Smart Farming System Research Articles

A design of a smart farm system for cattle monitoring

<span>The integration of technology in agriculture has led to the adoption of smart farming systems, which are becoming increasingly popular for optimizing resources, reducing labor costs, and improving efficiency. This article presents a design of a smart cattle monitoring farm system, which focuses on monitoring individual animal behavior and health, improving resource management, and optimizing overall farm efficiency. The proposed system integrates various internet of things (IoT) sensors, communication technologies, and cloud computing to provide a real-time monitoring solution <span>for cattle farms. The system uses machine learning (ML) algorithms to analyze data on cattle behavior, health, and performance, which can be accessed through web and mobile applications by farmers to proactively monitor their herd. The adoption of the system promises good results for intelligently transforming traditional cattle farming by reducing manual monitoring time and achieving a good accuracy in early detection of diseases, automated tracking of estrus cycles, and location mapping of cattle across the farm fields.</span></span>

Implementasi Profil Pelajar Pancasila melalui Kegiatan Ekstrakurikuler Robotik dan Smart Farmer di SMAS Santo Tarcisius Dumai

Santo Tarcisius High School is the first driving school in Dumai City, so Santo Tarcisius High School has the responsibility to implement the Pancasila Student Profile for its students. One of the characteristics of the Pancasila Student Profile is creative, namely being able to modify and produce something original, meaningful, useful and impactful. To realize the Pancasila Student Profile, an effort is needed to provide knowledge and skills to students in an interesting way, one of which is through robotics and smart farmer extracurricular learning activities. Based on the results of discussions with the school principal, the problems faced in implementing robotics and smart farming extracurricular activities include 1) Students have never had an understanding of computer and robot-based intelligent farming system technology, 2) They do not yet have teachers who are competent in the field of robotics, 3) Limited costs purchasing robotic electronic equipment and installing smart farmers at schools. Based on the problems above, the aim of this activity is to realize the Pancasila Profile, especially in the IT sector, through education on making Line Follower robots and smart farming systems for hydroponic plants. The implementation method is by dividing students into several groups to make Line Follower robots. The number of students in this activity is 150 students/class XI SMA Santo Tarcisius. Success in Beginner Community Service (PMP) is to improve students' skills in making robots and intelligent systems which is measured through the final product results, pre test and post test questionnaires, the product results from this PMP are line follower robots and smart farmer systems, the conclusion of the PMP This is to increase efficiency, productivity and sustainability in the agricultural sector through the application of the latest technology.

An automated weed detection approach using deep learning and UAV imagery in smart agriculture system

An automated weed detection approach using deep learning and UAV imagery in smart agriculture system

Greenhouse Gas (GHG) Emission Estimation for Cattle: Assessing the Potential Role of Real-Time Feed Intake Monitoring

This study investigated the impact of feeding systems on the determination of enteric methane (CH4) emissions factor in cattle. Real-time feed intake data, a crucial CH4 conversion rate (Ym value) parameter, were obtained using a roughage intake control (RIC) unit within a smart farm system. Greenhouse gas (GHG) emissions, including CH4 and carbon dioxide (CO2), from Holstein steers were monitored using a GreenFeed (GF) 344 unit. The results revealed satisfactory body weight (383 ± 57.19 kg) and daily weight gain (2.00 ± 0.83 kg), which are crucial factors. CO2 production exhibited positive correlations with the initial body weight (r = 0.72, p = 0.027), feed intake (r = 0.71, p = 0.029), and feed conversion ratio (r = 0.69, p = 0.036). Five different emission factors (EFs), EFA (New Equation 10.21A) and Equation 10.21 (EFB, EFC, EFD, and EFE), were used for GHG calculations following the Intergovernmental Panel on Climate Change (IPCC) Tier 2 approach. The estimated CH4 EFs using these equations were 69.91, 69.91, 91.79, 67.26, and 42.60 kg CH4/head/year. These findings highlight the potential for further exploration and adoption of smart farming technology, which has the potential to enhance prediction accuracy and reduce the uncertainty in Ym values tailored to specific countries or regions.

Comprehensive Analysis of Phenolic Compounds, Carotenoids, and Antioxidant Activities in Lactuca sativa var. longifolia Cultivated in a Smart Farm System

Smart farming is a promising strategy for future agriculture, and the global market for smart agriculture is growing rapidly. The aim of this study was to compare the quality of agricultural products based on their cultivation method. The components and antioxidant activities of Lactuca sativa (LS) var. longifolia cultivated using conventional agriculture methods (CAMs) and smart farm agriculture methods (SAMs) were compared. ABTS+ and DPPH radical scavenging activity assays were performed to compare the antioxidant activities of LS grown using CAMs and SAMs. High-performance liquid chromatography with a reverse-phase column was employed to analyze the phenolic and carotenoid compounds. In both the SAM and the CAM, β-carotene was found to be the most abundant compound. LS grown using the CAM had high total polyphenol content and ABTS+ radical scavenging activity, while LS grown using the SAM had high individual phenolic content and DPPH radical scavenging activity. This study developed a new method of SAM and confirmed that SAM produces a higher quantity of phenolic compounds and total flavonoid contents than CAM in LS. This study also highlights the SAM’s functioning, gives crop quality inspection guidelines, and standardizes LS. The new SAM is effective for application in undeveloped urban spaces, is more convenient than the CAM, and represents a way to develop and manage LS quality.

Correction to: A Review of Energy-Efficient Secured Routing Algorithm for IoT-Enabled Smart Agricultural Systems

Correction to: A Review of Energy-Efficient Secured Routing Algorithm for IoT-Enabled Smart Agricultural Systems

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture.

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics.

IoT Based Smart Agriculture Monitoring System

The population increase, climate change, and shortage of resources are posing hitherto unheard-of difficulties to the agriculture sector. The application of Internet of Things (IoT) technology to agriculture, or "smart agriculture," has become a game-changing response as a result. An IoT-based Smart Agriculture System intended to transform conventional farming methods is presented in-depth in this paper. In order to maximise crop output, preserve resources, and advance sustainable farming practises, the suggested system makes use of sensor technology, data analytics, and real-time monitoring. Numerous sensors for monitoring temperature, nutrient levels, and soil moisture are among the main elements of the Internet of Things-based Smart Agriculture System. These sensors' wireless transmission of data to a centralised cloud-based platform, where sophisticated data analytics algorithms process the data, is done. This platform is accessible to users via Keywords: Internet of Things (IoT), Sensor Technology, Data Analytics, Sustainability, Crop Monitoring, Agricultural Productivity.

Smart Farming System on Red Onion Plants Based on the Internet of Things

Smart Farming System on Red Onion Plants Based on the Internet of Things

Hydroponics: current trends in sustainable crop production.

The combination of Hydroponics with smart technology in farming is novel and has promise as a method for effective and environmentally friendly crop production. This technology eliminates the need for soil and reduces water usage by providing nutrients straight to the plant's roots. The Internet of Things (IoT), sensors, and automation are all used in "smart farming," which allows for constant monitoring of soil conditions, nutrient levels, and plant vitality to facilitate fine-grained management and optimization. The technology-driven strategy improves crop output, quickens growth rates, and keeps conditions ideal all year round regardless of weather or other environmental circumstances. In addition, smart farming lessens the need for organic chemical inputs, promotes environmentally safe methods of pest management, and minimizes the amount of waste produced. This ground-breaking strategy may significantly alter the agricultural sector by encouraging regionalized food production, enhancing food security, and adding to more resilient farming practices. This comprehensive review delves into current trends in Hydroponics, highlighting recent advancements in smart farming systems, such as Domotics, Data Acquisition, Remote Cultivation, and automated AI systems. The review also underscores the various types and advantages of smart farming hydroponic technology, emphasizing the requirements for achieving efficiency in this innovative domain. Additionally, it explores future goals and potential developments, paving the way for further advancements in hydroponic smart farming.

Implication of a Smart Farming System for Disease Detection and Crop Protection in Nepalese Agriculture

The paper explores the integration of advanced technologies such as IoT, apps, machine learning, and image recognition in the development of a smart farming system for disease detection and crop protection in Nepalese agriculture. Emphasizing the importance of timely disease detection in crop management, the paper discusses the utilization of IoT-based sensors for real-time monitoring of crop health parameters. Furthermore, it examines the application of image recognition techniques and machine learning algorithms for automated disease detection and identification. By leveraging these technologies, the smart farming system aims to address disease control challenges, optimize resource utilization, and promote sustainable agricultural practices in Nepal. The use of IoT and ML for Smart Farming Systems using timely disease detection and further crop management has been theoretically discussed.

Suaeda glauca Attenuates Liver Fibrosis in Mice by Inhibiting TGFβ1-Smad2/3 Signaling in Hepatic Stellate Cells.

Chronic liver injury due to various hepatotoxic stimuli commonly leads to fibrosis, which is a crucial factor contributing to liver disease-related mortality. Despite the potential benefits of Suaeda glauca (S. glauca) as a natural product, its biological and therapeutic effects are barely known. This study investigated the effects of S. glauca extract (SGE), obtained from a smart farming system utilizing LED lamps, on the activation of hepatic stellate cells (HSCs) and the development of liver fibrosis. C57BL/6 mice received oral administration of either vehicle or SGE (30 or 100 mg/kg) during CCl4 treatment for 6 weeks. The supplementation of SGE significantly reduced liver fibrosis induced by CCl4 in mice as evidenced by histological changes and a decrease in collagen accumulation. SGE treatment also led to a reduction in markers of HSC activation and inflammation as well as an improvement in blood biochemical parameters. Furthermore, SGE administration diminished fibrotic responses following acute liver injury. Mechanistically, SGE treatment prevented HSC activation and inhibited the phosphorylation and nuclear translocation of Smad2/3, which are induced by transforming growth factor (TGF)-β1 in HSCs. Our findings indicate that SGE exhibits anti-fibrotic effects by inhibiting TGFβ1-Smad2/3 signaling in HSCs.

Retracted: Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Retracted: Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Attack-Resistant, Energy-Adaptive Monitoring for Smart Farms: Uncertainty-Aware Deep Reinforcement Learning Approach

This work proposes an energy-adaptive monitoring system for a smart farm using solar sensors attached to cows. The proposed system aims to achieve a high monitoring quality in the smart farm under fluctuating energy and cyber attacks disrupting the collection of sensed data from solar sensors, such as protocol non-compliance, false data injection, denial-of-service, and state manipulation. We adopt Subjective Logic, a belief model, to consider multidimensional uncertainty in sensed data. We employ Deep Reinforcement Learning (DRL) for agents on gateways to collect high-quality sensed data from the solar sensors. The DRL agents aim to collect high-quality sensed data with low uncertainty and high freshness under fluctuating energy levels in solar sensors. We analyze the performance of the proposed energy-adaptive smart farm system in accumulated reward, monitoring error rate, and system overload. We conduct a comparative performance analysis of the uncertainty-aware DRL algorithms against their counterparts in choosing the number of sensed data to be updated to collect high-quality sensed data to achieve high resilience against attacks. Our results prove that Multi-Agent Proximal Policy Optimization (MAPPO) using the uncertainty maximization technique outperforms other counterparts, showing about 4% lower monitoring error rate and the system overload.

Smart Melon Farm System: Fertilizer IoT Solution

Embedded systems are increasingly being employed for a wide range of applications. Small and large enterprises can benefit from sensor networks and Internet of Things technologies. Farmers, particularly the next generation of farmers, are tremendously interested in the smart farm system. This is due to Thailand’s favorable geography, and young farmers are becoming more technologically literate. However, smart farm systems on the market are still too expensive and don’t meet small farms’ needs. Consequently, the study proposed a low-cost irrigation and fertilizer system for high-quality melon farms in Chiang Rai, Thailand. The system can properly carry out irrigation and fertilization operations on the melon farm, which require specific attention at various stages of production. As a result, the technique reduces human work while simultaneously supplying adequate water and nutrients to the entire plant. Moreover, farmers with Internet access can manually monitor or supervise the operation at any time, from any location, and on any device. The developed system may gather information from sensors and operational procedures for further analysis in order to increase output and reduce waste. The system is affordable since it was constructed primarily using open-source software and low-cost embedded components with an ergonomic architecture. The results of the comparison show that the automatic method outperforms the human approach in terms of quality and production yield.

A Review of Energy-Efficient Secured Routing Algorithm for IoT-Enabled Smart Agricultural Systems

A Review of Energy-Efficient Secured Routing Algorithm for IoT-Enabled Smart Agricultural Systems

Smart Farm Agriculture Design by Applying a Solar Power Plant

Every year as the world's population increases, land is getting full and not enough to be used in agriculture. Various types of technological developments have been abused to grow crops. The purpose of this research is to design a smart farm agriculture system by planting without soil and utilizing technological advances in the city. Smart farming is a technology in agriculture with the Internet of Things (IoT) to help farmers public people get data easy from the garden. Hydroponics is soil-less farming that uses minerals or fertilizers dissolved in water. Nutrient Film Technique (NFT) is one of the hydroponic methods using a thin layer on the flow of nutrients through pipe installations. Pumps that require continuous electrical power can harness solar power plants as an energy source. Off-grid system on solar panel plan produce electrical energy according to the required power without being connected to state electricity network. The design applies a multi-sensor system that includes nutrient sensors and pH sensors, as well as automatic solution pumps, temperature PZEM-004T sensors, and a data logger that collects data and connected to internet server with visual on app Thinger.io as monitoring platform. The results, pH is ranging from 6.5 to 7.5. The TDS sensor testing resulted in a 0.313% pH sensor error with an accuracy of 99.69%, and the TDS sensor testing resulted in a 1.18% TDS sensor error with an accuracy of 98.82% also the agriculture farm system testing, the testing in 1 until 2 weeks showed an error percentage of 38% in the pH solution and 38.73% in the nutrient solution. In addition, the solar panels generated a total power output of 1700.56 W, while the total load demand was 1165.74 W. Based on the testing results, the smart farming system can monitor nutrient and PH solution levels, the automatic pump controls a stable solution, and the power sourced from PLTS can supply the pump properly

Teaching embedded control system design of electromechanical devices using a lab-scale smart farming system

This article presents the design and monitoring of a lab-scale smart farming system through the integration of control and app designs that can be used for teaching embedded control application to electromechanical systems. A combination of sensors and actuators is used to develop an Arduino-based embedded feedback control system that could be implemented in a smart farming environment. Specifically, we look at controlling electromechanical devices to actuate the fan and water pump to provide the optimal temperature and moisture, respectively, to enhance plant growth in a smart farming setting. The effectiveness of the feedback control is tested by conducting a plant growth experiment. Using garden cress ( Lepidium sativum) as a case study, the plant grown in the controlled temperature and moisture settings shows substantially healthier growth compared to the one grown in the non-controlled environment. In addition, an app is designed and developed to transform the Arduino data stream from the sensors into valuable insights that could help the users to monitor and improve the overall crop health. The developed system in this paper enables students to learn integral skills from interdisciplinary engineering fields (e.g. systems, control, mechanical and computer) to solve an agricultural problem.

Resilient Localization and Coverage in the Internet of Things

The proliferation of the Internet of Things (IoT) has revolutionized traditional services, giving rise to emerging smart infrastructures by connecting the physical and digital worlds. Sensory data is essential in IoT-based systems for providing context-aware and location-based services. Hence, the accurate localization of IoT devices is paramount. Anchor misplacement can significantly affect location information and coverage services in IoT. We study the effect of anchor misplacement in typical IoT settings where sensors are randomly deployed, can be mobile, and may belong to multiple providers. We identify sensing coverage holes formed by anchor misplacement and analyze their presence and impact. To mitigate the impact of anchor misplacement on network reliability, we propose a framework to identify the affected sensor nodes and then identify and remove misplaced anchor nodes. The validity of our approach is verified, and its effectiveness is demonstrated by several experiments with different network topologies and parameters. Our results are promising and can be utilized in multiple coverage applications, such as smart agriculture systems and habitat monitoring, regardless of the sensors or deployment types. It also sheds light on best practices and methods for a reliable design of IoT-based systems.

IoT and ML‐based automatic irrigation system for smart agriculture system

AbstractThe development of the Internet of Things (IoT) and machine learning (ML) technologies has triggered smart agricultural systems. In smart agriculture, irrigation management plays a major role to reduce water waste. The monitoring settings, hardware modules, communication technology, and storage systems used in smart irrigation systems were analyzed to determine the optimal nature of water flow. This assessment aims to give an overview of the current state of the irrigation system by taking into account weather and soil moisture. This paper provides a comprehensive review of the utilization of various hardware modules in smart irrigation systems. Moreover, various communication technologies that aid in data transfer for efficient smart irrigation are reviewed. The ML method used for prediction is also evaluated, as well as based storage technologies like the cloud and databases used to store data for predictive irrigation systems. As a result, the paper provides an overview of all the factors that contribute to irrigation systems’ smart operation as well as potential future paths for improving agricultural systems through IoT.