Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece

Abstract

Agricultural technology integration has become a key strategy for attaining agricultural sustainability. This study examined the integration of technology in agricultural practices towards agricultural sustainability, using Greece as a case study. Data were collected using a questionnaire from 240 farmers and agriculturalists in Greece. The results showed a significant positive effect of technology integration on agricultural sustainability, with p-values indicating strong statistical relevance (types of technology used: p = 0.003; factors influencing technology adoption: p = 0.001; benefits of technology integration: p = 0.021). These results highlight the significant effects that cutting-edge technology like artificial intelligence, Internet of Things (IoT), and precision agriculture have on improving resource efficiency, lowering environmental effects, and raising agricultural yields. Our findings cast doubt on the conventional dependence on intensive, resource-depleting farming techniques and point to a move toward more technologically advanced, sustainable approaches. This research advances the conversation on sustainable agricultural practices by showcasing how well technology integration may improve sustainability results in Greek agriculture. The study emphasizes the significance of infrastructure investment, supporting legislation, and farmer education in order to facilitate the adoption of agricultural technology.

1. Introduction

Most countries have remained worried about the automation and technical upgrading in the agriculture industry. Fast population growth worldwide is driving up food consumption at the same time [1]. With the world’s population expected to exceed 10 billion people by 2050, we can appreciate the scope of these demands. Farmers are using an increasing number of hazardous pesticides and fertilizers, which degrades the soil and has an adverse effect on agricultural productivity. These traditional techniques are not keeping up with the expanding demand. Research on agricultural research costs up to USD 25.8 billion a year, and it has been reported that pests and diseases now damage almost 40% of the world crops [2]. Due to its widespread adoption across various industries, including agriculture, which faces numerous challenges daily, there has been significant advancement in artificial intelligence over the past 50 years [3,4]. Utilizing pesticides and fertilizers, managing product distribution and storage, managing weeds and illnesses that harm crops, and managing agricultural irrigation and drainage are all tasks that farmers must do [5]. Artificial neural networks (ANN), artificial intelligence (AI), machine learning (ML), deep learning (DL), intelligent systems, fuzzy logic, Internet of Things (IoT), and wireless communications are some of the new approaches being used to solve agriculture-based problems [2]. Through the utilization of physical quantity data, artificial intelligence systems can acquire a profound understanding of their environment. Armed with this knowledge, they possess the capability to address various challenges within the agriculture industry with remarkable efficacy [6]. Identifying and controlling pests and diseases, weed detection, crop harvesting, and plant disease monitoring are only a few of the enormous changes in agriculture that are facilitated by applying these areas’ skills to practices [7,8,9].

Ma and Wang (2020) [10] noted that due to the Green Revolution, food production has increased dramatically in agriculture during the previous several decades. An increase in irrigation, automation, specialization, and the use of chemical pesticides and fertilizers were among the innovations brought about by the Green Revolution [11]. In the 1960s, the Revolution brought about significant increases in output, particularly in Asia and Latin America, but these gains were not long-lasting. The data, for instance, show that the increase of rice yields in Asia fell precipitously in the 1980s, from 2.6% annual growth rate in the 1970s to 1.5% starting in 1981. This reduction was partially caused by rising costs for agrochemicals (pesticides and herbicides) and chemical fertilizers [12]. What is more, land degradation and environmental harm caused by agriculture are pervasive and unabated, and poverty and hunger continue despite the productivity increases linked to the Green Revolution [13,14].

More food production is necessary to reduce hunger, and more access to resources, expertise, and information that boosts productivity for farmers is necessary as well [15]. But most of the people who suffer from chronic hunger are smallholder farmers in developing nations who work on marginal soils and practice subsistence agriculture [1]. With a substantial impact on the GDP and employment of the nation, agriculture has long been a fundamental component of Greece’s cultural and economic legacy. Yet the need for sustainable farming methods has grown significantly at a time of population expansion, climate change, and resource depletion. A possible way to deal with these issues is via the incorporation of technology into agriculture. This paper explores the integration of technology in Greek agriculture and its role in promoting agricultural sustainability.

1.1. Purpose of the Study

Investigating the integration of technology in agricultural practices towards agricultural sustainability is a crucial and evolving area of research and application. Agriculture plays a fundamental role in feeding the global population, and sustainable practices are necessary to ensure food security while minimizing environmental impact. This study, therefore, focused on analyzing the role of integrating technology in agricultural practices towards agricultural sustainability.

The study was also based on the following objectives:

• To determine the different types of technology used in agriculture and their effect on agricultural sustainability in Greece,
• To establish the different factors influencing technology adoption and their effect on agricultural sustainability in Greece,
• To examine the benefits of technology integration in agriculture and their influence on effect on agricultural sustainability in Greece.

1.2. Research Hypotheses

Hypothesis 1 (H1). 

The types of technology used in agriculture positively affect agricultural sustainability in Greece.

Hypothesis 2 (H2). 

The factors influencing technology adoption have a positive relationship with agricultural sustainability in Greece.

Hypothesis 3 (H3). 

The benefits of technology integration in agriculture have a positive relationship with agricultural sustainability in Greece.

2. Literature Review

2.1. Technology Adoption and Smart Agriculture—Agriculture 4.0

The mechanisms controlling the application of innovations include dissemination and adoption. One way to think about diffusion is as extensive, aggregate adoption. A considerable amount of time passes between new technology’s creation and farmers’ acceptance of it [16,17]. Many variables may influence how new technology is adopted. Numerous elements that affect the adoption of technology are mentioned in the extensive literature on this subject [18]. The qualities of the technology, the farmer’s goal, the traits of the change agent, and the socioeconomic, biological, and physical context in which the technology is introduced are some of the elements that influence the degree of technology adoption [6]. A farmer’s age, level of education, income, family size, tenure status, usage of credit, value system, and beliefs are sociopsychological characteristics that are positively correlated with adoption [19,20]. In addition, the farmers’ ability to adapt may also be influenced by the personality of the local extension agents. Adoption is influenced by the reputation, relationship with farmers, and communication skills of extension agents working in tandem with the efficiency of the technology transfer process. Furthermore, the biophysical aspects of the agricultural region, such as the infrastructure and resources available to the farm, have a favorable impact on the social network of the farmers [8,21].

Furthermore, an adoption category based on the innovation decision period has been highlighted by Yépez–Ponce et al. (2021) [22]. The amount of time needed to complete the innovation–decision process is known as the innovation–decision period. A person’s choice to embrace an invention is determined by how long it takes them to become aware of it—this might take days, months, or years [23,24]. Furthermore, the steps of the decision-making process are shown by Rogers’ (1983) [25] innovation decision model [26,27,28]. These stages include learning about an innovation for the first time, deciding whether to accept or reject it, putting the new concept into practice if it is accepted, and verifying this choice [22].

Technology adoption among farmers depends on the dissemination of technology-related information [24,29]. Farmers often have conservative views and need a lot of time and knowledge to be convinced to use new technology [30]. Technical direction and trustworthy information are necessary for the effective commercialization of new innovations and technologies. Hence, farmers who are unsure about using new technology will find it easier to convince themselves with the help of demonstration plots and nearby farmers who have already made the switch. Farmers may benefit from the useful information that demonstration plots can provide to help them smoothly adopt new technologies [31].

Precision agriculture, often known as smart agriculture, is a system that combines cutting-edge smart technology with conventional agricultural methods to increase both the amount and quality of agricultural output [1]. Smart agriculture-use methods that are different from traditional ones, such as controlled irrigation and targeted, accurate application of herbicides and fertilizers, are used to raise production, decrease environmental impact, and increase efficiency and profitability [11,32,33]. Crops are severely impacted by a number of factors, including the soil, pests, diseases, and environment. This lowers production quality and quantity and causes major economic losses, as well as food emergencies. Right now, precision agriculture tries to provide answers. But the issue that emerges and worries us greatly is whether artificial intelligence (AI) can really assist in resolving the worldwide crop loss that imperils the planet’s food security [7].

Dhanaraju et al. (2022) [34] noted that artificial intelligence, big data, cloud and edge computing, smart sensors, Internet of Things (IoT) technology, robots, drones, and artificial intelligence are the primary digital technologies enabling the development and implementation of smart agricultural systems. The next phase of industrial agriculture, often described as “Agriculture 4.0”, or “precision, smart, or digital agriculture”, is powered by the integration of these technologies into the agricultural sector [6]. Nonetheless, a number of issues that must be addressed in the developing area of smart agriculture should be brought to light, including digitalization, the agricultural supply chain, ecological issues, and crop production [32,33].

Recent developments in smart agriculture, which make use of machinery, equipment, sensors, information technology, and computer vision, set them apart from conventional agricultural methods in a big way [6]. Aerial imaging, humidity sensors, robotics, GNSS, and other cutting-edge equipment and technology will be crucial to agriculture in the future. A decision-support system may optimize food supply chains, identify crop diseases, allocate resources more efficiently, and adjust to climate change in real time [35]. As an artificial intelligence method, fuzzy logic (FL) enables a controller to accurately perceive changes over time so that judgments may be made and actions taken in real time [6]. The FL approach is now extensively employed in the agricultural industry for a variety of purposes, including guiding robots used for harvesting and unmanned aerial vehicle (UAV) navigation for farm monitoring from above and picture capturing that is analyzed to make choices [6,36].

The real-time kinematic (RTK) technique is a satellite navigation method that is employed to improve the precision of location information for real-time applications. RTK achieves centimeter-level precision in determining the location of an object that is moving, such as an automotive or a surveying instrument, by employing a network of stationary base stations that transmit signals and mobile receivers. The methodology operates by conducting a comparative analysis between the timing of the carrier signal emitted by satellites and the phase received by the receiver. This process effectively mitigates errors arising from atmospheric conditions and various sources of interference [37,38,39].

2.2. Applications of AI in Agriculture

AI technologies are being used in agriculture in a variety of contexts, altering conventional agricultural methods. These are a few major fields where AI is having a big influence [7,40,41]. Artificial intelligence (AI) technologies, such as computer vision, robotics, natural language processing, and machine learning, have been integrated into tech business models in recent years. Applications of AI have the potential to lower costs associated with supporting smallholder farmers throughout the agriculture ecosystem, enhance the sustainable and efficient use of resources, and eliminate market asymmetries that keep farmers from participating in regional and global value chains. Examples of these applications include “smart” farm equipment and alternative credit scoring [40,42]. Due to developments in big-data analytics, cloud computing, and computing power, as well as lower costs for satellite imagery, remote sensors, and other hardware (like smartphones), mobile connectivity has become more accessible and affordable, making the use of AI technologies for agriculture commercially viable in recent years [43].

AI integrates real-time data from several sources, such as drones, Internet of Things sensors, and satellite photography, to allow precision agriculture. These data are analyzed by machine-learning algorithms, which provide insightful information on crop health, soil conditions, and resource needs. This enables farmers to apply pesticides, fertilizers, and irrigation with precision, improving crop yields and making the most use of available resources [44,45,46]. Computer-vision algorithms driven by AI provide automated crop management and monitoring. Drones and field-installed cameras may take pictures, and image-recognition algorithms can use those photos to find early indicators of weed infestations and nutritional deficits. This lessens the need for chemical treatments by empowering farmers to respond promptly, avoiding crop losses [23,24,47]. When paired with current agricultural and environmental data, AI models trained on previous data can reliably forecast crop yields [5]. AI systems are also able to recognize and categorize weeds, illnesses, and pests using visual cues and symptom assessments [6]. Deep-learning methods and computer-vision techniques are used by AI systems to identify possible crop dangers early on. This lessens the need for widespread pesticide applications by enabling farmers to implement focused preventative measures like localized interventions or precision spraying [8,12,48,49].

According to Soori et al. (2023) [2], artificial intelligence (AI)-driven irrigation systems improve water use by using sensor data. This increases total water-usage efficiency, encourages water conservation, and reduces water stress on plants [2]. Farm equipment is being revolutionized by AI technologies, which make automation and autonomous operations possible. AI-enabled robotic systems are very accurate and efficient in carrying out operations like planting, harvesting, and crop monitoring. This lowers the need for labor, boosts operational effectiveness, and raises total production [50].

2.3. More Technological Advancements in Agriculture

Agriculture across Europe has been adapting to the demands of the contemporary world, drawing on its rich historical legacy and cultural relevance [51]. Using cutting-edge technology is one of the main tactics for attaining sustainability and competitiveness in this industry. There are several innovations that have been incorporated into Greek agriculture, such as robotics and automation, sensor technology, remote sensing and Geographic Information Systems (GIS) applications, precision agriculture, and biotechnology, which includes genetically modified organisms (GMOs) (Figure 1).

2.3.1. Precision Agriculture

Precision agriculture has become more popular in Greece as a solution to the country’s resource shortage, climate change, and need for greater productivity. GNSS is a crucial tool in precision agriculture because it enables farmers to precisely map their fields and apply inputs where they are most needed. This is especially useful in Greece, where it is typical to have uneven terrain and varying soil conditions [52]. Farmers may maximize planting and harvesting processes, save waste, and increase yields with the use of GNSS equipment.

PA is one strategy to enable sustainable intensification by boosting yields without compromising ecosystem services. It includes a variety of approaches that improve the efficiency of agricultural production and distributed sensing technology (see Figure 2), whether that be through increasing yields or reducing waste. Since the 1980s, as technology like the GNSS and sensor technologies have advanced, PA utilization has increased [53]. Variable-rate planting, fertilizer, and herbicide applications for more targeted effectiveness and lowering chemical costs and environmental degradation [40], as well as crop forecasting and yield mapping to notify growers, buyers, and external stakeholders, about expected yields, are among the management practices made possible by precision approaches [6]. In order to evaluate the spatial and temporal variability of a farm, PA needs an efficient system. This process is essentially dependent on data collecting from a variety of sensing technologies, ranging from remote data collection from above to in situ data collection “on the ground” [52]. Here, we acknowledge the importance of the technologies used in PA (such as robotized agriculture and distributed sensing) and talk about the possible and present uses of solar power.

Wireless Sensor Network (WSN) adoption is one of the more recent advancements in agriculture [6]. WSN is made up of a network, or collection, of inexpensive, tiny sensors that are connected to a communication system to provide consumers access to real-time data collecting. As a result, WSN has several uses to improve agricultural productivity, including precision farming, greenhouse gas monitoring, and informational irrigation and fertilizer management [16]. An array of sensor nodes with four primary functions makes up a typical WSN system configuration: (i) a power source (such as a photovoltaic (PV) source of energy); (ii) the capacity to perceive environmental data; (iii) processing capacity; and (iv) communications capability. To link the sensor network to the outside world, each node will have a power source and a sensor system, such as a soil moisture sensor, that interacts with the other nodes and a nearby gateway node (Figure 2). The technologies used in this communication system may include Bluetooth, WiFi, GPRS, WiMax, ZigBee, and Wibree, but they also rely on power consumption, band frequency, and location and availability [6].

Dinesh and Vermeulen (2016) [54] noted that using WSN systems in agricultural applications has several advantages. When evaluating potentially localized qualities like soil moisture and nutrient availability, many sensors in one field help to overcome the limitations of field variability and soil type [55]. Solar power has been highlighted as one of the primary sources to use in the development of efficient energy capture and storage systems designed to solve this. By capturing solar energy during periods of high energy, such as daytime, sensors may operate during these hours and use stored energy, such as battery power, during periods of low sunlight or at night. At every sensor node, these power systems consist of a solar cell, battery, and control system [55].

2.3.2. Remote Sensing (RS)

We can study crops on a big scale in a synoptic, distant, and non-destructive way thanks to the highly helpful technique known as agricultural remote sensing. In most cases, a sensor is positioned on a platform, which might be a field robot, an unmanned ground vehicle (UGV), a satellite, or a remotely piloted aircraft (RPA) [46]. The sensor gathers electromagnetic radiation from plants that is reflected or released, and it is then processed to provide valuable data and products. These data include characteristics of the agricultural system and how they change across time and place. According to Martos et al. (2021) [46], functional characteristics are the biochemical, morphological, phenological, physiological, and structural physiognomies that control the fitness or performance of an organism (plant). Based on their individual characteristics, these features may be classified as typological, biological, physical, structural, geometrical, or chemical in origin. They differ from plant to plant and from one region to another. Leaf area index (LAI), chlorophyll content, soil moisture content, and other relevant information may be extracted with the use of RS, which offers an efficient relationship between plant radiance and the corresponding attributes [36]. However, knowledge of a number of variables is required to produce correct information from RS products, including the crop phenological stage, crop type, soil type, location, wind speed, precipitation, humidity, solar radiation, nutrient supply, etc. [13,14]. Plant density, organic computing, leaf biochemical content, green-cover percentage, leaf orientation, height, soil and vegetation temperature, and soil moisture are a few of the important informative items that RS provides [11,46].

To promote sustainable agriculture that can feed a global population that is expanding at a fast rate, a significant amount of information has been extracted from RS [56]. Selecting phenotypically superior varieties, optimizing crop management, evapotranspiration, agricultural phenology, crop production forecasting, ecosystem services (pertaining to soil or water resources), screening biodiversity in plants and animals, crop and land monitoring, and precision farming are some of the notable benefits or uses of RS [56,57].

2.3.3. GIS Applications

Greek agriculture has undergone a change thanks to Geographic Information Systems (GIS) applications, which provide farmers useful information and instruments for making decisions. In order to make well-informed choices regarding crop selection and land management, farmers may use GIS tools to examine spatial data, including soil types, elevation, and historical weather trends. To help with sustainable land-use planning, GIS may be used, for example, to determine which regions are best for growing a certain crop or to evaluate the danger of soil erosion. Greece’s varied terrain and microclimate make the use of GIS and remote sensing there very pertinent. By enabling farmers to precisely intervene and modify their methods in response to local circumstances, these technologies eventually increase the sustainability of agriculture [1,58].

The use of GIS applications has revolutionized global agriculture via technical breakthroughs. These instruments use drones in the air and satellite photos to track crop health, detect illnesses, and evaluate the state of the land [6]. When used in conjunction with Geographic Information Systems (GIS), they provide an accurate analysis of spatial data, such as soil types, elevation, and weather patterns, which helps decision-makers choose crops and manage land sustainably. Greece’s varied topography and microclimates make remote sensing and GIS indispensable for customizing agricultural operations to local circumstances, maximizing resource use, and elevating the sustainability of agriculture as a whole [7,59].

In order to create maps and study characteristics and geography for statistical and spatial approaches, the GIS consists of hardware and software that are intended to offer compilation, storage, retrieval, attributes analysis, and location data [55,60]. The GIS database defines the relationships between the variables that impact a crop on a specific agricultural field and includes information on field-soil types, nutrient status, topography, irrigation, surface and subsurface drainage, the amount of chemical treatments, and crop yield [34]. In addition to data storage and presentation, the GIS is used to compound and modify data layers for decision-making in order to evaluate current and alternative management.

GIS technology is essential for precision agriculture, a farming method that seeks to maximize resource use. Farmers may gather, organize, and evaluate geographical data on their farms with the use of GIS apps. This contains information on soil types, elevation, yield history, and past weather trends. A GIS system that incorporates these data allow farmers to make comprehensive field maps that help with decision-making. For example, depending on differences in soil characteristics and crop needs, they can accurately identify where to apply water, herbicides, and fertilizers [32,61]. With this focused approach, waste is decreased, environmental effects are minimized, and total resource efficiency is increased [6,12].

2.3.4. Sensor Technology

Although sensor technology is a key element of precision agriculture, it also merits discussion on its own for its function in tracking and improving other facets of Greek agriculture [46]. Different types of sensors exist, such as weather stations, soil sensors, and even drones fitted with specific sensors. Soil sensors are a vital source of information on temperature, moisture content, and nutrient levels. With the use of this knowledge, farmers may optimize resource usage and enhance crop health by planning irrigation and fertilization schedules in advance. Soil sensors are essential for effective water management in Greece, where water shortage is becoming an increasingly pressing issue [7].

Temperature, humidity, wind speed, and precipitation are among the meteorological data that weather stations with sensors may offer in real time [18]. For farmers in Greece, where climate change is increasing the frequency of severe weather events and climatic unpredictability, accurate weather information is essential [15]. Farmers are able to schedule irrigation, control pests and diseases, and decide when to sow and harvest using this information. In Greece, the usage of drones with multispectral cameras and other sensors for agricultural monitoring is growing. Farmers are able to evaluate crop health, identify regions that need care, and discover pests and illnesses due to their ability to take precise pictures of fields. Greece has steep terrain, making it difficult to reach every portion of a field [62].

Water status, fertility, and soil qualities are all provided via sensor technology, a key component of precision agriculture. As a result, in addition to the sensors that are presently on the market, additional sensors have been created based on desired characteristics. In order to optimize crop-development conditions, combat biotic and abiotic challenges, and boost crop yields, soil sensors and plant wearables analyze physical and chemical signals in the soil in real time, such as moisture, pH, temperature, and pollutants. The four nutrients that are most crucial for crop productivity are potassium (K), phosphorus (P), nitrogen (N), and soil organic materials (SOMs). The spatial fluctuation of soil nitrogen, both subsurface and surface, is measured using NIR reflectance-based sensors. Soil spectral reflectance in the infrared and visible wavelength ranges is evaluated in order to estimate SOM based on ideal wavelengths. Using NIR spectrophotometry, the soil’s nitrogen and phosphorus contents are estimated. Since ECa is sensitive to variations in soil texture and salinity, the soil apparent electrical conductivity (ECa) sensors continually gather data on the field surface. Optoelectronic, acoustic, impedance, and nanostructured biosensors are used to identify soil insects and pests [63].

2.3.5. Internet of Things

Smart farming is made possible by a new technology called the Internet of Things (IoT), which connects objects remotely [34]. In order to improve efficiency and performance across all sectors, the Internet of Things has started to have an impact on a wide variety of industries, including health, commerce, communications, energy, and agriculture [36]. Information on the impacts and practices of the Internet of Things is now available via apps. But when one looks at how far technology has come, it becomes clear that IoT technologies are essential to many aspects of farming, including the use of data acquisition, smart objects, sensors, mobile devices, cloud-based intelligent information, decision-making, and automation of agricultural operations [40,64].

IoT technology remotely pulls data from mobile phones and other devices and monitors plants and animals. Farmers are able to predict production levels and evaluate the weather thanks to sensors and tools [40]. More than ever, the Internet of Things contributes to water harvesting, flow-quantity monitoring and management, agricultural water-needs assessment, supply timing, and water conservation [10,65,66]. Based on the requirements of the soil and plants, sensors and cloud connection through the gateway may remotely monitor the condition and water supply. Farmers are unable to personally monitor and examine every plant in order to address nutritional shortages, pests, and diseases; nevertheless, IoT technology is still helpful and has helped farmers reach a new milestone in contemporary agriculture [32,34,67].

Nowadays, the Internet of Things (IoT) is a basic technology used in many other applications, not only smart home and city applications [6,68,69]. IoT technology may improve conventional farming techniques by combining cutting-edge technologies with creative ideas. An Internet of Things system’s sensors and actuators are what gather information about temperature, pressure, plants, weather, and other topics. After then, a cloud-based program receives the massive amount of data through the internet for processing and analysis so that wise judgments may be made [12].

IoT technologies can specifically aid in the development of an integrated control system for all agricultural parameters, including crop development, early disease detection, harvesting and post-harvest storage, preventing waste from inefficient harvesting, raising crop productivity, and so forth, in the field of smart agriculture. The term “smart agriculture”, as shown in Figure 3, describes the integration of IoT devices and methods, including sensors, cloud-based workflows, artificial intelligence, machine learning, and networking, into agricultural systems. It is a hardware and software combination whose main objective is to increase agricultural production [34]. The real-time correction process facilitates accurate navigation and positioning, rendering real-time kinematics (RTK) highly valuable in various domains, including precision farming, construction, and land surveying. In these fields, the acquisition of precise location data is of utmost importance for optimizing operations and facilitating informed decision-making [37,38,39].

Unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) are developing in a way that advances the idea of sustainable smart agriculture and enhances the value of data gathered via automated processing, analysis, and access [40,68]. Moreover, by tracking and forecasting weather patterns, IoT technology lessens the effect of climate change on agricultural output. This enables farmers to promptly manage weed, insect, and disease concerns, as well as monitor soil conditions [7,70]. Hence, the effective and optimal use of resources like water, insecticides, and agrochemicals is made possible by UAV and IoT-based technology. Furthermore, it has been shown that these intelligent technologies improve crop-performance quality and lessen the agricultural sector’s environmental impact [6,46].

2.4. Agricultural Sustainability

Agricultural sustainability, which is the capacity to satisfy current demands without endangering those of future generations, has become a crucial issue. A radical transformation in agricultural techniques is required due to many factors, including the world’s population expansion, changing climate, depletion of natural resources, and the need for higher productivity [54]. Sustainable agriculture is sometimes defined as environmentally responsible methods that either improve environmental quality and the natural resource base that supports the agricultural economy or have little-to-no negative impact on natural ecosystems [32]. Generally, this is accomplished by safeguarding, reusing, replenishing, and preserving the foundation of natural resources, such as land (soil), water, and wildlife, which support the preservation of natural capital. Synthetic fertilizers are administered based on need, even though they may be used to augment natural inputs. Synthetic chemicals that are known to negatively impact biodiversity, soil structure, and organisms are either avoided entirely or used sparingly in sustainable agriculture [10].

In agricultural systems, sustainability encompasses the ideas of persistence—the ability of a system to endure over extended periods of time—and resilience—the ability of a system to withstand shocks and pressures. It also covers a wide range of broader economic, social, and environmental effects [35,71,72]. An alternative agricultural system known as “sustainable agriculture” has arisen to guarantee environmental sustainability while addressing the many challenges encountered by resource-poor farmers. It speaks to agriculture’s ability over time to improve the quality of the environment while also producing enough food and other commodities and services in ways that are lucrative, socially conscious, and efficient with the economy [11]. This system combines integrated methods of producing soil, crops, and animals with a reduction or elimination of external inputs that may be hazardous to consumers’ and farmers’ health as well as the environment. Rather, it places emphasis on the use of methods that include and are tailored to regional natural processes, such as biological nitrogen fixing, nutrient cycling, soil regeneration, and natural enemies of pests, into the processes involved in the production of food [71].

A farm has to be financially successful in order to be really sustainable. Alternative, more lucrative uses of the land replace farms that are not economically feasible. There are many ways that sustainable agriculture may increase a farm’s financial sustainability [19,73]. While better crop rotation and soil management can boost yields in the short term, other environmental benefits from sustainable practices, such as increased water availability and soil quality, can boost farm values over the medium and long terms and enable payments for environmental services [55]. Achieving economic viability might also include taking steps like cutting down on practices that could endanger the environment, the health of farmers, and the welfare of customers. Instead, farmers would depend on the particulars of the production system, the cost of the equipment, chemical fertilizer, and pesticides (for farmers who can afford these inputs) [22]. Of course, a variety of other elements, such as household qualities like managerial skill, institutions, infrastructure, and market access, among others, influence economic sustainability in addition to crop-production techniques [8,10,74,75].

The standard of living for both the people who live and work on the farm and the people in the neighboring villages is a key component of social sustainability. It entails making certain that various participants in the agricultural production chain get fair income or returns [15]. Since sustainable agriculture makes more use of available labor, at least for certain methods, it might encourage community members to share in the agricultural value generated in the setting of high unemployment. This promotes social fairness and cultural cohesiveness [5]. Despite the fact that the aforementioned components are often addressed individually, they are not incompatible: Sustainable agriculture concurrently achieves social, economic, and environmental goals. Sustainable agricultural techniques are often not novel; rather, they are based on long-standing knowledge and procedures, some of which have recently undergone favorable scientific evaluation [57,58].

3. Materials and Methods

3.1. Research Design

The researcher utilized a cross-sectional survey design to understand the integration of technology in agricultural practices towards agricultural sustainability. A survey questionnaire was developed and administered through emailing to collect data. The survey contained different questions that were multiple choice and also based on a nominal scale. The researcher utilized this approach and effectively synthesized the different trends revealed by the collected data. The survey was sent to selected farmers and agriculturalists in Greece (see Appendix A).

3.2. Target Population

The study targeted the different farmers and agriculturalists in Greece. This population was targeted since they possibly could possess great knowledge concerning the integration of technology in agricultural practices towards agricultural sustainability.

3.3. Sample

The sample for our study was carefully selected to ensure a comprehensive representation of the farming community, encompassing a wide range of farming sizes, types, and geographic locations. The term “farmers” in this study is synonymous with “agriculturists”, as we focused on individuals actively engaged in agricultural practices, including crop production, livestock management, and other related agricultural activities. A sample size of 240 farmers or agriculturalists in Greece was selected for the study. This was determined using the formula developed by Yamane (1973) [76,77,78] as presented in Equation (1).

$$\mathrm{n}=\frac{\mathrm{N}}{1+{\mathrm{N}\mathrm{e}}^2}$$

(1)

where n = required sample size

• N = the target population
• e = level of significance
• 1 = constant

Using a 5% (0.05) level of significance

$$\mathrm{n}=\frac{2000}{1+2000 {\left(0.0025\right)}^2}\iff \mathrm{n}=240$$

To construct our sample, we utilized a stratified sampling technique, which allowed us to ensure that different subgroups within the farming community were adequately represented. These subgroups were identified based on factors such as farm size (small, medium, and large), type of farming (organic versus conventional), and geographic location (rural, peri-urban, and urban areas). This stratification ensured that our sample was not only representative of the broader farming population but also allowed us to explore potential differences and similarities among these subgroups.

3.4. Data Collection

The data-collection process involved administering a carefully constructed questionnaire through email. This questionnaire included closed-ended questions designed on a nominal scale, that ensured clarity and ease of understanding for respondents. The selection of questions was strategically done to cover the various dimensions crucial for the study, such as farm size (in hectares), type of cultivation, business size, prevalence of machinery versus manual labor, and other relevant factors. This comprehensive approach allowed for an in-depth analysis of the integration of technology in agriculture. Prior to distribution, official informed consent was obtained from all participants, ensuring ethical compliance. The survey targeted 240 farmers and agriculturalists who were chosen based on their willingness and availability to participate in the study. It is important to note that all 240 targeted respondents completed the survey, providing a complete dataset for analysis. This high response rate eliminated the need to seek additional participants and ensured that the data collected was both comprehensive and representative of the target population. The questionnaires were sent out with a one-week completion deadline. Upon the closing of this period, the researchers compiled the responses into a raw data file for further analysis, ensuring that all ethical considerations, including informed consent, were adhered to throughout the research process.

3.5. Data Analysis

The process of analyzing quantitative data included data editing and coding. The Statistical Package for Social Sciences (SPSS) Version 20.0 was then used to input and facilitate the data entry into the computer for analysis. The data were examined using descriptive statistics that yielded frequencies and percentages. The ANOVA statistics of adjusted R² and beta values were used to do regression analysis and determine significant levels. Regression analysis was used to ascertain the general predictive power of the different independent variables on the dependent variable under investigation. In this case, a multiple regression model was essential (Equation (2)) for determining different predictive values [78,79,80].

$$\mathrm{Y}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{X}}_1+{\mathsf{\beta }}_2{\mathrm{X}}_2+{\mathsf{\beta }}_3{\mathrm{X}}_3+\epsilon$$

(2)

where:

• Y = Agricultural sustainability
• β₀ = constant (coefficient of intercept);
• ${\mathrm{X}}_1$ = Types of technology used in agriculture
• ${\mathrm{X}}_2$ = Factors influencing technology adoption
• ${\mathrm{X}}_3$ = Benefits of technology integration in agriculture
• $\mathsf{\epsilon }$ = Represents the error term in the multiple regression model
• β₁…β₃ = represents the three independent variables’ regression coefficients, which were used to calculate how much effect each independent variable had on the dependent variable. In this study, we assumed there was no autocorrelation in the error term. However, it appears that we did not account for autocorrelation in our investigation. The 5% level of significance (0.05) was used to test the study’s hypotheses, and the null hypothesis was accepted or rejected based on the decision rule, which specifies that if p < 0.05, the null hypothesis should be accepted, and if p > 0.05, it should be rejected.

4. Results

The results for the characteristics of the study participants are presented in

Table 1. Personal information of respondents.

Item	Categories	Frequency	Percent
Respondent gender	Male	159	66.2
	Female	81	33.8
	Total	240	100%
Level of education	Certificate	35	14.6
	Diploma	63	26.3
	Degree	142	59.1
	Total	240	100%
Experience in the agriculture industry	Below 5 years	59	24.6
	5–10 years	63	26.3
	Above 10 years	118	49.1
	Total	240	100%

Source: Authors’ own work (2023).

.

A majority of the participating farmers and agriculturalists (66.2%) were male, and the remaining portion (33.8%) were female. Most of the respondents (59.1%) had obtained degrees, followed by 26.3% that had diplomas and only 14.6% that had certificates. This shows that data were collected from well-educated participants, which makes it highly reliable since they were able to interpret the questions on technology in agricultural practices. Furthermore, a majority of the respondents (49.1%) were highly experienced in the agriculture industry with above 10 years of experience, and only 24.6% had fewer than 5 years of experience.

4.1. Descriptive Results

The study established the types of technology used in agriculture, and the results are presented in Figure 4.

A majority of the study participants (35.4%) identified precision-agriculture technology as the common types of technology used in agriculture, followed by artificial intelligence technologies (27.9%), aerial drones (17.9%), satellite imagery and sensor technologies (7.9%), and agricultural robots (6.7%). However, 4.2% of participants, the smallest portion of respondents, mentioned other types of technology used in agriculture, such as the Internet of Things (IoT), digital farming technologies such as farm management software and data analytics, and GIS applications that enable farmers to analyze spatial data, such as soil types, elevation, and historical weather patterns.

The study established the different factors influencing agricultural technology adoption, and the results are presented in Figure 5.

The results in Figure 5 show that the major factors influencing agricultural technology adoption are the level knowledge and education on agricultural technologies (44.6%), the level of access to capital and credit (24.1%), the level of internet access and connectivity (17.9%), farm size and scale (6.3%), the nature of government policies (5%), and other factors influencing agricultural technology adoption (2.1%), which is the option that included the smallest number of participants.

The study also examined the different benefits of technology integration in agriculture, and the results are presented in

Table 2. Benefits of technology integration in agriculture.

Categories	Frequency	Percent (%)
Reduced environmental impact	28	11.7
Improved livestock management:	24	10.0
Enhanced crop monitoring by remote sensing	13	5.4
Resource efficiency	42	17.5
Increased productivity and yields	106	44.2
Reduced labor	19	7.9
Others	8	3.3
Total	240	100%

Source: Authors’ own work (2023).

.

In regard to the benefits of technology integration in agriculture, a majority of the farmers (44.2%) noted that technology integration leads to increased productivity and yields, followed by enhancing resource efficiency (17.5%), reduced environmental impacts (11.7%), and other benefits of technology integration in agriculture such as optimization of crop and livestock management by using data-driven insights (3.3%), which was the least popular option.

The study also established the different aspects of agricultural sustainability, and the results are presented in

Table 3. Aspects of agricultural sustainability.

Categories	Frequency	Percent (%)
Maintaining and improving soil quality through practices like crop rotation, cover cropping	32	13.3
Incorporating innovative technologies in agricultural practices	38	15.8
Responsible land-use planning	59	24.6
Sustainable crop- and livestock-production practices	93	38.8
Preserving and enhancing on-farm biodiversity	12	5.0
Others	6	2.5
Total	240	100%

Source: Authors’ own work (2023).

.

The results in Table 3 show that agricultural sustainability mainly comprises sustainable crop- and livestock-production practices (38.8%), followed by responsible land-use planning (24.6%), incorporating innovative technologies in agricultural practices (15.8%), and other aspects of agricultural sustainability (2.5%), which was the least popular option.

A cross-sectional analysis of technology integration in agriculture by farm size was also undertaken, and the results are presented in

Table 4. Cross-sectional analysis of technology integration in agriculture by farm size.

Farm Size	Technology Adoption Factor (Mean)	Technology Adoption Factor (Std. Dev.)	Most Frequently Used Technology	Most Reported Benefit
Large	3.02	1.18	IoT	Reduced Environmental Impact
Medium	3.15	1.24	Drones	Increased Productivity
Small	2.87	1.20	AI	Reduced Environmental Impact

Source: Authors’ own work (2023).

.

The results in Table 4 show that large farms, with an average technology adoption factor of 3.02 and a standard deviation of 1.18, show a moderate-to-high inclination towards adopting technology, with a particular preference for Internet of Things (IoT) technologies. This preference suggests a focus on leveraging connectivity and real-time data across extensive operations to enhance efficiency and decision-making. Medium-sized farms exhibit a slightly higher average technology adoption factor of 3.15, with a standard deviation of 1.24, indicating a somewhat more pronounced readiness or capability for embracing agricultural technologies. The predilection for drone technology among medium-sized farms suggests an emphasis on precise monitoring and data collection to optimize operations. Small farms, with the lowest average technology adoption factor of 2.87 and a standard deviation of 1.20, appear to face greater challenges or exhibit more cautious attitudes toward adopting new technologies. However, their preference for artificial intelligence (AI) indicates a strategic approach to technology adoption, focusing on solutions that offer significant operational optimizations and decision support within smaller scopes of operation.

4.2. Correlation Analysis

Correlation results showing the relationship between the different variables of the study are presented in

Table 5. Pearsons correlation results.

	Types of Technology Used in Agriculture	Factors Influencing Technology Adoption	Benefits of Technology Integration in Agriculture	Agricultural Sustainability
Types of technology used in agriculture	1
Factors influencing technology adoption	0.453 **	1
	0.002
Benefits of technology integration in agriculture	0.651	0.792 **	1
	0.064	0.010
Agricultural sustainability	0.648 **	0.524 **	0.803 **	1
	0.009	0.025	0.000

** Correlation is significant at the 0.05 level (2 tailed).

.

The correlation between types of technology used in agriculture and factors influencing technology adoption is positive and moderate (r = 0.453, p = 0.002), suggesting that as the variety of technologies used in agriculture increases, the factors that influence the adoption of these technologies become more prominent. This could imply that a greater exposure to or use of agricultural technologies might enhance the recognition of factors that influence their adoption, such as cost, availability, and perceived usefulness. Similarly, a strong positive correlation exists between the benefits of technology integration in agriculture and factors influencing technology adoption (r = 0.792, p = 0.010), indicating that the more positively the factors influencing technology adoption are perceived, the greater the benefits of technology integration are recognized. This strong correlation suggests that understanding and addressing the factors that influence technology adoption could significantly enhance the perceived and actual benefits of integrating technology into agricultural practices. The correlations involving agricultural sustainability reveal significant relationships with all other variables. The types of technology used in agriculture (r = 0.648, p = 0.009) and factors influencing technology adoption (r = 0.524, p = 0.025) both show significant positive correlations with agricultural sustainability. This indicates that both the diversity of technologies adopted and the factors influencing their adoption play crucial roles in enhancing agricultural sustainability. Furthermore, the strongest correlation is observed between the benefits of technology integration in agriculture and agricultural sustainability (r = 0.803, p = 0.000), underscoring the critical impact of technological benefits on achieving sustainable agricultural practices.

4.3. Results of Regression Analysis

Regression analysis was also conducted to establish how the integration of technology in agricultural practices affects agricultural sustainability, and the results are presented in

Table 6. Model summary.

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
	0.813 a	0.752	0.748	0.15201

^a Predictors: (constant): types of technology used in agriculture, factors influencing technology adoption, benefits of technology integration in agriculture.

. There was a positive multiple correlation coefficient (R) provided by the value of 0.813, which meant that the three independent variables positively correlated to improved agricultural productivity. Also, the value of R-Square confirms that the three independent variables bring a 75.2% change in agricultural productivity.

The one-way ANOVA was performed to determine if the linear regression model matched the data well, or whether the two independent variables were excellent predictors of the dependent variable. Since F (3, 237) = 305.214, p < 0.05, the model has been deemed a satisfactory match for the data (

Table 7. ANOVA analysis.

Model	Sum of Squares	df	Mean Square	F	Sig.
Regression	31.240	3	16.082	305.214	0.026
Residual	3.108	237	0.036
Total	61.178	240

Dependent variable: agricultural sustainability. Predictors (constant): types of technology used in agriculture, factors influencing technology adoption, benefits of technology integration in agriculture.

).

The unstandardized coefficients of the model were examined to establish how integration of technology in agricultural practices affects agricultural sustainability.

The beta coefficient (β1) was 0.397, and the p-value (0.003) was less than the significance level (0.05) (

Table 8. Regression coefficients.

Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.
	B	Std. Error	Beta
(Constant)	0.318	0.136		2.438	0.026
Types of technology used in agriculture	0.218	0.057	0.397	3.736	0.003
Factors influencing technology adoption	0.031	0.067	0.063	1.493	0.001
Benefits of technology integration in agriculture	0.274	0.026	0.213	3.195	0.021

Dependent variable: agricultural sustainability.

). We accept Hypothesis 1 that types of technologies used in agriculture positively affect agricultural sustainability.

The beta coefficient (β2) was 0.213; the p-value (0.021) was more significant than the significance level (0.05). We accept Hypothesis 2 that factors influencing technology adoption have a positive relationship with agricultural sustainability.

The beta coefficient of benefits of technology integration in agriculture 0.213, the p-value (0.021) was more significant than the significance level (0.05). We accept Hypothesis 3 that benefits of technology integration in agriculture have a positive relationship with agricultural sustainability.

5. Discussion

The results show that types of technology used in agriculture positively affect agricultural sustainability. Types of technologies such as IoT technology allow farmers to predict production levels and evaluate the weather thanks to sensors and tools by remotely pulling data from different mobile devices to monitor plants and animals [40]. The agricultural industry has recently benefited greatly from the growth of IoT technology, especially in terms of communication infrastructure [2]. This has included cloud-based intelligent analysis, interfacing, decision-making, automating agricultural activities, remote data collection, using cars and sensors through mobile devices and the internet, and linking smart items. The agricultural sector has seen a transformation thanks to these competencies in terms of increasing crop yields, managing climatic impacts, and optimizing resource use [81]. With its many uses, technology has the power to completely transform agriculture and promote sustainable methods in the process. Beginning in the 1950s and 1960s, environmental concerns sparked an interest in the sustainability of agricultural and food systems [12]. However, ideas on sustainability go back as least as far as the earliest extant texts from Greece, Rome, and China. Today, the main focus of sustainability concerns is the need to develop agricultural technologies and practices that improve food productivity while also having positive side effects on environmental goods and services, are accessible and effective for farmers, and do not negatively affect the environment, partly because the environment is an important asset for farming [6].

The findings showed that factors influencing technology adoption have a positive relationship with agricultural sustainability. The adoption in its aggregate may be seen as diffusion [72]. Furthermore, diffusion is the process through which an invention spreads across the participants in a social system over time via certain routes, according to Rogers (1983) [25]. Diffusion is the process through which a new concept, method, or technology spreads among a certain community, according to OECD research from 2001. Similar to how the adoption of technology is influenced, so too is the dissemination of technology by factors including relative benefit, complexity, divisibility, and compatibility [15]. Related studies show that farmers may base their choices on past weather trends and future projections by using GIS apps that include climate and meteorological data [82,83]. Access to precise and fast weather information is crucial for making choices on planting and harvesting schedules, pest and disease management, and irrigation scheduling in Greece, a country where climatic unpredictability and severe weather events are becoming more common as a result of climate change. Planning for sustainable land use requires the use of GIS technologies [19,56,65,84]. Decisions about conservation areas, land distribution, and the preservation of natural resources may be made by farmers and policymakers via the analysis of spatial data. Greece, with its different ecosystems and landscapes, needs to protect them in order to maintain ecological balance and support sustainable agriculture. Due to the strong reliance of global agriculture on weather patterns, monitoring the climate and weather is essential to agricultural management [10]. With the use of real-time weather data from remote-sensing satellites, farmers can prepare for severe weather occurrences like storms, floods, and droughts. Planting timetables, irrigation schedules, and harvesting operations may all be adjusted on time thanks to this information [58,85]. In addition, farmers may find patterns and connections by using GIS software to combine historical weather data with crop-production data. By reducing vulnerabilities and facilitating long-term planning and risk assessment, these data enable farmers to adjust to shifting climatic trends [11].

The findings revealed that the benefits of technology integration in agriculture have a positive relationship with agricultural sustainability. Farmers operating under time constraints or in narrow weather windows may also greatly benefit from WSN’s ability to acquire data in real time. However, despite the growing uptake and implementation of WSN systems, a number of significant obstacles still exist, such as the sensor nodes’ low battery capacity [6]. Monitoring crop health and development during the growing season is made possible through remote sensing, which is often done using satellite images and airborne drones [19,86,87]. Important data on variables including moisture content, insect and disease outbreaks, and chlorophyll content are provided by this technology. Farmers may maximize crop output and optimize resource usage by using these data to drive their choices about fertilization, irrigation, and pest management. One of the main benefits of remote sensing is its capacity to provide an aerial perspective of vast agricultural regions, enabling a macro-level evaluation of crop health [54,88]. In nations with vast agricultural areas, where human inspections would be expensive and time-consuming, this is especially advantageous. The findings showed that environmental and soil conservation have improved greatly from the agricultural use of technology. This agrees with Rizzo et al. (2023) [8], who argued that technologies relating to smart irrigation systems and precision farming have minimized soil erosion and water body pollution by cutting down on the excessive use of fertilizers and water resources [89,90].

Furthermore, by lowering the need for chemical pesticides, biotechnology may be able to improve ecosystem health and biodiversity [19,56,91]. Agriculture is now more economically viable as a result of technology integration. Farmers now earn more money and spend less on production because of higher agricultural yields and resource efficiency [11,36,83]. Furthermore, the sale of agricultural goods with technological enhancements has increased Greece’s agricultural export market, which has aided in the economic expansion of the nation. Further, the use of technology in Greek agriculture has impacted society in both beneficial and bad ways. It has, on the one hand, resulted in higher labor productivity and less physical strain on farmers. However, as technology replaces certain agricultural chores, worries have been expressed about the possible loss of rural labor. Small-scale farmers may also find it difficult to afford the expense of using cutting-edge agricultural technology [12,57,92].

6. Conclusions

This study examined the integration of technology in agricultural practices towards agricultural sustainability is Greece. The regression results show that types of technology used in agriculture positively affect agricultural sustainability (p-value < 0.05). Smart farming makes extensive use of sensors and data analytics. The results show that factors influencing technology adoption have a positive relationship with agricultural sustainability (p-value < 0.05). Furthermore, the results show that benefits of technology integration in agriculture have a positive relationship with agricultural sustainability (p-value < 0.05). Through an analysis of the Greek situation, this study provides insights into how technology might be used to improve agriculture and advance sustainability in various agroecological environments.

By studying the latest technological advancements and their application in agriculture, the research will contribute to the development of innovative solutions that address specific challenges in the field. The findings of the research will inform policymakers, agricultural extension agencies, and technology providers about the best practices and strategies for promoting sustainable agriculture through technology. This can lead to evidence-based policy decisions and practical applications in the field. The research can contribute to raising awareness among stakeholders about the benefits of technology integration in agriculture, fostering a culture of sustainability and responsible farming.

6.1. Recommendations

The Greek government should formulate policies that incentivize technology adoption in agriculture. This can include subsidies for purchasing modern machinery, tax incentives, and funding for research and development.

Owing to the role of technology in enhancing agricultural productivity, it is important for governments to focus more on investments in rural infrastructure, such as improved internet connectivity and storage facilities, are crucial to support the integration of technology in agriculture.

Farmers should be provided with training and education on modern agricultural technologies to ensure effective and sustainable adoption.

6.2. Suggestions for Future Research

The current study focused on integration technology on agriculture towards enhancing agriculture productivity. Future research should focus on investigating the potential of emerging technologies like blockchain and artificial intelligence in enhancing traceability and quality assurance in agricultural products.