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Article

Comparative Efficacy of UAVs (Unmanned Aerial Vehicles) and Ground-Based Bait Applications for Olive Fruit Fly (Bactrocera oleae) Control in Greek Olive Orchards

by
Georgia D. Papadogiorgou
1,
Konstantina Alipranti
2,
Vasileios Giannopoulos
3,
Sergey Odinokov
3,
Dimitris Stavridis
4,
Antonis Paraskevopoulos
5,
Panagiotis Giatras
6,
Stelios Christodoulou
3,
Kostas Dimizas
7,
Emmanouil Roditakis
2,
Emmanouela Kapogia
8,
Kostas Zarpas
1 and
Nikos T. Papadopoulos
1,*
1
Laboratory of Entomology and Agricultural Zoology, Department of Agriculture, Crop Production and Rural Environment, School of Agricultural Sciences, University of Thessaly, 38446 Volos, Greece
2
Laboratory of Agricultural Entomology and Pharmacology, Department of Agriculture, School of Agricultural Sciences, Hellenic Mediterranean University, 71004 Heraklion, Greece
3
Ionos-AgriDrones/Industrial Drone Solutions, 70013 Heraklion, Greece
4
General Directorate of Regional Agricultural Economy and Veterinary Services, Thessaly, 41110 Larisa, Greece
5
General Directorate of Regional Agricultural Economy and Veterinary Services, Messinia, 24133 Trifilia, Greece
6
Directorate of Agricultural Economy & Veterinary Services—Department of Quality & Phytosanitary Control, 29100 Zakynthos, Greece
7
Elanco Hellas S.A.C.I., 15231 Athens, Greece
8
Ministry of Rural Development and Food, 10432 Athens, Greece
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(9), 2158; https://doi.org/10.3390/agronomy15092158
Submission received: 18 July 2025 / Revised: 19 August 2025 / Accepted: 8 September 2025 / Published: 9 September 2025
(This article belongs to the Section Pest and Disease Management)
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Abstract

The use of unmanned aerial vehicles (UAVs) in agricultural pest management has emerged as a promising alternative to conventional methods, particularly in challenging terrains. This study assessed the effectiveness of UAV-based versus ground-based bait spraying for controlling the olive fruit fly Bactrocera oleae in four regions in Greece (Larisa, Zakynthos, Trifillia, and Crete) over a four-year period (2021–2024). In each region, three olive orchards were selected: one received UAV-based bait applications, one was treated using standard ground-based bait application, and the third served as an untreated control. UAV applications were conducted using the M6E hexacopter, while ground treatments followed conventional protocols. Infestation levels were evaluated through systematic fruit sampling, assessing both overall and active infestations. Climatic and orchard data were also recorded to interpret variability in treatment outcomes. Results showed that both UAV and ground treatments significantly reduced infestation compared to the control. Active infestation ranged from 14.2–22.5% in control-untreated plots, 4.6–7.8% in UAV plots, and 5.3–8.4% in ground-treated plots. A significant year × treatment interaction indicated variable efficacy across years, with clearer treatment effects in 2021–2022. UAV applications were as effective or superior to ground spraying, especially in hard-to-reach areas. These findings support the integration of UAVs into pest management programs as a sustainable and efficient alternative for olive fly control.

1. Introduction

The olive tree (Olea europaea L.), originally native to tropical and warm temperate regions, is one of the oldest cultivated crops in the Mediterranean basin [1,2,3]. This evergreen perennial tree belonging to the Oleaceae family is primarily cultivated for its fruits, which are widely used to produce olive oil—the predominant oil derived from olives and a key product in Mediterranean agriculture [4]. While olive cultivation has expanded beyond the Mediterranean, the region as a whole remains the principal zone of production, accounting for approximately 90% of the global olive-growing area. The eastern Mediterranean is one of the key subregions contributing to this dominance [4]. Olive cultivation is predominantly concentrated in southern Europe, with Spain as the leading producer, covering 53% of the area (2.4 million ha), followed by Italy (24%; 1.4 million ha), Greece (15%; 1 million ha), and Portugal (7%; 0.5 million ha) [1]. Olive yields can reach up to 22 tons per hectare, with the weight of individual olives varying between 1 and 12 g, depending on the variety [5]. Olive oil, being a major export commodity, plays a crucial role in the economic development of olive-producing regions, and the European olive oil strategy focuses on maintaining its global market position by encouraging the production of high-quality products that benefit producers, processors, traders, and consumers alike [1].
The olive fruit fly Bactrocera oleae (Rossi) (Diptera: Tephritidae) is the main insect pest affecting olive production globally, and particularly in the Mediterranean region [6,7]. This monophagous pest feeds exclusively on the genus Olea, with newly hatched larvae feeding on the pulp of olive drupes, causing direct damage to olive yields [8,9,10]. In the Mediterranean region, the pest can complete multiple generations, ranging from one to four, depending on environmental factors such as temperature, altitude, and closeness to the sea [11,12]. The level of fruit infestation can vary even up to 100%, influenced by environmental factors [13,14], with annual economic losses in the olive industry exceeding one billion USD in the region [15].
The management of B. oleae has traditionally relied on chemical insecticides, particularly organophosphates (OPs) and pyrethroids [16,17]. Among these, dimethoate has been widely used in bait spray formulations due to its proven efficacy in controlling adult olive fly populations and its rapid action in mitigating infestations [16,18]. Bait sprays, a priority in olive fly management for decades [19,20], are recognized as an essential tool for B. oleae and other fruit-feeding tephritid control. Their use is strongly recommended by the IOBC Guidelines for the Integrated Production of Olives [21]. Bait spray applications against the olive fly combine an insecticide (e.g., organophosphate at 0.3%) with a food attractant (e.g., 2% hydrolyzed protein) and applied to a small portion of the tree canopy, typically 1–2 m3, using no more than 300 mL of solution per tree [22]. This localized application targets adult flies, minimizing environmental exposure compared to cover sprays [23]. Cover sprays, in contrast, are applied to the entire canopy until runoff using a much larger volume of spray solution (10–15 L per tree). The insecticide dose in cover sprays is approximately ten times lower (e.g., organophosphate at 0.03%) than in bait sprays. However, cover sprays are primarily recommended as a curative measure when bait sprays fail, and fruit infestation surpasses economic threshold levels, typically 8–10% for oil-producing olive varieties, depending on fruit size [22,24].
In Greece, the protection of olive production from the olive fruit fly is achieved through ground-based bait-spray applications as part of the National Programme for Integrated Olive Fruit Fly Control. Known as the “Collective Control of the Olive Fruit Fly,” this program was established in 1937, funded by the Ministry of Rural Development and Food, and is supervised by the Agricultural Economy and Veterinary Directorates across the 36 olive-producing regional units [24]. The application of this method is based on the monitoring of the insect population through a network of glass McPhail traps and applying three to five bait sprayings via ground application depending on the year, variety, and the climatic conditions of the region of the country [22]. While this program has achieved significant success over the decades, it heavily relied on the use of organophosphate insecticides, such as dimethoate. However, the intense use of dimethoate has led to the development of field evolved resistance of B. oleae and it was associated with target site insensitivity [25]. Furthermore, effective in controlling olive fly populations, dimethoate posed toxicological risks to human health and non-target organisms, including pollinators like honeybees (Apis mellifera), birds, aquatic species, and terrestrial wildlife. Its environmental persistence further raised concerns about contamination, making it incompatible with the goals of sustainable agriculture [24,26]. As a result, dimethoate was withdrawn from the EU market in 2020, prompting a shift toward alternative insecticides [27]. Currently, bait sprays primarily employ pyrethroids (e.g., deltamethrin) and spinosad, which offer effective control with reduced environmental impact. However, the prolonged and widespread use of any insecticide class with a specific mode of action may still contribute to resistance development in B. oleae populations [22], underscoring the need for integrated and adaptive pest management strategies.
The effectiveness of bait spray applications is further influenced by environmental factors such as air temperature, wind speed, and humidity levels in the spraying area. These parameters are critical to the success of spray operations and must remain within specific thresholds to prevent the targeted adult flies from dispersing to nearby areas. However, without the support of advanced computer and communication technologies, it is challenging for spray operators to manage these variables effectively. Spray applications often cover large areas, making it difficult for tractor operators to accurately memorize the sprayed zones. This can result in over-spraying, under-spraying, or off-target spraying, which not only reduces the quality of olive oil and table olives but also leads to adverse environmental and human health impacts. Moreover, spray operators often lack the means to determine the appropriate spray volume per area or to identify areas that should not be sprayed, further exacerbating these issues [26].
The use of unmanned aerial vehicles (UAVs), commonly known as drones, is rapidly increasing in commercial agriculture due to their capabilities in crop monitoring and operations such as spraying and sowing [28,29,30,31,32]. By 2035, about 25,000 UAVs are expected to operate in Europe, in activities including chemical spraying and precision farming [33]. UAV-based spraying offers efficient, low-cost, and environmentally friendly solutions, especially in complex terrains less accessible to ground equipment [28,34]. Challenges include limited tank capacity and battery life [28,35,36]. Cost models indicate UAV spraying is 1.45 to 2 times more expensive than traditional methods due to higher capital costs and shorter economic lifespan [37]. UAVs also reduce pesticide use and operational time compared to ground systems [38]. However, regulatory restrictions in many countries, including the EU Directive 2009/128/EC, generally prohibit aerial spraying except where specific exemptions apply, mainly due to concerns about drift and environmental exposure [39].
The aim of the present study was to assess the effectiveness of using UAVs in the application of bait sprays for controlling the olive fruit fly in olive groves of varied topographical characteristics, in comparison with traditional ground-based bait sprays application methods used in olive fly control. This study seeks to evaluate the operational efficiency, coverage, and cost-effectiveness of UAVs in pest management, particularly in challenging terrains where conventional methods may be less efficient. By comparing UAV-based and ground-based spraying techniques, this research provides insights into the potential advantages and limitations of UAVs, including factors such as spray precision, pesticide consumption, labor requirements, and environmental impact. Additionally, our study aims to explore how the use of UAVs can contribute to the optimization of pest control strategies in diverse agricultural environments, promoting sustainable and efficient olive cultivation practices. Overall, we provide much-needed empirical evidence on the practicality and sustainability of UAV-assisted spraying techniques, offering a comprehensive comparison with conventional practices.

2. Materials and Methods

2.1. Study Design and Experimental Sites

Trials were implemented over a four-year period (2021–2024) in various regions across Greece (Larisa, Zakynthos, Trifillia and Crete) (Figure 1). In each regional unit, three olive orchards were selected for the study. In the first orchard, ground-based bait spraying was conducted following the standard practices of the olive fruit fly control program. In the second orchard, bait spraying was applied using UAVs. The third orchard received no treatments for B. oleae control and served as an untreated control. For ground-based bait spraying, approximately 300 mL of bait solution was applied to the northern–inner side of every second or third tree. For UAV-based bait application, the application was performed from a height of 2–3 m above the tree canopy. The specific sites where drone-based bait application were applied are provided in the Supplementary Materials (Supplementary Figures S1–S5).

2.2. Bait Application Methods and UAV Specifications

Each area received three applications between September and October. The experimental orchards at each research site covered approximately 2.5 hectares. The bait formulation used in all applications was a commercial concentrated bait containing 0.024% (w/v) spinosad (1986/24.08.2009, Greece) as the active ingredient. Spinosad is a naturally derived insecticide consisting primarily of spinosyn A and spinosyn D, produced by the actinomycete Saccharopolyspora spinosa. The formulation includes a protein-based attractant and other food-derived volatile compounds. For each application, the required volume of insecticide was diluted with tap water resulting in a final spinosad concentration of 0.024% (w/v) in the spray mixture. No additional attractants or adjuvants were added.
Aerial bait applications were conducted using the M6E (10-L) hexacopter drone (Figure 2), operated by IONOS under UAS Operator Registration GRCw8dbc2ffalk1m and authorized by the Hellenic Civil Aviation Authority (HCAA) under the Specific Category—SORA framework (Operational Authorisation Number: GRCw8dbc2ffalk1m-G001-AMEND-01). The Targeted Laminar Jet Application (patented GREEN JET method) was employed, delivering precisely 50 mL of bait solution per tree, with one tree treated for every three. Depending on the application and selected site, 12–23 L of water were used per 2.5 ha orchard. This method ensures virtually no drift issues due to precise laminar jet deflection control. Flights were carried out at speeds of 3.7–4.5 m/s and heights of 3–5 m above the canopy, under wind conditions up to 5.5–8 m/s. Experimental use of insecticide was approved by the Ministry of Agricultural Development and Food for the period 2020–2026.
For ground-based bait spraying, the insecticide dose was approximately 2.5 L per application, with water volumes ranging from 150 to 180 L per orchard (2.5 ha). This corresponded to a targeted bait solution delivery of about 300 mL/ha on the treated trees, following the same 1:3 tree treatment pattern used in UAV applications. For both applications, the insecticide used contained the active ingredient spinosad and was applied at a rate of 1000 mL/ha.

2.3. Monitoring of Olive Fruit Fly Populations and Fruit Sampling

To evaluate the effectiveness of bait spraying treatments, olive fruit sampling was conducted in each region from three experimental plots corresponding to the treatments: ground-based application, UAV-based application, and untreated control. In each plot, 100 olive fruits were randomly collected from the central area in the plot, ensuring adequate distance from population monitoring traps to avoid potential interference. Olives were collected from multiple trees within each plot, and all sampled trees were marked. In addition, fallen olives were collected from the ground beneath four trees per plot to complement the assessment. All collected olives were examined individually under a stereoscope (×10–40 magnification) to detect signs of B. oleae infestation. Each fruit was assessed externally for oviposition stings, puncture marks, and exit holes, and then dissected using a scalpel to inspect the interior for the presence of larvae, pupae, egg galleries, and feeding tunnels. Olives were classified as infested if any developmental stage of the insect or characteristic damage was observed.

2.4. Infestation Assessment

The infestation rate was calculated as the proportion of olives showing any signs of infestation (e.g., oviposition punctures, egg galleries, larval entry points, or exit holes) divided by the total number of fruits examined. An olive was classified as infested if any of these external or internal indicators of B. oleae activity were present, regardless of larval viability.
To further assess active infestation, we examined infested fruits for the presence of fresh larval tunnels within the pulp, characterized by moist, uncollapsed galleries typically containing live larvae or recent feeding signs. Although active infestation includes eggs, first, and second instar larvae, in this study we estimated infestation levels referring to the presence of third instar larvae, pupae, and exit holes, which represent later stages associated with more significant damage. The active infestation rate was calculated in two ways: first, as the proportion of olives with larval tunnels relative to the total number of olives examined (including both infested and non-infested fruits); and second, as the proportion of olives with tunnels relative only to the subset of infested olives. While we acknowledge that larval tunnels can persist after larvae have pupated or died, we took care to distinguish between fresh (active) and old (inactive) tunnels by considering their condition, color, and moisture content during dissection.

2.5. Climatic and Orchard Data

To account for potential environmental and agronomic factors affecting the efficacy of bait treatments and infestation levels, key climatic parameters and orchard characteristics were recorded during this study.
  • Climatic Conditions
Meteorological data were collected in each bait-spraying application. The recorded parameters included temperature (°C), which influences the development and activity of B. oleae; relative humidity (%), affecting the persistence of the bait solution and the feeding behavior of the flies; and wind speed (m/s), which impacts the distribution and potential drift of bait sprays, particularly relevant for UAV applications. These data were obtained from nearby meteorological stations and are summarized in Supplementary Table S1.
  • Orchard Characteristics
Supplementary Table S2 presents the key characteristics of the surveyed sites, including location (site, latitude, and longitude), olive tree variety, tree age, and irrigation regime. The predominant olive variety across the pilot sites was Koroneiki, present in four out of five locations, while the Amphisis cultivar was found exclusively in the remaining site. Regarding irrigation practices, only the orchards in Trifillia were irrigated; the other sites relied solely on rainfed cultivation. Tree age varied between 15 and 30 years across the surveyed orchards. Key characteristics of the study sites, including location (site name, latitude, and longitude), olive variety, tree age, and irrigation regime, are detailed in Supplementary Table S2.

2.6. Statistical Analysis

To examine the effects of year and treatment on infestation status, we employed a generalized linear model (GLM) in SPSS 29. Since the response variable was binary (1 = infested, 2 = not infested), we specified a binomial distribution with a logit link function. Year and treatment were included as fixed factors, and their interactions were also assessed. Model fit was evaluated using deviance residuals, and the significance of main effects and interactions was determined via Wald chi-square tests. Pairwise comparisons were adjusted by using the Bonferroni correction test. As the experiment was not conducted at each site in every year, we focused on comparing infestation levels between treatments across the four-year period, excluding site as a factor. Site-specific data are provided in the Annex for reference. The significance level for all comparisons was set at α = 0.05.

3. Results

Detailed data on infestation levels per plot and year are provided in Appendix A, Appendix B and Appendix C (Figure A1, Figure A2 and Figure A3 and Table A1, Table A2 and Table A3).

3.1. Comparative Effectiveness of UAV-Based and Ground-Based Bait Applications on Bactrocera oleae Infestation

The mean infestation levels of B. oleae over four years period, comparing three pest control methods are given in Figure 3 (Table 1). Statistical analysis confirmed that both the year and treatment significantly affected infestation levels (p < 0.001). Infestation levels were higher in 2021 across all treatments (p < 0.001) (Table 2). Pairwise comparisons indicated that infestation rates were higher in the control group compared to both UAV bait applications and ground-based bait applications (p < 0.001) (Table 3). Comparing UAV- and ground-treated plots, the UAV application resulted in markedly lower infestation levels (p < 0.001, Table 3). Furthermore, the significant year × treatment interaction suggests that the effectiveness of the spraying methods varied over the years. Significant differences were detected among treatments in certain years (e.g., 2021 and 2022), but not in others (e.g., 2024), (Supplementary Table S3).

3.2. Assessment of Bactrocera oleae Active Infestation Levels Based on Total Olives Examined Under Different Control Strategies

The average active infestation levels of B. oleae, calculated from the total olives examined during the four-year study period are presented in Figure 4. The data were assessed under three pest management strategies (Table 4). Statistical tests revealed that both year and treatment had a significant impact on infestation levels (p < 0.001). Active infestation was highest in 2021, irrespective of the treatment method used (p < 0.001; Table 5). Post hoc comparisons indicated that infestation levels in the untreated control were significantly higher than in both the aerial and ground treatments (p < 0.001; Table 6). Between the UAV- and ground-treated plots, active infestation levels were significantly lower in the UAV treatment (p < 0.001; Table 6). Additionally, a significant interaction between year and treatment was identified, demonstrating that the efficacy of the treatments varied across years. Differences between treatments were significant in some years (e.g., 2021 and 2022). For example, in 2021, mean active infestation was 48.88% in control plots, 28.16% in UAV-treated plots, and 41.78% in ground-treated plots. Similar trends were observed in 2022. In contrast, no significant differences were detected among treatments in 2023 and 2024, when overall infestation pressure was lower (Supplementary Table S4).

3.3. Assessment of Bactrocera oleae Active Infestation Levels Under Different Control Strategies

The mean active infestation levels of B. oleae over four years, comparing the three pest management strategies, are presented in Figure 5 (Table 7). Statistical analysis indicated that both the treatment method and the year had a significant effect on active infestation levels (p < 0.001). The highest infestation rates were observed in 2024, regardless of the treatment method (p < 0.001; Table 8). Pairwise comparisons revealed significantly higher infestation levels in the untreated control compared to both treatments (p < 0.001; Table 9) Consistent with previous findings, active infestation was also higher in the ground-treated plots compared to the UAV-treated plots (p < 0.001; Table 9). A significant interaction between year and treatment further suggested that the effectiveness of the control strategies differed between years. Notable treatment-related differences were found in 2021 and 2022, whereas in 2024, infestation levels did not differ across treatments (Supplementary Table S5).

4. Discussion

This study evaluated the effectiveness of UAV-bait applications and ground-based bait application strategies for controlling B. oleae infestations over a four-year period. Overall, bait applications applied via UAVs performed similarly to, and in some years, better than conventional ground-based applications. As expected, untreated (control) orchards consistently exhibited higher infestation levels across all years. Notable interannual variability was observed, with 2024 recording the lowest infestation rates. While both UAV and ground treatments significantly reduced infestation relative to the control, their relative efficacy fluctuated depending on the year (significant year by treatment interaction), reflecting the influence of environmental or operational factors on treatment performance. It seems that the effectiveness of control strategies was not uniform over time and may have been influenced by fluctuating environmental or operational factors. These findings reflect the non-uniform performance of control strategies over time and emphasize the dynamic nature of pest management in olive agroecosystems. It is important to note that data availability varied across locations and years due to logistical constraints in collaboration with the Directorate of Regional Agricultural Economy and Veterinary Services of Greece. Therefore, results should be interpreted with caution, especially when comparing years with incomplete site representation.
The consistently higher infestation levels observed in the untreated control plots in our study underscore the critical need for effective pest management against the olive fly. The absence of treatment in these plots allowed the olive fruit fly populations to increase, resulting in significantly higher infestation rates compared to treated plots. This finding is in line with previous studies, which have demonstrated that untreated olive groves experience rapid population buildup and increased fruit damage due to uncontrolled fly activity [24]. On the other hand, our findings confirmed that both UAV- and ground-based applications of spinosad significantly reduced olive fruit fly infestation levels. This result highlights the flexibility of spinosad applications, demonstrating that both systems can achieve effective control when implemented correctly. In 2024, infestation levels were similarly low across all treatments, including the untreated control plots. This exception is likely to be due to unusually low olive fruit fly pressure during that year, which limited population development regardless of management strategy. These findings are consistent with earlier studies showing that spinosad can be as effective as dimethoate in managing B. oleae populations while also offering a more favorable toxicological and ecological profile. In addition, its approval for use in organic agriculture within the European Union further supports its role as a sustainable alternative in integrated pest management (IPM) programs, particularly in regions where environmental sensitivity and residue limitations are of concern [40].
Across the four years of our study (2021–2024), UAV-based bait applications were found to be either more effective or equally effective compared to ground-based treatments for controlling B. oleae. This improved efficacy may be attributed to UAVs’ ability to execute precise, programmable flight paths and deliver targeted applications that minimize off-target spraying and optimize pesticide use. These advantages are particularly relevant under conditions where terrain complexity or operator variability may reduce the accuracy of ground-based applications. While UAVs are limited by smaller tank capacities and shorter operational times per flight [37], their precision can offer distinct benefits in certain environmental conditions or plot configurations. In the years when UAVs achieved better control than ground-based treatments (2021 and 2022), active infestation levels were relatively high. This suggests that UAVs may be particularly advantageous under greater pest pressure, where accurate and consistent bait application is critical for suppressing growing populations. Similar results have been reported in other cropping systems, such as maize, where rapid drone-based application of insecticides significantly reduced Spodoptera frugiperda populations, achieving over 94% control efficacy [41,42]. In 2023 and 2024, when overall B. oleae active infestation levels were low, both UAV and ground-based applications achieved comparable efficacy. Under such conditions, the uniformity of the plots and the lower pest pressure may have allowed both systems to perform equally well. Our results are consistent with previous comparative studies in citrus orchards, where UAV-applied spinosad bait treatments against Ceratitis capitata were found to be as effective or even more so than ground-based applications, demonstrating UAVs as a viable and sustainable alternative for precision bait spraying in tree crops [43]. Beyond pest control efficacy, recent ecotoxicological research on Spintor® Fly has shown similarly limited impacts on soil fauna and beneficial arthropods regardless of application method [44], suggesting that this system does not substantially alter the ecological footprint of the treatment. These findings support the integration of UAVs into environmentally responsible (IPM) programs, especially in light of EU regulatory pressures to reduce pesticide use and promote sustainable alternatives. Overall, the mixed results across years highlight that while UAVs are at least as effective as ground spraying for olive fruit fly control, their precision capabilities may confer additional benefits under specific conditions, particularly when pest pressure is high, supporting their broader adoption as a complementary or alternative pest management tool.
The observed annual and site-specific variations in infestation levels and treatment efficacy can be attributed to a combination of environmental, biological, and operational factors that fluctuated over time. Olive fruit fly population dynamics and the number of generations per year depend on several factors, including microclimate (temperature and humidity), fruit availability, and quality [11,45,46,47,48]. Laboratory studies indicate that temperatures of 35 °C and above are lethal to pupae [49], with larval development occurring between a lower threshold of 10–12.5 °C and an upper threshold of 30–32 °C [50,51]. Field observations confirm larval development at temperatures between 12 and 35 °C [52], though high temperatures cause substantial mortality in eggs and young larvae.
At both local and landscape scales, B. oleae population dynamics are shaped by additional influences, including microclimate variability, topography, and interactions with natural enemies [46]. Olive groves are often embedded in landscapes with diverse microclimatic conditions, which can significantly affect pest pressure. For example, land cover types surrounding orchards provide resources and habitats for predators and parasitoids that regulate fly populations [53]. Elevation also plays a role, with higher B. oleae populations often recorded in cooler mid- to high-altitude areas during summer, while lowland areas tend to experience peak populations in autumn when temperatures align more closely with the pest’s optimum range [11]. Endogenous factors such as reproductive quiescence and site-specific microhabitat features, even including ground morphology between nearby plots with similar agricultural practices, further contribute to differences in population levels and resulting damage [9,46,54].
These general ecological and physiological patterns are reflected in the field results of this study. For instance, in Larisa, UAV bait applications in 2023 resulted in substantially lower infestation (25.94%) compared to previous years, which may be linked to cooler application-time temperatures (22–25 °C), higher humidity, and consistent wind patterns that are likely to have favored bait adherence and increased fly exposure. Conversely, in Trifilia in 2024, very high infestation levels (over 90% despite treatment) coincided with lower temperatures (18 °C) at application time and high humidity (up to 90%). Such cool, moist conditions probably reduced adult fly activity during bait spraying, resulting in fewer flies feeding on the bait. Additionally, high humidity may have washed off or diluted bait droplets shortly after application, further lowering the efficacy. Although adult activity was temporarily reduced during treatment, this period was short-lived; once weather conditions improved, surviving adults resumed oviposition, along with newly emerged flies from pupae present in the soil or nearby untreated areas. The high humidity during this period is likely to have favored the survival of eggs and larvae inside olives, compounding the reduced control efficacy and ultimately leading to the exceptionally high infestation levels observed.
Wind speed is another important operational factor that can influence the uniformity of bait deposition, especially for UAV applications. According to Pontikakos et al. (2010) [55], the air speed during spraying should be less than 8 m/s, as high wind speeds inhibit insect flight activity. When wind speeds are too high, olive fruit flies are less likely to fly and feed on the sprayed bait, allowing more flies to survive treatment. In Zakynthos, where infestation levels were low in 2023 and wind speeds were mild (0.8–2.7 m/s), both drone and ground treatments showed similar efficacy. In contrast, in Larisa and Heraklion, stronger winds during certain applications (up to 5.5 m/s) may have caused droplet drift or reduced bait persistence, particularly impacting ground-based treatments. These higher wind speeds may also have contributed to reduced fly activity, indirectly lowering bait uptake and treatment effectiveness.
In our study, active infestation levels in Larisa, where the olive variety was predominantly Amfissis, were notably higher compared to other sites where the olive variety was Koroneiki. This aligns with previous findings that larger olive varieties tend to experience greater infestation rates [56]. For example, Garantonakis et al. (2016) [57] reported that the largest olives exhibited the highest infestation among the varieties tested over several years. Similarly, multiple studies [58,59,60] have documented that larger-sized olive fruits are more heavily infested than smaller ones, with evidence suggesting that within the same variety, flies preferentially oviposit on bigger fruits. These preferences may be related to larger fruits providing more resources or more favorable microhabitats for larval development, thereby increasing susceptibility to B. oleae infestation.
Finally, the agronomic context also played a role in shaping treatment outcomes. In Trifillia, mean active infestation levels in 2024 were notably high, with the plots being irrigated. Also, during spraying period low temperature and high relative humidity may also affected the infestation level as described above. Research by Bjelis et al. (2008) [61] showed that B. oleae infestation intensity is significantly higher in irrigated growing conditions compared to dry ones. This difference is primarily due to two factors: the external morphological characteristics of fruits, such as larger size and stretched exocarp in irrigated trees, which attract female flies for oviposition, and the improved internal nutritive quality of the fruit’s endocarp, which provides a favorable environment for larval development. These findings suggest that irrigation practices may indirectly increase susceptibility to olive fruit fly infestation by altering fruit morphology and quality. Further investigations assessing cultivar resistance under varying irrigation and drought conditions would be valuable for developing practical pest management strategies.
Taken together, these findings underscore the complex interplay of environmental, biological, and agronomic factors that influence B. oleae population dynamics and control efficacy in olive groves. While both UAV- and ground-based spinosad bait applications proved effective overall, their relative performance fluctuated with variations in temperature, humidity, wind speed, olive variety, and irrigation practices. The precision and programmability of UAVs offer clear advantages under conditions of high pest pressure and challenging terrain, supporting their role as a valuable tool in integrated pest management strategies. However, the variability observed across years and sites highlights the importance of tailoring control approaches to local conditions, including microclimate and agronomic practices, to optimize outcomes. Future research focusing on cultivar resistance, irrigation management, and the integration of UAV technology will be critical to advancing sustainable and effective control of the olive fruit fly.

5. Conclusions

This four-year study provides robust evidence that UAV-based bait spraying is an effective and reliable alternative to conventional ground-based applications for controlling B. oleae in olive groves. Overall, both UAV and ground treatments significantly reduced infestation compared to untreated controls, with UAV applications performing equally well or better in years of higher pest pressure (2021 and 2022). These findings demonstrate the precision and operational advantages of UAVs, particularly in complex terrain or under high infestation scenarios, making them a valuable tool for (IPM). In summary, UAV bait spraying is a practical and often superior method, provided applications are adapted to local conditions.
For olive growers, these findings highlight the importance of considering local microclimate, irrigation regime, and variety selection when planning olive fruit fly control. UAV-based bait applications offer an efficient, precise, and environmentally friendly option, especially under high pest pressure or challenging terrain. However, treatment timing should be carefully aligned with favorable weather conditions to maximize bait efficacy. Future research should focus on cultivar resistance to B. oleae and optimizing irrigation to reduce infestation risk, alongside further integration of UAV technology into IPM programs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15092158/s1, Figure S1: Olive grove in the Larisa area, where bait spraying for olive fruit fly control was carried out using UAVs. Figure S2: Olive grove in Zakynthos where bait spraying for olive fruit fly control was conducted using UAVs. Figure S3: Olive grove in Trifilia where UAVs were used to apply bait sprays for olive fruit fly control. Figure S4: Olive grove in Lasithi where UAVs were used to apply bait sprays for olive fruit fly control. Figure S5: Olive grove in Heraklion where bait spraying for olive fruit fly control was conducted using UAVs. Table S1: Climatic Parameters Recorded During Bait Spraying Applications and Monitoring Period. Table S2: Key Agronomic and Geographic Features of Surveyed Sites. Table S3: Pairwise comparisons between year and treatment of estimated marginal means based on infestation levels. The mean difference is significant at the level 0.05. Table S4: Pairwise comparisons between year and treatment were conducted on the estimated marginal means of infestation levels, calculated from the total number of olives examined. Mean differences were considered statistically significant at the 0.05 level. Table S5: Pairwise comparisons between year and treatment were conducted on the estimated marginal means of infestation levels. Mean differences were considered statistically significant at the 0.05 level.

Author Contributions

Methodology, G.D.P., K.A., V.G., S.O., D.S., A.P., P.G., S.C., K.D., E.R., E.K., K.Z. and N.T.P.; Formal analysis, G.D.P. and N.T.P.; Resources, D.S., A.P., P.G., S.C., K.D., E.R., E.K. and N.T.P.; Data curation, G.D.P., K.A., V.G., S.O., D.S., A.P., P.G., S.C., K.D. and E.R.; Writing—original draft, G.D.P.; Writing—review & editing, G.D.P., K.A., V.G., S.O., D.S., A.P., P.G., S.C., K.D., E.R., E.K., K.Z. and N.T.P.; Supervision, E.K. and N.T.P.; Funding acquisition, E.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Hellenic Republic, from the budget of the national olive fruit fly control program of the Ministry of Rural Development and Food.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

Authors Vasileios Giannopoulos, Sergey Odinokov and Stelios Christodoulou were employed by the company Ionos-AgriDrones/Industrial Drone Solutions; author Kostas Dimizas was employed by the company Elanco Hellas S.A.C.I. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
UAVsUnmanned aerial vehicles
OPsOrganophosphates
HCAAHellenic Civil Aviation Authority
GLMsGeneralized linear models

Appendix A. Infestation Under Different Pest Control Strategies

Agronomy 15 02158 g0a1
Figure A1. Mean infestation levels of Bactrocera oleae across five sites over four study years were assessed. Three treatment methods were evaluated: aerial application using UAV bait application, ground based-bait application spraying, and an untreated control.
Figure A1. Mean infestation levels of Bactrocera oleae across five sites over four study years were assessed. Three treatment methods were evaluated: aerial application using UAV bait application, ground based-bait application spraying, and an untreated control.
Agronomy 15 02158 g0a1
Table A1. Overview of Bactrocera oleae Infestation by Site and Year.
Table A1. Overview of Bactrocera oleae Infestation by Site and Year.
Mean Infestation (%)
SiteYearUAV Bait ApplicationGround Based-Bait ApplicationUntreated Control
Larisa202152.3163.7663.23
202237.0447.3350.80
202342.4045.8075.00
2024
Zakynthos2021
2022
202319.4022.80
2024
Trifillia2021
202219.2020.0015.41
2023
20242.412.163.33
Lasithi2021
202243.6626.6661.00
2023
2024
Heraklion2021
202215.6623.3311.33
2023
2024

Appendix B. Active Infestation Levels Based on Total Olives Examined Under Different Control Strategies

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Figure A2. Mean active infestation levels of Bactrocera oleae from total olives, across five sites over four study years were assessed. Three treatment methods were evaluated: aerial applications using UAV, ground based-bait application, and untreated control.
Figure A2. Mean active infestation levels of Bactrocera oleae from total olives, across five sites over four study years were assessed. Three treatment methods were evaluated: aerial applications using UAV, ground based-bait application, and untreated control.
Agronomy 15 02158 g0a2
Table A2. Overview of Bactrocera oleae Active Infestation (% of Total Sampled Olives) by Site and Year.
Table A2. Overview of Bactrocera oleae Active Infestation (% of Total Sampled Olives) by Site and Year.
Mean Infestation (%)
SiteYearUAV Bait ApplicationGround Based-Bait ApplicationUntreated Control
Larisa202128.1641.7848.88
202221.1938.9139.80
202311.0013.4036.80
2024
Zakynthos2021
2022
20233.804.40
2024
Trifillia2021
20222.043.065.00
2023
20242.252.003.33
Lasithi2021
202220.5013.6647.00
2023
2024
Heraklion2021
20224.667.336.66
2023
2024

Appendix C. Active Infestation Under Different Control Strategies

Agronomy 15 02158 g0a3
Figure A3. Mean active infestation levels of Bactrocera oleae across five sites over four study years were assessed. Three treatment methods were evaluated: aerial applications using UAV, ground based-bait application, and untreated control.
Figure A3. Mean active infestation levels of Bactrocera oleae across five sites over four study years were assessed. Three treatment methods were evaluated: aerial applications using UAV, ground based-bait application, and untreated control.
Agronomy 15 02158 g0a3
Table A3. Overview of Bactrocera oleae Active Infestation by Site and Year.
Table A3. Overview of Bactrocera oleae Active Infestation by Site and Year.
Mean Infestation (%)
SiteYearUAV Bait ApplicationGround Based-Bait ApplicationUntreated Control
Larisa202153.8465.5277.31
202257.2282.2078.34
202325.9429.2549.06
2024
Zakynthos2021
2022
202319.5819.29
2024
Trifillia2021
202210.6515.3432.43
2023
202493.1092.30100
Lasithi2021
202246.9451.2577.04
2023
2024
Heraklion2021
202229.7831.4258.82
2023
2024

References

  1. Arvaniti, O.S.; Rodias, E.; Terpou, A.; Afratis, N.; Athanasiou, G.; Zahariadis, T. Bactrocera oleae Control and Smart Farming Technologies for Olive Orchards in the Context of Optimal Olive Oil Quality: A Review. Agronomy 2024, 14, 2586. [Google Scholar] [CrossRef]
  2. Boskou, D. Olive Oil: Chemistry and Technology; AOCS Publishing: Urbana, IL, USA, 2006. [Google Scholar]
  3. Vossen, P. Olive Oil: History, Production, and Characteristics of the World’s Classic Oils. HortScience 2007, 42, 1093–1100. [Google Scholar] [CrossRef]
  4. Bassi, D.; Cirilli, M.; Rossini, L. Most Important Fruit Crops in Mediterranean Basin (Mb); Milano University Press: Milan, Italy, 2024. [Google Scholar]
  5. Gonzalez-Fernandez, I.; Iglesias-Otero, M.A.; Esteki, M.; Moldes, O.A.; Mejuto, J.C.; Simal-Gandara, J. A Critical Review on the Use of Artificial Neural Networks in Olive Oil Production, Characterization and Authentication. Crit. Rev. Food Sci. Nutr. 2019, 59, 1913–1926. [Google Scholar] [CrossRef]
  6. Abd El-Salam, A.M.E.; Salem, S.A.-W.; Abdel-Rahman, R.S.; El-Behery, H.H.; Magd Elden, M.A. Effects of Climatic Changes on Olive Fly, Bactrocera oleae (Rossi) Population Dynamic with Respect to the Efficacy of Its Larval Parasitoid in Egyptian Olive Trees. Bull. Natl. Res. Cent. 2019, 43, 173. [Google Scholar] [CrossRef]
  7. Caselli, A.; Petacchi, R. Climate Change and Major Pests of Mediterranean Olive Orchards: Are We Ready to Face the Global Heating? Insects 2021, 12, 802. [Google Scholar] [CrossRef] [PubMed]
  8. Benelli, G. Aggressive Behavior and Territoriality in the Olive Fruit Fly, Bactrocera oleae (Rossi) (Diptera: Tephritidae): Role of Residence and Time of Day. J. Insect Behav. 2014, 27, 145–161. [Google Scholar] [CrossRef]
  9. Daane, K.M.; Johnson, M.W. Olive Fruit Fly: Managing an Ancient Pest in Modern Times. Annu. Rev. Entomol. 2010, 55, 151–169. [Google Scholar] [CrossRef]
  10. Malheiro, R.; Casal, S.; Baptista, P.; Pereira, J.A. A Review of Bactrocera oleae (Rossi) Impact in Olive Products: From the Tree to the Table. Trends Food Sci. Technol. 2015, 44, 226–242. [Google Scholar] [CrossRef]
  11. Kounatidis, I.; Papadopoulos, N.T.; Mavragani-Tsipidou, P.; Cohen, Y.; Tertivanidis, K.; Nomikou, M.; Nestel, D. Effect of Elevation on Spatio-Temporal Patterns of Olive Fly (Bactrocera oleae) Populations in Northern Greece. J. Appl. Entomol. 2008, 132, 722–733. [Google Scholar] [CrossRef]
  12. Marchi, S.; Guidotti, D.; Ricciolini, M.; Petacchi, R. Towards Understanding Temporal and Spatial Dynamics of Bactrocera oleae (Rossi) Infestations Using Decade-Long Agrometeorological Time Series. Int. J. Biometeorol. 2016, 60, 1681–1694. [Google Scholar] [CrossRef]
  13. Katsoyannos, P. Olive Pests and Their Control in the Near East; FAO: Rome, Italy, 1992. [Google Scholar]
  14. Mazomenos, B.E. Dacus Oleae. World Crop Pests 1989, 3, 169–177. [Google Scholar]
  15. van Asch, B.; Pereira-Castro, I.; Rei, F.T.; da Costa, L.T. Marked Genetic Differentiation between Western Iberian and Italic Populations of the Olive Fly: Southern France as an Intermediate Area. PLoS ONE 2015, 10, e0126702. [Google Scholar] [CrossRef]
  16. Bempelou, E.; Varikou, K.; Anagnostopoulos, C.; Charalampous, A.; Samari, N.; Economou, L.; Garantonakis, N.; Liapis, K. Toxicity of Insecticides in the Adult and Larva Olive Fruit Fly, after Estimation of the Dislodgeable Foliar and Fruit Residues in Olive Trees by LC-MS/MS and GC-MS/MS. Agriculture 2023, 13, 543. [Google Scholar] [CrossRef]
  17. Roessler, Y.; Robinson, A.S.; Hooper, G. Fruit Flies: Their Biology, Natural Enemies and Control. In World Crop Pests; Robinson, A.S., Hooper, G., Eds.; Elsevier: London, UK, 1989. [Google Scholar]
  18. Tomlin, C.D.S. The Pesticide Manual—A World Compendium, 13th ed.; British Crop Protection Council (BCPC): Alton, UK, 2003. [Google Scholar]
  19. Manousis, T.; Moore, N.F. Control of Dacus oleae, a Major Pest of Olives. Int. J. Trop. Insect Sci. 1987, 8, 1–9. [Google Scholar] [CrossRef]
  20. Nadel, D.J.; Golan, Y. Control of the Olive Fly by the Protein Hydrolysate Baiting Method through Aerial and Ground Application. FAO Plant Prot. Bull. 1966, 14, 47–53. [Google Scholar]
  21. Malavolta, C.; Delrio, G.; Boller, E.F. IOBC Technical Guidelines III. In Guidelines for the Integrated Production of Olives; IOBC/WPRS Bulletin: Wageningen, The Netherland, 2002; Volume 25, pp. 1–8. [Google Scholar]
  22. Varikou, K.; Garantonakis, N.; Birouraki, A.; Ioannou, A.; Kapogia, E. Improvement of Bait Sprays for the Control of Bactrocera oleae (Diptera: Tephritidae). Crop Prot. 2016, 81, 1–8. [Google Scholar] [CrossRef]
  23. Varikou, K.; Garantonakis, N.; Birouraki, A. Residual Attractiveness of Various Bait Spray Solutions to Bactrocera oleae. Crop Prot. 2015, 68, 60–66. [Google Scholar] [CrossRef]
  24. Haniotakis, G.E. Olive Pest Control: Present Status and Prospects. IOBC WPRS Bull. 2005, 28, 1. [Google Scholar]
  25. Vontas, J.; Hernández-Crespo, P.; Margaritopoulos, J.T.; Ortego, F.; Feng, H.-T.; Mathiopoulos, K.D.; Hsu, J.-C. Insecticide Resistance in Tephritid Flies. Pestic. Biochem. Physiol. 2011, 100, 199–205. [Google Scholar] [CrossRef]
  26. Pontikakos, C.M.; Tsiligiridis, T.A.; Yialouris, C.P.; Kontodimas, D.C. Pest Management Control of Olive Fruit Fly (Bactrocera oleae) Based on a Location-Aware Agro-Environmental System. Comput. Electron. Agric. 2012, 87, 39–50. [Google Scholar] [CrossRef]
  27. European Economic Area Joint Committee. Decision of the EEA Joint Committee No 33/2020 of 20 March 2020 Amending Annex II to the EEA Agreement [2023/347]; European Economic Area Joint Committee: Brussels, Belgium, 2023. [Google Scholar]
  28. Cavalaris, C.; Tagarakis, A.C.; Kateris, D.; Bochtis, D. Cost Analysis of Using UAV Sprayers for Olive Fruit Fly Control. AgriEngineering 2023, 5, 1925–1942. [Google Scholar] [CrossRef]
  29. del Cerro, J.; Cruz Ulloa, C.; Barrientos, A.; de León Rivas, J. Unmanned Aerial Vehicles in Agriculture: A Survey. Agronomy 2021, 11, 203. [Google Scholar] [CrossRef]
  30. Kim, J.; Kim, S.; Ju, C.; Son, H.I. Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications. IEEE Access 2019, 7, 105100–105115. [Google Scholar] [CrossRef]
  31. Sylvester, G. E-Agriculture in Action: Drones for Agriculture; FAO: Rome, Italy, 2018. [Google Scholar]
  32. Velusamy, P.; Rajendran, S.; Mahendran, R.K.; Naseer, S.; Shafiq, M.; Choi, J.-G. Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges. Energies 2021, 15, 217. [Google Scholar] [CrossRef]
  33. SESAR Joint Undertaking. Report: European Drones Outlook Study; SESAR Joint Undertaking: Brussels, Belgium, 2016. [Google Scholar]
  34. Zhang, R.; Hewitt, A.J.; Chen, L.; Li, L.; Tang, Q. Challenges and Opportunities of Unmanned Aerial Vehicles as a New Tool for Crop Pest Control. Pest Manag. Sci. 2023, 79, 4123–4131. [Google Scholar] [CrossRef] [PubMed]
  35. Tsouros, D.C.; Bibi, S.; Sarigiannidis, P.G. A Review on UAV-Based Applications for Precision Agriculture. Information 2019, 10, 349. [Google Scholar] [CrossRef]
  36. Zhang, C.; Kovacs, J.M. The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review. Precis. Agric 2012, 13, 693–712. [Google Scholar] [CrossRef]
  37. Cavalaris, C. Energy Efficiency for in-Farm Unmanned Aerial System Applications. In Unmanned Aerial Systems in Agriculture; Academic Press: Cambridge, MA, USA, 2023; pp. 231–244. ISBN 978-0-323-91940-1. [Google Scholar]
  38. Gertsis, A.; Karampekos, L. Evaluation of Spray Coverage and Other Spraying Characteristics from Ground and Aerial Sprayers (Drones: UAVs) Used in a High-Density Planting Olive Grove in Greece. In Information and Communication Technologies for Agriculture—Theme IV: Actions; Springer Optimization and Its Applications; Springer International Publishing: Cham, Switzerland, 2021; Volume 185, pp. 255–268. ISBN 978-3-030-84155-3. [Google Scholar]
  39. European Parliament and Council. Directive 2009/128/EC Establishing a Framework for Community Action to Achieve the Sustainable Use of Pesticides. Off. J. Eur. Union 2009, 7, 1–7. [Google Scholar]
  40. Gonçalves, M.F.; Santos, S.A.P.; Torres, L.M. Efficacy of Spinosad Bait Sprays to Control Bactrocera oleae and Impact on Non-Target Arthropods. Phytoparasitica 2012, 40, 17–28. [Google Scholar] [CrossRef]
  41. Subramanian, K.S.; Pazhanivelan, S.; Srinivasan, G.; Santhi, R.; Sathiah, N. Drones in Insect Pest Management. Front. Agron. 2021, 3, 640885. [Google Scholar] [CrossRef]
  42. Song, X.-P.; Liang, Y.-J.; Zhang, X.-Q.; Qin, Z.-Q.; Wei, J.-J.; Li, Y.-R.; Wu, J.-M. Intrusion of Fall armyworm (Spodoptera Frugiperda) in Sugarcane and Its Control by Drone in China. Sugar Tech 2020, 22, 734–737. [Google Scholar] [CrossRef]
  43. Chueca, P.; Fonte, A.; Beltrán, R.; Carrillo, I.; Garcerá, C. UAV for Spraying Liquid Bait Treatments to Control Ceratitis capitata in Citrus Is an Effective Alternative to Terrestrial Applications. In Precision Agriculture’25; Wageningen Academic: Wageningen, The Netherlands, 2025; pp. 650–656. [Google Scholar]
  44. D’Alessandro, A.; Coletta, M.; Torresi, A.; Dell’Ambrogio, G.; Renaud, M.; Ferrari, B.J.D.; La Terza, A. Evaluation of the Impact of Plant Protection Products (PPPs) on Non-Target Soil Organisms in the Olive Orchard: Drone (Aerial) Spraying vs. Tractor (Ground) Spraying. Sustainability 2024, 16, 11302. [Google Scholar] [CrossRef]
  45. Tzanakakis, M.E. Seasonal Development and Dormancy of Insects and Mites Feeding on Olive: A Review. Neth. J. Zool. 2003, 52, 87. [Google Scholar] [CrossRef]
  46. Katsikogiannis, G.; Kavroudakis, D.; Tscheulin, T.; Kizos, T. Population Dynamics of the Olive Fly, Bactrocera oleae (Diptera: Tephritidae), Are Influenced by Different Climates, Seasons, and Pest Management. Sustainability 2023, 15, 14466. [Google Scholar] [CrossRef]
  47. Burrack, H.J.; Connell, J.H.; Zalom, F.G. Comparison of Olive Fruit Fly (Bactrocera oleae (Gmelin)) (Diptera: Tephritidae) Captures in Several Commercial Traps in California. Int. J. Pest Manag. 2008, 54, 227–234. [Google Scholar] [CrossRef]
  48. Baratella, V.; Pucci, C.; Paparatti, B.; Speranza, S. Response of Bactrocera Oleae to Different Photoperiods and Temperatures Using a Novel Method for Continuous Laboratory Rearing. Biol. Control 2017, 110, 79–88. [Google Scholar] [CrossRef]
  49. Genç, H.; Nation, J.L. Survival and Development of Bactrocera oleae Gmelin (Diptera: Tephritidae) Immature Stages at Four Temperatures in the Laboratory. Afr. J. Biotechnol. 2008, 7, 2495–2500. [Google Scholar]
  50. Tsitsipis, J.A. Effect of Constant Temperatures on Larval and Pupal Development of Olive Fruit Flies Reared on Artificial Diet. Environ. Entomol. 1980, 9, 764–768. [Google Scholar] [CrossRef]
  51. Fletcher, B.S. Life History Strategies of Tephritid Fruit Flies. In Fruit Flies, Their Biology, Natural Enemies and Control; Elsevier: Amsterdam, The Netherlands, 1989; pp. 195–208. [Google Scholar]
  52. Tsitsipis, J.A. Effect of Constant Temperatures on the Eggs of the Olive Fruit Fly, Dacus oleae (Diptera, Tephritidae). Ann. Zool. Ecol. Anim. 1977, 9, 133–139. [Google Scholar]
  53. Ortega, M.; Pascual, S. Spatio-Temporal Analysis of the Relationship between Landscape Structure and the Olive Fruit Fly Bactrocera oleae (Diptera: Tephritidae): Effect of Landscape on B. oleae. Agric. For. Entomol. 2014, 16, 14–23. [Google Scholar] [CrossRef]
  54. Nestel, D.; Carvalho, J.; Nemny-Lavy, E. The Spatial Dimension in the Ecology of Insect Pests and Its Relevance to Pest Management. In Insect Pest Management; Horowitz, A.R., Ishaaya, I., Eds.; Springer: Berlin/Heidelberg, Germany, 2004; pp. 45–63. ISBN 978-3-642-05859-2. [Google Scholar]
  55. Pontikakos, C.M.; Tsiligiridis, T.A.; Drougka, M.E. Location-Aware System for Olive Fruit Fly Spray Control. Comput. Electron. Agric. 2010, 70, 355–368. [Google Scholar] [CrossRef]
  56. Garantonakis, N.; Varikou, K.; Birouraki, A. Influence of Olive Variety on Biological Parameters of Bactrocera oleae (Diptera: Tephritidae). Appl. Entomol. Zool. 2017, 52, 189–196. [Google Scholar] [CrossRef]
  57. Garantonakis, N.; Varikou, K.; Markakis, E.; Birouraki, A.; Sergentani, C.; Psarras, G.; Koubouris, G.C. Interaction between Bactrocera oleae (Diptera: Tephritidae) Infestation and Fruit Mineral Element Content in Olea Europaea (Lamiales: Oleaceae) Cultivars of Global Interest. Appl. Entomol. Zool. 2016, 51, 257–265. [Google Scholar] [CrossRef]
  58. Dominici, M.; Pucci, C.; Montanari, G.E. Dacus Oleae (Gmel.) Ovipositing in Olive Drupes (Diptera, Tephrytidae)1. J. Appl. Entomol. 1986, 101, 111–120. [Google Scholar] [CrossRef]
  59. Iannotta, N.; Perri, L.; Tocci, C.; Zaffina, F. The Behaviour of Different Olive Cultivars Following Attacks by Bactrocera oleae (Gmel.). In Proceedings of the III International Symposium on Olive Growing, Crete, Greece, 22–26 September 1997; pp. 545–548. [Google Scholar]
  60. Rizzo, R.; Caleca, V. Resistance to the Attack of Bactrocera Oleae (Gmelin) of Some Sicilian Olive Cultivars. In Proceedings of the Olivebioteq, Second International Seminar “Biotechnology and Quality of Olive Tree Products Around the Mediterranean Basin”, Mazara del Vallo, Marsala, Italy, 5–10 November 2006; Volume 2, pp. 291–298. [Google Scholar]
  61. Bjelis, M.; Masten Milek, T.; Šimala, M. Olive Fruit Infestation by Olive Fruit Fly Bactrocera oleae Gmel. in Dry and Irrigated Growing Conditions in Dalmacija. Cereal Res. Commun. 2008, 36, 1731–1734. [Google Scholar]
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Figure 1. Map showing the dispersion of selected sites considered for field trials to test the efficacy of the UAV-applied bait application methods against the olive fruit fly. Map template obtained from https://d-maps.com.
Figure 1. Map showing the dispersion of selected sites considered for field trials to test the efficacy of the UAV-applied bait application methods against the olive fruit fly. Map template obtained from https://d-maps.com.
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Figure 2. Drone Used for Aerial Bait Applications.
Figure 2. Drone Used for Aerial Bait Applications.
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Figure 3. Mean infestation levels of olive fruit by Bactrocera oleae over four study years were assessed. Three treatment methods were evaluated: aerial application using UAV bait application, ground based-bait application spraying, and an untreated control. Different letters above bars indicate significant differences among treatments within the same year (p < 0.05).
Figure 3. Mean infestation levels of olive fruit by Bactrocera oleae over four study years were assessed. Three treatment methods were evaluated: aerial application using UAV bait application, ground based-bait application spraying, and an untreated control. Different letters above bars indicate significant differences among treatments within the same year (p < 0.05).
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Figure 4. Mean active infestation levels of olive fruit by Bactrocera oleae based on the total number of olives examined over four study years were assessed. Three treatment methods were evaluated: aerial application using UAV bait application, ground based-bait application, and an untreated control. Different letters above bars indicate significant differences among treatments within the same year (p < 0.05).
Figure 4. Mean active infestation levels of olive fruit by Bactrocera oleae based on the total number of olives examined over four study years were assessed. Three treatment methods were evaluated: aerial application using UAV bait application, ground based-bait application, and an untreated control. Different letters above bars indicate significant differences among treatments within the same year (p < 0.05).
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Figure 5. Mean active infestation levels of olive fruit by Bactrocera oleae over four study years were assessed. Three treatment methods were evaluated: aerial application using UAV bait application, ground based-bait application spraying, and an untreated control. Different letters above bars indicate significant differences among treatments within the same year (p < 0.05).
Figure 5. Mean active infestation levels of olive fruit by Bactrocera oleae over four study years were assessed. Three treatment methods were evaluated: aerial application using UAV bait application, ground based-bait application spraying, and an untreated control. Different letters above bars indicate significant differences among treatments within the same year (p < 0.05).
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Table 1. Results of the generalized linear models (GLMs) testing the effects of year and treatment on infestation level.
Table 1. Results of the generalized linear models (GLMs) testing the effects of year and treatment on infestation level.
FactorWald x2dfp
Intercept16,666.1111<0.001
Year4583.2003<0.001
Treatment650.9262<0.001
Year × Treatment1569.3606<0.001
Table 2. Comparisons between years of estimated marginal means based on infestation levels. The mean difference is significant at the 0.05 level.
Table 2. Comparisons between years of estimated marginal means based on infestation levels. The mean difference is significant at the 0.05 level.
Comparison GroupsMean Difference ± SEdfp (Bonferroni)
2021 × 202220.74 ± 0.3941<0.001
2021 × 202310.32 ± 0.4631<0.001
2021 × 202457.13 ± 1.1061<0.001
Table 3. Comparisons between treatments of estimated marginal means based on infestation levels. The mean difference is significant at the 0.05 level.
Table 3. Comparisons between treatments of estimated marginal means based on infestation levels. The mean difference is significant at the 0.05 level.
Comparison GroupsMean Difference ± SEdfp (Bonferroni)
Untreated control × UAV bait application16.64 ± 0.6901<0.001
UAV bait application × Ground-based bait application−4.14 ± 0.7501<0.001
Untreated control × Ground- based bait application12.50 ± 0.7041<0.001
Table 4. Results of the generalized linear models (GLMs) evaluating the effects of the year, and treatment on active infestation levels, based on the total number of olives examined.
Table 4. Results of the generalized linear models (GLMs) evaluating the effects of the year, and treatment on active infestation levels, based on the total number of olives examined.
FactorWald x2dfp
Intercept7645.2731<0.001
Year2455.1823<0.001
Treatment849.0002<0.001
Year × Treatment388.6726<0.001
Table 5. Comparisons of estimated marginal means between years, based on active infestation levels calculated from the total number of olives examined. Mean differences are considered significant at the 0.05 level.
Table 5. Comparisons of estimated marginal means between years, based on active infestation levels calculated from the total number of olives examined. Mean differences are considered significant at the 0.05 level.
Comparison GroupsMean Difference ± SEdfp (Bonferroni)
2024 × 20219.77 ± 0.3951<0.001
2024 × 202220.57 ± 0.5661<0.001
2024 × 202337.08 ± 0.9101<0.001
Table 6. Comparisons between treatments of estimated marginal means based on active infestation levels of total examined olives. The mean difference is significant at the 0.05 level.
Table 6. Comparisons between treatments of estimated marginal means based on active infestation levels of total examined olives. The mean difference is significant at the 0.05 level.
Comparison GroupsMean Difference ± SEdfp (Bonferroni)
Untreated control × UAV bait application17.41 ± 0.86131<0.001
UAV bait application × Ground-based bait apllication−7.108 ± 0.6841<0.001
Untreated control × Ground- based bait application10.30 ± 0.6111<0.001
Table 7. Results of the generalized linear models (GLMs) testing the effects of year and treatment on active infestation level.
Table 7. Results of the generalized linear models (GLMs) testing the effects of year and treatment on active infestation level.
FactorWald x2dfp
Intercept29,553.4651<0.001
Year2757.2953<0.001
Treatment542.6062<0.001
Year × Treatment210.6556<0.001
Table 8. Comparisons between years of estimated marginal means based on active infestation levels. The mean difference is significant at the 0.05 level.
Table 8. Comparisons between years of estimated marginal means based on active infestation levels. The mean difference is significant at the 0.05 level.
Comparison GroupsMean Difference ± SEdfp (Bonferroni)
2024 × 202129.57 ± 1.3111<0.001
2024 × 202229.81 ± 1.2661<0.001
2024 × 202361.74 ± 1.3951<0.001
Table 9. Comparisons between treatments of estimated marginal means based on active infestation levels. The mean difference is significant at the 0.05 level.
Table 9. Comparisons between treatments of estimated marginal means based on active infestation levels. The mean difference is significant at the 0.05 level.
Comparison GroupsMean Difference ± SEdfp (Bonferroni)
Untreated control × UAV bait application20.29 ± 0.8891<0.001
UAV bait application × Ground-based bait apllication−8.757 ± 0.9911<0.001
Untreated control × Ground- based bait application11.53 ± 0.8861<0.001
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Papadogiorgou, G.D.; Alipranti, K.; Giannopoulos, V.; Odinokov, S.; Stavridis, D.; Paraskevopoulos, A.; Giatras, P.; Christodoulou, S.; Dimizas, K.; Roditakis, E.; et al. Comparative Efficacy of UAVs (Unmanned Aerial Vehicles) and Ground-Based Bait Applications for Olive Fruit Fly (Bactrocera oleae) Control in Greek Olive Orchards. Agronomy 2025, 15, 2158. https://doi.org/10.3390/agronomy15092158

AMA Style

Papadogiorgou GD, Alipranti K, Giannopoulos V, Odinokov S, Stavridis D, Paraskevopoulos A, Giatras P, Christodoulou S, Dimizas K, Roditakis E, et al. Comparative Efficacy of UAVs (Unmanned Aerial Vehicles) and Ground-Based Bait Applications for Olive Fruit Fly (Bactrocera oleae) Control in Greek Olive Orchards. Agronomy. 2025; 15(9):2158. https://doi.org/10.3390/agronomy15092158

Chicago/Turabian Style

Papadogiorgou, Georgia D., Konstantina Alipranti, Vasileios Giannopoulos, Sergey Odinokov, Dimitris Stavridis, Antonis Paraskevopoulos, Panagiotis Giatras, Stelios Christodoulou, Kostas Dimizas, Emmanouil Roditakis, and et al. 2025. "Comparative Efficacy of UAVs (Unmanned Aerial Vehicles) and Ground-Based Bait Applications for Olive Fruit Fly (Bactrocera oleae) Control in Greek Olive Orchards" Agronomy 15, no. 9: 2158. https://doi.org/10.3390/agronomy15092158

APA Style

Papadogiorgou, G. D., Alipranti, K., Giannopoulos, V., Odinokov, S., Stavridis, D., Paraskevopoulos, A., Giatras, P., Christodoulou, S., Dimizas, K., Roditakis, E., Kapogia, E., Zarpas, K., & Papadopoulos, N. T. (2025). Comparative Efficacy of UAVs (Unmanned Aerial Vehicles) and Ground-Based Bait Applications for Olive Fruit Fly (Bactrocera oleae) Control in Greek Olive Orchards. Agronomy, 15(9), 2158. https://doi.org/10.3390/agronomy15092158

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