Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application

Smart agriculture modifies traditional farming practices,and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies.In today’s world where technology is everything,these technologies are utilized to streamline regular tasks and procedures in agriculture,one of the largest and most significant industries in every nation.This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems.Divided into three main sections-security analysis,system architecture and design and risk assessment of Cyber-Physical Systems(CPS)applications-the study delves into various elements crucial for smart farming,such as data sources,infrastructure components,communication protocols,and the roles of different stakeholders such as farmers,agricultural scientists and researchers,technology providers,government agencies,consumers and many others.In contrast to earlier research,this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling,penetration testing,and vulnerability assessments.Important discoveries highlight the concerns connected to unsecured communication protocols,possible threats from malevolent actors,and vulnerabilities in IoT devices.Furthermore,the study suggests enhancements for CPS applications,such as strong access controls,intrusion detection systems,and encryption protocols.In addition,risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards,addressing issues like data breaches,system outages,and automated farming process sabotage.The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor.This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards.The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach,which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

Review of IoT and electronics enabled smart agriculture

The population increases at an exponential rate as human society advances,and pollution is increasingly depleting the availability of resources such as water and land.All these problems are thought to require the use of smart agriculture.By reducing use of chemical fertilizers and pesticides,smart agriculture could mitigate land pollution and increase the sustainability of agricultural practices while also greatly enhancing the agro-ecological environment,yield,and quality of crops.The steps to make agriculture smart are made possible through data and communication technology,which helps with automatic operation and cultivation.Moreover,advances in wireless communication protocols will bring agriculture to a more intelligent stage.This study provides an overview of IoT technology and its application in the smart agriculture industry to make crop production automatic and intelligent by assessing their architecture(IoT devices,communication technologies,and processing),their applications,and research timelines.The communication protocols that have established uses in agriculture are reviewed first in this article.Various wireless communication protocols such as WiFi,ZigBee,SigFox,LoRa,RFID,NFMI,Terahertz,and NB-IoT were summarized,and their applications in various fields were also studied.These protocols in smart agriculture can effectively and efficiently address environmental data,water saving,monitoring of animal behavior,accuracy,power efficiency,cost reduction due to low power consumption,accuracy,wide transmission,simple in operation and cost effective.The most commonly used microcontrollers are Arduino(to develop autonomous machines),Raspberry Pi(to store data),and 8-bit microcontroller(to process data).In addition,it is important to take advantage of modern communication technology to enhance crop production.This study also examines the future opportunities and trends for IoT applications in smart agriculture,along with the ongoing challenges and issues that need addressing.Furthermore,it provides crucial insights and guidance for future research and the development of IoT solutions.These advancements aim to improve agricultural productivity and quality while facilitating the transition to a more sustainable agroecological future.

Construction and Application Exploration of Smart Agriculture Based on Big Data Technology

Big data finds extensive application and many fields.It brings new opportunities for the development of agriculture.Using big data technology to promote the development of smart agriculture can greatly improve the effect of agricultural planting,reduce the input of manpower and material resources,and lay a solid foundation for the realization of agricultural modernization.In this regard,this paper briefly analyzes the construction and application of smart agriculture based on big data technology,hoping to provide some valuable insights for readers.

Building an interoperable space for smart agriculture

The digital transformation in agriculture introduces new challenges in terms of data,knowledge and technology adoption due to critical interoperability issues,and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.DEMETER(Building an Interoperable,Data-Driven,Innovative and Sustainable European Agri-Food Sector)addresses these challenges by providing an overarching solution that integrates various heterogeneous hardware and software resources(e.g.,devices,networks,platforms)and enables the seamless sharing of data and knowledge throughout the agri-food chain.This paper introduces the main concepts of DEMETER and its reference architecture to address the data sharing and interoperability needs of farmers,which is validated via two rounds of 20 large-scale pilots along the DEMETER lifecycle.This paper elaborates on the two pilots carried out in region of Murcia in Spain,which target the arable crops sector and demonstrate the benefits of the deployed DEMETER reference architecture.

Augmented IoT Model for Smart Agriculture and Farm Irrigation Water Conservation

In Northern Nigeria, irrigation systems are operated manually. Agriculture has over the years been practiced primitively by farmers, especially in sub-Saharan Africa. This is due to the absence of intelligent technological know-how where its practice could be leveraged upon. Agricultural practice is constrained by some major challenges ranging from traditional way of farming, understating of concepts, practices, policy, environmental and financial factors. The aim of this study was to optimize an IoT-based model for smart agriculture and irrigation water management. The objectives of the study were to: design, implement, test and evaluate the performance of the optimized IoT-based model for smart agriculture and irrigation water management. The method used in the study was the prototyping model. The system was designed using balsamiq application tools. The system has a login page, dashboard, system USE-CASE diagrams, actuators page, sensor page and application interface design. Justinmind tool was used to show the flow of information in the system, which included data input and output, data stores and all the sub-processes the data moves through. The Optimized IoT model was implemented using four core platforms namely, ReactJS Frontend Application development platform, Amazon web services IoT Core backend, Arduino Development platform for developing sensor nodes and Python programming language for the actuator node based on Raspberry Pi board. When compared with the existing system, the results show that the optimized system is better than the existing system in accuracy of measurement, irrigation water management, operation node, platform access, real-time video, user friendly and efficiency. The study successfully optimized an IoT-based model for smart agriculture and irrigation water management. The study introduced the modern way of irrigation farming in the 21st century against the traditional or primitive way of irrigation farming that involved intensive human participation.

Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

This paper presents a comprehensive review of emerging technologies for the internet of things(IoT)-based smart agriculture.We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT,such as unmanned aerial vehicles,wireless technologies,open-source IoT platforms,software defined networking(SDN),network function virtualization(NFV)technologies,cloud/fog computing,and middleware platforms.We also provide a classification of IoT applications for smart agriculture into seven categories:including smart monitoring,smart water management,agrochemicals applications,disease management,smart harvesting,supply chain management,and smart agricultural practices.Moreover,we provide a taxonomy and a side-by-side comparison of the state-ofthe-art methods toward supply chain management based on the blockchain technology for agricultural IoTs.Furthermore,we present real projects that use most of the aforementioned technologies,which demonstrate their great performance in the field of smart agriculture.Finally,we highlight open research challenges and discuss possible future research directions for agricultural IoTs.

A Survey on Smart Agriculture:Development Modes,Technologies,and Security and Privacy Challenges

With the deep combination of both modern information technology and traditional agriculture,the era of agriculture 4.0,which takes the form of smart agriculture,has come.Smart agriculture provides solutions for agricultural intelligence and automation.However,information security issues cannot be ignored with the development of agriculture brought by modern information technology.In this paper,three typical development modes of smart agriculture(precision agriculture,facility agriculture,and order agriculture)are presented.Then,7 key technologies and 11 key applications are derived from the above modes.Based on the above technologies and applications,6 security and privacy countermeasures(authentication and access control,privacy-preserving,blockchain-based solutions for data integrity,cryptography and key management,physical countermeasures,and intrusion detection systems)are summarized and discussed.Moreover,the security challenges of smart agriculture are analyzed and organized into two aspects:1)agricultural production,and 2)information technology.Most current research projects have not taken agricultural equipment as potential security threats.Therefore,we did some additional experiments based on solar insecticidal lamps Internet of Things,and the results indicate that agricultural equipment has an impact on agricultural security.Finally,more technologies(5 G communication,fog computing,Internet of Everything,renewable energy management system,software defined network,virtual reality,augmented reality,and cyber security datasets for smart agriculture)are described as the future research directions of smart agriculture.

An Interpretable Artificial Intelligence Based Smart Agriculture System

With increasing world population the demand of food production has increased exponentially.Internet of Things(IoT)based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time.Interpretability can be an important factor to make such systems trusted and easily adopted by farmers.In this paper,we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production.The strength of the proposed system is in its interpretability which makes it easy for farmers to understand,trust and use it.The use of fuzzy logic makes the system customisable in terms of types/number of sensors,type of crop,and adaptable for any soil types and weather conditions.The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms.To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type,soil moisture,soil temperature,plant life cycle,irrigation requirement and water application timing for Maize as our target crop.The experimental results show that our proposed system is interpretable,can detect anomalous data,and triggers actions accurately based on crop requirements.

Demand for Meteorological Services in Smart Agriculture and Countermeasures

According to the current situation of modern meteorological services and smart agriculture in Tongliao City,the demand for meteorological services in smart agriculture was analyzed,including accurate meteorological services,point-to-point meteorological services,improved agro-meteorological disaster prevention system,and a comprehensive platform for agricultural services.Besides,some countermeasures to strengthen meteorological services for smart agriculture were proposed,such as promoting the construction of agro-meteorological big data,jointly carrying out the work of meteorological information into villages and households,promoting the construction of modern agricultural meteorological service demonstration areas,and advancing weather modification capacity construction.

Smart Agriculture and IoT Technology

This paper firstly describes the main applications of Internet of Things(IoT)in modern agriculture and achievements made on the basis of these technologies.It introduces the role of IoT in modern agricultural practices such as vertical farming(VF),hydroponics and phenotyping.Then,it analyzes the potential of wireless sensors and IoT in agriculture,and incoming challenges when integrating this technology with traditional agriculture.In addition,it lists the sensors that can be used in specific agricultural applications,and the main current and future agricultural application scenarios and platforms based on IoT.It also reviews the relevant research being carried out by major technology companies at home and abroad.It is intended to help researchers and agricultural engineers to implement the technology based on the IoT and realize the construction of smart parks.

Computer vision in smart agriculture and precision farming:Techniques and applications

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)technologies.This transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and sustainability.This survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision agriculture.By providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural environments.The focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming tasks.This paper first discusses various salient crop metrics used in digital agriculture.Then this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and planning.After establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.

Image classification on smart agriculture platforms:Systematic literature review

In recent years,smart agriculture has gained strength due to the application of industry 4.0 technologies in agriculture.As a result,efforts are increasing in proposing artificial vision applications to solvemany problems.However,many of these applications are developed separately.Many academic works have proposed solutions integrating image classification techniques through IoT platforms.For this reason,this paper aims to answer the following research questions:(1)What are themain problems to be solvedwith smart farming IoT platforms that incorporate images?(2)What are the main strategies for incorporating image classification methods in smart agriculture IoT platforms?and(3)What are the main image acquisition,preprocessing,transmission,and classification technologies used in smart agriculture IoT platforms?This study adopts a Systematic Literature Review(SLR)approach.We searched Scopus,Web of Science,IEEE Xplore,and Springer Link databases from January 2018 to July 2022.Fromwhich we could identify five domains corresponding to(1)disease and pest detection,(2)crop growth and health monitoring,(3)irrigation and crop protectionmanagement,(4)intrusion detection,and(5)fruits and plant counting.There are three types of strategies to integrate image data into smart agriculture IoT platforms:(1)classification process in the edge,(2)classification process in the cloud,and(3)classification process combined.The main advantage of the first is obtaining data in real-time,and its main disadvantage is the cost of implementation.On the other hand,the main advantage of the second is the ability to process high-resolution images,and its main disadvantage is the need for high-bandwidth connectivity.Finally,themixed strategy can significantly benefit infrastructure investment,butmostworks are experimental.

Triboelectric nanogenerators for smart agriculture

Rapid iterations of sensing,energy,and communication technologies transform traditional agriculture into standardized,intensive,and smart modern agriculture.However,the energy supply challenge for the plentiful sensors or other microdevices constraints the extensive application of intelligent technologies in agriculture.Triboelectric nanogenerator(TENG),which efficiently converts mechanical energy into electrical energy through contact electrification and electrostatic induction,is considered a promising way to build next-generation intelligent energy supply networks.By efficiently harvesting low-frequency mechanical energy from the agricultural environment,including wind,rain,and water flow energy,TENGs can be a strong contender for distributed power for microdevice networks in smart agriculture.In addition,highly customizable TENGs can be combined with microdevices in agriculture to enable self-powered agricultural monitoring and production strategy adjustment.By deeply exploring the application potential of TENG in agriculture,it is conducive to further promoting unmanned production,refinement,and intelligence of agricultural production and enhancing agriculture's ability to combat natural risks.

Clean Energy Consumption of Power Systems Towards Smart Agriculture: Roadmap, Bottlenecks and Technologies

Over the past decades,both agriculture and power systems have faced serious problems,such as the power supply shortage in agriculture,and difficulties of clean energy consump-tion in the power system.To address and overcome these issues,this paper proposes an idea to combine smart agriculture and clean energy consumption,use surplus clean energy to supply agriculture production,and utilize smart agriculture to support power system with clean energy penetration.A comprehensive review has been conducted to first depict the roadmap of coupling a agriculture-clean energy system,analyze their feasibilities and advantages.The recent technologies and bottlenecks are summa-rized and evaluated for the development of a combined system consisting of smart agriculture production and clean energy consumption.Several case studies are introduced to explore the mutual benefits of agriculture-clean energy systems in both the energy and food industries.

Deep learning for smart agriculture:Concepts,tools,applications,and opportunities

In recent years,Deep Learning(DL),such as the algorithms of Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN)and Generative Adversarial Networks(GAN),has been widely studied and applied in various fields including agriculture.Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique.This article provides a concise summary of major DL algorithms,including concepts,limitations,implementation,training processes,and example codes,to help researchers in agriculture to gain a holistic picture of major DL techniques quickly.Research on DL applications in agriculture is summarized and analyzed,and future opportunities are discussed in this paper,which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly,and further to facilitate data analysis,enhance related research in agriculture,and thus promote DL applications effectively.

Edge computing:A tractable model for smart agriculture?

Establishing food security remains a global challenge;it is thus a specific objective of the United Nations Sustainable Development Goals for 2030.Successfully delivering productive and sustainable agricultural systemsworldwide will form the foundations for overcoming this challenge.Smart agriculture is often perceived as one key enabler when considering the twin objectives of eliminating world hunger and undernourishment.The practical realization,deployment,and adoption of smart agricultural systems remain distant due to a confluence of technological,social,and economic factors.Edge computing offers a potentially tractable model for mainstreaming smart agriculture.A synergistic relationship exists,which,if harnessed productively,would increase the penetration of smart agricultural technologies across Majority-Minority world boundaries.The paper considers the prevailing context of global food security,smart agriculture and the pervasive issue of internet access.A survey of the state-of-the-art in research utilizing the Edgemodel of computing in agriculture is reported.Results of the survey confirm that the Edge model is actively explored in a number of agricultural domains.However,research is rooted in the prototype stage,and detailed studies are currently lacking.While potential is demonstrated,several systemic challenges must be addressed to manifest meaningful impact at the farm level.

Service design for climate-smart agriculture

Holistic information systems for climate-smart agriculture demands the seamless integration of various categories of climate,meteorological and weather data.Any actor in the agricultural value chain may harness weather forecasts at the short and medium-range,local weather history,and prevailing climatic conditions,to inform decision-making.Weather is fundamental to many day-to-day operations,especially at farm-level,influencing decision-making at various spatial and temporal scales.Many operational decisions ideally require hyper-localized service provision.In practice,integrating weather information into decision-support services demands a comprehensive understanding of various categories of weather-related data,their genesis,as well as the specific standards and data formats used by the meteorological community.This paper considers the weather as a crucial context for the delivery of farm-level operational services in smart agriculture,highlighting critical issues for reflection by system designers during the service design and implementation phases.

Internet of Things Based Smart Irrigation System Using ESP WROOM32

Farming has been the most prominent and fundamental activity for generations.As the population has been mul-tiplying exponentially,the demand for agricultural yield is growing relentlessly.Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour.In the era of digitization,smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management,optimize the process of farming and enhance the yield of crops,hence,ensuring sustainable growth of the increasing production.By implementing modern technologies in the field of farming we can enable telemetry through which farmers can remotely monitor and gather real time data on the desired parameters.It also gives accurate and precise measurements when compared to traditional measurement techniques.This research paper focuses on an IoT based approach for smart monitoring using ESP WROOM 32 microcontroller that helps farmers identify real-time parameters of temperature,moisture and humidity of their field.Real-time data on temperature,moisture,and humidity enables farmers to make informed decisions about irrigation and crop protection.Furthermore,the use of smart monitoring ensures accurate and precise measurements,surpassing the limitations of traditional techniques.

A novel nondestructive detection approach for seed cotton lint percentage using deep learning

Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.

Performance of Deep Learning Techniques in Leaf Disease Detection

Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species.Consequently,the identification of leaf diseases is essential in this field of agriculture.Diseases brought on by bacteria,viruses,and fungi are a significant factor in reduced crop yields.Numerous machine learning models have been applied in the identification of plant diseases,however,with the recent developments in deep learning,this field of study seems to hold huge potential for improved accuracy.This study presents an effective method that uses image processing and deep learning approaches to distinguish between healthy and infected leaves.To effectively identify leaf diseases,we employed pre-trained models based on Convolutional Neural Networks(CNNs).There are four deepneural networks approaches used in this study:ConvolutionalNeuralNetwork(CNN),Inception-V3,Dense Net-121,and VGG-16.Our focus was on optimizing the hyper-parameters of these deep learningmodels with prior training.For the evaluation of these deep neural networks,standard evaluation measures are used,such as F1-score,recall,precision,accuracy,and AreaUnderCurve(AUC).The overall outcomes showthe better performance of Inception-V3 with an achieved accuracy of 95.5%,as well as the performance of DenseNet-121 with an accuracy of 94.4%.VGG-16 performed well as well,with an accuracy of 93.3%,and CNN achieved an accuracy of 91.9%.