International Journal of Advances in Agricultural Science and Technology
Editor- In-Chief : Dr. Rajeev Mehra Title proper : International Journal of Advances in Agricultural Science and Technology Issn : 2348-1358 Subject : Agricultural Science Publisher : Elixir Publishers Dates of publication : 2013 Frequency : Quarterly Language : English Country : India Medium : Online Indexed by : CROSSREF Indexed by : ROAD Indexed by : THE KEEPERS Title DOI of the Journal: http://doi.org/10.62604/issn.2348-1358
THE EFFECTS OF AI ON Agricultural Quality Management Systems — An Examination of Shanghai SQ Agrotech Co., Ltd.
As an extensive management concept, Total Quality Management (TQM) stresses the need of all organizational members actively working to improve the quality of goods and services via continuous process improvement. Originally implemented in China's industrial sector, Total Quality Management (TQM) has now spread across many other industries, most notably agriculture. Precision agriculture, smart agriculture, and green agriculture have all found new life in China's agricultural enterprises thanks to TQM and the incredible advances and widespread uses of technologies like 5G, big data, satellite remote sensing, and Artificial Intelligence (AI). These innovations have been supported by government policies in China. As a result, new possibilities and obstacles have arisen for the implementation and development of TQM in farming. In this study, we will focus on the behavioral and awareness aspects of the five-factor quality management model that is based on total quality management theory: characteristic quality, tool quality, system quality, behavioral quality, and awareness quality. This study uses a questionnaire survey and interviews with Shanghai SQ Agricultural Technology Company's management and staff to investigate how AI is changing the quality of awareness and behavior in agricultural enterprises, as well as to identify the main challenges and potential solutions that AI faces when applied to quality management in these sectors. These terms are related to the following: agricultural enterprises, five-dimensional quality management framework, total quality management, artificial intelligence (AI).
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The Colletotrichum Gloeosporioides Epidemic in Mangoes
The earliest reported occurrence of anthracnose was 45th SMW, while the highest incidence was 31.95% on 52nd SMV. From the thirteenth to the fourteenth SMW, the illness advanced more rapidly (11.35 to 26.41%), but from the fifth to the tenth SMW, it advanced more slowly (26.73 to 9.63%). It is evident that the mango anthracnose illness may begin as early as 45 SMW (the 2nd week of November) and continue until 20 SMW (the 3rd week of May), and the window period for the disease can be defined as the 49th to 52nd SMW (30.61 to 31.95 %). To stop the illness from spreading and losses from occurring, appropriate management actions should be implemented between the 47th and 52nd MSW. Topics covered include mangoes, epidemiology, anthracnose, and Colletotrichum Gloeosporioides.
Commodity Futures and Their Role in Agricultural Sector Hedging
The significance of futures and options contracts for the control of price risk in the agriculture industry is examined in this article. There is a pressing need to establish market mechanisms that would enable agricultural producers to plan, market, and hedge their output in light of the global volatility in the prices of agricultural goods and inputs. Examining the use of commodity derivatives in agricultural hedging techniques is the primary goal of this article. Research suggests that the agricultural industry may greatly benefit from developing futures and options commodities exchanges. These exchanges could use futures and options contracts to mitigate the risk of negative price fluctuations for agricultural inputs and outputs. Topics covered: commodity derivatives, futures, options, and commodity exchanges.
Modeling the Unpredictable Growth of Agricultural Monopolies' Output and Corporation Tax
The primary objective of this work is to provide a basic model for the production growth of a chaotic agricultural monopoly that may produce stable equilibria, cycles, or anarchy. In this work, one important hypothesis is that the coefficient π= f ( 1 -n ) (a - d) is very important for understanding the local stability of the output of an agricultural monopoly. Here, d is the coefficient of the firm's total cost function, an is the coefficient of the inverse demand function, n is the corporation tax rate, and f is the coefficient that represents the relationship between the after-tax profit of the firm in period t and the output of the firm in period t+1. The Agricultural Monopoly Firm, Agricultural Output, Corporation Tax, and Chaos are all related terms.
Agricultural Productivity and Chaotic Economic Growth
This paper's overarching goal is to provide a fundamental model for agricultural development that, given a set of parameters, may produce stable equilibria, cycles, or chaos.  A key hypothesis of this work is based on the idea that the coefficient π = ( − ) plays a crucial role in explaining local economic stability , where α – the coefficient of marginal agricultural labour productivity ; β – the coefficient of average agricultural labour productivity.
Agricultural productivity, Economic growth , Stability, Chaos
Three Crucial Pulses: ABA-Induced Alterations to Their Pigment Contents, Photosynthetic Gas Exchange Properties, Leaf Area, and Dry Matter Accumulation
An experiment was conducted at TNAU, Coimbatore, to study the reaction of three significant pulses—Soyabean, greengram, and blackgram—to ABA that was sprayed from outside the plant. Pulse responses to drought stress demonstrate that chemical signals control stomatal aperture and photosynthesis. The exact mechanism by which Abscisic acid (ABA) chemical signals regulate the stomatal opening remains unknown, nevertheless. Thus, the purpose of this study was to determine the gas exchange parameter changes caused by ABA in the dominant soybean, green gram, and black gram varieties, and to find the crops that can improve water use efficiency by ABA treatments that maintain higher photosynthetic rates and lower transpiration rates. We grew all of the pulses in well-watered soil and gave them an ABA treatment. After the 22nd day after sowing, gas exchange metrics exhibited substantial alterations when an exogenous 10-5M ABA solution was applied for three consecutive days. Photosynthesis, leaf area, total dimethyl phosphorus, and photosynthetic pigments exhibited remarkable stability in greengram when treated with ABA sprays, although stomatal conductance decreased in all pulses. The transpiration rate was low in greengram compared to its control, while it was high in soyabean and blackgram. Clearly, under extended drought circumstances, ABA-induced increases in greengram represent an improvement in water-use efficiency. Pulses, ABA, stomatal conductance, transpiration, photosynthesis, pigment contents, leaf area, and total dry matter accumulation are all terms that fall under this umbrella.
Pulses, ABA, stomatal conductance, transpiration, photosynthesis, pigment contents, leaf area
Investigating the Differences in Ef (Eragrostis tef (Zucc.) Trotter) Varieties' Grain and Straw Yield and Straw Quality Attributes
Tef, scientifically known as Eragrostis tef (Zucc.) Trotter, is a native Ethiopian grain that is good for both humans and cattle. Over the course of two years, 35 different tef varieties were cultivated in two different areas of Ethiopia, along with a control variety, to study the differences in grain and straw production as well as straw quality attributes. Acid detergent fiber (ADF), neutral detergent fiber (NDF), acid detergent lignin (ADL), metabolizable energy (ME), crude protein (CP), in vitro organic matter digestibility (IVOMD), and straw yield (STY) were the parameters that were examined. The interaction between the variety of tef, the cultivation environment, and ME, CP, IVOMD, and CP were considerably (P < 0.01) impacted. Compared to Holetta, a high-altitude site, for both 2016 and 2015, the GY, CP, IVOMD, and ME characteristics performed better at mid-altitude in Debre Zeit (P < 0.01). Consistently, there were positive and statistically significant relationships found between GY and STY, CP, ME, and IVOMD, and NDF, ADF, and ADL). In contrast, a negative and statistically significant correlation was found between yield and quality attributes. This research also classified variations into four groups according to the quality of their straw, grain production, and overall performance; some varieties even outperformed the average in these categories. The current tef breeding approach mostly aims at improving grain output, thus it would be beneficial to promote varieties like Melko, which have greater straw yield and quality features, for immediate use as a dual-purpose variety or as a parental line to change this.
yield, straw yield, straw quality, Tef variety, and variation.
Design and Implementation of a Wireless Electronic Scale Leveraging the MSP430 Microprocessor
Vegetable, fruit, and cookie shops should record all transaction details to better understand customer preferences and stock their shelves. The MSP430 microcontroller and the PTR2000 wireless connection module form the basis of the electronic scale's design. The scale is able to do more than just weigh people; it can also communicate with a host computer and follow commands. The strain bridge output signal circuit, time/data circuit, memory circuit, wireless module, and so on is all shown. Moving-average filtering is used to refine the data from the measurements. Visual Basic is used to create the user interface on the computer side.
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Development of a Digital Feedback Control Algorithm for Two-Stage DC-DC Converters
The two-stage dc-dc converter's construction process is shown, and a methodical digital management technique is suggested for it. The dynamic reaction of the structure is very complicated because of the large number of reacting components. The steady-state study is used to check the two-stage converter's development. The fleeting reaction of the structure must be analysed, and a tiny signal model is required for this. The system is then adjusted with the help of a computer device that acts as a latency adjuster. Overshoot is reduced and stability is achieved by increasing the crossover frequency relative to the uncompensated converter and improving the second resonant frequency. The design process and the digital controller's steady-state and intermittent excellent characteristics are demo nitrated with experimental data.
Spatiotemporal Pattern-Based Malware Detection Using the Dark-TRACER Framework
There is an urgent need to swiftly recognize patterns in hacking and implement appropriate defences as their prevalence rises around the world. Because there is no genuine contact taking place in the darknet, an observation and analysis of random hacks is made easier. Similar spatial patterns are seen on the darknet, where adware is spreading outbreaks through indiscriminate monitoring. Focusing on the unusual alignment of spatial patterns in darknet traffic data, we hope to solve the issue of early discovery of virus activities. Three different machine learning techniques were used in our prior research to suggest algorithms that could autonomously predict and identify unusual spatial patterns of darknet traffic in real time. In this work, we combined the previously suggested techniques into a unified system called Dark-TRACER and tested its ability to identify these malware behaviours using quantitative methods. Our large-scale darknet monitors (to /17 network sizes) were used to collect statistics on darknet activity from October 2018, through October 2020. The findings show that the techniques' flaws cancel each other out, leading to a perfect memory rate for the suggested methodology. Dark-TRACER also finds malicious activity on average 153.6 days before it is disclosed to the public by trustworthy third-party security study groups.
Hybrid Deep Learning with DEA-RNN for Identifying Cyberbullying in Twitter Data
Cyberbullying (CB) is on the rise in today's online communities. With so many people of all ages using social media, it's crucial that these sites be protected from harassment. In order to identify CB on the Twitter platform, this article introduces a mixed deep learning model dubbed DEA-RNN. To fine-tune the Elman RNN's characteristics and shorten training time, the suggested DEA-RNN model blends Elman type RNNs with an improved Dolphin Echolocation Algorithm (DEA). Using a dataset of 10,000 tweets, we conducted extensive testing on DEA-RNN and compared its results to those of other state-of-the-art algorithms like RNNs, SVMs, Multinomial Naive Bayes, and Random Forests. (RF). The testing findings indicate that DEA-RNN performs better than the alternatives in every situation tested. In terms of identifying CB on Twitter, it did better than the other methods that were taken into account. With an average of 90.45% accuracy, 89.52% precision, 88.98% memory, 89.25% F1-score, and 90.94% sensitivity, DEA-RNN performed best in case 3.
Real-Time Recognition of Underwater Sonar Signals Using Incremental Stream Mining and Conflict Detection
Recognizing sonar sounds is a crucial step in finding large items submerged in the ocean. Rather than relying on visual cues, the military often uses sonar signals to guide them through the depths of the ocean and/or detect the presence of enemy submarines. In particular, data mining's classification technique has been used to sonar signal detection to determine the nature of reflected surfaces. By training a classification model using the whole dataset in batches, classification algorithms in the conventional data mining technique provide reasonable accuracy. It's well knowledge that data streams are continuously acquired from sonar waves. The prior classification techniques may not be applicable to incremental classifier learning, notwithstanding their efficacy in conventional batch training. To meet the need for fast speed, data preparation time must be minimized despite the infinitely large data streams that might result from sonar signals. To avoid having to learn everything about a dataset all at once, this study introduces a new approach to data mining that is well-suited to the gradual elimination of noisy data through quick conflict analysis of the data stream. Through rigorous simulation studies, we find strong evidence for the methodology's success.
Adaptive Rate-Based Optimization Strategies for Deep Neural Networks
Profound learning structures are turning out to be more confounded, bringing about weeks, if not months, of tutoring time. This drowsy schooling is brought about by "evaporating inclinations," in which the angles utilized by engendering are gigantic for loads interfacing profound (layers close to the yield layer) and little for loads associating shallow (layers close to the information layer), bringing about sluggish learning inside the shallow layers. Besides, low arch seat factors have been displayed to create during non-raised illnesses, like profound neural organizations, which essentially eases back learning [1]. In this paper, we present an advancement technique for profound neural organization training that plans to tackle the two issues referenced above by utilizing study costs that are explicit to each layer in the organization and versatile to the ebb and flow of the element, permitting us to foster burden information at low curve components. This empowers us to learn quicker in the organization's shallow layers and break out extreme mistakes of low shape saddle parts in a short measure of time. We utilize our procedure to huge picture gloriousness datasets like as MNIST, CIFAR10, and Image Net, and exhibit that it further develops exactness while diminishing the measure of time required for preparing over immense strategies.
Enhancing Security and Data Integrity in Cloud Computing via Decentralized Solutions
Security threats are widespread in the cloud computing architecture. Threats to infrastructure, data privacy, data integrity, and stable infrastructure are all examples. These days, cloud infrastructures may be either centralized or decentralized, depending on the desired level of centralization and degree of independence from other nodes. Unfortunately, there are a number of security risks associated with relying on a centralized cloud service. Due to geo-redundancy technology and Reid Solomon erasure coding, decentralized cloud computing is more robust to outages, and data is better safeguarded.
A Reputation Generation Framework Across Platforms Using Aspect-Based Sentiment Analysis
The active growth of Internet-based applications such as social networks and e-commerce websites leads people to generate a tremendous amount of opinions and reviews about products and services,Thus, it becomes very crucial to automatically process them. Over the last ten years, many systems have beenproposed to generate and visualize reputation by mining textual and numerical reviews. However, they have neglected the fact that online reviews could be posted by malicious users that intend to affect the reputation of the target product. Besides, these systems provide an overall reputation value toward the entity and disregard generating reputation scores toward each aspect of the product. Therefore, we developed a system that incorporates spam _ltering, review popularity, review posting time, and aspect-based sentiment analysis to generate accurate and reliable reputation values. The proposed model computes numerical reputation value for an entity and its aspects based on opinions collected from various platforms. Our proposed system alsooffers an advanced visualization tool that displays detailed information about its output. Experiment results conducted on multiple datasets collected from various platforms (Twitter, Face book, Amazon . . . ) show theef_cacy of the proposed system compared with state-of-the-art reputation generation systems
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Multimodal Deep Attention Model for Disaster Detection via Social Media
In order to detect suicidal inclinations and kinds, this research details content analysis of text. The article goes on to detail the steps necessary to build a sentence classifier using a neural network trained using a variety of Python tools dedicated to machine learning. Teen suicide and online "groups of death" are major issues, and people are looking for solutions to put an end to suicide propaganda targeted at young people. Evaluation of previously published data pertaining to so-called "groups of death" and their online dissemination.
Deep Learning-Based Prediction of Plant Growth and Yield in Greenhouse Environments
For greenhouse growers and farmers in general, accurate forecasts of plant development and productivity are critical. Growers can enhance environmental control, match supply with demand, and minimise costs by developing models that accurately forecast growth and output. Powerful new analytical tools may be gained from recent advances in Machine Learning and in particular, Deep Learning (DL). Tomato yield forecasting and Ficus benjamina stem development will be predicted using ML and DL approaches in a controlled greenhouse setting in the proposed project. In the prediction formulations, we use a novel deep recurrent neural network (RNN) based on the LSTM neuron model. The RNN architecture models the intended growth parameters using the previous yield, growth, and stem diameter measurements, as well as microclimate circumstances. Support vector regression and random forest regression are compared in a researchutilising the mean square error criteria in order to assess the effectiveness of the various approaches. Results from the EU Interreg SMARTGREEN project (2017-2021) in two greenhouses in Belgium and the UK have shown great promise, according to the statistics given.
Deep neural networks for deep learning (RLSNNNs)
recurrent long short-term memory (RLSNNNs)
A Cloud Computing-Based Information System for the Development of Tourism Villages
An information system based on cloud computing may do wonders for economic growth in a certain area. Good management and exploitation of the area's current potential will allow it to be put to good use in the expansion of its environs' supply of useful things. Cloud-based information management has the potential to be utilized in a number of different kinds of advertising to better reach and engage target audiences. Segments of the local economy. In an attempt to promote tourism growth, this paper will describe a Cloud-Computing-powered information system for managing the village's infrastructure. This study is a literature review. The research team collects its knowledge through reading and analyzing a wide range of sources, such as academic publications, papers, data sets, and other materials. The findings of this research show that using Cloud computing in tourist communities ensures that data is safely kept in a centralized location. Cloud computing eliminates the need for consumers to supply physical storage devices like hard drives by storing data electronically. In addition, the availability of the Cloud-based information system may help the larger community learn more about the village's tourist potential.
A Study of Cloud Computing Techniques for Securing Data and Managing Security Parameters
The term "cloud computing" refers to a relatively new computing paradigm that transforms traditional IT and computer solutions into more conventional utilities such as public water and power. Understanding the security dangers and determining the proper security measures utilized to minimize them in Cloud Computing is the primary goal of this study. One of the biggest problems with Cloud Computing is how to keep user data safe while it's being stored and processed.
An Evaluation of the Cloud/IX Operating System for ARM-Based Data Center Servers
A transition from costly hardware to a large number of inexpensive servers has become a dominant trend in data center design architecture, creating new challenges for data center architects and necessitating the adoption of fresh approaches. In this paper, we explore alternative approaches to designing distributed systems in the spirit of the Plan9 operating system. We begin with an overview of application and research initiatives such as the porting of Plan9 to the IBM Blue Gene/L supercomputer, the usage of Plan9 in data centers and clouds, and the development of distributed embedded systems. Then, we present Cloud/IX, an OS for ARM-based server systems that, like Plan9, is based on the Plan9 architecture and runs on top of a variant of Plan 9 called 9front. We also detail the infrastructure and findings of an experimental evaluation of Cloud/IX on a real-world, multi-server farm in a data center.
Performance Optimization of Computer Vision Applications: A Case Study on STHORM Architecture
In the realm of embedded many-core architectures, computer vision applications are a major motivator. A harmony between computation and communication is vital for such systems to reach their full potential, however many computer vision techniques provide a highly data dependent actions that make the work harder. The developer needs access to tools for rapid and accurate application-level performance analysis in order to optimize application performance. Here, we go through the steps required to migrate and fine-tune a face recognition program for the STHORM many-core accelerator by way of the STHORM OpenCL software development kit. We isolate the key bottlenecks and separate the effects of the application, the openly programming paradigm, and the STHORM Openly software development kit (SDK). Finally, we demonstrate how these problems might be fixed in the near future to provide programmers even more leeway to enhance the efficiency of their applications.
Performance Analysis of Sequential and Parallel GRASP Algorithms for the 0-1 Multidimensional Knapsack Problem
The knapsack problem is a widely known problem in combinatorial optimization and has been object of many researches in the last decades. The problem has a great number of variants and obtaining an exact solution to any of these is not easily accomplished, which motivates the search for alternative techniques to solve the problem. Among these alternatives, metaheuristics seem to be suitable on the search for approximate solutions for the problem. In this work we propose a sequential and a parallel implementation for the multidimensional knapsack problem using GRASP metaheuristic. The obtained results show that GRASP can lead to good quality results, even optimal in some instances, and that CUDA may be used to expand the neighbourhood search and as a result may lead to improved quality results.
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Solar Power Monitoring System Using Arduino
The purpose of this project is to design and build a solar energy monitoring system that makes use of Arduino Board technology to accomplish its objectives. A number of parameters were assessed in this research, including thermal conductivity, light intensity, voltage conductivity, and current conductivity, among others. A temperature sensor was used to keep tabs on the temperature of the room. The intensity of the light was measured with the help of a light dependent resistor (LDR) sensor. Consequently, we employed a voltage divider to measure the voltage since the voltage generated by the solar panel is too high for the Arduino, which is functioning as the receiver in this experiment. To finish it, we used a current sensor module that was capable of detecting the current generated by the solar array to take a reading on the current. The Arduino was given these settings as input values, and the result was shown on a Liquid Crystal Display (LCD) screen on the computer. On the LCD display screen, the temperature, the light intensity, the voltage, and the current amounts are all shown in real time. In order to display the result on an LCD screen, the Arduino must transform the analogue input of a parameter to a digital output and then back to analogue. This project will also feature a design that will ensure that the device casing is portable and easy to move, amongst other things.
Advances in Biology: Investigating the Health Effects of Microwave-Heated Meals
With the increase in the use of microwave technology in the workplace, it has piqued the attention of many researchers, leading to an increase in the number of articles addressing the health hazards connected with microwave exposure. With this study, the researchers sought to determine the physiologic effects of microwave-heated food on the blood and organs of experimental mice, and the results were encouraging. This study sought to examine the effects of feeding on microwave-heated meals with the goal of improving their nutritional value. These experiments were carried out on male Swiss albino mice at two distinct ages: one month before (before puberty) and three months after (after puberty). The results of all of the albumin and bilirubin tests indicated that the levels of both parameters had increased, although the protein concentration had decreased in all of the tests. Following exposure to oxidative stress, glutathione peroxidase and superoxide dismutase levels considerably decreased, but malondahyde levels significantly increased. [*] It was discovered that physiological anomalies were caused by the oxidative stress that occurred. A recent study found that microwave radiation has a deleterious influence on liver functioning, resulting in histological and physiological abnormalities of the organ.
Automation in Agriculture Using Smart Irrigation Technology
Problems in the agricultural sector are, as is well known, a big problem right now. The Union Minister of Agriculture and Farmers Welfare, Narendra Singh Tomar, reported that 5,579 farmers in India will take their own lives in 2020. Scholars and activists have offered a wide variety of reasons for farmer suicides, but among the most prevalent are [2]heavy financial loads, agricultural failure, mental health problems, and personal problems. So, how can we find a solution that benefits farmers? "Agricultural Automation" is the correct response. Soil erosion, for example, might be an issue for farmers whether there is an abundance of water or not enough. The required crop quality is not yet available. Water shortage is another issue; so, water efficiency is key. No matter the weather—be it scorching heat that causes sunstroke, severe illnesses brought on by heavy rains, or dangerously cold temperatures that deteriorate farmers' health—they must water the crops manually. Agricultural automation has the potential to resolve most of these issues.
Design and Analysis of a Circular Microstrip Patch Antenna for WiMAX Applications
The micro strip antennae have become pretty famous within the fields of cell and cell communications, further to RFID programs with the appearance of diverse simulation equipment, inexperienced designs of MSA’s in considered one of a kind patch configurations are being practiced, thinking about exceptional substrate or high-quality strate mixtures at recognized microwave frequencies of interest. In this dissertation, investigations are carried on the overall performance trends of round patch micro strip published antenna at 3.5 GHz that's suitable for Wi MAX packages.
Physico-Chemical Analysis of Musi River Water- Justification for Agricultural utilization
This study evaluates water quality in Hyderabad's Musi River (Himayathsagar to Nalgonda) using key parameters as per Indian Standards. While pH met irrigation norms, Electrical Conductivity showed moderate salinity. Alarmingly, Biological Oxygen Demand exceeded irrigation limits by 3-4 times and drinking standards by 150 times, indicating severe organic pollution from untreated waste. The findings highlight the river's unsuitability for agricultural or domestic use without treatment, necessitating urgent remediation measures to restore water quality and protect public health.
PEANUT CROP PEST DETECTION AND CLASSIFICATION USING MACHINE LEARNING, CONVOLUTIONAL FUZZY LOGIC, AND EVITA
Image recognition and classification have both benefited greatly from the fast development of Vision Transformer (ViT) techniques. Specifically for the purpose of identifying, segmenting, and classifying pests, this study presents an Enhanced Vision Transformer Architecture (EViTA). With EViTA, we want to increase the accuracy of pest image prediction by capitalising on ViT's advantages over CNNs. Among the preprocessing methods used in this approach are Moth Flame Optimisation (MFO) for normalising and flattening images, and a dual-layer transformer encoder for integrating pest picture segments of different sizes. The effectiveness of EViTA has been shown by extensive studies that used three insect datasets that impact peanut crops. The findings are encouraging. Research into supplementary methods, including as DenseNet, InceptionV3, and Xception TL models, also points to avenues for accuracy gains above 94%. Also, by using the Flask framework, it is possible to create an authentication-ready testing front end that is easy for users to navigate. EViTA introduces a new way of looking for pests, which might greatly improve farming and pest control. The potential for EViTA to perform better on pest detection tasks might be enhanced with further study and optimisation.
MODEL-BASED CLOUD GENERATION: A NOVEL APPROACH FOR SECURING CLOUD ENVIRONMENTS
One major security risk in cloud computing settings is authorization. Its goal is to control user access to system resources. The implementation of security standards is difficult and error-prone due to the abundance of resources connected with REST APIs, which are common in cloud computing environments.In this research, we suggest an implementation of security cloud monitor to mitigate this issue. To express the functional and security requirements, we use a model-driven approach. Cloud monitors are then created using the models. Contracts that are used to automatically verify implementation are contained in the cloud monitors. We implement cloud monitor using the Django web framework, and we validate our solution using OpenStack.
LEAF DISEASE IDENTIFICATION USING AN ATTENTION MECHANISM AND A LIGHTWEIGHT DEEP RESIDUAL NETWORK
The need for very accurate identification in the field of leaf disease detection is higher than ever before. This project presents SE-VRNet, a lightweight model developed specifically for the purpose of detecting leaf diseases on mobile devices. Its target audience is the agriculture industry, which heavily uses smartphones and tablets. Utilising state-of-the-art methods such as the deep variant residual network (VRNet) and an attention mechanism in the squeeze-and-excitation (SE) module, SE-VRNet extracts disease-related characteristics from leaf pictures with great skill, guaranteeing accurate diagnosis and classification of different leaf diseases.By accurately recognising diseases in leaf pictures, SE-VRNet overcomes obstacles such as scattered lesion sites and varying area widths. Lightweight models are crucial for mobile devices, and this experiment highlights the necessity of computing economy without sacrificing accuracy, which SE-VRNet does. The model's practicality and efficacy are emphasised, providing a hopeful answer for farmers regarding the prompt control of diseases and the preservation of crops.The team is aiming for a detection threshold of 0.85mPA or higher and is investigating other approaches like YoloV5 and YoloV8 to further improve performance. With a 99.80% training accuracy and a 96% test accuracy on self-generated data, the base paper's results with SE-VRNet provide a solid basis for this investigation into diverse deep learning models. These models promise improved efficiency in detecting leaf diseases and enhancing agricultural resilience.
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