Volume 10 | Issue 8 | August-2022

 Abstract

India is the agriculture-based country. Our ancient people completely depended on the agricultural harvesting. Agriculture is a source of livelihood of majority Indians and has great impact on the economy of the country. In dry areas or in case of inadequate rainfall, irrigation becomes difficult. So, it needs to be automated for proper yield and handled remotely for farmer safety. Increasing energy costs and decreasing water supplies point out the need for better water management. Irrigation management is a complex decision-making process to determine when and how much water to apply to a growing crop to meet specific management objectives. If the farmer is far from the agricultural land he will not be noticed of current conditions. So, efficient water management plays an important role in the irrigated agricultural cropping systems. A low-cost alternative solution for efficient water management currently in use is drip irrigation systems that consist of an automated controller to turn on & off the control values, which in turn helps the farmers by managing the water supply to the crop fields and further maintains the moisture levels of soil that helps in better crop production. This project probes into the design of the automated irrigation system based on Arduino. This Embedded project is to design and develop a low-cost feature which is based on embedded platform for water irrigation system. This project uses temperature and soil moisture sensors to detect the water quantity present in agriculture. The project uses Arduino micro controller which is controller to process the information. The aim of the implementation was to demonstrate that the automatic irrigation can be used to reduce water use

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 Keywords

GSM: Global system of mobile

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 Abstract

Agriculture is India's economic backbone. Scientists are tirelessly striving to improve the cultivation of crops. This system combines all the factors like nutrient requirements, moisture content monitoring, animal intrusion and early crop disease detection methods to alert farmers. Soil nutrients are critical for achieving high crop yields and producing high- quality products. This system is used to design and develop a working system that uses Ph sensor to monitor the Ph values and to maintain the amount of alkalinity and acidity in soil for the efficient plant growth. It uses soil moisture sensor to regulate the moisture levels of the soil preventing less yield due to inadequate and nutrients erosion due to excess soil moisture. It gives an effective suggestion system for the crops using NPK sensor to prevent crop dehydration due to excess and stunted growth, discoloring on the edges to leaves due to insufficient supply of nutrients. It uses camera module to avoid the animal intrusion using alarm alert system using computer vision algorithms. Plant infections may be detected early, which helps farmers prevent losses using deep learning algorithms. After recognizing the symptoms of leaf diseases, automatic approaches for classifying plant diseases can also help with decision-making also to create a user-friendly mobile application for easy access to all the information required and deploy it on cloud so that it can be accessible from anywhere. Hence, Automatic detection and monitoring is very much important for the sustainable agricultural revenue.

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 Keywords

Agriculture, NPK, Ph, CNN, crop recommendation

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 Abstract

People get to know one another through sharing their ideas, thoughts, and experiences with others around them. There are several ways to accomplish this, the best of which is the gift of "Speech." It will be unjust if we overlook those who are denied this priceless gift: the deaf and dumb. The usage of "Sign Language" is the only mode of communication available to the deaf and dumb. Mute people can make hand gestures with their fingers, which will be transformed into speech so that regular people can understand them. The deaf and dumb communicate through sign language, which is difficult to decipher for those who are not familiar with it. As a result, software for the phone that can translate motions into text and speech is required. This will be an innovative step toward allowing deaf and dumb people to communicate with the general population without the use of a secondary device other than a Smartphone. We'll be working on an app that will make it easier for people who are deaf or hard of hearing to communicate. Flutter is used for the front end, and Python script is used for the backend. It attempts to provide dumb people with an effective and economical means to express them by converting motions to text and deaf people's speech to text and gestures. This application will create a better society where community sees them as one and people will look into their abilities than disabilities. This application will create a better society where community sees them as one and people will look into their abilities than disabilities.

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 Keywords

Flutter, Python, Decipher

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 Abstract

A brain tumour is a misshapen cell growth that can be malignant or non-cancerous in nature. The most deadly condition is a brain tumour, which can be detected with automated procedures on MRI images and identified quickly and accurately. Several methods of diagnosis and segmentation of brain tumors have been suggested by many researchers for effective tumor detection. Because of the high diversity in tumor tissue of different patients, automating this process could be a challenging risk. Medical resonance imaging is a challenging and innovative field in the medical science field. In the medical field, the techniques of ML( Machine learning ) and Deep learning holds a significant stand. Brain tumor detection using MRI images have many applications. Brain tumor detection from MRI images is one in all the emerging fields in medical science. There are different brain tumor detection methods. Detection and segmentation methods are used to detect the brain tumor from the MRI images. Here a brief review of the different brain tumor detection methods has been discussed. The different methods include Convolution Neural Network, Artificial Neural Network, Resnet - 50 and CNN with Transfer Learning.

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 Keywords

Brain Tumor, MRI images, Convolution Neural Network, Artificial Neural Network, Resnet - 50, CNN with Transfer Learning

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 Abstract

The Areca nut, often known as betel nut, is a tropical crop. India is the world's second-largest producer and consumer of areca nut. A multitude of illnesses afflict it throughout its life cycle, from root to fruit. The current method of disease detection is simple observation with the naked eye, and farmers must thoroughly study each crop on a regular basis to detect diseases. We proposed a method that uses Convolution Neural Network (CNN) and Support Vector Machine (SVM) algorithm to detect whether the given areca nut is of bad quality or good quality. We created our own dataset comprising of 208 photos of good and bad quality areca nuts to train and test the model. By using CNN we have obtained the accuracy of 97.12% and by using SVM we obtained the accuracy of 93.68%.

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 Keywords

Areca nut, Convolution Neural Network, Support Vector Machine

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 Abstract

Lives of animals are valuable. As global citizens, we must work to ensure the survival and development of animals in order to maintain the ecosystem's balance and stability. Wildlife monitoring collects data on wildlife species, numbers, habits, quality of life and habitat conditions to aid researchers in understanding the status and dynamics of wildlife resources and to serve as a foundation for effective wildlife resource protection, sustainable use and scientific management. Continuous hunting has resulted in the extinction of many animal species and the government has enacted legislation and is undertaking surveys to conserve certain species. Conducting surveys is a difficult undertaking, especially if don't have the necessary resources are not available. Conducting Survey is the Challenging undertaking, especially without the assistance of technology. To address this, Species Classification wireless camera implementation is carried out. The smart camera is combined with a phython based programming that includes a pretrained Tensor Flow model Some species are rare to find and even when they are, predicting their classification is challenging. From a human perspective, various species present in various settings appear in various sizes, shapes, colours, and angles. Superior choice of technique for detecting and classifying distinct species is to be carried out in order to better conserve and preserve them.

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 Keywords

LIDAR, FSP, SVM, CNN, Forest stand

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Adders are an essentially universal component of today's integrated circuits. The constantly developing computing industry demands not just faster arithmetic units, but also smaller and less power consumption arithmetic circuits. The adder must be quick and also efficient in chip area to meet its requirements. In order to construct an adder of 32-bit using eight 4-bit adder for our project, we used the Carry look ahead adder. It is also known as fast adders because it consumes less time over other adders by propagating carry before the sum output is obtained, resulting in brilliant performance. We designed the schematic and layout of CLA using LT spice simulation programmed in Electric binary using 45nm technology.

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 Keywords

Adder, CMOS, Electric, CLA

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Transportation-related greenhouse gas emissions are one of the most serious environmental concerns, and they are increasing at an alarming rate. As a result, solar energy for transportation may be able to alleviate this issue. The goal of the proposed effort is to offer a technology that promotes green energy; for example, assume a situation in which we might utilize solar energy to charge an electric vehicle, with solar panels built-in, but the next question is if this is practical during the rainy season. During the rainy season, charging internal solar panels is problematic. The SPEV system includes a charging wire that connects to both the car and a 230v wall outlet. A security system, drive guiding system, route detection, and other features are included into the electric car. Support for Android apps, Wi-Fi, Battery Update, and LoRa. Our research findings are thoroughly examined. Hence The use of a solar-powered electric vehicle (SPEV) results in fewer pollution. The majority of automobiles are powered by gasoline. Hazardous gases are emitted by these vehicles. This contributes to global environmental contamination. In recent years, academics have proposed the use of hybrid vehicles to minimize pollution. Many countries have chosen to reduce pollution by employing electric vehicles as one of their answers (EV). EVs have been rising in popularity in recent years. The electric vehicle's battery is a critical component. The efficient utilization of battery is a critical parameter for electric vehicles. The efficiency of electric vehicles can be improved by using renewable energy sources such as solar energy could be increased In electric vehicles, the energy management system (EMS) is crucial. Because the number of subsystems and components in electric vehicles is growing, enhancing EV efficiency through the use of EMS is desirable. Various EMS systems are explored in this research, and an energy model for effective battery use to increase EV performance is proposed

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

SPEV; Solar powered Electric Vehicle EV; Electric Vehicle EMS; Energy Management System

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Digit recognition is a suitable model issue for learning about the Neural Networks, and it will pave the way for sophisticated Deep Learning techniques.Manymethods are found in literature to recognize and classify the digits which are written by hand (handwritten digits).ThispaperexplainshowtorecognizeandclassifyhandwrittendigitsusingCNNandMATLABforenhancedperformance. A deep convolutional neural network (CNN) is a type of neural network that is used to recognise images. This method determines how important the deep layer improvements are for processingthe image byusing MATLAB, we provide the implementationof necessary for constructing and applying Convolution Neural Network to a high-quality data set known as MNIST which is a collection of more than 60,000 handwritten digits dataset for training purpose and 10,000 digitsdatasetfortesting purpose.Whentestedthedevelopedmodelforclassification, we got 99.60% accuracy and prove to be betterthanotherclassifier. Many hidden convolution layers and more hidden artificial neurons could improve the accuracy of the outcome.

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 Keywords

Convolution, Padding, Stride, Softmax, Maxpooling, ReLU

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Internet of Things, Biometric, Finger print authentication, Embedded system, Voting system

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Creative Commons Attribution 4.0 and The Open Definition