Remote Sensing with UAVs: Addressing Over-Fertilization and Pesticide Management for Improved Agricultural Efficiency in Turkey

Abstract

The rapid development in the technologies of Unmanned Aerial Vehicles has ushered in a new era in precision agriculture, revolutionizing farm practices across the world. Equipped with multispectral, hyperspectral, and thermal imaging systems, UA Vs offer unparalleled capability for crop health monitoring, assessment of soil conditions, optimization in the use of fertilizers and pesticides, and improved water management. The current study investigates the huge potential for UA V technology to answer critical agricultural challenges, such as over-fertilization, excess pesticide use, and water shortages in Turkey, which have historically prevented sustainability and further efficiency. UA Vs can help farmers improve resource use significantly, lessen environmental impact, and yield better predictions with real-time data and precise intervention. This research reviews international case studies from areas like the Mediterranean and carries out regional questionnaires among Turkish farmers to analyze their UA V adoption barriers, such as high initial costs, inability to access technical expertise, and limited awareness of UA V capability. Large-scale and government-subsidized small-scale implementation of UA Vs within Turkish agriculture could help develop an overall modernization of agricultural techniques, increase profitability, and contribute to national objectives related to sustainability. Through such UA V-driven innovations, resource-use inefficiencies can be resolved alongside other environmental challenges, while long-term agricultural productivity and resilience are ensured for Turkey.

Keywords: UA V technology, remote sensing, precision agriculture, Turkey, sustainability, crop management, resource optimization

Introduction

Agriculture has traditionally been based on labor-intensive methods whereby farmers would observe field conditions and crop health by manual means. Traditional approaches to farming have usually led to several inefficiencies in resource usage and thus resulted in poor crop yields with greater labor costs. However, precision agriculture (PA) brought new technologies such as GPS, sensors, and data analytics, which revolutionized farming. Precision agriculture helped apply the most critical inputs of water, fertilizers, and pesticides with unprecedented precision. The sector has thus been able to receive a remarkable boost in productivity and sustainability [1]. This has lessened the burden, in terms of time and effort, placed on farm management while also encouraging better resource utilization [25; 18].

Generally, the rate of adoption of PA technologies has been increasing across the world. Examples of this increase have been seen in countries as; United States, Canada, and Australia, where digital agriculture solutions have completely revolutionized farming. In contrast, in Turkey, the rate of adoption can be considered quite low, although interest in the use of such technologies to raise agricultural efficiency is rapidly growing globally [25; 2]. Several reports indicate that drone technology seems to be the most promising approach in combating agricultural challenges in Turkey for modernization and better productivity [9].

UA V technology provides a great opportunity for farmers with active real-time monitoring abilities to enable fast assessments over large areas of agricultural fields without necessarily having an excessive amount of manual intervention. These will help develop an effective potential for reducing operational costs, hence making good precision in decision-making for the benefit of crop management. Studies have shown that UA V-based systems can reduce pesticide and fertilizer use and increase yield prediction accuracy for better sustainability in farming [26]. The new use of UA Vs in remote sensing, especially with multispectral imaging, is adding depth to farmers’ conclusions on their field conditions. This technology is considered vital for balancing the increasing global demand for food with the pressing need for environmental sustainability [5, 16].

Multi-spectral imaging enabled with UA V-supported remote sensing holds much transformational potential for agriculture. With the ability to take images in multiple wavelengths, including those in the Near-Infrared (NIR) part of the spectrum, UA Vs can work out plant health, soil conditions, and nutrient levels with remarkable accuracy. This imagery makes use of reflected light from the crop canopy in calculating such indices as NDVI (Normalized Difference Vegetation Index) for the computation of nitrogen and pesticide needs [27]. It has been reported in countries like China and Brazil that UA Vs may decrease pesticide consumption by up to 37% and raise crop productivity [29, 30, 28]. The fact that UA Vs are cost-effective and can deploy much quicker than ground-deployed methods bolsters a solid foundation on which UA Vs should be employed in Turkey, since agricultural efficiency improves through such technologies [26].

Despite its significant potential, UA V technology remains underutilized in Turkish agriculture, presenting a substantial opportunity for improvement. This research examines how deploying UA Vs to detect excessive pesticide spraying and over-fertilization can enhance resource management, increase crop yields, and reduce operational costs across Turkey’s agricultural sector.

Our study investigates three primary benefits of UA V technology adoption: improved resource management, enhanced yield monitoring, and greater economic sustainability. We hypothesize that increased implementation of UA V-based precision agriculture technologies will significantly improve crop yields while enhancing cost efficiency and time management through optimized resource utilization.

The paper also addresses current economic and technological challenges limiting UA V adoption in Turkey and proposes solutions based on successful implementation strategies from other countries. Our framework includes a critical review of UA V technology, its agricultural applications, analysis of relevant case studies, and recommendations for improving UA V adoption rates across Turkey.

Advanced UA V Technologies in Precision Agriculture

Unmanned Aerial Vehicles (UA Vs), commonly known as drones, have emerged as a pivotal technology in modern farming, transforming traditional practices through precision agriculture. By capturing high-resolution imagery and collecting environmental data across fields, UA Vs enable farmers to make evidence-based decisions regarding crop health, irrigation needs, fertilizer application, and pest management. This technology addresses the dual challenge of meeting increasing global food demand while promoting environmental sustainability [18]. UA Vs offer particular advantages over conventional monitoring methods due to their ability to rapidly survey large areas and navigate challenging terrains, making them adaptable to diverse agricultural landscapes. The two primary UA V designs used in agriculture—rotocopters and fixed-wing models—serve different purposes: rotocopters provide detailed observation capabilities through their hovering functionality and maneuverability, while fixed-wing UA Vs, with their extended flight times and greater coverage range, are ideal for monitoring extensive agricultural operations [5, 16]. These technological attributes position UA Vs as essential tools for modernizing agricultural practices, especially in regions like Turkey, where terrain diversity and varying farm sizes create unique monitoring challenges.

Multispectral and Hyperspectral Imaging

UA Vs can be equipped with multispectral and hyperspectral sensors that capture data beyond the visible spectrum, providing valuable insights into plant health and vegetation indices [19]. UA Vs with multispectral cameras have transformed traditional crop monitoring by capturing data across several key electromagnetic bands beyond visible light [8]. These sensors typically collect information in four to ten distinct spectral regions, including visible light (red, green, blue) and near-infrared (NIR) wavelengths. This multispectral data enables farmers to calculate critical vegetation indices such as the Normalized Difference Vegetation Index (NDVI), which measures plant vigor by comparing near-infrared light reflection (high in healthy vegetation) against red light absorption (linked to chlorophyll activity). Similarly, the Green Chlorophyll Index (GCI) provides specific insights into chlorophyll concentration, while other indices like the Enhanced Vegetation Index (EVI) offer improved sensitivity in high-biomass regions [5]. These quantifiable measurements allow farmers to identify spatial variations in crop development, detect stress factors before visual symptoms appear, and implement targeted interventions with precision. Multispectral imaging has proven particularly valuable for irrigation management, fertilizer application optimization, and early pest detection, enabling zone-specific treatments that reduce input costs while maximizing yield potential.

Hyperspectral imaging represents a significant technological advancement beyond multispectral capabilities, capturing hundreds of narrow, contiguous spectral bands across the electromagnetic spectrum. While multispectral sensors might collect data in 4-10 bands with bandwidths of 40-150 nm, hyperspectral systems typically record 100-300 bands with much narrower bandwidths of 5-10 nm [19]. This extraordinary spectral resolution creates detailed spectral signatures unique to specific crop conditions, enabling identification of subtlephysiological changes imperceptible to other sensing methods. The technology can detect variations in biochemical properties, including nitrogen, phosphorus, and potassium concentrations within plant tissues, differentiate between various disease pathogens before visible symptoms manifest, and identify moisture stress with remarkable precision [5]. Hyperspectral data processing often employs advanced machine learning algorithms to interpret the complex spectral information, creating highly detailed prescription maps for variable-rate applications. Despite higher equipment costs and data processing requirements compared to multispectral systems, hyperspectral imaging provides unparalleled insights for research applications and high-value crop production, where early detection of stress factors and diseases can prevent significant economic losses.

Thermal Imaging

Thermal imaging sensors integrated into UA Vs can capture data related to the canopy temperature of plants, providing critical information about their water status and stomatal conductance [8]. These sensors detect infrared radiation that can supply the variable of temperature to farmers over their fields. Variations in canopy temperature might indicate water stress, inadequate irrigation coverage, or even disease outbreaks. As summarized by Husain et al. (2023), “thermal imaging helps in identifying water-deficient zones and optimizes irrigation schedules, thus conserving water resources and enhancing crop resilience.”

Olson and Anderson (2021) also highlight the significance of thermal imaging in monitoring crop water stress, noting that UA Vs equipped with thermal sensors offer the Crop Water Stress Index (CWSI), which is a real indication of the actual status of plant hydration. This index provides substantial decision support for irrigation management by farmers, thus averting under- or over-watering. Farmers can thereby achieve optimization of water use, which may become crucial in arid areas where water scarcity is prevalent.

Image Stitching and Data Processing

One of the principal challenges in UA V-based agricultural monitoring is effectively managing the substantial volume of overlapping imagery captured during flight operations. As Olson and Anderson (2021) explain, “Image stitching is a key component for delivering high-resolution maps over large areas and enables high-resolution crop variability assessment and site-specific input applications.” This process involves algorithmically combining hundreds of individual high-resolution images into a single geometrically corrected orthomosaic map [12, 5]. The resulting composite provides a comprehensive, spatially accurate representation of the entire field that preserves the detailed resolution necessary for identifying subtle variations in crop performance. These orthomosaics reveal specific regions experiencing stress due to nutrient deficiencies, pest infestations, soil compaction, or water limitations that might otherwise remain undetected through traditional scouting methods. The geometric correction applied during processing ensures that measurements taken from these maps maintain spatial accuracy, enabling precise localization of problem areas for targeted intervention.

The transformation of raw UA V imagery into actionable agricultural intelligence requires sophisticated data processing beyond basic image stitching. Specialized software analyzes orthomosaics to extract critical information, including vegetation indices, plant height estimates, and canopy cover measurements [5]. As Husain et al. (2023) note, recent advancements in artificial intelligence and machine learning have dramatically improved the efficiency of this process, enabling “real-time analysis and faster decision-making” that enhances farm management responsiveness. These technological improvements, coupled with the emergence of cloud-based computing services, have significantly streamlined data processing workflows, reducing farmers’ dependence on expensive on-site hardware and specialized technical expertise [5]. The processed data ultimately generates practical outputs such as fertilizer prescription maps for variable rate application equipment and targeted pest management recommendations [5]. This complete workflow—from image capture to actionable recommendations—creates a digital decision support system that empowers farmers to implement precision agriculture practices with greater confidence and efficiency, maximizing both economic and environmental benefits through optimized resource utilization.

Yield and Nutrient Assessment

UA V-based remote sensing has revolutionized agricultural monitoring by offering efficient and non-destructive methods for yield and nutrient assessment. These aerial systems, equipped with advanced sensors, can capture detailed multispectral and hyperspectral imagery across large agricultural areas in minimal time. When this imagery is processed and analyzed, it provides crucial data enabling researchers and farmers to accurately estimate key crop parameters, including leaf nitrogen content, leaf area index, and plant biomass, all of which serve as valuable indicators for potential yield prediction [12]. For example, specialized studies have successfully demonstrated how UA V-based 3D photogrammetry techniques can measure crop height with remarkable precision, establishing strong correlations between these measurements and final corn crop yields [12]. This application is further emphasized by Olsson and Anderson (2021), who confirm that drones excel at “estimating leaf nitrogen content, which is very important for the optimization of fertilizer application and maximization of crop yields.” By enabling real-time monitoring of plant nutrient status through detailed spectral analysis, UA V technology facilitates precision fertilization strategies that optimize nutrient application rates and timing, thereby promoting healthy crop development while significantly reducing the environmental risks associated with over-fertilization and nutrient runoff into water systems [5, 16].

Furthermore, UA Vs have demonstrated exceptional capability in comprehensive nutrient management and yield forecasting through their sophisticated sensor systems. These platforms can simultaneously collect and integrate multiple data types, generating detailed crop health profiles that go beyond basic assessment. Husain et al.’s research highlights how UA V systems effectively employ specialized vegetation indices such as the Soil-Adjusted Vegetation Index (SA VI) to evaluate not only crop conditions but also underlying soil health and nutrient bioavailability patterns across fields. This advanced spatial analysis enables the creation of high-resolution nutrient management zone maps that allow farmers to implement variable-rate application technologies with unprecedented precision. By adjusting fertilization strategies according to real-time field data [5, 16], farmers can target inputs specifically where and when they’re needed, substantially reducing unnecessary application in already nutrient-sufficient areas. This precision approach delivers significant economic benefits through input cost reduction while simultaneously advancing environmental sustainability goals by minimizing nutrient leaching, runoff, and greenhouse gas emissions associated with excess fertilizer application, creating a win-win scenario for both agricultural productivity and ecological protection.

Precision Pesticide Application and Weed Detection

UA Vs are increasingly employed for precision pesticide application and weed detection, offering an environmentally friendlier alternative to conventional methods that often result in chemical overapplication. Equipped with precision spray systems, UA Vs can apply pesticides in a site-specific manner, targeting only affected areas rather than entire fields [11, 1]. According to Husain et al., drone technology enables targeted pesticide application that can reduce chemical use by as much as 30% while simultaneously lowering operational costs. This precision approach significantly minimizes the risk of pesticide drift and potential negative impacts on surrounding ecosystems. Weed detection using UA V imagery often employs deep learning-based methods to accurately identify and map weed infestations, creating detailed maps that enable variable rate chemical weeding and lead to significant savings in crop protection products[6, 8].

Furthermore, UA Vs enhance weed management through sophisticated detection capabilities using high-resolution cameras and spectral data analysis. These systems can effectively differentiate between crops and weeds, enabling precise herbicide application only where needed. “UA Vs fitted with high-tech imaging are able to detect infestations of weeds before they get out of hand, thus enabling the farmer to take control of them while still in their tender stages”[5]. This early intervention capability is crucial for maintaining optimal crop health and preventing yield losses from weed competition [8]. The integration of advanced imaging technologies with precision application systems creates a comprehensive approach to pest and weed management that simultaneously improves agricultural efficiency, reduces environmental impact, and enhances economic outcomes for farmers [11, 1].

Advanced UA V technologies in multispectral and hyperspectral imaging, integrated with thermal imaging, advance the new era in precision agriculture. Enabled by such technologies, farming can be optimized with increased resource use, yields, and management of more sustainable agricultural methods. The analysis of existing literature, when put together, creates a scenario whereby UA V not only changes the traditional ways of monitoring agriculture but also form the future innovations for sustainable farming. Equipped with thorough and timely information on crop health, water stress, nutrient levels, and management of pests, UA Vs put farmers in a better position to make informed decisions for increasing yields while lessening environmental impacts.

Agricultural challenges caused by climate change, resource scarcity, and increasing food demand continue to worsen. With these further challenges to farming, the role of UA Vs in precision agriculture will continue to play a vital part. Their application can further enhance the efficiency of agricultural operations beyond what was previously mentioned, in turn contributing to or supporting food security and sustainability goals. In each source, research and continued investments into the technology of UA Vs are highly emphasized as imperative for truly exploiting their potential in changing global agriculture.

Support Section I (Analysis and Literature Review)

Research conducted in various parts of the world has demonstrated that UA V technologies are of vital importance for optimizing resource use in agriculture; thus, they are envisioned as a promising solution for improving crop management in Turkey. However, despite their potential for further development and wider adaptation, the use of UA V technology for remote sensing of excessive pesticide spraying, over-fertilization, and yield monitoring in Turkish agriculture remains minimal. This situation creates immense opportunities to optimize resource use and improve crop yields, thereby enhancing cost and time management. Therefore, this section will review the existing literature to support the hypothesis that UA Vs can effectively address resource management inefficiencies in the agricultural sector. With UA Vs capable of monitoring pesticide and fertilizer use as well as crop health, the potential for driving sustainability, reducing input costs, and enhancing productivity becomes significant. These capabilities could substantially transform the landscape of Turkish agriculture, offering new pathways toward more efficient and environmentally sound farming practices..

How UA V Technology Has Impacted Agricultural Practices Globally

Unmanned Aerial Vehicle (UA V) technology has revolutionized agricultural practices worldwide by providing producers with enhanced assessment capabilities and facilitating more efficient decision-making processes. The global adoption of precision agriculture technologies, with UA Vs playing a pivotal role, continues to accelerate, with industry projections estimating a market value of $43.4 billion by 2025[4]. This remarkable growth trajectory underscores the agricultural sector’s increasing recognition of UA V technology’s transformative potential.

UA Vs offer farmers sophisticated options for assessing critical factors affecting agricultural systems. Color sensors mounted on UA Vs can accurately estimate leaf color, plant height, lodging, canopy cover, stand count, and even flower and fruit quantities[5]. Research has demonstrated UA V-based height estimation reaching an R-squared value of 0.51 with dry grain yield when measured at the beginning of August, illustrating its significant potential for yield prediction and comprehensive crop monitoring[12]. Beyond visual data, spectral sensors can estimate indirect leaf nitrogen content, yield potential, leaf area index (LAI), leaf chlorophyll content, and plant biomass. Additionally, thermal sensors capture vital data for estimating canopy temperature, stomatal conductance, water use efficiency, and plant water potentials[5]. Notably, Green Normalized Difference Vegetation Index (GNDVI) data acquired by UA Vs during anthesis and full crop development has produced accurate biomass estimates, with some studies reporting higher accuracy yield estimates with GNDVI as early as 5 weeks into a crop’s lifecycle [5].

The fusion of multiple data types significantly enhances assessment capabilities, with combinations of color, spectral, and thermal data products improving reliability and accuracy. Three-dimensional characterizations such as crop height or volume, derived from LiDAR or high-resolution color cameras and combined with conventional 2D data, can increase the accuracy of yield and quality estimates[5]. Further, the research has shown that combining plant height measurements with NDVI values has improved biomass estimation accuracy[5]. Additionally, radiometric correction of UA V imagery can significantly reduce grey value variation in overlapping images, from 14–18% to 6–8% under varying illumination conditions, further enhancing data quality[19].

The rich data gathered by UA Vs drives strategic decisions that enhance input efficiency, potentially maximizing return on investment while simultaneously reducing environmental impact. Studies conducted on Australian grain farms have estimated an increase ranging from $4/ha to $23/ha, with an average of $13/ha (8%) attributable to precision agriculture technology, including benefits from variable rate fertilizer application and reduced overlap in input distribution[21]. In another compelling case study from an Italian cereal farm, researchers documented a 53% reduction in pesticide usage during the post-adoption phase, largely attributable to precision sprayers[6]. This same farm experienced a 28% decrease in fuel consumption due to the implementation of newer, more efficient machinery equipped with advanced satellite guidance systems[6]. It is evident that UA V technology helps ensure precise pesticide application, utilizing fewer chemicals and minimizing environmental contamination[5, 16].

Precision agriculture technologies incorporating UA V data have demonstrated impressive efficiency improvements across multiple categories. Recording and mapping technologies (RMT) can lead to fertilizer savings ranging from 1.6–82% in peer-reviewed papers and 5–70% in EU projects. Variable rate technologies (VRT) have shown fertilizer savings ranging from 5–59% in peer-reviewed literature and 22–30% in EU projects. Guidance and Controlled Traffic Farming (CTF) technologies have reported fertilizer savings ranging from 1–26%, while Farm Management Information Systems (FMIS) have demonstrated fertilizer savings ranging from 14.7–46% in peer-reviewed papers and 5–70% in EU projects[14].

The applications of UA Vs in agriculture span numerous domains, including creating detailed fertilizer prescription maps, conducting hail crop damage assessments, enabling early pest detection, and generating crop yield and quality predictions[5]. UA V-captured multispectral imagery is used to assess plant health and nutrient status, enabling the creation of variable rate fertilizer prescription maps [3]. Methods like Ordinary Kriging (OK) and Inverse Distance Weighting (IDW) are employed for spatial prediction of soil properties for these maps, with modern approaches integrating data from digital elevation models and multispectral satellite images to enhance prediction accuracy [16].

The integration of UA V-based imagery with machine learning algorithms offers enhanced yield assessment accuracies while reducing reliance on labor-intensive ground-based surveys. Machine learning applied to UA V imagery has achieved classification accuracies ranging from 67 to 95% for identifying diseases like yellow rust, grapevine bacterial disease, myrtle rust, and citrus greening[5]. UA Vs equipped with multispectral cameras effectively assess plant health using Normalized Difference Vegetation Index (NDVI) and other specialized indices, detecting stress factors not visible to the naked eye[5, 16]. One study demonstrated that vegetation fraction mapping in wheat crops using a low-cost camera mounted on a UA V achieved impressive accuracies of 87.73–91.99% when operating at a height of 30 meters[8].

For crop yield and quality predictions, Partial Least Squares Regression (PLS-R) is widely used to analyze spectral data obtained by UA Vs, with PLS-R models for maize grain yield showing coefficient of determination (R²) values up to 0.86 in validation[15]. Hyperspectral data from UA Vs have been employed for wheat growth monitoring and yield estimation using methods like Multiple Linear Regression (MLR), Simple Linear Regression (SLR), and Partial Least Squares Regression (PLSR)[8]. The deployment of hyperspectral cameras on UA Vs shows particular promise due to their ability to capture broader spectral ranges than simpler multispectral cameras.

A major contribution of UA V technology has been its ability to gather phenotypic data quickly, efficiently, and non-destructively, significantly advancing crop improvement through genetics and plant breeding programs[5]. For instance, NDVI data generated from UA V imagery successfully identified quantitative trait loci (QTL) associated with drought adaptive traits in durum wheat, accounting for 89.6% of the phenotypic variance[5]. Biplot analysis, which can utilize data collected by UA Vs, has proven to be an effective tool for selecting superior genotypes and increasing efficiency in selection [3]. The rapid and comprehensive data collection capabilities offered by UA Vs greatly expedite field data acquisition for developing germplasm with desired agronomic traits.

In conclusion, UA V technology has transformed global agricultural practices by enhancing precision, efficiency, and sustainability. Research indicates that Farm Management Information Systems supported by UA V data show potential for yield increases ranging from 5-14%, fertilizer savings of 14.7-46%, and water savings of 10-50% [21]. As the technology continues to evolve and become more accessible, its adoption across different agricultural contexts promises further improvements in productivity and sustainability, helping address the growing global demand for agricultural products.

How Does Remote Sensing Save Resources Compared to Traditional Methods?

One key advantage lies in crop health monitoring. Traditional methods often involve visual inspection and manual collection of ground samples from random locations. This can be difficult, time-consuming, and may not provide a comprehensive overview, especially on large farms [13, 8]. Drones, however, can perform daily monitoring of crops to detect potential threats such as diseases, pests, and slow growth rates more effectively. They utilize aerial remote sensing, capturing images of different wavelengths to measure various parameters [1]. Drones can be equipped with different sensors, such as color, spectral (including near-infrared – NIR), and thermal cameras, allowing for the assessment of plant health indicators that might be invisible to the naked eye. This enables earlier detection of problems and facilitates quicker corrective actions, potentially preventing crop spoilage [8, 3]. The ability of drones to fly at low altitudes allows them to obtain high-resolution images with greater precision compared to satellites, which can be affected by cloud obstruction and have lower spatial resolution. While manned aircraft can cover large areas, they are often more cost-prohibitive than drones [2, 13].

In pesticide and fertilizer spraying, drone-mounted sprayers offer enhanced coverage ability and increased chemical effectiveness compared to conventional methods, which can be less effective in controlling pests and diseases [11]. Traditional spraying can also result in a higher cost of application. Drones can carry pesticide tanks (up to 40 liters in some cases) and follow pre-mapped routes to spray crops according to specific requirements; this capability allows for spot spraying in infected areas, reducing the overall amount of pesticides used and minimizing environmental impact [1, 8]. Drones can effectively cover fields with difficult access for tractors and aircraft. Furthermore, they can provide a more uniform and precise application of pesticides, operating at a standard delivery height and speed. Similarly, drones can be used for the application of fertilizers, making the process easier and potentially more efficient than traditional on-ground tools like tractors [13, 3]. Variable rate application of fertilizers and pesticides, guided by drone-collected data, directly translates to savings in resources [4].

Drones contribute significantly to resource management. The high spatial and temporal resolution data provided by drones enables site-specific management. For instance, drones with thermal sensors can detect temperature variations indicating water stress, allowing for efficient utilization of water through targeted irrigation and preventing water wastage [1, 13]. By identifying areas of nutrient deficiency or pest infestation, drones facilitate the precise application of fertilizers and pesticides, leading to reduced usage of these inputs. Studies suggest potential herbicide savings of 20% to 50% through precision application [14]. The data collected by drones, such as soil moisture information, can also be used to automate irrigation scheduling systems, further reducing water usage [3].

The efficiency in data acquisition by drones is a major benefit. They can cover large areas in short time periods and offer freedom from positioning and timing limitations compared to other remote sensing platforms [1]. The ability to collect data frequently allows for the detection of changes over time, which is crucial for effective agricultural management [8]. This data can then be analyzed using Artificial Intelligence (AI) and deep learning models for tasks like crop yield prediction, disease detection, weed mapping, and nitrogen status estimation, providing valuable insights for better management decisions [17]. This instantaneous data analysis capability allows for quicker responses to emerging issues [13, 1].

Several sources highlight the potential for cost and time savings with the use of drones. Farmers have reported that spraying and fertilization activities using drones save time and reduce costs due to less labor being required and the low cost of battery charging compared to fuel costs for traditional machinery [[13, 4]. Drone usage can significantly reduce labor costs associated with manual crop monitoring and pesticide application. For example, some studies indicate that drones save up to 20 times the labor of farmers [13].

Drones offer greater accessibility and coverage, especially in complex or difficult-to-reach areas. They can navigate terrains that are challenging for tractors and manned aircraft, ensuring that all parts of a field can be monitored and treated effectively [1]. Fixed-wing drones are particularly suited for large-scale surveys and monitoring vast agricultural areas, while multi-copters are often preferred for crop health monitoring and targeted interventions due to their maneuverability and hovering capabilities [16, 5].

Furthermore, drones play a crucial role in high-throughput phenotyping, allowing researchers and plant breeders to gather phenotypic data quickly, efficiently, and nondestructively. This includes estimating leaf color, plant height, canopy cover, stand count, flower count, and fruit count using various sensors [20]. Spectral sensors can estimate leaf nitrogen content, yield, leaf area index, and plant biomass, while thermal sensors can provide data on canopy temperature and plant water status [5].

In conclusion, drones offer numerous advantages over traditional agricultural methods by providing more efficient and precise crop monitoring, targeted resource application, improved data acquisition and analysis, potential cost and time savings, enhanced accessibility, and valuable tools for research and development in precision agriculture [18]. The increasing technological advancements and decreasing costs of UA Vs are making them an increasingly important tool for modern agriculture [8, 11].

Cost and Efficiency Comparisons

The increasing accessibility of UA V technology has emerged as a viable option for agricultural applications [8]. By 2019, complete agricultural-grade UA V systems—which included a UA V platform like the DJI Matrice 100, a sensor such as the MicaSense Red Edge MX, image processing software (e.g., Pix4D Fields), and necessary hardware—became available for under $30,000 [5]. This represents a significantly lower initial investment compared to traditional aerial imaging systems such as piloted aircraft, making UA Vs a more financially feasible solution for farmers [1]. Additionally, the proliferation of cloud-based computing services has reduced the need for substantial upfront investments in on-site image processing infrastructure, enabling farmers to efficiently process UA V-captured imagery without dedicated hardware and software expenses [5].

In terms of efficiency, UA Vs can cover significant areas in relatively short periods, leading to considerable time and labor savings compared to traditional ground-based monitoring methods [11]. Rotocopter UA Vs can cover 1–8 hectares per battery charge, while fixed-wing UA Vs can cover 10–40 hectares, depending on the altitude [5]. This ability to rapidly assess large fields allows for frequent monitoring and the acquisition of high-resolution data, which is difficult to achieve with manual ground inspections [1]. Farmers often find it challenging to regularly inspect every part of extensive land holdings, a task that drones can perform effectively through routine aerial surveillance, providing aerial views of their harvest and information on water systems, soil variations, pests, and fungal infestations [1]. Unlike satellite images, which can be obstructed by clouds, UA Vs offer greater reliability, along with freedom from positioning and timing limitations associated with satellite and manned aircraft-based remote sensing [11].

The adoption of UA V technology offers substantial cost-saving benefits for farmers, primarily through the enablement of Variable Rate Technologies (VRT). UA V-collected data facilitates the targeted application of inputs, resulting in significant reductions in resource use [13, 18]. Studies have indicated the potential for fertilizer usage reductions of up to 80% and pesticide use reductions of up to 80% [14]. This precise application, sometimes guided by GPS coordinates in drone systems, not only lowers input costs but also minimizes environmental impact by reducing the overall amount of chemicals used. For example, savings in herbicides have been reported in the range of 20% to 50% through precision application [5, 16, 14].

Moreover, the integration of UA Vs within broader robotic systems and smart machines has shown potential for significant decreases in labor costs (up to 97%) and fuel consumption (up to 50%) for specific agricultural tasks like spraying and fertilization [14]. Some reports suggest that drones can save up to 20 times the labor of farmers [13]. The automation of procedures through digital agricultural technologies (DATs), including UA Vs, streamlines operations and diminishes the discomfort associated with protective clothing worn during manual spraying [14].

Beyond cost reductions, improved yield and better resource management contribute to overall economic benefits for farmers. VRT, enabled by UA V data, has been linked to yield increases of up to 62% [14]. UA Vs equipped with sensors like thermal cameras can also enable efficient utilization of water through targeted irrigation, preventing water wastage [4]. Projects utilizing IoT and satellite data have demonstrated fertilizer cost savings ranging from 5% to 70%, highlighting the potential of data-driven approaches [14]. The comprehensive data provided by UA Vs, when effectively analyzed using techniques like vegetation indices and machine learning, translates into an economic benefit that enhances farmer profitability. In fact, the perceived economic benefit is a significant driver for farmers considering the adoption of drones [4].

In conclusion, the decreasing cost of agricultural-grade UA V systems, coupled with their efficiency in data collection and the accessibility of cloud-based processing, makes them a powerful tool for implementing precision agriculture practices like VRT. These practices directly lead to substantial cost savings for farmers through reduced input usage, potential decreases in labor and fuel, and improved yields, ultimately contributing to enhanced economic sustainability [1, 5].

Research Challenges

While the global success of UA V technologies in agriculture is evident, applying these innovations to Turkey’s unique agricultural landscape brings a number of challenges that must be addressed. One major issue pertains to limited data on UA V technology adoption and its impacts within Turkey. Because UA Vs are relatively recently introduced to Turkish agriculture, there is a noticeable deficiency in terms of localized research and empirical studies that should be pursued regarding the practical benefits and limitations of UA Vs under Turkish farming conditions.

This scarcity of data itself acts as an adoption barrier because many Turkish farmers and policymakers receive relatively few insights regarding how UA Vs can directly affect yield improvement, cost reduction, and resource management within their specific agricultural sectors. More importantly, scaling up the knowledge globally to the local level requires attention to specific problems that farmers in Turkey face with heterogeneous soil types and climate conditions, and crop varieties grown across regions. Other than this data limitation, two big challenges are specialized training and technical capability for flying UA Vs and analyzing generated data. While the technology of UA Vs in and of itself holds tremendous promise, certain relatively complex aspects involved in flying drones and making analyses of high-resolution multispectral images place demands for a level of technical expertise that may not be readily available in Turkish countryside settings. In this regard, the relatively limited availability of experienced UA V operators and data analysts within farming communities in Turkey is likely to dampen the rate of diffusion of this technology.

Besides these, very high upfront costs of UA Vs and continued maintenance and training requirements create financial barriers against their adoption by small and medium-sized farms that do not have the required capital investment in such technology. Overcoming these challenges will involve strategic efforts, including the establishment of local UA V service providers, government incentives to reduce adoption costs, and initiatives to build technical capacity within the workforce in rural areas.

Additional Barriers and Considerations

Other considerations and barriers to UA V implementation involve the cost of training and technical expertise. Mediterranean studies indicate that the ability to process NDVI and other UA V data requires a high degree of technical knowledge on the part of UA V operators and data analysts alike- a resource that may be limited in rural Turkish areas [6].

A developed network of local UA V service providers would make precision agriculture more accessible to Turkish farmers because the individual farms would not need to make investments in expensive equipment and training.

Overall, international experiences underline the transformational role that UA V technology can play in agriculture: from reducing input costs to yield prediction and supporting sustainable farming. A suitably adapted application can henceforth provide the means for UA V-driven precision agriculture to contribute toward attaining environmental sustainability, productivity, and economic resilience in the farming sector of Turkey.

Support Section II (Surveys and Turkey)

Agriculture in Turkey, representing nearly 23% of the country’s total workforce, faces significant challenges related to resource efficiency, environmental sustainability, and adaptation to climate variability [10]. With an average farm size of only 5.9 hectares—considerably smaller than EU (17.4 ha) and US (18.0 ha) averages—Turkish agriculture operates under different structural constraints than more consolidated agricultural systems elsewhere. The Cukurova region, accounting for 5% of Turkey’s total cultivation area with 1,091,000 hectares of cropping land, exemplifies both the potential and challenges of Turkish agriculture [10].

Turkish farming is experiencing transformation pressures from multiple directions, including climate change effects, freshwater supply shortages, and economic pressures from rising input costs like fertilizers [9]. The current agricultural production system faces particular strain as inflation (44.38%) and high interest rates (42.50%) create financial barriers for farm investments [23, 24]. Further, the average interest rate of 46.7% of Turkey from the 1970s to the last decade indicates the long-lasting economic trifle farmers face [22]. These economic conditions make cost-effective technological solutions particularly relevant for Turkish farmers.

Precision farming technologies offer promising solutions specifically tailored to Turkey’s agricultural challenges. Studies demonstrate that variable-rate fertilizer application could achieve fertilizer savings ranging from 4% to 37%, depending on soil variability—a significant consideration given that fertilizer costs currently represent approximately $643.33 per hectare for wheat production [9]. These savings would be particularly impactful in regions with high soil nutrient variability, though implementation potential varies significantly between geographic regions like Central Anatolia and the more diverse Cukurova region.

The economic viability of precision farming in Turkey depends heavily on farm size, with annual equipment costs ranging from $13-131 per hectare (for 500-50 ha farms, respectively). This suggests that while larger operations may readily adopt these technologies, structural adaptations may be necessary for Turkey’s predominantly small-scale farming sector to benefit from precision agriculture advancements [9].

Farmer Readiness and Perceived Benefits

Survey-based research across multiple Turkish provinces provides valuable insights into farmers’ readiness to adopt drone technology and their perceptions of its benefits. In the Southeastern Anatolia Region, a survey conducted between September 2022 and January 2023 with 249 producers in Mardin and Şanlıurfa revealed that 27.7% of respondents had used unmanned aerial vehicles for agricultural purposes in the previous production season. The study found a statistically significant correlation between drone use and education levels (p=0.000), with higher education corresponding to greater adoption rates. The surveyed farms averaged 26.4 hectares, substantially exceeding both the national average of 5.9 hectares and the regional average of 10.4 hectares, with farmers averaging 19.5 years of agricultural experience [4].

Regarding economic benefits, farmers generally recognized the potential advantages of drone technology, with a mean score of 3.497±1.07 on a 5-point Likert scale. Specifically, they acknowledged that drones could increase crop productivity (mean 3.534), reduce work costs (mean 3.426), increase profit (mean 3.438), and be cost-effective (mean 3.590). The perceived usefulness of drones scored even higher at 3.561±1.10, with farmers believing that drones would make their jobs easier (mean 3.598), provide better results than other systems (mean 3.534), enable faster work (mean 3.554), improve work performance (mean 3.554), and allow for work diversification (mean 3.566) [4].

Despite recognizing these benefits, farmers expressed moderate concerns about the technical aspects of drone operation. Statements regarding ease of use received somewhat lower scores, with “I think drones are easy-to-use tools” averaging 3.317 and “It will be easy for me to learn to use a drone” averaging 3.385. Confidence in their ability to use drones was similarly moderate, with statements like “I think using a drone is complicated and difficult” (mean 2.767), “It is difficult for me to use a drone” (mean 2.671), and “I think I am not a farmer who is good at working with digital tools like drones” (mean 2.590) indicating some hesitation. These lower confidence scores likely reflect the prevalence of rental services, where farmers might not directly operate the drones themselves [4].

The research identified two significant factors positively influencing drone adoption intention: perceived usefulness and perceived economic benefit. Interestingly, perceived ease of use and trust attitude did not significantly impact adoption intentions. Farmers were primarily motivated by potential reductions in workload, enhanced productivity, and lower operational expenses rather than by how easy the technology might be to use [4, 7].

High initial investment emerged as the primary barrier to adoption, as evidenced by a survey of 384 farmers in Aydın province. Only 3.12% of these farmers were initially willing to purchase drone technology, while a much larger percentage (32.6%) expressed willingness to rent Autonomous Unmanned Aerial Vehicle (AUA V) technology at an average price of TRY 287.54 per hectare. The significance of this barrier was further confirmed by the 67.2% of farmers who strongly agreed that the high initial purchase cost was a major obstacle to adoption. Notably, when government subsidies were mentioned, potential purchase interest more than tripled to 10.67%; as Parmaksiz and Cinar (2023) note, “Farmers preferred cooperatives as providers of UA V rental services”, highlighting the importance of policy interventions in technology adoption. The study also found that young, technology-oriented farmers with higher agricultural incomes showed greater interest in drone adoption [7].

A complementary survey in the Çukurova region (covering Hatay, Osmaniye, Adana, and Mersin provinces) assessed awareness of precision agriculture (PA) technologies among agricultural stakeholders. While 90.2% of participants (including agricultural engineers, farm equipment dealers, and farmers) reported following new agricultural trends, 51.8% had not heard the term ‘Precision Agriculture’ before. However, after receiving brief training, 97.6% believed PA technologies would benefit Turkish agriculture, 88.4% wanted more detailed training, and 89.7% expressed interest in using these technologies. This dramatic shift in interest after minimal education suggests that knowledge gaps represent a significant but potentially addressable barrier to adoption [10, 3].

Based on these findings, several promising pathways emerge for increasing drone technology adoption in Turkish agriculture. First, given the significant difference between purchase and rental willingness, cooperative-based rental services could significantly accelerate adoption. Second, the tripling of purchase interest when subsidies were mentioned highlights the critical role of government support in overcoming financial barriers. Third, the correlation between education and adoption, along with high interest in training after basic exposure, suggests that educational initiatives could substantially boost adoption rates. Finally, developing favorable borrowing channels specifically designed for agricultural technology investments could help address the initial cost barrier that currently limits widespread adoption [7, 10]

These findings collectively demonstrate that while Turkish farmers recognize the potential efficiency and economic advantages of drone technology, successful widespread adoption will require addressing financial barriers and knowledge gaps through coordinated efforts by government agencies, agricultural cooperatives, and educational institutions. The relatively high willingness to rent suggests a practical intermediate step toward technology integration in Turkish agriculture, particularly if facilitated by appropriate organizational structures and policy frameworks [10, 3].

How UA Vs Address Turkey’s Agricultural Challenges

The agricultural sector in Turkey has long been plagued by resource-wasting and environmentally destructive practices, which have led to issues such as overuse of water, soil degradation, and excessive chemical inputs. Traditional farming techniques, while deeply rooted in the culture, have often failed to adapt to the growing need for sustainability and efficiency. However, the advent of UA V (Unmanned Aerial Vehicle) precision agriculture presents a significant opportunity to address these challenges. UA Vs offer the potential to revolutionize Turkey’s agricultural practices by providing real-time data and insights that can lead to smarter decision-making, increased efficiency, and reduced environmental impact.

Improved Efficiency in Fertilizers and Pesticides

Turkey’s agricultural sector faces challenges related to efficient resource management and increasing productivity. Precision agriculture technologies (PATs) can be considered a tool for farm management that allows the agricultural entrepreneur to optimize inputs and reduce costs [6]. UA V technology can address these challenges by enabling precision application of fertilizers and pesticides, based on site-specific information collected by multispectral and hyperspectral sensors [7]. UA Vs can carry out a wide variety of agricultural operations in support of precision agriculture, including fertilizer application. This targeted approach can help reduce the overuse of agricultural inputs, which is crucial as Turkey has begun to become more foreign-dependent in agriculture. By optimizing the quantity and timing of fertilizer and pesticide applications according to the needs of different areas within a field, UA Vs can contribute to cost reduction and environmental sustainability [12, 7]. For instance, Recording and Mapping Technologies (RMT), which include UA Vs with sensors, can lead to significant fertilizer savings. One study achieved an 80% reduction in fertilizer doses applied in a vineyard through the use of aerial imagery and ground detection. Variable Rate Technologies (VRT), often implemented using UA Vs, have demonstrated the potential for fertilizer savings of up to 59.6%. In one case, a 31% reduction in potassium-based fertilizer and a 59% reduction in phosphate consumption were achieved [14]. Furthermore, regarding pesticides, UA Vs help ensure very precise pesticide spraying, which uses fewer chemicals as the zones are specific in number, minimizing environmental contamination. VRT has shown potential for pesticide savings ranging between 8% and 80%. Sensor-based variable-rate application (VRA) achieved an 8% fungicide savings in winter wheat, and another study reported a 51.9% reduction in spray volume for air-assisted spraying based on real-time disease spot identification. Autonomous robotic systems integrated into modern agricultural practices have also led to substantial Plant Protection Product (PPP) savings, with one system achieving 66% herbicide savings [14, 5, 16]. These precise application methods not only reduce input costs but also contribute to protecting the environment and human health through the controlled use of fertilizers and pesticides [7]. In Turkey, Tekin et.al.’s study examining variable-rate fertilizer application in wheat production suggested that applying fertilizer considering the variations in soil nutrients could be economically justified, with the degree of variability being a key factor [9].

Water Conservation and Irrigation Efficiency

Water scarcity is a significant concern in many agricultural regions, including parts of Turkey. UA Vs equipped with thermal sensors can play a vital role in improving irrigation efficiency and promoting water conservation [7]. By identifying areas experiencing water stress through canopy temperature measurements, farmers can implement variable rate irrigation (VRI), applying water only where and when needed. This precision in irrigation can lead to significant water savings, as demonstrated in international case studies where VRT irrigation systems have achieved water savings of 20% to 50% in vineyards and pear orchards [8, 14]. Recording and Mapping Technologies (RMT) contribute to the necessity of water conservation in agriculture. One study implementing an automatic irrigation scheduling system in an olive orchard, using soil moisture sensor data, resulted in a 24% reduction in water usage. Innovative irrigation scheduling software using model-predicted crop water stress achieved water savings between 16% and 35% [14]. Empirical data from the Precision Crop Management initiative, utilizing IoT sensors, satellite imagery, and drone technology, successfully reduced irrigation costs by 10% in wheat cultivation. Guidance and Controlled Traffic Farming (CTF) technologies have also been linked to water savings, with one study observing water reductions of 13.0% and 9.0% for different crops through precision irrigation in CTF systems. Furthermore, CTF was linked to a 65% increase in rainfall-use efficiency, leading to reduced runoff and water conservation. Variable Rate Technologies (VRT) have also demonstrated potential in significantly reducing water consumption, with computer simulations showing variable water savings up to 26% with optimized specific zone control in center-pivot irrigation. Soil moisture sensor-based irrigation scheduling achieved water savings ranging from 7.5% to 19%, and VRI systems reduced irrigation water use by 8–20% for soybeans and 25% for corn. The HydroSense project in Greece, using VRI in cotton fields, showed 5 to 34% savings in water consumption. These examples highlight the potential for UA V-based technologies to address water scarcity challenges in agriculture, including in regions like Turkey [14].

Environmental Impact on Turkish Agriculture

The detrimental ecological footprint of traditional agricultural practices in Turkey poses significant environmental challenges that demand urgent attention. Unconscious watering, spraying and fertilization methods widely employed across Turkish agricultural regions severely damage ecosystems, contaminate groundwater, and destroy beneficial insects and soil microorganisms [3]. Traditional pesticide application techniques enable these harmful chemicals to permeate the environment, posing risks to human health, with farmers often directly exposed to toxins during manual spraying operations. The risk of pesticide residues and their negative impact on ecosystems represents one of Turkey’s most pressing pollution problems, mirroring similar concerns worldwide [7].

Conventional fertilization approaches in Turkey typically treat agricultural land as homogeneous units, applying standardized amounts of fertilizer without accounting for spatial variability, resulting in substantial environmental degradation. This uncontrolled use of fertilizers and pesticides introduces heavy metals into soil systems, triggering contamination that affects all living organisms and ultimately compromises human health through the food chain [16]. Additionally, unconscious stubble burning practices severely impact groundwater quality and destroy beneficial soil organisms [3]. Traditional agricultural development’s singular focus on increased productivity has historically pushed natural systems beyond sustainable limits, while practices such as deep tillage and vegetation burning alter the living conditions of soil microorganisms, causing deterioration of their environment and decreasing soil biota diversity [7]. These conventional methods have contributed to alarming rates of water resource depletion, soil contamination, and excessive salinization of irrigated lands, leading to significant losses of arable territory [3].

The strategic deployment of drone technology in Turkey’s agricultural landscape offers a promising pathway to reduce environmental harm through precision farming methods. Research indicates that drones can transform conventional agricultural practices by enabling farmers to apply resources with unprecedented accuracy, addressing a fundamental environmental challenge in modern farming [5, 16]. This technological intervention represents a significant advancement in global efforts to balance agricultural productivity with environmental conservation.

Drones equipped with spraying capabilities enable site-specific delivery of pesticides and fertilizers, which substantially reduces overall chemical usage. This precision minimizes residue risks and their negative environmental impacts, addressing a globally recognized concern [8, 9]. Variable rate application (VRT) technology, facilitated by drones, proves essential for environmental sustainability by enabling precise application of water, fertilizers, and pesticides, thereby improving resource efficiency [14].

Research on Digital Agricultural Technologies (DATs) shows that VRT can achieve up to 80% reduction in pesticide use and 60% decrease in fertilizer usage. Additionally, initiatives like the Smart Orchard Spray Application within the IOF2020 project demonstrated significant environmental benefits: 22% to 39% reduction in greenhouse gas emissions through optimized pesticide application, along with 48% reduction in spray drift. Similarly, an autonomous system achieved 66% herbicide savings through precise weed detection and targeted spraying [14].

Another major environmental advantage comes from improved crop health monitoring capabilities. Drones effectively monitor crop health, enabling early detection of diseases, nutrient deficiencies, and water stress [1]. This early detection allows for targeted interventions rather than broad, indiscriminate treatments, potentially reducing the overall need for extensive chemical use. For example, vegetation indices like ExG and VEG, derived from low-cost camera UA V imagery, have achieved high accuracy (83.74–91.99%) in vegetation fraction mapping, demonstrating their potential for precise weed management [8, 5]. Multispectral and hyperspectral sensors on drones can identify changes in crop nutrient status, enabling precision fertilization and preventing over-fertilization and nutrient runoff [5, 16].

Agricultural drones (AUA Vs) are widely recognized as a technology that significantly contributes to sustainable agriculture by reducing input use. This aligns with the global need for policy changes aimed at reducing environmental problems in agriculture. The optimization of input consumption—applying only the required quantities of fertilizer and pesticide—represents a key aspect of this contribution [7, 3].

By enabling more precise farming methods and offering opportunities to optimize inputs and reduce costs, drone technology supports environmentally conscious production. The integration of UA V data with GIS and predictive modeling can further optimize resource usage and enhance crop resilience to stress [8, 4]. A case study of an Italian cereal farm that adopted precision agriculture technologies demonstrated a 53% reduction in pesticides after adoption [6]. These advancements collectively indicate a strong potential for drones to play a crucial role in fostering more sustainable agricultural practices in Turkey.

Farmer Concerns

Turkish farmers face significant economic hurdles when considering new agricultural technologies, particularly agricultural drones (AUA Vs). The high initial investment cost, ranging from USD 15,000 to 25,000, represents a major barrier to adoption, with 67.2% of surveyed farmers citing this as prohibitive. This substantial upfront cost translates to extremely low purchase intention, with only 3.12% of farmers indicating they would consider buying such technology [7]. Beyond the purchase price, farmers worry about ongoing expenses including subscription fees, training costs, maintenance requirements, and eventual replacement costs. These cumulative “adjustment costs” significantly impact the perceived economic viability of new technologies [18]. Without clear evidence of sufficient return on investment and profitability, farmers remain reluctant to make such substantial financial commitments [4]. Farm size also plays a crucial role in technology adoption decisions, with approximately 10.3% of farmers rejecting Precision Agriculture technologies, specifically citing high investment requirements and small field size as significant barriers [10].

Farmers express considerable hesitation regarding the ease of use and practicality of new technologies like drones. Despite acknowledging potential time and cost savings, many remain uncertain about operational aspects such as spraying effectiveness, coverage capabilities, and their own ability to operate drones even after training. Factor analysis reveals that some farmers feel uneasy using new technological tools, consider themselves too old to learn, find the learning process troublesome, and view new technology as an additional burden [7]. The technological complexity of solutions like drones and aircraft/satellite systems has limited their adoption even in more technologically advanced markets like the United States [18]. Interestingly, while approximately half of the surveyed Turkish farmers believed traditional spraying methods were inefficient (47.6%) and potentially harmful to their health (53.4%), they still expressed uncertainty about alternatives [7].

Legal procedures and regulations governing advanced agricultural technologies create additional adoption barriers. Complex or restrictive legal arrangements can impede the implementation of potentially beneficial technologies. In certain regions of Turkey, particularly near the Syrian border, security issues and the presence of signal jammers introduce additional restrictions on drone usage. Farmers must obtain necessary permits regarding flight conditions and adhere to legislation from the General Directorate of Civil Aviation [4, 7]. Data ownership, privacy, and security have emerged as significant concerns in digital agriculture, with ongoing debates centering on farmers’ concerns about ownership of data generated from their fields and potential compensation, alongside data privacy and security considerations [18].

Government intervention could significantly impact adoption rates, with recommendations including incorporating drones into existing state machinery and equipment support programs, providing financial assistance such as interest-free loans for AUA V purchases and extending current agricultural equipment subsidies to cover AUA V technology [7]. For example, in Malaysia, research suggests the government should offer different incentives specifically for rural farmers using advanced technologies like drone cameras [13]. Agricultural cooperatives are identified by Turkish farmers as organizations best positioned to deliver rental services for expensive technologies like drones, potentially accelerating adoption among small and medium-sized operations by leveraging collective farmer funds [7].

TARNET, a technology company affiliated with Agricultural Credit Cooperatives, could play a vital role in facilitating access to AUA V technology by managing subsidy programs or offering affordable rental services [7]. Agricultural extension services can facilitate drone adoption by providing information and guidance, while technology providers increasingly offer cloud-based farm management information systems [18]. Research institutions and industry representatives provide essential evidence about farmers’ technology adoption behaviors, informing policy decisions. The social aspects of technology diffusion—including communication, exhibitions, fairs, and field days—play crucial roles in increasing awareness of new technologies. Developing efficient rental markets represents a viable approach to overcoming high initial purchase costs, especially since most farmers who used drones did so by
renting them [10, 7].

Data Collection Challenges

One of the considerable limitations in this research was the very limited nature of previous studies and case analyses on the adoption of UA Vs in the agricultural sector of Turkey. In contrast to countries where various aspects of UA V technologies have been studied and piloted, there is limited, if any, prior research undertaken within Turkey to fully understand the potential for the technology within a local context. The limited number of case studies and field data restricted the depth and breadth of this research; it was required to rely heavily on international examples or small-scale projects. In this respect, this shortcoming in current localized research reflects a serious requirement for a more extensive research investigation related to the potential of integrating UA Vs within Turkish agriculture.

Moreover, the very process of gaining relevant data was particularly difficult due to the fact that there are no records or organized systems tracking the use of UA Vs within Turkey. This lack of structured data systems made it extremely challenging to assess the current state of UA V implementation or measure its impact across different farming regions. Future research would greatly benefit from the establishment of comprehensive data collection frameworks, such as national registries or cooperative databases, to track UA V utilization and outcomes. Such initiatives would ultimately allow researchers to conduct more powerful analyses in the development of a clear understanding of UA Vs’ potential benefits and challenges within Turkey’s unique agricultural landscape.

Implications for Turkey’s Agricultural Sector

Regardless of the very promising potential, several obstacles stand in the way of this technology being widely used in Turkey’s agricultural sector. A very important factor is that the farmers are not aware, and there is a lack of technical knowledge, especially in the less developed regions. Farmers are skeptical about such technologies being viable; often, they think that it would be too expensive or complicated to use. This is a major knowledge gap, as it prevents farmers from fully grasping how UA Vs could improve productivity by reducing costs. Such problems need awareness campaigns, hands-on training, and demonstration projects that will outline the practical benefits of the technology.

Another major barrier is, of course, the economic one, especially for small-scale farmers who cannot afford the initial costs of the UA Vs or the related services. In this regard, Turkey can introduce government-backed financial incentives, such as subsidies, soft loans, or tax exemptions, to make UA Vs more accessible. Cooperative-led initiatives could also play an important role, whereby farmers are allowed to pool resources and rent UA Vs rather than purchase them outright, reducing the economic burden on individual farmers.

Furthermore, the absence of centralized data systems and extensive research on UA V usage in Turkey represents a barrier to scaling. Without reliable data regarding UA V performance and trends in adoption, it is challenging to develop effective policies or tailor solutions to farmers’ needs. Building national databases and encouraging partnerships between universities, cooperatives, and government bodies could help close this gap and make informed, data-driven decisions to further promote UA V use.

Overcoming these challenges will help Turkey unlock the transformational potential of UA V agriculture. Such technologies can lead to sustainable development through the efficient use of resources, reduction in environmental impact, and enhanced agricultural productivity. With proper support mechanisms, UA Vs could act as an agent of change for modernizing the agricultural sector of Turkey by helping farmers adapt to global challenges with long-term economic and environmental sustainability.

Conclusion

Unmanned Aerial Vehicles (UA Vs) hold a revolutionary capability for the modernization and sustainability of Turkish agriculture. This study has explored the benefits of UA V-based remote sensing technologies in addressing some of the most pressing challenges in Turkey’s agricultural sector, such as over-fertilization, excessive pesticide use, and inefficient resource management. Drawing upon global and local case studies, the findings confirm the critical role UA Vs can play in achieving significant advancements in productivity, cost efficiency, and environmental sustainability.

Utilization of airborne UA V technologies in agriculture has yielded impressive worldwide results because it allows access in real time to high-resolution, spatially detailed data that can be used for precision farming. For example, multispectral and hyperspectral imaging are able to help unlock information about plant health, soil and nutrient availability, and empower Turkish farmers to apply fertilizer and pesticide at the end of their fields. Not only does it decrease the amount of chemical waste, but also this targeted app reduces the environmental pollution, which should be seriously taken into account in areas such as the Konya Plain, where intensive irrigation and fertilizing lead to soil degradation. Additionally, UA Vs equipped with thermal imaging sensors optimize water use by identifying areas of water stress, which is essential for arid regions in Turkey facing water scarcity.

Examples from elsewhere, including the Mediterranean and Australia, showcase the economic and operational benefits of UA V technology. Research has demonstrated pesticide use reductions up to 30% and substantial cost savings via better fertilizer application. For Turkish farmers, these results do so place UA Vs at the forefront of a potential cost reduction by containing or even increasing crop yield. Additionally, pilot projects at local levels in Turkey, encompassing examples of UA V-aided pesticide spraying in paddy fields, have also demonstrated the capacity of the technology to save time, reduce labour costs, and support sustainable farming.

Despite these benefits, several barriers impede the widespread adoption of UA Vs in Turkey. High upfront costs, a lack of technical expertise, and limited awareness about the capabilities of UA Vs are among the most significant challenges. Surveys show that many farmers, especially farmers running small and medium-sized fields, consider UA Vs to be too expensive or too complicated. Moreover, the absence of centralized data systems and empirical studies on UA V adoption within Turkey creates additional obstacles, as farmers and policymakers lack the localized insights necessary to fully understand the technology’s impact.

In order to address these limitations, some strategic efforts should be taken in order to encourage the use of UA V technologies. Specifically, government-sponsored measures like subsidies, interest-free loans, and tax breaks can help alleviate the economic pressure on farmers. Cooperative-based rental services for unmanned air vehicles represent an alternative solution that can be used by farmers to get access to the latest technology with minimal capital outlay. Furthermore, awareness campaigns and hands-on skills programs are crucial in addressing the knowledge gap, especially in rural areas where technical skills are lacking. Such efforts should be supported with the establishment of national databases and research protocols to record UA V applications and results that would provide data-driven decisions regarding the integration of UA Vs in agriculture in Turkey.

In the future term, the application of UA V technology has the promise to turn Turkish agriculture into a model of sustainability and productivity. Through providing precision agriculture capability, UA Vs can alleviate losses of resources, environmental impairment, and yield enhancement, all of which are highly relevant to addressing climate change and the demand for more food in the world. On the other hand, the popularization of UA Vs is compatible with Turkey’s sustainability goals, potentially leading to sustainable long-term economic resilience and environmental protection.

In conclusion, while the road to widespread UA V adoption in Turkey’s agricultural sector is fraught with challenges, the opportunities far outweigh the obstacles. Under the optimal policy environment, along with technological advancement and farmer training, UA Vs may provide disruptive changes to Turkish agriculture, an increase in productivity, sustainability, and competition in the global market. The success stories from other countries and the initial results from local pilot projects provide a clear roadmap for how Turkey can harness the full potential of UA V technology to modernize its agricultural practices and ensure food security for future generations.

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