findings have been reported by Thompson et al., (1996); Green and Beestman (2007); Chen et al., (2022) and Brankov et al., (2023).
 
Effect of adjuvants on weed control efficiency
 
Weed control efficiency (WCE) differed significantly among the treatments at both observation stages (Table 6). Among the herbicide-treated plots, the silicon-based adjuvant (T₁) recorded the highest WCE of 76.65% and 74.99% at 40 and 60 DAT, respectively, followed by APSA (72.12% and 71.66%) and methylated seed oil (68.41% and 70.12%). In contrast, UAV application without adjuvant (T₄) and knapsack spraying (T₅) recorded comparatively lower WCE of 64.01% and 64.00% and 67.87% and 68.26%, respectively.


       
The enhanced performance of the silicon-based adjuvant is primarily associated with its superior ability to lower the surface tension of the spray solution, thereby promoting greater droplet spreading, wetting and retention on the target surface. Under low-volume UAV spraying, these properties improve canopy interception and droplet deposition while reducing losses due to droplet rebound and drift. In comparison, APSA mainly enhances droplet adhesion and wetting, whereas methylated seed oil primarily facilitates herbicide penetration through the leaf cuticle after deposition. Consequently, the greater influence of organosilicone adjuvants on droplet behaviour contributes to superior spray performance and herbicide efficacy under UAV application (Singh et al., 2026). The comparatively poorer performance of knapsack spraying may be due to less uniform spray deposition, limited canopy penetration and operator-dependent variability, particularly under flooded transplanted rice conditions. Similar findings have been reported by Hewitt (2008); Chen et al., (2022) and Brankov et al., (2023).
 
Effect of adjuvants on herbicide efficiency index (HEI), crop resistance index (CRI) and weed persistence index (WPI)
 
Herbicide efficiency index (HEI), crop resistance index (CRI) and weed persistence index (WPI) differed significantly among the weed management treatments (Fig 3). Among the herbicide-treated plots, UAV application with the silicon-based adjuvant (T₁) recorded the highest HEI and CRI, along with the lowest WPI, indicating superior herbicide efficacy, crop competitiveness and reduced weed persistence. APSA (T₂) and methylated seed oil (T₃) also improved these indices over UAV application without adjuvant (T₄) and knapsack spraying (T₅), whereas the weedy check (T₇) recorded the lowest HEI and CRI and the highest WPI (Ashraf et al., 2025) and (Bhagavathi et al., 2026).


       
The superior performance of the silicon-based adjuvant may be attributed to improved droplet spreading, deposition and herbicide uptake resulting from its lower surface tension. In combination with the rotor-induced downwash of the UAV, this enhanced spray penetration and herbicide interception, leading to lower weed biomass, reduced weed persistence and improved crop competitiveness. APSA and methylated seed oil also enhanced herbicide performance but were comparatively less effective due to their lower spreading ability under UAV spraying conditions. Similar findings have been reported by Rana and Kumar (2014); Rao et al., (2017); Chen et al., (2022) and Brankov et al., (2023).
 
Effect on yield attributes and yield
 
Yield attributes and yield were significantly influenced by adjuvant-based herbicide application (Table 7). Among the herbicide-treated plots, the silicon-based adjuvant (T₁) recorded the highest total grains per panicle (173.2), grain yield (5422.7 kg ha^-1) and straw yield (6384 kg ha^-1), remaining statistically comparable with the weed-free check (5716.9 kg ha^-1). APSA (T₂) and methylated seed oil (T₃) also improved yield over UAV application without adjuvant (T₄), but were inferior to the silicon-based adjuvant. The higher yield obtained under adjuvant-assisted treatments was primarily due to lower weed density, reduced nutrient removal by weeds, improved crop growth and enhanced physiological efficiency. Effective weed suppression during the critical period minimized crop-weed competition and allowed greater partitioning of assimilates towards reproductive growth.


       
The results are consistent with findings of Green and Beestman (2007); Chen (2021); Brankov et al., (2023), who reported that adjuvant-assisted herbicide applications improve herbicide performance, crop growth and final grain yield. Silicon-based adjuvant consistently outperformed APSA and methylated seed oil adjuvants, indicating its greater potential for improving herbicide efficacy under UAV-based spraying systems. The interaction between UAV rotor downwash and the organosilicon adjuvant likely improved herbicide distribution and uptake, leading to superior weed control and crop productivity (Narappa et al., 2026).

This study evaluated the effectiveness of three adjuvants (Silicon-based, all-purpose spray and methylated seed oil) in UAV based herbicide application for transplanted rice. The results showed that adjuvant addition significantly improved herbicide performance compared with non-adjuvant application. Among the treatments, the silicon-based adjuvant produced the best overall results, likely dur to enhanced droplet spreading, retention and herbicide penetration on target surfaces. Adjuvant-assisted UAV spraying also achieved higher yields than conventional knapsack application while substantially reducing water use, highlighting its potential for precision agriculture. Nevertheless, further research is needed to better understand the mechanisms governing adjuvant performance under varying field conditions.

Authors acknowledge the SRM Institute of Science and Technology, Baburayanpettai, Chengalpattu, India for providing necessary facilities and valuable assistance in conducting this research work.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.