Automated Irrigation System Based on LoRa and ML for Marginal Farmers

2020 ◽  
Vol 10 (3) ◽  
pp. 345-353
Author(s):  
Selvam Loganathan ◽  
Kavitha Perumal
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Algorithm ◽  
Irrigation System ◽  
Cost Effective ◽  
Automated System ◽  
Correct Prediction ◽  
The World ◽  
Soil Status ◽  
Automated Irrigation System

Background & Objective:: India is one of the foremost agricultural producers in the world; on the other hand, the consumption of water for agricultural purposes in India has been among the highest in the world. Indiscriminate use of inadequate irrigation techniques has led to a critical water deficit in the country. Now with the development of (IoT) Precision Farming and Precision Irrigation are becoming very popular. This paper proposes a cost-effective Automated Irrigation System based on LoRa and Machine Learning, which can be of great help to marginal farmers, for whom agriculture is hardly a profitable venture, mainly due to water scarcity. Methods: In this automated system, LoRa technology is used in Sensor and Irrigation node, in which sensors collect data on soil moisture and temperature and send it to the server through a LoRa gateway. Then the data is fed into a Machine Learning algorithm, which leads to correct prediction of the soil status. Results: Hence, the field needs to be irrigated only if and when it is needed. Conclusion: The system can be remotely monitored using a web application that can be accessed by a mobile phone.

scholarly journals Smart irrigation using WSN based on IOT

2018 ◽  
Vol 7 (2.8) ◽  
pp. 331 ◽  
Author(s):  
VP Krishna Anne ◽  
Kuricheti R V Siva Naga Durg ◽  
Rama Krishna Muddineni ◽  
Surya Gowtham Peri
Keyword(s):  
Web Application ◽  
Irrigation System ◽  
Automated System ◽  
Communication Link ◽  
Threshold Values ◽  
Crop Water ◽  
Crop Field ◽  
Self Rule ◽  
Temperature Measure ◽  
Automated Irrigation System

To set right the usage of water for crops of agriculture an automated irrigation system has been implemented. A moisture soil sensor; and a temperature measure sensor which is called as network of the distributed wireless is used at base of the plant. Along with these, we implemented a gateway unit. which gathers information and regulate it and by activating the triggers actuators, it can send and receive the transmits data to and from the web application. I proposed the algorithm which having the temperature and soil moisture threshold values that embedded in a gate way based on micro controller. It implemented panels of the photovoltaic; and having a duplex communication link; and works with the interface i.e. cellular-Internet which offers that data inspection & irrigation timing. All this can be programmed by using a web page. Implemented automated Crop water saving system tested for 136 days in sage crop field. It can be saved 90% water compared to others. The main 3 advantages of this automated system make it place successfully in any place for 18 months. As it is energy self-rule, cost less, so it can be efficiently useful in limited water geographical lands.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

Android-Based Skin Cancer Recognition System Using Convolutional Neural Network

2021 ◽  
pp. 59-85
Author(s):  
Sercan Demirci ◽  
Durmuş Özkan Şahin ◽  
Ibrahim Halil Toprak
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Cancer ◽  
Early Diagnosis ◽  
Learning Algorithm ◽  
Recognition System ◽  
Machine Learning Algorithm ◽  
The Sun ◽  
Human Beings ◽  
The World

Skin cancer, which is one of the most common types of cancer in the world, is a malignant growth seen on the skin due to various reasons. There was an increase in the number of the cases of skin cancer nearly 200% between 2004-2009. Since the ozone layer is depleting, harmful rays reflected from the sun cannot be filtered. In this case, the likelihood of skin cancer will increase over the years and pose more risks for human beings. Early diagnosis is very significant as in all types of cancers. In this study, a mobile application is developed in order to detect whether the skin spots photographed by using the machine learning technique for early diagnosis have a suspicion of skin cancer. Thus, an auxiliary decision support system is developed that can be used both by the clinicians and individuals. For cases that are predicted to have a risk higher than a certain rate by the machine learning algorithm, early diagnosis could be initiated for the patients by consulting a physician when the case is considered to have a higher risk by machine learning algorithm.

Web-Based Machine Learning Application for Heart Disease Prediction

2022 ◽  
pp. 383-393
Author(s):  
Lokesh M. Giripunje ◽  
Tejas Prashant Sonar ◽  
Rohit Shivaji Mali ◽  
Jayant C. Modhave ◽  
Mahesh B. Gaikwad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Web Application ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Web Based ◽  
The World ◽  
Comprehensive Survey ◽  
Health Organization

Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.

Plural marking patterns of nouns and their associates in the world’s languages

2020 ◽  
Vol 44 (1) ◽  
pp. 231-269
Author(s):  
Rong Chen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Component Structure ◽  
Universal Distribution ◽  
Two Component ◽  
The World ◽  
Plural Marking

Abstract Plural marking reaches most corners of languages. When a noun occurs with another linguistic element, which is called associate in this paper, plural marking on the two-component structure has four logically possible patterns: doubly unmarked, noun-marked, associate-marked and doubly marked. These four patterns do not distribute homogeneously in the world’s languages, because they are motivated by two competing motivations iconicity and economy. Some patterns are preferred over others, and this preference is consistently found in languages across the world. In other words, there exists a universal distribution of the four plural marking patterns. Furthermore, holding the view that plural marking on associates expresses plurality of nouns, I propose a hypothetical universal which uses the number of pluralized associates to predict plural marking on nouns. A data set collected from a sample of 100 languages is used to test the hypothetical universal, by employing the machine learning algorithm logistic regression.

scholarly journals Data-Driven Intelligent Application for Youtube Video Popularity Analysis using Machine Learning and Statistics

2021 ◽  
Author(s):  
Wenxi Gao ◽  
Ishmael Rico ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Web Application ◽  
Data Driven ◽  
Accuracy Evaluation ◽  
Video Content ◽  
The Public ◽  
Left Behind ◽  
The World ◽  
Web App

People now prefer to follow trends. Since the time is moving, people can only keep themselves from being left behind if they keep up with the pace of time. There are a lot of websites for people to explore the world, but websites for those who show the public something new are uncommon. This paper proposes an web application to help YouTuber with recommending trending video content because they sometimes have trouble in thinking of the video topic. Our method to solve the problem is basically in four steps: YouTube scraping, data processing, prediction by SVM and the webpage. Users input their thoughts on our web app and computer will scrap the trending page of YouTube and process the data to do prediction. We did some experiments by using different data, and got the accuracy evaluation of our method. The results show that our method is feasible so people can use it to get their own recommendation.

scholarly journals Smart irrigation system based on IoT

2020 ◽  
Author(s):  
arushi dheer ◽  
M. L. sharma ◽  
krishna tripathi
Keyword(s):  
Large Scale ◽  
Soil Analysis ◽  
Irrigation System ◽  
Cost Effective ◽  
Water Shortage ◽  
Home Garden ◽  
Agriculture Sector ◽  
Indian Agriculture ◽  
Shared Network ◽  
Automated Irrigation System

<div><div><div><div><p>Agriculture is the backbone of the Indian economy. The Indian agriculture sector accounts for 18% of the gross domestic product and employs nearly 50% of the country's workforce, with increasing population, water shortage and ever-growing demand for food. Since the acres of land available for cultivation remains unchanged, it is critical that we take steps towards increasing productivity and optimizing water usage to increase yield from the land currently available for cultivation. Soil Analysis has become an essential factor for effective cultivation. The need for the automated irrigation system is to overcome over-irrigation and under-irrigation.[1] This research paper proposes an automated irrigation system using Arduino microcontroller, which is cost-effective and can be used on a farm field or average home garden. IoT is an upcoming technology with huge prospects. IoT is a technology which connects things, people, applications, data. Internet of Things (IoT)is a shared network of objects or things which can interact with each other provided the Internet connection—using this technology to implement this system at a lower scale to act as a base model. With the implementation of this project at a large scale, it could bring a significant change in the overall yield and water consumption in agriculture.</p></div></div></div></div>

scholarly journals Detection of Novel Corona Virus Using Machine Learning and Image Recognition

2020 ◽  
Vol 6 (12) ◽  
pp. 394-397
Author(s):  
Dhruv Garg and Saurabh Gautam
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Recent Past ◽  
Great Precision ◽  
The Past ◽  
Corona Virus ◽  
The World ◽  
Affected Person ◽  
Precise Machine ◽  
Self Learning

In the recent past whole of the world has come to a standstill due to a novel airborne virus. The airborne nature of this disease has made it highly contagious which has led to a great number of people being infected very fast. This requires a new method of testing that is faster and more precise. Machine Learning has allowed us to develop sophisticated self-learning models that can learn from data being fed and decide on entirely new options. In the past we have used different Machine Learning algorithm to make models on different biomedical dataset to detect various kind of acute or chronic diseases. Here we have developed a model that successfully detects severe cases of Novel corona virus affected person with great precision.

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