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 Agricultural Science; Vol. 7, No. 1; 2025 
ISSN 2690-5396   E-ISSN 2690-4799 

https://doi.org/10.30560/as.v7n1p158 

158                             Published by IDEAS SPREAD 
 

Exploration of the Safety and Threats Associated with Smart 
Agriculture-related Technologies 

Dong Hu1, Jiayue He1 & Qian Wang2 

1 Zhaotong University, Zhaotong, Yunnan Province, China 
2 Suijiang County Vocational Senior High School, Zhaotong, Yunnan Province, China 

Correspondence: Qian Wang, Suijiang County Vocational Senior High School, Zhaotong, Yunnan Province, 
China. 

 

Received: May 18, 2025   Accepted: June 27, 2025   Online Published: June 29, 2025 

 

Abstract 

Smart agriculture integrates cutting-edge information technologies such as big data, artificial intelligence, and 
blockchain, deeply integrating them into production decision-making and circulation links related to agriculture, 
forming a new agricultural business model and solution with significant advantages of intensification, precision, 
automation, and informatization. Therefore, properly handling the relationship between Agricultural Big Data 
technology and data security becomes particularly critical.The concept of Agricultural Big Data, comprehensively 
analyzing various current viewpoints. Subsequently, through specific cases, it elaborates on the driving role of 
Agricultural Big Data in various links of the agricultural supply chain. To further delves into the unique 
characteristics of Agricultural Big Data, including its ubiquity, sociality, and interdisciplinarity. Starting from the 
common problems of big data, it introduces specific issues in the agricultural field and proposes targeted security 
solutions based on actual smart agriculture application scenarios. This paper aims to provide a new perspective for 
future research on solving data security issues in the field of smart agriculture, to promote the more rapid and 
secure development of smart agriculture. 

Keywords: agriculture, smart agriculture, data security, technology 

1. Introduction 

The progress of production methods in the agricultural field is closely linked to the overall level of social 
development. It can be said that the level of technological application in agriculture reflects the development of 
social productive forces. Given the special status of agriculture in providing basic subsistence materials for humans, 
its development process must not only adapt to changes in social productive forces but also to changes in human 
needs. Especially with the rapid development of information technology, the digitization, intellectualization, and 
networking of industrial fields have become an inevitable trend[1]. The term "smart agriculture" refers to the new 
agricultural business model and solution formed by the deep integration of frontier information technologies such 
as big data, artificial intelligence, and blockchain with production decision-making and circulation links related to 
agriculture. Through network technology, it achieves comprehensive interconnection of personnel, agricultural 
machinery, and crops, thereby realizing real-time monitoring of the agricultural production process, obtaining 
dynamic data, and promoting the accelerated flow of production factors such as information, technology, and funds 
through these data. Smart agriculture has the advantages of intensification, precision, automation, and 
informatization. In this process, it can not only realize intelligent management of agricultural product production 
and processing but also promote the gradual formation of an efficient and precise production and marketing 
ecosystem in circulation channels, thus reshaping the agricultural industry chain[2]. 

Agricultural Big Data is one of the important supports for realizing smart agriculture, providing smart agriculture 
with a digital foundation, a basis for scientific decision-making, and a source of intelligent capabilities, making 
agricultural production more intelligent, efficient, and sustainable. In the fields related to agricultural production, 
the benefits of Agricultural Big Data have gradually emerged: in production and circulation links, it helps 
accelerate the cultivation of high-quality crop varieties, enhance the transparency of agricultural information, and 
achieve food traceability; through continuous and extensive collection and correlation analysis of Agricultural Big 
Data, it becomes the "think tank" of modern agricultural production and management, providing direction guidance 
for agricultural practitioners in production categories, optimized seed selection, and production planning; in market 
consumption links, it becomes the vane of modern agricultural market consumption, guiding market trends and 



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agricultural product demand; at the macro level of agricultural policy formulation, it provides diverse and accurate 
data support and analysis conclusions to assist relevant government departments at all levels in making precise 
decisions.With the development and promotion of Agricultural Big Data technology, data security issues have also 
become increasingly prominent, bringing risks and hidden dangers to the construction and development of smart 
agriculture[3]. The destruction, misuse, and leakage of Agricultural Big Data may synchronously affect agricultural 
production, supply, and market in the fields where smart agriculture is applied. Therefore, in the field of smart 
agriculture, it is necessary to combine the common risks of big data technology with the industry characteristics 
of Agricultural Big Data to conduct in-depth analysis and research on the data security risks of Agricultural Big 
Data and propose feasible solutions. 

2. Analysis of Data Security Risks in Smart Agriculture 

2.1 Security Risks in Agricultural Big Data Collection 

Agricultural Big Data covers various aspects of the entire crop production cycle, including breed selection, farming 
techniques, and management measures. These links are comprehensively influenced by various factors such as 
meteorology, resources, the environment, the market, transportation, and safety. Taking rice breed selection as an 
example, the considerations include meteorological conditions such as temperature, humidity, and precipitation; 
resource conditions such as organic matter content, nutrient content, and pH value in the soil; as well as economic 
factors such as market demand and transportation efficiency. Data collection refers to the process of inputting 
external data into the system through a certain medium. Active collection involves human intervention and is a 
conscious and targeted data collection behavior; passive collection usually occurs through automated tools and 
systems without direct human participation. Survey questionnaires and experimental records are common methods 
in active collection. However, due to the professional competence or insufficient attention of staff, the collected 
data may be useless or incomplete[4]. To solve this problem, improvements need to be made in questionnaire design, 
survey planning, and professional competence training for interviewers. Common passive collection methods 
include web crawlers, sensor information collection, and system data collection. Due to the extensive scope, 
complexity, and high degree of automation involved in passive collection, there are risks such as intellectual 
property rights infringement, infringement of personal privacy data, violation of data owner rights, unreliable data 
sources, and uncontrollable data quality. It is necessary to clarify the collection scope, quantity, and depth 
according to the purpose and use of the data collection and follow the principle of data minimization to ensure data 
compliance; on the other hand, data classification and identification should be implemented in the data collection 
stage to ensure data security and traceability of data provenance quality[5].  

In specific scenarios of smart agriculture, agricultural sensors are installed in various locations, making 
Agricultural Big Data have significant comprehensive characteristics during data collection and bringing 
corresponding risks. To solve these problems, physically, protective devices such as protective covers can be 
considered for sensors, and technically, a device monitoring system can be established to ensure the normal 
operation of the devices, and password verification and other identity verification mechanisms can be added to the 
collectors to ensure the security and reliability of configuration modifications. Satellite remote sensing has unique 
advantages in obtaining information such as crop coverage, vegetation growth status, and land use in the 
agricultural field, but it also faces risks of incomplete and inaccurate data collection at the data security level. For 
example, when clouds are thick, ground radiation cannot penetrate the cloud layer, resulting in incomplete imaging 
results of remote sensing satellites; the mixed effect of ground objects makes it difficult for remote sensing 
satellites to accurately distinguish between ground object types, leading to biases in crop coverage statistics; in 
addition, the wireless transmission characteristic of satellite remote sensing data also makes it vulnerable to attacks 
where intruders can invade and tamper with the data. These issues are potential risks inherent in satellite remote 
sensing technology. 

2.2 Security Risks in Data Storage and Transmission 

Data transmission involves transmitting data from its source to its destination through one or multiple data links 
according to established procedures. This process includes both wired and wireless transmission technologies, 
each with its advantages. Wired technologies, such as Ethernet, USB, and HDMI, provide stable transmission 
channels; while wireless technologies, such as Wi-Fi, Bluetooth, and Z-wave, break through the limitations of 
physical connections, bringing convenience to mobile devices and remote communications. However, data 
transmission also faces various risks, including information leakage caused by attackers intercepting and 
interpreting the data during transmission, data integrity destruction through man-in-the-middle attacks that hijack 
and tamper with data, and distributed denial-of-service attacks that block communication channels, making 
information unreachable. To address these risks, it is necessary to develop appropriate data transmission security 



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strategies and procedures based on specific scenarios and adopt corresponding security measures to ensure the 
identity authentication of the transmitting and receiving entities, verify data integrity, have data recovery control 
measures, and audit and monitor changes to security strategies.Agricultural Big Data is ubiquitous and social, 
encompassing a wide range of data that includes sensitive information critical to the security of national 
agricultural development strategies, such as soil, geology, water quality, topography, hydrological conditions, and 
climate. If this information is transmitted in plaintext or using weak encryption protocols and algorithms, it can 
easily be stolen by domestic and foreign criminals or hostile organizations, posing a threat to national agricultural 
security and national security. To mitigate such risks, the use of trusted encryption technologies should be 
mandated to establish secure encryption channels, utilizing trusted algorithms for data encryption and employing 
technologies such as digital signatures and digital certificates to verify the identities of communicating parties[6-8]. 

Data storage refers to the process of statically preserving data in various data media and formats, providing 
capabilities for management, retrieval, and destruction. Stored objects include collected data, analysis and 
processing result data, and temporary procedural data. In the context of big data, significant differences between 
big data storage technologies and traditional data storage technologies should be noted. Currently, commonly used 
big data storage technologies, such as distributed file systems, column-based databases, and SQL databases, can 
store unstructured, semi-structured, and structured data, catering to the volume, velocity, and variety characteristics 
of big data, and offering advantages over the centralized storage model of traditional databases. However, as big 
data storage technologies develop, security risks also gradually emerge, including unauthorized data access leading 
to leaks, ransomware attacks following data encryption or corruption, and issues such as unclear and insufficiently 
granular user security permission control rules. The intersectionality and comprehensiveness of Agricultural Big 
Data allow attackers to derive critical information through correlation analysis of centrally stored data, 
compromising data confidentiality. For example, attackers may infer the location of solar power generation 
facilities by querying non-sensitive data and public information in combination. During the data storage phase, 
security controls should be developed to use correlation models and analysis scripts for security rule detection of 
centrally stored data, requiring strong business modeling capabilities. Furthermore, homomorphic encryption 
technology can effectively address the issues arising from the intersectionality and comprehensiveness of 
Agricultural Big Data, allowing for operations such as search, filtering, and computation on data in an encrypted 
state without the need for decryption, thereby protecting data privacy[9-10]. 

2.3 Security Risks in Data Processing and Computing of Agricultural Big Data 

In the realm of data processing, it is imperative to closely monitor the entire lifecycle of data processing, 
encompassing both the handling of data content and the outcomes of data anonymization processes. At the 
inception of the processing workflow, given the multi-source nature of big data, data processors must first ensure 
the security of data sources. In accordance with relevant legal provisions, data processors should require data 
providers to specify the origins of the data. Failure to verify data sources prior to processing vast amounts of data 
may introduce malicious data, which can lead to incorrect associations during data aggregation and correlation 
processing, subsequently misleading subsequent data analysis and computation, and potentially providing 
attackers with opportunities to implant malicious code or launch attacks through the malicious data. Therefore, 
formulating stringent data source review strategies to ensure data security and compliance is of paramount 
importance. Prior to anonymization, it is essential to explicitly list the data assets requiring anonymization and 
establish corresponding classification, grading standards, and anonymization procedures based on the business 
characteristics of relevant industries. Appropriate anonymization techniques, such as generalization, suppression, 
and pseudonymization, should be selected for different data types[11]. However, due to the vast volume of big data, 
attackers may infer anonymized personal privacy data through correlation mining analysis. Consequently, 
establishing corresponding validation methods for anonymization effectiveness is necessary after anonymization 
processing to avoid the inclusion of recoverable sensitive data in the processing results, ensuring the effectiveness 
and compliance of data anonymization, and documenting the anonymization process to meet subsequent security 
audit requirements. 

During the handling of data outliers, attention must be paid to the risk of unauthorized access by data processors, 
which could lead to data breaches. The capability of data preprocessing often relies on the data platform, and data 
processors need to ensure data traceability. Nevertheless, vulnerabilities in the platform can compromise data 
availability. Although data analysis and data computing are distinct, they are interconnected. Data analysis focuses 
on interpreting data using statistical methods to extract knowledge for business decision-making, while data 
computing emphasizes processing, analyzing, inferring data, or generating new information using mathematical 
and computer science methods, with a strong emphasis on technical implementation and computational efficiency. 



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In practice, data analysis and computing often work synergistically, contributing to intelligent decision-making, 
problem mining, business process optimization, result prediction, and efficiency enhancement[12]. 

3. Discussion on Technological Risks in Smart Agriculture 

3.1 Social Trust Risks in Smart Agricultural Technologies 

Social trust risk refers to the risk of negative ethical impacts on social trust due to improper application of 
technologies, such as abuse or misuse. Take blockchain technology as an example. Its core function is to create 
trust, providing a relatively low-cost means to establish complex trust relationships. In the agricultural sector, 
particularly in the consumption process, the application of blockchain technology is mainly embodied in 
agricultural product traceability.By recording information on every link of agricultural products from production 
to consumption on the blockchain, it ensures information security, transparency, and authenticity, enabling 
consumers to comprehensively monitor the quality of agricultural products and thereby establishing a solid trust 
mechanism among producers, suppliers, and consumers. Blockchain traceability technology has obvious 
advantages: firstly, the information on the chain is immutable; secondly, it reduces the workload of inspection and 
quarantine, avoids repeated inspections due to distrust, and lowers costs[13]. 

However, technological ethical risks still exist and require governance. The zero-state problem arises when the 
accuracy of the data in the first block of the blockchain is questioned. This can occur if there is no third-party 
service to protect stakeholders, if proper due diligence is not conducted on the data, or if the person entering the 
data makes errors or maliciously alters the information. For example, in a blockchain used to track crop information 
in a supply chain, the first block may erroneously indicate that a truck is loaded with crops from a specific origin, 
while in reality, the crops come from another origin. Individuals involved with the truck's contents may have been 
deceived or bribed along the way. Current blockchain technology can only ensure that uploaded data is not 
tampered with but cannot guarantee the authenticity of the data. In the absence of third-party supervision, if the 
items recorded on the blockchain have historically been targets of fraud, bribery, and hacking, then the agricultural 
information traceability system built on blockchain will lose social trust, leading to serious social ethical issues. 
Furthermore, the vast amount of data itself can lead to a certain degree of loss of control. Big data is extremely 
complex in content and form, to the extent that a single ordinary computer cannot process it, requiring the support 
of distributed technologies and cloud computing, which inherently poses a high technical threshold. As technology 
develops, big data continues to change in scale, dimensions, and types, exposing various imperfections. With the 
enhancement of human data capture capabilities, big data continues to grow and become more complex, making 
it practically very difficult to comprehensively analyze all data, beyond the reach of human capabilities. Therefore, 
our ability to control big data is limited, and big data is, to some extent, out of human control. On the one hand, 
there is the issue of agricultural data privacy, where farmers' personal information should be used and protected 
reasonably to prevent data from being out of control, i.e., abused or leaked. On the other hand, the raw data of 
Agricultural Big Data is generated by farmers during the production process, but individual farmers do not have 
the capability to process this data. Therefore, the processing of such big data is usually done by powerful large 
enterprises. In some countries, after large enterprises obtain and use the data generated by farmers, the farmers are 
unaware of this[14]. The neglect of data ownership has sparked widespread concern among farmers. 

3.2 Ecological and Environmental Issues in Smart Agricultural Technologies 

Although many promotions and advertisements for smart agriculture often emphasize its green and safe 
characteristics and focus on protecting the ecological environment, smart agriculture and green agriculture are 
actually two different concepts. Without specific attention to ecological safety, smart agriculture can also pose 
ecological risks. In terms of technology application, smart agriculture utilizes smart sensors to monitor soil 
moisture, uses drones for farmland patrols, and leverages artificial intelligence to identify crop pests and diseases. 
In contrast, green agriculture emphasizes the harmonious coexistence of environmental protection and agricultural 
production. Green agriculture tends to adopt environmentally friendly and sustainable production methods, such 
as using organic fertilizers instead of chemical fertilizers, to reduce environmental pollution. In terms of focus, 
smart agriculture primarily aims to improve production efficiency and yield while reducing labor costs to enhance 
economic benefits. Green agriculture, on the other hand, pays more attention to environmental protection and 
improving the quality of agricultural products, aiming to promote agricultural development while ensuring 
environmental protection and the green purity of agricultural products. Smart agriculture focuses more on the 
application of modern technology and the improvement of production efficiency, while green agriculture 
emphasizes environmental protection and the quality and safety of agricultural products. Therefore, in the 
development process of smart agriculture, it is necessary to increase attention to ecological and environmental 
safety risks[15]. 



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4. Solutions to Risks in Smart Agriculture 

4.1 Information Sharing and Mechanism Improvement 

In traditional trust-building mechanisms, product quality certification and consumer rights protection are avenues 
through which consumers place significant trust. However, with technological advancements, information sharing 
has gradually become a powerful guarantee for enhancing trust between supply and demand due to its ability to 
significantly reduce the risks associated with information asymmetry. The more information agricultural producers 
disclose, the better consumers understand agricultural products, leading to enhanced security and trust. Therefore, 
producers should proactively ensure information openness and transparency. For example, they can establish 
accounts on social media platforms to disclose production information. Surveys have shown that some consumers 
choose agricultural products from a particular company because it provides the most comprehensive production 
information on its online platform. Additionally, interaction and participation are crucial ways to build trust. 
Allowing consumers to participate in producers' activities can significantly increase their trust in the producers. 
Therefore, practical ways to effectively enhance corporate trust include inviting consumers to visit production sites 
or farms in person or engaging with consumers through online platforms. Strengthening the cultivation of 
information technology talents in the agricultural sector and guiding enterprises to actively participate in the 
development of specialized software and socialized service technologies for smart agriculture through professional 
institutions can provide more technical support for information sharing between producers and consumers. 

4.2 Integration of Smart Agriculture and Ecological Agriculture 

Ecological agriculture, as an agricultural model that meets ecological environmental requirements, helps improve 
the ecological environment, reduce soil erosion and land degradation, and thereby maintain the stability and 
healthy operation of ecosystems by minimizing the negative impact of human activities on the natural environment. 
At the same time, it can protect the diversity and interrelationships of ecosystems, enhance the self-restoration 
capability of agricultural ecosystems, and rebuild damaged ecological environments. The integration of smart 
agriculture and ecological agriculture embodies a systems and holistic perspective. Smart agriculture emphasizes 
technological innovation and informatization application, while ecological agriculture emphasizes ecological 
environmental protection and resource conservation in agricultural production processes. The integration and 
innovation of smart and ecological agriculture can bring more opportunities for agricultural development. Through 
the comprehensive integration of technologies, rather than relying on a single technology, the optimization of the 
large agricultural system can be achieved. The shift from purely pursuing economic benefits to achieving 
comprehensive improvements in economic, social, and ecological benefits forms a sustainable composite 
agricultural ecosystem. Furthermore, if smart agriculture fails to comprehensively consider the use of new 
technologies, it may lead to a distorted societal perception of agriculture's role. In fact, in the continuous pursuit 
of new technology applications, human productivity has been greatly enhanced. Alongside choosing the 
integration of smart and ecological agriculture, under today's efficient social production conditions, there is ample 
room to accommodate some low-efficiency ecological agriculture. This approach not only meets the diversified 
needs of the market but also, more importantly, ensures stability at the societal values level[16-17]. 

5. Conclusion 

In reality, many large technology companies are also important governance entities. It is precisely because these 
technology companies actively govern the commercial and technological platforms they control that they can 
jointly promote the healthy operation of the entire society in the current digital economy era. At the same time, as 
an important cornerstone of social governance, the public should also actively participate in the governance of 
technological ethical risks, especially by proactively safeguarding their legitimate rights and interests. Therefore, 
the governance of technological ethical risks involves multiple entities and requires collaborative and joint efforts 
from various parties. In summary, as a new model that further liberates productive forces and enhances the level 
of agricultural modernization, smart agriculture has become a major trend in future agricultural development. 
However, it should be noted that smart agriculture itself is a combination of a series of advanced and cutting-edge 
technologies that are still developing, often accompanied by a series of technological ethical risks that deserve our 
attention and vigilance, and more importantly, require solutions for governance. Therefore, by continuously 
innovating the governance system, proactively taking responsibility, and persistently exploring effective ways to 
solve problems, we can achieve healthier development of smart agriculture. Establishing a framework for big data 
security risks in smart agriculture scenarios, we can derive specific issues based on proprietary features in the 
agricultural sector from the common problems of big data. In exploring strategies for Agricultural Big Data 
security issues, the strategies for data security issues in the field of smart agriculture are still in the preliminary 
exploration stage, requiring cross-disciplinary talents with knowledge of both agricultural technology and data 



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security. Only by deeply understanding the characteristics and features of Agricultural Big Data can we propose 
targeted security solutions for actual scenarios to ensure the secure application of Agricultural Big Data in the 
supply chain, thereby promoting the rapid development of smart agriculture. 

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    /UKR <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>
    /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing.  Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
  >>
  /Namespace [
    (Adobe)
    (Common)
    (1.0)
  ]
  /OtherNamespaces [
    <<
      /AsReaderSpreads false
      /CropImagesToFrames true
      /ErrorControl /WarnAndContinue
      /FlattenerIgnoreSpreadOverrides false
      /IncludeGuidesGrids false
      /IncludeNonPrinting false
      /IncludeSlug false
      /Namespace [
        (Adobe)
        (InDesign)
        (4.0)
      ]
      /OmitPlacedBitmaps false
      /OmitPlacedEPS false
      /OmitPlacedPDF false
      /SimulateOverprint /Legacy
    >>
    <<
      /AddBleedMarks false
      /AddColorBars false
      /AddCropMarks false
      /AddPageInfo false
      /AddRegMarks false
      /ConvertColors /ConvertToCMYK
      /DestinationProfileName ()
      /DestinationProfileSelector /DocumentCMYK
      /Downsample16BitImages true
      /FlattenerPreset <<
        /PresetSelector /MediumResolution
      >>
      /FormElements false
      /GenerateStructure false
      /IncludeBookmarks false
      /IncludeHyperlinks false
      /IncludeInteractive false
      /IncludeLayers false
      /IncludeProfiles false
      /MultimediaHandling /UseObjectSettings
      /Namespace [
        (Adobe)
        (CreativeSuite)
        (2.0)
      ]
      /PDFXOutputIntentProfileSelector /DocumentCMYK
      /PreserveEditing true
      /UntaggedCMYKHandling /LeaveUntagged
      /UntaggedRGBHandling /UseDocumentProfile
      /UseDocumentBleed false
    >>
  ]
>> setdistillerparams
<<
  /HWResolution [2400 2400]
  /PageSize [612.000 792.000]
>> setpagedevice

