Acceptance of an IoT System for Strawberry Cultivation: A Case Study of Different Users

Abstract

The Internet of Things (IoT) has been impacting multiple industries worldwide for over a decade. However, less developed countries have yet to make the transition to these technologies. South America is among the regions with the least IoT influence in all sectors, indicating a need for studies to explore IoT acceptance among various users in this region. This study analyzes two different users of a monitoring and irrigation system for strawberry (Fragaria × ananassa) farming. Monitored variables include soil moisture, and ambient temperature and humidity, with irrigation performed via water pumping from a reservoir. The system is based on the M5Core2 development kit for the local station and the IoT platform ThingSpeak for remote access. It features a web user interface consisting of an application developed in HTML using a plugin on ThingSpeak. Thus, the system can be used locally via a touchscreen and remotely through a web browser. Measurements are cross-verified with commercial meters to ensure their reliability, and users are asked to fill out a Technology Acceptance Model (TAM) for IoT to gauge their acceptance level. Additionally, an interview is conducted that explores four critical factors, aimed at understanding their experience and interaction with the system after a period of usage. The findings confirm the validity of the monitored variables and demonstrate a global acceptance rate of slightly over 80%, albeit with varying user acceptance perspectives. Specifically, the technical user exhibits greater acceptance than the crop administrator, evidenced by a mean discrepancy of 1.85 points on the TAM scale.

1. Introduction

The Internet of Things (IoT) is a technology that has been implemented across multiple sectors for various purposes, ranging from basic monitoring needs to complex, real-time programming applications [1,2,3,4]. Its role is especially prominent in industrial processes, where the main requirements include scalability, interoperability, security, privacy, reliability, and low latency [5,6]. Smart cities have been equipped with numerous IoT devices aiming to monitor and make optimal decisions for urban environments [7]. The widespread adoption of these solutions has even shifted focus towards developing energy-efficient and sustainable IoT technologies [8,9]. Moreover, IoT-based automation of crucial components for agricultural and human life needs, such as water monitoring [10], underscores the importance of continuing to deploy solutions that serve humanity.

It is clear that IoT is here to stay, but the question remains whether we are all ready to embrace it. Factors to consider for IoT acceptance include the level of automation allowed, management capability, and compatibility with users’ daily lives [11,12,13]. In wearable devices, perceived privacy risks have a greater impact on IoT acceptance, although this factor is present in all IoT technologies [14,15]. In education, acceptance analyses consider utility, user attitude facilitation, and high availability [16], while others extend beyond the technology itself to include factors like pedagogical content [17]. In healthcare IoT solutions, acceptance is mainly influenced by perceived utility and ease of use [18], with technology costs and user age also being significant considerations [19]. The industrial sector has shown good acceptance, particularly due to benefits like error reduction and service time improvements [20]. Universal design is another concept studied in IoT acceptance, emphasizing the need to cater to the needs and habits of all users, especially in smart cities with a diverse user base, including the disabled, elderly, and children [21].

Specifically, in agriculture, IoT can significantly contribute by increasing production, improving product quality, preventing food loss, and reducing the deterioration of resources such as water, fertilizers, and operational costs [22,23,24,25]. Consequently, some governments are encouraging the use of this technology through financial incentives and policies [26]. However, adopting IoT in agriculture is challenging, requiring technical knowledge from farmers throughout the crop cultivation cycle, from planting to harvest [27]. The most notable application of IoT in agriculture is in greenhouses, where the benefits of this technology are maximized [28,29]. Furthermore, achieving the best results with IoT requires additional elements like data analytics and deep learning [30,31,32]. Regarding acceptance, some countries face difficulties in improving their agricultural processes with IoT, where utility, complexity, subjective norms, reliability, and costs significantly influence adoption [33].

In terms of interdisciplinary integration within Industry 4.0 globally, there has been significant growth over the last two decades, especially in artificial intelligence, big data, and IoT. In South America, Brazil is the only country that has made significant advancements in these fields [34]. Regrettably, the development of smart agriculture based on IoT is limited to developed countries [23]. Regarding IoT in agriculture in South America, adoption levels are led by Brazil and Argentina, followed by Uruguay and Chile. The most reported adoption barriers were technology costs, lack of training, limited number of suppliers, and unawareness of the benefits [35]. In this region, there is resistance to adopting smart devices, with social influence and performance expectancy being the main factors affecting acceptance [36]. Specifically, in Ecuador, the literature shows few studies using IoT to improve processes, with economic factors being the main limitations [37,38].

In this context, analyzing the acceptance of IoT systems remains a current literature necessity, especially in developing countries. The research highlights of this work include a bilateral view of an IoT system regarding acceptance, a mixed-method analysis of user perspectives through a TAM and interviews, and a replicable application that is easy to implement for remote control and monitoring of crops. This paper conducts a perspectives analysis of an IoT-based monitoring and irrigation system for strawberry cultivation, aiming to contrast the acceptance between technical personnel and cultivation administrators. The document is organized into six sections, starting with the current section, which introduces the research topic. Section 2 describes related work found in the literature; Section 3 presents the materials and methods used; Section 4 details the obtained results; Section 5 discusses these findings; and finally, Section 6 outlines the main conclusions of the research.

2. Related Works

In related works, proposals are analyzed involving technologies that assess user acceptance or usability. For instance, a study investigated Malaysian consumers’ acceptance of IoT in the retail sector through online surveys [39]. The findings revealed a positive and significant impact of IoT, particularly due to factors like performance expectancy, facilitating conditions, perceived enjoyment, trust, technological autonomy, and risk. Conversely, another study evaluated the usability of an agricultural irrigation system based on IoT technology, tested by users both experienced and inexperienced in IoT and agriculture [40]. However, this research focused solely on assessing the user interface’s usability in supporting irrigation decision-making.

Another investigation explored the factors affecting Iranian agricultural staff and consultants’ attitudes and behavioral intentions toward adopting precision agriculture technologies through surveys [41]. The results indicated a unanimous intention to use this technology, with experts agreeing that behavioral attitudes significantly influence the intention to use precision agriculture technologies. Key determinants also included individual innovation, trust, ease of use, and perceived usefulness. In a similar vein, the adoption of precision agriculture technologies in North Dakota was examined [42]. Acceptance surveys targeted operators, showing that larger operations are more inclined to adopt these technologies. Additionally, it was found that corn production positively correlates with adopting multiple technologies, while wheat production negatively correlates with the adoption of variable rate technologies.

A study examined the social influence and system characteristics on the acceptance of an agricultural and food traceability system in Taiwan [43], also looking into the utility and ease of use’s effects on attitudes and the intention to reuse. Results suggested that image and visibility positively impact the system’s perceived utility, system quality affects ease of use, and trust positively influences the intention to reuse. Another research assessed the extended Technology Acceptance Model’s applicability in predicting small farmers’ adoption of climate-smart agriculture technologies in Malawi and Zambia [44]. The findings highlighted differences in ease of use, usefulness, and climate risk perceptions between genders, suggesting gender-specific policy strategies for behavioral change.

The social drivers behind the acceptance and adoption of automated technology in Australian cotton farms were also studied [45]. Participants included producers using automated technology, those not currently using it but considering future adoption, and those not using it with no intention to adopt. The findings revealed that social and personal factors significantly motivate producers to adopt automated technology, with differences in perceived utility among the producer groups. A similar effort developed an IoT-based system for agricultural monitoring to enhance farmers’ acceptance of IoT technology, with user experience tests among ten participants affirming the system’s feasibility [46]. The system was positively received as attractive, user-friendly, exciting, and innovative compared to conventional agricultural monitoring systems.

Lastly, a recent literature review on technology acceptance in smart agriculture reviewed 16 articles, noting that perceived effectiveness was the most examined factor, while opportunity costs, burdens, and ethics were overlooked [47]. The review calls for formal, standardized, and specific methods to support acceptance evaluation and proposes creating an instrument for measuring technology acceptance in agriculture to enhance social impact. IoT acceptance studies in agriculture are vital for devising strategies that promote the deployment of these systems. Yet, related research in South America remains limited. Our study is chiefly driven by the acceptance analysis of an IoT-based monitoring and irrigation system for strawberry farming from various user perspectives, utilizing low-cost components.

3. Materials and Methods

This research builds on the system depicted in Figure 1, where the goal is to connect users to strawberry (Fragaria × ananassa) cultivation by monitoring and controlling irrigation. The cultivation of this strawberry requires temperatures of 15–25 °C, relative humidity of 60–80%, loam to sandy-loam soils with a pH of 5.5–6.5, and good drainage. Drip irrigation with 25–40 mm of water per week and fertilization every 2–4 weeks with nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur are ideal. Harvesting is conducted manually in spring–summer, with storage at 0–4 °C. This requires an electronic circuit with a processor, sensors, and an actuator, and this local component must include internet communication for data exchange. Once the cultivation is connected to the internet, an IoT platform that allows for continuous data storage and visualization is needed. This platform should include an API—Application Programming Interface—to develop an application with a user interface for remote system management.

3.1. Diagnosis

3.1.1. Initial Conditions

At the beginning of this study, the irrigation procedures in the blackberry plantation were carried out entirely manually. At that time, there was no data reading involved; the irrigation of the cultivation was manually performed, connecting the pump twice a week, once a day, for a period of 4 to 6 h per session. The exact amount of water used for irrigation is unknown, as it was sourced from water bodies without any measurement or recording systems. This method was adopted due to the plantation’s location in a remote agricultural area, far from urban centers, where only a farmers’ association was responsible for supplying water from natural sources. The area under study has a temperature range from 8 to 22 °C and a relative humidity varying between 56% and 87%. While details about the soil type are limited, the average pH in the region is 5.64. Strawberry cultivation in this area requires temperatures between 15 and 25 °C, relative humidity of 60–80%, and soil types ranging from loamy to loamy-sand with a pH of 5.5–6.5. These conditions have enabled local farmers to successfully harvest strawberries for several generations.

The individuals in charge of the plantation live 35 min away, traveling to the site each time they needed to carry out the irrigation. Given this context, the aim of this study is to transform this traditional practice into a more efficient and technologically advanced process. It is intended for users to be able to manage irrigation remotely, relying on temperature and humidity data, which would allow them to make more informed decisions and optimize water use in strawberry cultivation.

3.1.2. Participants

The management of the cultivation is overseen by two key figures: the administrator and the technical user. The administrator, who is the owner of the crop, is not only responsible for carrying out the regular irrigation but also takes on the duty of implementing phytosanitary control practices, following the guidelines set by the technical user. This task involves constant supervision and the application of preventive and corrective treatments to maintain the health and productivity of the plants.

The technical user, in turn, plays a consultative and supervisory role within the entire farmers’ association. With their specialized knowledge, they assess the conditions of the plantations and establish agricultural management guidelines, including irrigation, fertilization, and pest and disease control programs. Their technical recommendations are designed to optimize agricultural practices and ensure the quality and sustainability of the product.

3.2. Components

The selection of system components can be split into physical and non-physical. The components used are shown in Figure 2. The M5Core2 development kit is used as the local processor; the kit is based on the EP32 and includes Wi-Fi and a USB TYPE-C interface for charging its built-in 390 mAh battery and for program downloads. It also features a serial port, a 2.0-inch capacitive touchscreen, and power and reset buttons, among other integrated add-ons; this is an M5stack brand product [48], with an average cost of USD 45 in the U.S.A. This device does not require additional circuitry for application development due to its built-in touchscreen interface, facilitating setup with its multiple features compatible with this proposal, including WiFi connectivity. For monitoring the crop, basic temperature and humidity variables are considered; the DHT22 sensor measures ambient temperature and humidity [49]. It measures humidity in the range of 0–100% RH with an accuracy of ±2% and temperature from −40 to 80 °C with an accuracy of ±0.5 °C. It consumes 1.5 mA during measurement and 0.3 mA in standby, with a sampling period of 2 s. The FC-28 sensor measures soil moisture, providing an analog output [50]. The sensor consists of two probes that are inserted into the soil. Water pump control for irrigation uses a water pump activated by a contactor, which is, in turn, triggered by a low-voltage relay. For non-physical components, ThingSpeak serves as the IoT platform, simplifying data storage and display. ThingSpeak also offers plugins for developing custom applications, here primarily using HTML for lay-outing. The web application is developed using a ThingSpeak plugin that includes the server, focusing solely on the client side developed in HTML.

To justify the variables acquired by the sensors that are part of the proposed model,

Table 1. Justification of the monitored variables.

Sensor	Voltage Levels	Output	Variable	Purpose
DHT 22	3.3 V to 5.5 V	Digital (single-bus)	Air temperature	Air temperature is crucial in irrigation decision-making for strawberries, as it affects evapotranspiration and the water needs of the plants. Higher temperatures increase water demand, while lower temperatures reduce it.
			Air humidity	Air humidity influences irrigation decisions for strawberries by affecting transpiration and disease risk. In high-humidity environments, plants transpire less, which may reduce the frequency of irrigation, but it also increases the risk of fungal diseases. Conversely, in low humidity conditions, plants lose water more rapidly, requiring more frequent irrigation to maintain water balance.
FC-28	3.3 V to 5 V	Analog	Soil humidity	Soil moisture is a crucial factor in irrigation decision-making for strawberries, as it indicates the availability of water for the plants. Low soil moisture signals the need for immediate irrigation to prevent water stress and ensure healthy growth. Conversely, if the soil retains adequate moisture, irrigation can be delayed, optimizing water resource use.

is provided. This table details each sensor, including their voltage levels and their influence on the process. The information helps to understand the role of each sensor in the system, ensuring the accuracy and relevance of the data collected. Air temperature, humidity, and soil moisture are key factors in irrigation decisions for strawberries. Higher temperatures and lower humidity increase water demand, while high humidity reduces transpiration but raises disease risks. Soil moisture indicates when irrigation is needed; low levels require immediate action to prevent water stress, while adequate moisture allows for delayed irrigation, optimizing water use.

3.3. Connections

The electronic circuit setup follows the connection diagram in Figure 3, illustrating how to connect the elements to the M5Core2 kit, manufactured by M5Stack which is located in Shenzhen city in China. The DHT22 sensor uses port G19 for serial communication, and the FC-28 sensor connects to port G35 as an analog input. The relay control uses port G27, set as a digital output, to supply alternating current to the LS ELECTRIC brand MC-12B contactor [51], managing the irrigation pump.

3.4. Programming

The main program on the M5Core2 kit is shown in the flowchart of Figure 4. After configuring the kit’s input and output parameters and establishing a WiFi connection to the internet, the cycle starts with the local reading and display of sensor-obtained data, simultaneously sending these readings to ThingSpeak via HTTP. Then, the irrigation status is updated based on ThingSpeak’s latest actions. The system also enables local pump control using the M5’s button A. Button C is set to turn off the system.

3.5. Web Application

The web application designed for this project’s users is shown in Figure 5. It features graphs of soil moisture, ambient temperature and humidity, and the pump’s status. Moreover, the user interface includes buttons to turn the pump on and off. This web application can be accessed through a browser on a computer or mobile device, making access easy. The application’s security relies on the licensed ThingSpeak account credentials, ensuring availability.

The primary purpose of the system is to facilitate the monitoring and remote control of cultivation irrigation. To achieve this goal, software has been installed on local devices that manages bidirectional communication, allowing for the remote transmission and reception of data. This enables users, especially technical users, to exercise direct and informed control over irrigation operations, leveraging their knowledge in agronomy to make decisions based on accurate and up-to-date data.

This capability for manual intervention is crucial, as it allows for the adjustment of irrigation according to the specific needs of the cultivation, taking into account factors such as weather conditions, plant growth stages, and the technical user’s direct observations in the field. The decision to not fully automate the irrigation system was made following the recommendation of the technical user overseeing the cultivation. This approach reflects a preference for maintaining a level of human control in the process, ensuring that each irrigation action is carried out with a comprehensive understanding of the agronomic context and the specific needs of the cultivation, which is essential for optimizing water use and promoting sustainable agriculture.

3.6. Instruments

For the acceptance, the Technology Acceptance Model (TAM) for IoT as described in [52] is selected. This tool comprises 7 factors and 25 indicators assessing aspects such as usefulness, trust, ease of use, social influence, enjoyment, intention to use, and sense of control. The user responds to each indicator using a 7-point Likert scale, where 1 indicates strong disagreement and 7 indicates strong agreement. To analyze the results, these values are then scaled from 0 to 100%. The indicators applied in this study are listed in

Table 2. IoT acceptance model indicators.

ID	Indicator	ID	Indicator
Perceived Usefulness (PU)		Trust (TR)
PU1	Using IoT system would enable me to collect data more quickly.	TR1	IoT system is trustworthy.
PU2	Using IoT system would make it easier for me to make more efficient decisions.	TR2	IoT system provides reliable information.
PU3	Using IoT system would significantly reduce my time collecting data.	TR3	IoT system keeps its promises and commitments.
PU4	In general, I would find using IoT system to be advantageous.	TR4	IoT system keeps my best interests in mind.
Perceived ease of use (PEOU)		Social influence (SI)
PEOU1	Learning to use IoT system is easy for me.	SI1	People who are important to me would recommend using IoT system.
PEOU2	I find my interaction with IoT system clear and understandable.	SI2	People who are important to me would find the use of IoT system beneficial.
PEOU3	I think using IoT system is easy.	SI3	People who are important to me would find using IoT system a good idea.
Perceived enjoyment (PE)		Behavioral intention to use (BI)
PE1	I have fun using IoT system.	BI1	If I give a chance, I intend to use IoT system.
PE2	Using IoT system is pleasurable.	BI2	I am willing to use IoT system in the near future.
PE3	Using IoT system gives enjoyment to me.	BI3	I will frequently use IoT system.
Perceived behavioural control (PBC)		BI4	I will recommend IoT system to others.
PBC1	The use of IoT system is entirely within my control.	BI5	I will continue using IoT system in the future.
PBC1	I have the resource, knowledge and ability to use IoT system.
PBC1	I am able to skillfully use IoT system.

.

In addition to the acceptance model, a qualitative instrument was employed through an interview aimed at assessing four components: Benefits, Opportunities, Disadvantages, and Risks. These components are grounded in a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis from the users’ perspective. The open-ended questions used are detailed in

Table 3. Interview questions.

ID	Component	Question
E1	Benefits	What are the main benefits you have experienced with the monitoring and irrigation system in your strawberry plantation?
E2	Opportunities	What future opportunities do you foresee this technology bringing to the strawberry plantation?
E3	Disadvantages	Have you identified any drawbacks or negative aspects in using the monitoring and irrigation system in your strawberry plantation?
E4	Risks	What do you consider to be the main vulnerabilities or challenges associated with the monitoring and irrigation system of the strawberry plantation?

.

4. Results

The proposed system was installed as illustrated in the photographs of Figure 6. The left photo shows the strawberry crops and the right photo displays the control station, including the M5Stack, relay, and the contactor that triggers the irrigation pump. Sensors were strategically positioned at the plantation’s edge to ensure water reached the entire crop. This site benefits from WiFi internet connectivity, negating the need for additional equipment to connect the device to the network.

The users underwent comprehensive training in managing the applications, specifically in using the application developed for the M5Stack locally, as well as the web platform designed for remote access. During these training sessions, they were taught in detail how to navigate and operate the user interfaces of both applications, with a special focus on understanding the functionality and purpose of each element within them. They were instructed on how each button, indicator, and interactive chart in the interfaces plays a crucial role in the monitoring and management of irrigation. Furthermore, emphasis was placed on the importance of accurately interpreting the real-time temperature and humidity data, as these are essential for making informed decisions about irrigation. The training included simulated practices where users could directly engage with the applications. This hands-on approach helped users become acquainted with the technology, thereby increasing their confidence and proficiency in using the applications in both the M5Stack and web environments.

4.1. Validation

The measurements from low-cost sensors were validated against commercial sensors as references. Soil moisture was validated using an analog hygrometer by Xingbailong, located in Shenzhen city in China, with the comparison of soil moisture readings between the proposed system and the reference shown in Figure 7a. This yielded a mean relative error in soil moisture of 2.64% compared to the reference readings. Air humidity and temperature were validated using a Digital HTC-2 thermo-hygrometer by Mengshen, located in Wuhan city in China. The comparison of air humidity and temperature readings between the proposed system and the reference is shown in Figure 7b,c, respectively. The mean relative error for air humidity was 2.43%, and for air temperature, it was 2.83% compared to the reference readings.

4.2. Acceptance

Once installed and operational, the IoT system was used for a month by two individuals responsible for strawberry cultivation: a technician, responsible for overseeing the strawberries’ proper growth, with agricultural training, and the administrator, the owner of the cultivation. Both users completed an acceptance questionnaire after this period, noting that the IoT system was fully funded by this project. Acceptance results by TAM indicator are shown in Figure 8, indicating a high overall acceptance with a mean score of 5.64 out of 7, and the lowest score in the PU factor (median PU = 4.5).

The individual acceptance scores of the users are depicted in the radial chart of Figure 9. The technician showed the highest acceptance with a mean of 6.56 out of 7, while the administrator showed the lowest acceptance with a mean of 4.72 out of 7. The administrator rated the PU factor lowest, and both users scored low in the TR factor.

The findings suggest a difference in acceptance between the two users.

Table 4. Results of IoT system acceptance.

Factor	Mean Score (Technical)	Mean Score (Administrator)	Global
PU	6	3.25	4.63
PEOU	7	5	6
TR	5.75	5.5	6.63
SI	6.33	4	5.17
PE	7	5	6
PBC	7	5.33	6.17
BI	7	5	6
Total Mean	6.57	4.72	5.64
Standard deviation	0.65	0.84	1.19

presents the detailed results of the acceptance test, broken down by factor and user. The greatest variability between users is observed in the PU factor, where the difference reaches 2.75 points. On the other hand, factors such as PEOU, PE, and BI show more uniform differences, with a variation of 2 points for each. In contrast, the TR factor displays the most homogeneous results among users, with a minimal difference of just 0.25 points, indicating that this factor influences both users similarly. Regarding the overall score, the acceptance test achieves a mean of 5.64 out of a maximum of 7 points, which corresponds to an acceptance percentage of 80.57%. In our scenario, a mean difference of 1.85 points on a 7-point scale has been identified between the users, representing 26.42% of the total range of the scale.

4.3. Interview

4.3.1. Technical User

The interview with the technical user of the plantation yielded the following results. For component E1, the user stated “the available soil moisture information allows me to make decisions about irrigation and thus intervene in a timely manner”. He also mentioned that “the ability to activate irrigation remotely prevents the need for unnecessary travel to perform this activity”. In component E2, the following assertion was made: “we expect a more abundant fruit production because we are intervening timely with the lack of water in the strawberry plants”. For component E3, the user commented “it is difficult to view the data when using the app on the phone, but on the computer, it is easier”, and also noted that “at the beginning, it was hard to understand whether the pump was on or off”. Finally, in component E4, the user highlighted that “the data obtained are not sufficient to improve the quality of the strawberries; it would be interesting to have the soil pH information”.

4.3.2. Administrator

The interview with the plantation administrator yielded the following results. In component E1, the statement obtained was “it saves me time and money to have the data and be able to irrigate from home, as the plantation is far away for me and I used to go twice a week, and I also don’t have to send the technical very often”. In component E2, the response was “as we can irrigate whenever we want, we expect the strawberry production to be thicker and to earn more money, so maybe more members of the farmers’ association will want this technology”. In component E3, the user stated “I am not sure about the data shown by the application, I have seen moisture on sunny days”, and also mentioned “when the pump is turned on from home, I always have doubts about whether it is working”. Finally, in component E4, the two main ideas highlighted were “we don’t have knowledge of the equipment installed in the crop and we don’t know how to maintain it on our own” and “I was told that the service has an annual cost, I’m still not sure if it’s worth paying for the license next year”.

5. Discussion

This study develops an IoT-based monitoring and irrigation system for strawberry cultivation utilizing low-cost technologies, such as M5Stack, electronic components, and the ThingSpeak platform. The economic factor is significant when adopting new technology, though the literature indicates it is often a neglected factor in technology acceptance studies [47]. Moreover, the system allows for local and remote monitoring and irrigation management, incorporating user interfaces with a simplicity that promotes system use.

IoT applications in agriculture typically enjoy high acceptance, facilitating the monitoring of essential variables for agricultural production. This finding aligns with other agricultural systems that incorporate IoT [46,53]. In our case, the system achieved an 80.57% overall acceptance rate, indicating satisfactory system performance, although not all TAM factors scored highly. The PEOU and PE factors received positive acceptance, crucial for other industries as well [39,41]. Conversely, the PU factor scored the lowest, a heavily examined factor when assessing technology acceptance in smart agriculture [47]. The TR factor also scored low, a significant aspect in agriculture incorporating IoT and related technologies, especially in precision agriculture [41].

The social influence (SI) factor also showed lower acceptance in our study. The literature supports this combination, where analysis of technology acceptance identified a positive relationship between these factors [43]. This suggests the system’s projection is not optimal, directly affecting the perceived utility of the IoT system. Meanwhile, PEOU received one of the highest scores, attributed to the system’s well-integrated and comprehensive operation [43,45]. PEOU is positively related to the quality of the technological system, especially valued by the technician in charge of strawberry cultivation, who rated this factor the highest.

This study revealed a difference in acceptance between the technician and the administrator of the cultivation. The literature review found no evidence that a user’s organizational position affects related technology acceptance. The qualitative results reveal differences in the perception and acceptance of the system between the technical user and the administrator. The technical user shows greater acceptance and appreciation of the system, valuing the provided information as crucial for optimizing the irrigation process. However, they express an interest in accessing a broader spectrum of data, indicating a desire to delve deeper into the analysis and management of the cultivation. On the other hand, the administrator, while receptive to the information offered by the system, expresses certain reservations. They exhibit skepticism regarding the practical implementation of data-driven irrigation, questioning the effectiveness and accuracy of the irrigation tasks’ execution. Furthermore, there is a notable concern from this user about the long-term sustainability of using the system, driven by two main factors: the lack of sufficient training to operate the system effectively and the costs associated with maintaining it. Although gender-based differences in PU and PEOU have been noted [44], as well as differences in technology adoption in agriculture based on the type of cultivation [42], comparisons between producers with and without these technologies have shown that non-users view automation features as unnecessary [45]. This highlights an opportunity for future research to consider various user types to uncover factors that influence technology acceptance based on role.

Regarding the environmental impact, remote irrigation control promotes more conscious and efficient water management, facilitating the optimization of its use. However, we did not delve into a detailed analysis of this aspect. The main reason for this limitation is the absence of historical records or control over water usage times before implementing our proposal. This lack of previous data makes direct comparison and quantitative evaluation of the impact that our system has had on improving water use efficiency challenging.

The limitations of this research include the instrument used, system size, and participant number. The TAM from [52], designed for IoT systems, was chosen due to the absence of a specific instrument for these technologies in agriculture [47]. In the context of our study, the processor selected for the monitoring and irrigation system is distinguished by its scalability. This processor has a wide range of available ports, facilitating the simultaneous connection of multiple devices and sensors. Furthermore, it offers the flexibility to integrate various types of communication technologies, which could simplify and reduce the number of physical connections required. Regarding the system’s applicability to different types of cultivations, it has been determined that there is no significant limitation to its implementation in the region. Most local crops rely primarily on irrigation as a key agricultural practice, making our system particularly relevant and beneficial. Moreover, this approach of controlled and monitored irrigation helps to leverage and improve soil quality, a vital aspect of the agriculture of the area. The system’s scale was limited to a few devices due to the cultivation’s size, making a single actuator sufficient for irrigation, though measuring other agricultural variables and a more advanced application could be beneficial. As for the number of participants, they represented all available users in strawberry cultivation, although future studies might explore acceptance across multiple plantations with similar systems.

Based on this discussion,

Table 5. Advantages and disadvantages of the proposal.

Aspect	Advantages	Disadvantages
Cost	Use of low-cost technologies, other studies underestimate the economic factor [47,54].	Limited number of sensors and actuators.
Monitoring and Management	Allows local and remote monitoring, simple user interface [43,45].	Concerns about practical implementation and long-term sustainability.
Acceptance	80.57% overall acceptance, with high acceptance in PEOU and PE, characteristic of similar proposals [46].	PU and TR have low acceptance, an important factor according to [41]; SI is also low, supported by [43].
Perception	Technical show greater acceptance and appreciation of the system.	Administrators have reservations about the effectiveness and accuracy of automated irrigation, characteristic of non-users [45].
Scalability and Flexibility	Multiple available ports and integrable communication technologies.	Limited to few devices and a single actuator for irrigation, a problem for achieving precision agriculture [41].
Applicability in Different Crops	No significant limitations for implementation.	Did not measure other advanced agricultural variables, which influences adoption in other types of crops [42].

presents the advantages and disadvantages of our proposal compared to the works in the literature.

Finally, this proposed IoT-based system for strawberry cultivation offers numerous advantages over the traditional method. Instead of relying on the farmer’s experience and manual techniques, the IoT system uses DHT22 and FC-28 sensors to monitor temperature, ambient humidity, and soil moisture in real time, providing accurate data accessible remotely through a web application on ThingSpeak. This approach allows for automated and precise drip irrigation, adjusted according to soil conditions, optimizing water use and ensuring a consistent supply. Additionally, the ability to access and control the system remotely is especially beneficial for plantations located far from urban areas. In contrast, the traditional method can result in significant production variability due to the lack of precise data and reliance on manual practices, often leading to inefficient use of water and resources. Therefore, the IoT system not only enhances the precision and efficiency of crop management but also facilitates data-driven decision-making, establishing better conditions for the acceptance of these technologies in less developed areas.

6. Conclusions

This study analyzes two perspectives on an IoT system for strawberry cultivation. The proposed system facilitates cultivation monitoring and irrigation through a web application, using the M5Core2 development kit and the ThingSpeak IoT platform. After validating measurements, two users assessed the system’s acceptance using an IoT TAM. The findings indicate a difference in acceptance between the technical and administrative user, with the latter showing lower acceptance. These outcomes suggest a diminished utility perception and negative societal impact for the administrator. Additionally, this difference in acceptance might relate to the limited application of IoT in similar processes in developing countries. Both users exhibited low scores in the TR factor, indicating a system distrust despite no reported malfunctions during the trial period, possibly due to unfamiliarity with this technology type or the use of low-cost technology. Ultimately, the overall TAM score reached 80.57%, which suggests a positive acceptance. However, this result should be approached with caution.

6.1. Limitations

The main limitations of the study include the small scale of the system and the limited number of users involved in the cultivation. The current implementation was tested on a relatively small number of devices and participants, which may not fully capture the variability and challenges encountered in larger or more diverse agricultural settings. This limitation restricts the generalizability of the findings and may not reflect the broader applicability of the system in different contexts or with different crops.

6.2. Future Works

Future research could address these limitations by expanding the scope of the study to include a larger and more diverse set of monitoring devices that capture additional agricultural variables. By incorporating more sophisticated sensors and actuators, the system could provide more comprehensive data on soil moisture, temperature, humidity, and other critical factors that influence crop health and yield. Additionally, future iterations of the application could be enhanced with advanced features such as real-time alerts and recommendations, which would assist farmers in making timely and informed decisions. These features could be integrated into a standalone mobile user interface, making the system more accessible and user-friendly for farmers who rely on mobile devices for their daily operations.

Moreover, involving a broader range of users, including farmers from different regions and with various types of crops, would provide a more comprehensive understanding of the system’s usability and effectiveness. This would also help to identify any region-specific or crop specific challenges that need to be addressed. Overall, these enhancements would not only improve the functionality and user experience of the system but also contribute to a more robust and scalable solution for precision agriculture.