An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

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.

Effects of smart agricultural production investment announcements on shareholder value:Evidence from China

The advent of the era of the smart economy has made agricultural production more intelligent.An increasing number of companies have launched a series of investment activities aimed at smart agricultural production(SAP).However,whether smart agricultural production investment(SAPI)impacts the stock market has yet to be confirmed.Therefore,based on the sample data of 118 listed companies in China from 2010 to 2019,this study empirically examines the impact of SAPI announcements on shareholder value,as indicated by abnormal returns of stocks.Further,we tested the moderating effect of certain characteristic factors on abnormal stock returns.The research results illustrate a significant positive connection between SAPI announcements and shareholder value.Moreover,considering the announcement content and company factors,this study investigates the impacts of different investment targets and industries on the market reaction to SAPI announcements.We find that non-agricultural companies have a more positive market reaction to SAPI than agricultural companies;the higher the liability-asset ratio,the more positive will be the stock market reaction to SAPI.

Deep learning based computer vision approaches for smart agricultural applications

The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies.At the core of artificial intelligence,deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into reality.Computer vision techniques,in conjunction with high-quality image acquisition using remote cameras,enable non-contact and efficient technology-driven solutions in agriculture.This review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to harvesting.Recent works in the area of computer vision were analyzed in this paper and categorized into(a)seed quality analysis,(b)soil analysis,(c)irrigation water management,(d)plant health analysis,(e)weed management(f)livestock management and(g)yield estimation.The paper also discusses recent trends in computer vision such as generative adversarial networks(GAN),vision transformers(ViT)and other popular deep learning architectures.Additionally,this study pinpoints the challenges in implementing the solutions in the farmer’s field in real-time.The overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and accuracy.However,the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.

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 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.

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.

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable farming.Deep Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf diseases.However,current DL methods often require substantial computational resources,hindering their application on resource-constrained devices.We propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this.The Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification.The proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet approach.More specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato diseases.The model could be used on mobile platforms because it is lightweight and designed with fewer layers.Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

In The Wireless Multimedia Sensor Network(WNSMs)have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets.By utilising portable technologies,it achieves solid and significant results in wireless communication,media transfer,and digital transmission.Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature,moisture content,and other environmental conditions in recent decades.WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appro-priate audio and video information.Many video sensor network studies focus on lowering power consumption and increasing transmission capacity,but the main demand is data reliability.Because of the obstacles in the sensor nodes,WMSN is subjected to a variety of attacks,including Denial of Service(DoS)attacks.Deep Convolutional Neural Network is designed with the stateaction relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture.The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network.It reduces the attacker detection delay and improves the detection accuracy.In order to protect the network against DoS assaults,an improved machine learning technique must be offered.An efficient Deep Neural Network approach is provided for detecting DoS in WMSN.The required parameters are selected using an adaptive particle swarm optimization technique.The ratio of packet transmission,energy consumption,latency,network length,and throughput will be used to evaluate the approach’s efficiency.

Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets,and Future Directions

In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.

Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes.Therefore,Internet of Things(IoT)technology can be applied to monitor and detect harmful insect pests such as red palm weevils(RPWs)in the farms of date palm trees.In this paper,we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier,namely InceptionResNet-V2.The sound sensors,namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the farm.Palm trees are labeled based on the sensor node number to identify the infested cases.Then,the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model,i.e.,InceptionResNet-V2.The proposed infestation classifier has been successfully validated on the public TreeVibes database.It includes total short recordings of 1754 samples,such that the clean and infested signals are 1754 and 731 samples,respectively.Compared to other deep learning models in the literature,our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio recordings.The resulted classification accuracy score was 97.18%.Using 10-fold cross validation,the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53%and±1.69,respectively.Applying the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work.

Assessment of Climate Variability in Kisii Kenya and Its Implications on Food Security

Climate change and variability have been singled out as one of the modern challenges that affect economies of several countries leading to food scarcity and food insecurity in various parts of the world and represent a fundamental contemporary environmental shock. Kenya is no exception. This research was conducted in Kisii County, a perceived Kenyan national bread basket and investigated the trend in climate variability between the years 1983-2013. The objective of the study was to examine the precipitation and temperature trend in Kisii County. The research question was to find out whether there was any significant trend and pattern of rainfall and temperature as indicators of climate variability. The study examined climate variability for thirty one years (1983 to 2013). Data was obtained from Kenya Meteorological Department and their annual means were computed. Mann Kendall statistic test was applied to establish whether the observed trend of precipitation and temperature was significant. From the analysis, rainfall did not show any significant trend in Kisii County whilst temperature revealed a significantly upward trend over the years, at 95% confidence level. The study recommends a need to incorporate weather prediction and early warning systems by the Ministry of Agriculture in Kisii County and also promote afforestation programmes to protect water catchments. To build resilient systems to climate shocks, introduction of high temperature tolerant food crops as well as adoption of climate smart agriculture (CSA) should also be explored.

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.

Land Use Land Cover Dynamics and Farmland Intensity Analysis at Ouahigouya Municipality of Burkina Faso,West Africa

Sahel zone has been reported as one of the most vulnerable regions to climate change, so serious attention must be paid to this zone by researchers and development actors who are interested in environmental-human dynamics and interactions. The aim of this study was to bring more insight into the impact of actions aiming at reducing land degradation, regreening the Sahel, stopping population migration and reducing the pressure on land in the Sahelian zone. The study focused on farmland dynamic in Ouahigouya municipality based on remote sensing data from 1986 to 2016 using intensity analysis. The annual time interval change was 0.77% and 2.46% for 1986-2001 and 2001-2016, respectively. Farmlands gained from mixt vegetation, water bodies and from bar lands. Mixed vegetation and water bodies were both active during both intervals while the other land use such as woodland and bar land were dormant. Combining land use land cover analysis and intensity analysis was found to be effective for assessing the differentiated impact of the various land restoration actions.

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.