Agricultural research has strengthened the optimized
economical profit, internationally and is very vast and
important field to gain more benefits.
In future agriculture is the only scope for all the people. But
today number of people having land, but they don’t know how
to yield the crops.
So many of people are doing useless agriculture by
cultivating the crop on improper soil. To implement the
application to identify the types of soil,water source of that
land whether that land is based on rain or bore water. And
suggest what of crop is suitable for that soil. So through this
application provide application for the people to know about
the agriculture. There is no any application to know about the
cultivation. However, it can be enhanced by the use of different
technological resources, tool, and procedures. Predict the type
of crop which one is suitable for that particular soil, weather
condition, temperature and so on. So for, using machine
learning with the set of data set are identified the crop for the
corresponding soil.
Wildlife entering in to populated areas has
recently become very Popular. The space for wild
animals is decreasing as humans are encroaching
forests. It creates great loss to property and life
when wild animals enter in to cities. We use latest
advances in technology such as Internet of Things
(IoT) to create an alert system of possible wildlife
leaving the forest and also the message will send to
the users Mobile to alert them. We use low cost
motion detectors and Passive Infrared sensors to
achieve this. We relay information of such motion
to a control centre to take further actions. We also
include making loud noise through speakers in
which the animals cannot enter in to land. The
basic idea of IoT is to connect different sensors
and establish communication and also provide
services. In this article, we make use of several IoT
devices at the periphery of natural reserve to
create an alert system. This system can also be
used to find out smugglers and other people
illegally entering in to the forest.
Despite the perception people may have regarding the agricultural process, the reality is that today’s agriculture
industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT)
based technologies redesigned almost every industry including ‘‘smart agriculture’’ which moved the industry
from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture
methods and creating new opportunities along a range of challenges. This article highlights the potential of
wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this
technology with the traditional farming practices. IoT devices and communication techniques associated with
wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for
specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are
listed.For the sake of obtaining the interest data, wireless sensor networks (WSNs) are used to collect the
interest data in the farm field and send the obtained data to the servers via wireless communication. Since the
WSNs usually operate in the unlicensed spectrum ,the available resource elements (REs) are scarce especially
when a large number of sensor nodes are deployed in the farm field.
Wildlife entering in to populated areas has
recently become very Popular. The space for wild
animals is decreasing as humans are encroaching
forests. It creates great loss to property and life
when wild animals enter in to cities. We use latest
advances in technology such as Internet of Things
(IoT) to create an alert system of possible wildlife
leaving the forest and also the message will send to
the users Mobile to alert them. We use low cost
motion detectors and Passive Infrared sensors to
achieve this. We relay information of such motion
to a control centre to take further actions. We also
include making loud noise through speakers in
which the animals cannot enter in to land. The
basic idea of IoT is to connect different sensors
and establish communication and also provide
services. In this article, we make use of several IoT
devices at the periphery of natural reserve to
create an alert system. This system can also be
used to find out smugglers and other people
illegally entering in to the forest.
Despite the perception people may have regarding the agricultural process, the reality is that today’s agriculture
industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT)
based technologies redesigned almost every industry including ‘‘smart agriculture’’ which moved the industry
from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture
methods and creating new opportunities along a range of challenges. This article highlights the potential of
wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this
technology with the traditional farming practices. IoT devices and communication techniques associated with
wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for
specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are
listed.For the sake of obtaining the interest data, wireless sensor networks (WSNs) are used to collect the
interest data in the farm field and send the obtained data to the servers via wireless communication. Since the
WSNs usually operate in the unlicensed spectrum ,the available resource elements (REs) are scarce especially
when a large number of sensor nodes are deployed in the farm field.
IoT Based Remote Monitoring and Controlling for Shutter Systemsijtsrd
The technological advancement of the current technology has affected the processes of the most of the economic and social related businesses. The aims of this advancement are to serve and make human life more comfortable and however, there are still lots of areas in our daily life where manual processes are used. Taking as an example in the water control and management systems, where many authorities use manual systems for water controland management. Especially nowadays most of the countriesare still using manual system for controlling and monitoring the dams. Due to the complicated and time consuming process in a manual system, a model for Remote Monitoring and Controlling of Dams is proposed that uses remote control technology, linked to the web technology, to attain great success in monitoring and controlling water levels in managing dams. This paper is to present a new solution which it is to implemental proposed system called as RMCD ""Remote Monitoring and Controlling of Dams"". With the proposed system it will allow the user to control and monitor the dams remotely which it is saving a lot of efforts, reducing the cost and also increasing the monitoring quality as the users are going to use automated system rather than using of manual system. Shaik Roshan | Dr. M. Muthuvinayagam | S. Manivannan ""IoT Based Remote Monitoring and Controlling for Shutter Systems"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30123.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30123/iot-based-remote-monitoring-and-controlling-for-shutter-systems/shaik-roshan
Since the development of advanced metering and digital technology, Smart City has been equipped based on things IOT in different electronic devices, and therefore smarter than before. The purpose of this paper is to carry out the concept of smart cities and their motivation and application of a comprehensive review. In addition, the survey describes the intelligent networking technology and smart cities and major components of urban functions. This also explains the major challenges and experiences around the world. Mukesh Kumawat | Mr. Durgesh Kumar | Dr. Garima Mathur ""IOT Based Smart Cities"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23246.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23246/iot-based-smart-cities/mukesh-kumawat
A Survey on Wireless Sensor Network For Agriculturerahulmonikasharma
Wireless sensor Network is widely used in agriculture field. It provides a new direction of research in agricultural domain. WSN gives various benefits such as crop monitoring, crop management and water management. The aim of this paper to review the need of WSN in agriculture field such as real time data capturing from field, devices used etc. Farming is one of the major domain where WSN is used. It is very difficult to do the field management manually. With the help of WSN farmer can capture real time data from field and it will be beneficial for crop management.
IoT Based Water Level Monitoring System with an Android Applicationrahulmonikasharma
The principle objective of our assignment is to display the water level as well as controlling with IoT and android application. Wastage of water within the modern state of affairs, merely because of overflowing tanks isn't less expensive. Conventional water tanks can neither reveal nor manage the water stage in tank, main to huge amount of wastage. A few different technologies had sure drawbacks in a few or the alternative way. The want of removal of those quick-comings and offering an efficient and competitively priced solution has been the principle focus of this assignment. The IoT platform we are using is Arduino which is an open source. The water level within the water tank is split into most, minimal and nominal degrees indicated by using exclusive colorings for each. An ultrasonic sensor is positioned on the floor of the tank to sense the water stage and the distance is dispatched to the android utility through Arduino. We can display the tank manually using an on/off button provided within the android utility. The android application is a user interface which displays the tank format, a button for guide operation and a led for indicating the motor popularity.
The Internet of Things (IoT) has provided promising opportunities to create powerful industrial and domestic applications. One of its main applications is smart metering. The existing analogue meter in residential area requires consistent human monitoring, which leads to computational errors. Huge labor force, their negligence and money invested are the drawback of such meters. Therefore, a cost effective and low power smart-meter that can monitor the daily consumption of water in residential area need to be developed, in order to conserve water. Here in this research, SOC based smart water meter is developed to provide cost effective solution. Further, the developed system is implemented in real time to investigate the reliability and feasibility.
A Review on Associative Classification Data Mining Approach in Agricultural S...Editor IJMTER
Data mining in agriculture is a very recent research topic. It consists in the
application of data mining techniques to agriculture. Recent technologies are nowadays able to
provide a lot of information on agricultural-related activities, which can then be analyzed in
order to find important information. A related, but not equivalent term is precision agriculture.
This research aimed to assess the various classification techniques of data mining and apply
them to a soil science database to establish if meaningful relationships can be found. A large
data set of soil database is extracted from the Soil Science & Agricultural department, Bhopal
M.P and National Informatics Centre, The application of data mining techniques has never
been conducted for Bhopal soil data sets. The research compares the different classifiers and
the outcome of this research could improve the management and systems of soil uses
throughout a large number of fields that include agriculture, horticulture, environmental and
land use management.
Hyperparameters analysis of long short-term memory architecture for crop cla...IJECEIAES
Deep learning (DL) has seen a massive rise in popularity for remote sensing (RS) based applications over the past few years. However, the performance of DL algorithms is dependent on the optimization of various hyperparameters since the hyperparameters have a huge impact on the performance of deep neural networks. The impact of hyperparameters on the accuracy and reliability of DL models is a significant area for investigation. In this study, the grid Search algorithm is used for hyperparameters optimization of long short-term memory (LSTM) network for the RS-based classification. The hyperparameters considered for this study are, optimizer, activation function, batch size, and the number of LSTM layers. In this study, over 1,000 hyperparameter sets are evaluated and the result of all the sets are analyzed to see the effects of various combinations of hyperparameters as well the individual parameter effect on the performance of the LSTM model. The performance of the LSTM model is evaluated using the performance metric of minimum loss and average loss and it was found that classification can be highly affected by the choice of optimizer; however, other parameters such as the number of LSTM layers have less influence.
IoT Based Remote Monitoring and Controlling for Shutter Systemsijtsrd
The technological advancement of the current technology has affected the processes of the most of the economic and social related businesses. The aims of this advancement are to serve and make human life more comfortable and however, there are still lots of areas in our daily life where manual processes are used. Taking as an example in the water control and management systems, where many authorities use manual systems for water controland management. Especially nowadays most of the countriesare still using manual system for controlling and monitoring the dams. Due to the complicated and time consuming process in a manual system, a model for Remote Monitoring and Controlling of Dams is proposed that uses remote control technology, linked to the web technology, to attain great success in monitoring and controlling water levels in managing dams. This paper is to present a new solution which it is to implemental proposed system called as RMCD ""Remote Monitoring and Controlling of Dams"". With the proposed system it will allow the user to control and monitor the dams remotely which it is saving a lot of efforts, reducing the cost and also increasing the monitoring quality as the users are going to use automated system rather than using of manual system. Shaik Roshan | Dr. M. Muthuvinayagam | S. Manivannan ""IoT Based Remote Monitoring and Controlling for Shutter Systems"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30123.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30123/iot-based-remote-monitoring-and-controlling-for-shutter-systems/shaik-roshan
Since the development of advanced metering and digital technology, Smart City has been equipped based on things IOT in different electronic devices, and therefore smarter than before. The purpose of this paper is to carry out the concept of smart cities and their motivation and application of a comprehensive review. In addition, the survey describes the intelligent networking technology and smart cities and major components of urban functions. This also explains the major challenges and experiences around the world. Mukesh Kumawat | Mr. Durgesh Kumar | Dr. Garima Mathur ""IOT Based Smart Cities"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23246.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23246/iot-based-smart-cities/mukesh-kumawat
A Survey on Wireless Sensor Network For Agriculturerahulmonikasharma
Wireless sensor Network is widely used in agriculture field. It provides a new direction of research in agricultural domain. WSN gives various benefits such as crop monitoring, crop management and water management. The aim of this paper to review the need of WSN in agriculture field such as real time data capturing from field, devices used etc. Farming is one of the major domain where WSN is used. It is very difficult to do the field management manually. With the help of WSN farmer can capture real time data from field and it will be beneficial for crop management.
IoT Based Water Level Monitoring System with an Android Applicationrahulmonikasharma
The principle objective of our assignment is to display the water level as well as controlling with IoT and android application. Wastage of water within the modern state of affairs, merely because of overflowing tanks isn't less expensive. Conventional water tanks can neither reveal nor manage the water stage in tank, main to huge amount of wastage. A few different technologies had sure drawbacks in a few or the alternative way. The want of removal of those quick-comings and offering an efficient and competitively priced solution has been the principle focus of this assignment. The IoT platform we are using is Arduino which is an open source. The water level within the water tank is split into most, minimal and nominal degrees indicated by using exclusive colorings for each. An ultrasonic sensor is positioned on the floor of the tank to sense the water stage and the distance is dispatched to the android utility through Arduino. We can display the tank manually using an on/off button provided within the android utility. The android application is a user interface which displays the tank format, a button for guide operation and a led for indicating the motor popularity.
The Internet of Things (IoT) has provided promising opportunities to create powerful industrial and domestic applications. One of its main applications is smart metering. The existing analogue meter in residential area requires consistent human monitoring, which leads to computational errors. Huge labor force, their negligence and money invested are the drawback of such meters. Therefore, a cost effective and low power smart-meter that can monitor the daily consumption of water in residential area need to be developed, in order to conserve water. Here in this research, SOC based smart water meter is developed to provide cost effective solution. Further, the developed system is implemented in real time to investigate the reliability and feasibility.
A Review on Associative Classification Data Mining Approach in Agricultural S...Editor IJMTER
Data mining in agriculture is a very recent research topic. It consists in the
application of data mining techniques to agriculture. Recent technologies are nowadays able to
provide a lot of information on agricultural-related activities, which can then be analyzed in
order to find important information. A related, but not equivalent term is precision agriculture.
This research aimed to assess the various classification techniques of data mining and apply
them to a soil science database to establish if meaningful relationships can be found. A large
data set of soil database is extracted from the Soil Science & Agricultural department, Bhopal
M.P and National Informatics Centre, The application of data mining techniques has never
been conducted for Bhopal soil data sets. The research compares the different classifiers and
the outcome of this research could improve the management and systems of soil uses
throughout a large number of fields that include agriculture, horticulture, environmental and
land use management.
Hyperparameters analysis of long short-term memory architecture for crop cla...IJECEIAES
Deep learning (DL) has seen a massive rise in popularity for remote sensing (RS) based applications over the past few years. However, the performance of DL algorithms is dependent on the optimization of various hyperparameters since the hyperparameters have a huge impact on the performance of deep neural networks. The impact of hyperparameters on the accuracy and reliability of DL models is a significant area for investigation. In this study, the grid Search algorithm is used for hyperparameters optimization of long short-term memory (LSTM) network for the RS-based classification. The hyperparameters considered for this study are, optimizer, activation function, batch size, and the number of LSTM layers. In this study, over 1,000 hyperparameter sets are evaluated and the result of all the sets are analyzed to see the effects of various combinations of hyperparameters as well the individual parameter effect on the performance of the LSTM model. The performance of the LSTM model is evaluated using the performance metric of minimum loss and average loss and it was found that classification can be highly affected by the choice of optimizer; however, other parameters such as the number of LSTM layers have less influence.
Implemented various classification models using R language to identify which one performs best for prediction of soil fertility and which properties are important in defining the fertility of soil.
Data Mining Assignment Sample Online - PDFAjeet Singh
A data mining assignment sample may include tasks such as data preprocessing, exploratory data analysis, modeling, and evaluation. For example, students may be asked to clean and preprocess a dataset, perform exploratory data analysis to gain insights into the data, build predictive models using techniques such as classification or regression, and evaluate the performance of the models using metrics such as accuracy or precision.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have anaccurate model for rainfall prediction. Recently, several data-driven modeling approaches havebeen investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third of the data was used for training the model and One-third for testing.
Crop yield prediction using data mining techniques.pdfssuserb22f5a
Agriculture is the main source of occupation which forms the backbone of our country. It involves the production of crops which may be either food crops or commercial crops. The productivity of crop yield is significantly influenced by various parameters such as rainfall, farm capacity, temperature, crop population density, humidity, irrigation, fertilizer application, solar radiation, type of soil, depth, tillage and soil organic matter. An accurate crop yield prediction support decision-makers in the agriculture sector to predict the yield effectively. Machine learning techniques and deep learning techniques play a significant role in the analysis of data for crop yield prediction. However, the selection of appropriate techniques from the pool of available techniques imposes challenges to the researchers concerning the chosen crop. In this paper, an analysis has been performed on various deep learning and machine learning techniques. To know the limitations of each technique, a comparative analysis is carried out in this paper. In addition to this, a suggestion is provided to further improve the performance of crop yield prediction.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Application of informative textural Law’s masks methods for processing space...IJECEIAES
Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.
Soil Characterization and Classification: A Hybrid Approach of Computer Visio...IJECEIAES
This paper presents soil characterization and classification using computer vision & sensor network approach. Gravity Analog Soil Moisture Sensor with arduino-uno and image processing is considered for classification and characterization of soils. For the data sets, Amhara regions and Addis Ababa city of Ethiopia are considered for this study. In this research paper the total of 6 group of soil and each having 90 images are used. That is, form these 540 images were captured. Once the dataset is collected, pre-processing and noise filtering steps are performed to achieve the goal of the study through MATLAB, 2013. Classification and characterization is performed through BPNN (Back-propagation neural network), the neural network consists of 7 inputs feature vectors and 6 neurons in its output layer to classify soils. 89.7% accuracy is achieved when back-propagation neural network (BPNN) is used.
The effects of multiple layers feed-forward neural network transfer function ...IJECEIAES
In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.
PREDICTION OF STORM DISASTER USING CLOUD MAP-REDUCE METHODAM Publications
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information the patterns, associations, or relationships among all this data can provide information. Spatial Data Mining (SDM) is the part of data mining. It is mainly used for finding the figure in data that is related to space. In spatial data mining, to get the result analyst use geographical or spatial information which require some special technique to get geographical data in the appropriate formats. SDM is mainly used in earth science data, crime mapping, census data, traffic data, and cancer cluster (i.e. to investigate environment health hazards). For real time processing, Stream data mining is used for the prediction of storm using spatial dataset with the help of stream data mining strategy i.e. CMR (Cloud Map-Reduce). In this, Stream data mining is presented to detect the storm disaster of coastal area which is located in Central America country and then by taking the dataset of coastal area which are having various regions. There are various regions i.e. Panama, Greater Antilles, Mexico golf which is used to detect the storm. It also detects that, is the region is affected or not, if affected then which area of that region is affected and from this it is helpful to predict the storm before the disaster occur. In this, two parameters are used to test the technique i.e. processing time and computational load and using this parameter compare the previous and proposed technique. The main aim of this is to predict storm disaster before it happen with the help of directionality and velocity of storm.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Soil health analysis for crop suggestions using machine learningEditorIJAERD
Indian economy is depending on agriculture. Agriculture is the main source of income for most of the
population. So farmers are always curious about yield prediction. Many factors are responsible like soil, weather, rain,
fertilizers and pesticides to increase yield production. Agriculture being a soil-based industry, an increase in yield can
only be attained by ensuring that the soil provides a balanced and an adequate supply of nutrients. Soil testing is pivotal
in understanding the deficiencies in soil and avoiding nutrient imbalance. This survey and study focuses on the different
soil types, crop types and soil test reports. Soils are complex mixtures of air, water, minerals, organic matter, and
countless organisms that are the decaying remains of once-living things. We can say soil is an important ingredient of
agriculture. There are several types of soils and each type of soil can have different kinds of features and different kinds
of crops grow on different types of soils. We must know which type of crop is go better in our soil. We can apply machine
learning techniques to classify soil and to predict the crop suitable.
An analysis and impact factors on Agriculture field using Data Mining Techniquesijcnes
In computing and information huge amount of data was provided in the storage. The task is to extract the specified data from the raw data. Data mining is one of the techniques that will extract the data. Data mining techniques are used in many places. The techniques like K-means, K nearest neighbor, support vector machine, bi clustering, navie bayes classifier, neural networks and fuzzy C-means are applied on agricultural data. There are many factors in agriculture. The main factors for the farmer are climate, soil and yield prediction. Farmer must know To improve their production select suitable crop for suitable climate. This paper provides the various concepts of Data mining, their applications and also discusses the research field in agriculture. This paper discusses the different types of factors that impact in the agriculture field.
Similar to PREDICTIVE DATA MINING ALGORITHMS FOR OPTIMIZED BEST CROP IN SOIL DATA CLASSIFICATION (20)
In this Project, a multi-input DC-DC converter is proposed and studied for hybrid electric vehicles (HEVs). Compared to conventional works, the output gain is enhanced, photovoltaic (PV) panel and energy storage system (ESS) are the input sources for proposed converter. The Super capacitor is considered as the main power supply and roof-top PV is employed to charge the battery, increase the efficiency and reduce fuel economy. The converter has the capability of providing the demanded power by load in absence of one or two resources. Moreover, power management strategy is described and applied in control method. A prototype of the converter is also implemented and tested to verify the analysis.
A mobile application which can save time, effort and money by giving technical video explanation based on the recognized image for learners. The collected video information contains all the detailed explanation of the scanned image done by the user. This application groups Augmented reality , 3d objects , sound , video , AI for image recognition into an easy and compact application for the benefit of the every learner. This web application is built C#, UNITY, VUFORIA with the help of Vuforia server connected by asserts. This mobile application serves as an easier way to access details and documentaries related to recognized object. It includes an automatic AR Camera and image target along with the video player which makes it easier for recognizing the image and to learn. The database is done with Vuforia cloud database. The UI provides an excellent user friendly environment that makes the communication more interactive.
In this engaged life, people tend to forget
scheduling their meetings and events that are very
important in their day to day life. They wish to have
someone who keeps on prompting them to lineup the work
and to make sure that they accomplished the scheduled
task on time. The solution to this problem is proposed
through an Android Application named “Nudge” using
artificial intelligence. This application is built using
Android Studio IDE 3.3 released by the Google. The
application is used to trigger alarms based on locations,
where the user can set a reminder to a location and select a
range within which the alarm should be triggered. Once
the user move into that range, the alarm goes on. This
application is also used to set reminders based on the mails
the user receives. Suppose, if a user receives a mail
containing any schedule for a meeting or an event, this
application will automatically set a reminder to that
particular event in the OS calendar. This application also
includes facility to directly set a reminder to the OS
calendar.
Keywords-Location, reminder, alarm, calendar, gmail, date
In the EXISTING SYSTEM, Ballot
based Voting is present, but still there is no system to
avoid Proxy Casting and Recasting is implemented.
We do not have an option to see our casted Vote also.
There is no security in this current application. In the
PROPOSED SYSTEM, a novel electronic voting
system based on Blockchain that addresses some of
the limitations in existing systems and evaluates some
of the popular blockchain frameworks for the purpose
of constructing a blockchain-based e-voting system.
In the MODIFICATION part of the project, we
integrate Aadhaar card linked mobile number for
OTP generation, only then the voter can cast the vote,
this system prevents casting and re-casting of proxies.
The Android application is widely used in all
sectors. In our application it helps to track the
location of the bus. By waiting near the bus stop
for a long time this application helps to reduce the
waiting time of the students using Google map. The
arrival time of a bus is calculated based upon the
pickup point and the starting time of a bus in the
shuttle routes. Providing the accurate bus timing
will be helpful for all the students, staff to catch the
bus at the right time. In a college bus driver is
assigned with a mobile phone where we can use to
track the current location of the bus. Notification
will be sending to the students and parents once
the bus is nearby pick up point and drop point by
using Geo-fencing. Quick Response code is used to
track the attendance of the student whether they
boarded the bus at the pickup point. Common
alert message will be sent to students, staffs and
faculty about the holidays or any crucial
information.
Geo-fencing, Google map, GPS
(Global Positioning System)
The objective of this paper is to use the
Android mobile application to purchase grocery
items. This would help the consumers to purchase the
grocery items through their mobile application and
get the grocery items delivered to their home, office
or anywhere else directly. The users can also select
the nearby provision store or any other supermarket
which is present in their location and then select their
items. The application is thoroughly built only in the
Android platform and is supported only for Android
mobiles. Thus, the application guarantee about userfriendly
in use, provides help desk, security services
etc. This would save the time of the purchaser instead
of going to the grocery store to snap up the items and
standing on a big queue for debiting the products
andwould also help the shop proprietor to develop
their business which will not depreciate their business
revenue for adapting this system by the consumers. It
also helps retailers to create brand awareness so that
the consumers feel self-assured to themselves about
the products procure through online. This would lead
the consumers to increase their familiarity in
purchasing grocery products through an online
application using the internet. The shopkeepers
should also ensure timely delivery of ordered grocery
products without any imperfection in the products
and also ensuring the damage to the products at the
time of deliverance.
Our future battle field system will have more difficulties to maintain security, because of increasing military competitive. Ability to understand, predict and adopt the vast array of inter-networked things is very difficult. Unwanted fire, unauthorized human intervention and other object movement will play major important role for affecting military environment. This project aims to help our future military environment by introducing new technology LoRaWAN in IoT (Internet of Things). LoRaWAN (Long Range Wide Area Network) is a state -of- art commercial of the self (COTS) technology. This project consist of sensors, embedded microcontrollers equipped with LoRaWAN, embedded processors equipped with LoRaWAN and cloud technology. By introducing this new technology in our future military environment we can easily find out criminal activities and fire hazards.
These days heart diseases are
considered as the major health issue. It
includes heart attack and cardiac arrest.
Heart attack is the global leading cause
of death for both the genders and
occurrence is not always know.
Sometimes heart attack is often
compared with other type of pain and
not often dealt with it. Hence, this
project is to implement the heart rate
monitoring using IOT. The patients are
expected to carry or wear a hardware
sensor. The sensor with note the heart
rate and transmits it through internet.
The patient may be expected to set the
high and low heart rate individually. On
reaching the high rate or going below
the expected heart rate, an emergency
alert notification is sent to the patient’s,
guardian, doctor and
ambulance(optional) android devices.
—In a laboratory experiment was conducted on
the utilization of Ethanol-Diesel emulsion in a single
cylinder direct injection diesel engine, a single cylinder,
water cooled, four stroke diesel engine was used. The
principal goals of the present work are to obtain emission
data and combustion characteristics for this type of Diesel
Engine, and to identify the ratio of Emulsion which is
effective in reducing emissions. Experiments were
conducted with emulsions viz (90%diesel + 10%ethanol),
(80% diesel + 20% ethanol), (70% diesel + 30%ethanol) as
fuel. While AVL smoke meter was employed to measure
the smoke density in HSU, the exhaust gas analyzer was
used to measure the NOx emission. High volume sampler
was employed to measure the particulate matter emitted at
the exhaust. The combustion characteristics were studied
using AVL combustion analyser. From the experimental
investigation it was found that the smoke, particulate
matter and Oxides of Nitrogen emissions were reduced
marginally. From the pressure curve and cumulative heat
release curve, it was observed that the combustion started
earlier and the rate of pressure rise increased marginally.
The present research work demonstrates the
preparation of Copper Oxide Nanoparticles (CuO NPs) and
investigates the thermo mechanical properties of the CuO NPs
embedded in the polymer composites experimentally. In this study,
CuO NPs were produced by aqueous precipitation method and
morphology of the NPs was studied using Field Emission
Transmission Electron Microscope (FESEM). Epoxy resin and glass microsphere were considered the base material for the preparation of
the Nano based polymer composites. In order to fabricate the Nano
based polymer composites, CuO NPs with 1.0wtpercentage were
embedded in the base material by means of compression moulding
press. Nano composites proved higher thermal conductivity
enhancement rather than the base material. While comparing to the
base material, the maximum four-point bending strength of 415 MPa
was obtained from the Nano based polymer composites. The test
results obtained from the TG study revealed that an addition of CuO
NPs had acted as the thermal retardant and CuO NPs had delayed
thermal degradation of the Nano based polymer composites. Based
on the test results, it can be suggested that the newly fabricated
nanocomposites have achieved the improved thermal and mechanical
properties.
The Surface roughness prediction method using
artificial neural network (ANN) and Adaptive Neuro Fuzzy
Inference System (ANFIS) are developed to investigate the
effects of cutting conditions during turning of EN8 material.
The ANN model of surface roughness parameters (Ra) is
developed with the cutting conditions such as cutting speed,
feed rate and depth of cut as the affecting process
parameters.
The experiments are planned and totally 27 settings
with three levels defined for each of the factors in order to
develop the knowledge based system. The ANN training
method is used for back-propagation training algorithm
(BPTA) and also for training the Adaptive neuro fuzzy
inference system (ANFIS). We have compared the
Artificial Neural Network and Adaptive neuro fuzzy
inference systems.
The main objective of this paper is to
determine casting defects generally happening in an
aluminium die casting process and efforts have been
taken to identify the tools which eliminate the casting
defects. In global prospective this study briefs the
application of the various tools that are used in the
industries for improvement of quality in foundry
industry. In our national prospective these tools are not
so popular, hence this study will help us to utilise the
available technology through which the productivity is
enhanced with safe and economical means. The QC
tools were used to analyse the casting condition of the
given pattern with three dimensional simulations for the
result preparation. This work has been carried out to
improve the quality of the pattern which is made with
gravity die casting process and this was achieved
through continuous quality control operation with QC
tools, then it was taken to test in some simulation
software. The latest trend available in casting and
foundry shops are the scientific approach in
optimization of all kind of fields including optimization
of defects in castings. These trends are incorporated in
the analysis of aluminium die casting.
Friction Stir Welding, a type of welding which was
discovered in the year of 1991 with a few countable methods
and processes. But today it is one of the necessary and
important type of welding techniques. To develop it, several
researchers showed their interests in this technique. Today,
it acts as the heart of welding of automobiles. Thousands of
inventions has been made in field of Friction Stir Welding
and also successfully being implemented. If a researcher
tries to make some research in this field, he has to go
through thousands of journals where hours of time is being
consumed. To solve that problem several Re-view journals
are being published and also successfully solved this issue
of time consumption. In this paper, similarly a re-view of
several important and different types of papers are discussed
with their results, outcomes, the parameters being performed
for analysis.
This paper also discusses about various methods and various
metals as tools and job materials. It will be much easier and
lenient to understand from this paper to research. The
authors of the papers also clearly explained about the usages
and applications of their methods and provided several
statistical data for clear observation of their methods
Pure and Al substituted Langanite
(La3Ga5.5Nb0.5O14) ceramics have been synthesized
by solid state sintering method and studied their
structural, dielectric and electrical properties. The
crystalline nature was confirmed by powder XRD
studies. The ac conductivity and dielectric
properties of La3Ga5.5-xAlxNb0.5O14 samples were
examined by using complex impedance technique.
Surface morphology and elemental composition
were studied by energy-dispersive x-ray
spectroscopy and scanning electron microscopy.
The frequency dependence of dielectric constant,
dielectric loss and AC conductivity were studied in
the frequency range of 100 KHz to 3 MHz at
different temperatures. The activation energy was
calculated using Arrhenius plot. The lattice
parameter, grain size, dielectric constant and AC
conductivity of pure LGN ceramics were deeply
affected by Al substitution in pure LGN.
In this paper, the non-invasive
methodology for removing Fetal Electrocardiogram
(FECG) is gained by subtracting the balanced
variation of maternal electrocardiogram (MECG)
movement from the abdominal electrocardiogram
(AECG) banner. The banner assessed from the
mother's guts (AECG) is regularly overpowered by
maternal heartbeat. The maternal portion of the
AECG is the nonlinearly changed variation of
MECG. This paper uses an Adaptive Neuro-Fuzzy
Interference System (ANFIS) structure. It is used
for finding the non-direct change and the ensuing
banner are set up with people based request
figurings. This strategy involves some specific
issues which are a direct result of the low force of
the fetal ECG which is sullied by various
wellsprings of checks. It joins maternal ECG,
electromyogram (EMG) signals, power line
impedances and sporadic electronic upheavals.
Along these lines, we are proposing an improved
multimode PSO estimation for overcoming this
issue. In addition, methods like wavelet change,
flexible filtering, thresholding are moreover used.
We have furthermore empowered the thoracic and
stomach signals using MATLAB programming. It
uses only two signs recorded at the thoracic and
stomach regions of mother's skin. Similarly, our
test shows that the proposed count can expel FECG
banner immediately in pregnancy period and that is
one of the basic focal points of figuring.
Almost since the first days of flight, man has been
concerned with the safe escape from an aircraft, which
was no longer flyable. Early escape equipment
consisted of a recovery parachute only. As aircraft
performance rapidly increased during World War II, it
became necessary to assist the crew members in
gaining clear safe separation from the aircraft. This
was accomplished with an ejector seat, which was
powered by a propellant driven catapult - the first use
of a propulsive element in aircrew escape Since then,
this collection of componentry has evolved through
several generations into today's relatively complex
systems, which are highly dependent upon propulsive
elements. Ejection seats are one of the most complex
pieces of equipment on any aircraft, and some consist
of thousands of parts. The purpose of the ejection seat
is simple: To lift the pilot straight out of the aircraft to
a safe distance, then deploy a parachute to allow the
pilot to land safely on the ground
The current paper is mainly about maintaining a secure
environment and also free from thefts that are happening
in our home. The present paper discusses about the
detection of intruders with the help of the various
devices and software.. OpenCV(open source computer
vision) is the major software that is being used in our
present work. For detecting faces we are using various
algorithms like Haar cascade, linear SVM, deep neural
network etc. The main method that we have proposed in
our work is, if any person comes in front of the pi
camera, first it will look for potential matches that we
have already stored in our system If the module finds a
match then it continues to record until any intruder
comes. If the face is not recognized then the unknown
person’s face will be captured and a snap shot will be
sent to the user’s email. The device is developed using
Raspberry Pi b+ with 1.4 GHz quad core processor,
raspberry pi camera module and a Wireless dongle to
communicate with user’s email.
OpenCV, Rassberry pi, python
The aim of this article is to explain the readers the technique of sending data from the sensor
through the Raspberry pi and communicating it to the Thingspeak cloud which is an IOT
platform. To explain the procedure a simple ultrasonic sensor HC-SR-04 which senses objects
up to a distance of 13 ft connected with a raspberry Pi board is used.
This tutorial describe about Raspberry Pi board, Ultrasonic sensor, circuit design for sensing of
data, Python programming script with step by step interpretation. Finally procedure for creating a
channel in ThinSpeak for uploading our data and to read the uploaded data from a remote
desktop/mobile is also included in this tutorial.
Ferrites are the unique magnetic materials which that exhibit electrical as well as magnetic properties and
hence are commercially and scientifically important magnetic material . A brief introduction to various
types of ferrites materials is explained herein. The chemical composition of ferrites including spinel type,
garnet type and hexagaonal type is given in this review. The most interesting applications in electronic
devices High frequency devices and in biotechnology are discussed. Since its discovery in 1950,
hexaferrite has an increasing degree of interest and is still growing exponentially. Hexaferrites are the
extremely important material both commercially and technologically and it accounts for the bulk of the
total magnetic materials manufactured globally. Hence the classification of Hexaferrite is discussed in
detail in this review.
Online Bus pass generation is useful for user who are facing problems with the current manual work of bus pass
registration and renewal. The user need to register by submitting their details through online .The administrator will
verify the user details and if they are satisfied they will approve bus pass. The pass will be generated and send to
userid. The user can login with their user idno and password and then renewal can be performed
More from IJTRET-International Journal of Trendy Research in Engineering and Technology (20)
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
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PREDICTIVE DATA MINING ALGORITHMS FOR OPTIMIZED BEST CROP IN SOIL DATA CLASSIFICATION
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PREDICTIVE DATA MINING ALGORITHMS FOR
OPTIMIZED BEST CROP IN SOIL DATA
CLASSIFICATION
G. Divya1
, G. Bharathi Mohan2
1
PG student, 2
Assistant Professor, Jaya Engineering College, Thiruninravur, Chennai-602 024.
divyaganesan8682@gmail.com
Abstract
Agricultural research has strengthened the optimized
economical profit, internationally and is very vast and
important field to gain more benefits.
In future agriculture is the only scope for all the people. But
today number of people having land, but they don’t know how
to yield the crops.
So many of people are doing useless agriculture by
cultivating the crop on improper soil. To implement the
application to identify the types of soil,water source of that
land whether that land is based on rain or bore water. And
suggest what of crop is suitable for that soil. So through this
application provide application for the people to know about
the agriculture. There is no any application to know about the
cultivation. However, it can be enhanced by the use of different
technological resources, tool, and procedures. Predict the type
of crop which one is suitable for that particular soil, weather
condition, temperature and so on. So for, using machine
learning with the set of data set are identified the crop for the
corresponding soil.
Keyword-Data Mining, Soil Testing Agriculture, Analysis,
Artificial Intelligence.
I. INTRODUCTION
Data mining is a vital area of modern research world for
processing, analyzing and evaluating large data sets; to
identify associations, classifications, and clustering, etc.
Between different attributes and predict the best results with
relevant patterns[1][2].Significantly, these methods can be used
in the field of agriculture and can produce extraordinary
significant benefits and predictions that can be used for
commercial and scientific purposes. Traditionally, Agriculture
decision- making is based on experts’ judgments and these
judgments may not apply to classify the soil suitability and
may lead the lower crop yield. The explicit data set
management by the data mining techniques and algorithms
contain the huge analytical potential for accurate and valid
results and these can help to automate the classification
process, depending on the predefined parameters developed by
Agriculture research centers. Decision tree, Naïve Bayes
algorithm, Rule- Based classification, Neural Networks,
Support Vector Machine and Genetic Algorithm etc. are very
well-known algorithm for data classification and further for
knowledge discovery.
Traditionally, Agriculture decision-making is based on ex-
perts’ judgments [3] and these judgments may not apply to
classify the soil suitability and may lead the lower crop yield.
Decision tree [4], Naïve Bayes algorithm [5][6][7], Rule-
Based classification, Neural Networks[5][6], Support Vector
Machine [8] and Genetic Algorithm [9] etc., are very well-
known algorithm for data classification and further for knowl-
edge discovery.
In this research, we intended to understand the related
domain, analyzed the behavior of different data mining clas-
sification algorithms on the soil data set and evaluating the
most predictive and accurate algorithm. The data set has been
accumulated from different soil surveys that were conducted
at numerous agricultural areas located in Tamil Nadu District
and Andhra.
II. PROBLEMSTATEMENT
The soil is highly important and subservient organism torun
the ecosystem and the importance of soil in agriculture is
understandable because that is the basic bedrock of the
agricultural industry. In Pakistan, the soil characterization is
a basic component and has the potential to increase the yield
per acre, but unfortunately due to not having any appropriate
technological resources that are difficult to distinguish and
classify the soil so that the suitable crops can be grown at the
right location. Moreover, there are many other factors that
may be affected the soil quality parameters, for example,
traditional cropping system, the application of fertilizers, and
irrigation, etc. Therefore, it is highly important to maintain a
system that can classify the soil in adequate quantities for best
practices. The primary objectives of ours study are:
o To classify the soil under different agro ecological zones
in Kasur district, Punjab, Pakistan by different classification
algorithm available in data mining.
o To recommend the relevant crops depending on their classi-
fication.
o To evaluate the performance of predictive algorithms for
better knowledge extraction.
III. METHODS
The rapid growth of interest in data mining is due to the
(i)falling cost of large storage devices and increasing ease
2. International Journal of Trendy Research in Engineering andTechnology
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of collecting data over networks (ii) development of robust
and efficient machine learning algorithms to process this data,
and (iii) falling cost of computational power, enabling use of
computationally intensive method sf or data analysis.
Though, there are lots of techniques available in the data
mining, few methodologies such as Artificial Neural
Networks, K nearest neighbor, K means approach, are popular
currently depends on the nature of the data.
Artificial Neural Network: Artificial Neural Networks (ANN)
is systems inspired by the research on human brain (Hammer-
strom, 1993). Artificial Neural Networks (ANN) networks in
which each node represents a neuron and each link represents
the way two neurons interact. Each neuron performs very
simple tasks, while the network representing of the work of
all its neurons is able to perform the more complex task. A
neural network is an interconnected set of input/output units
where each connection has a weight associated with it. The
network learns by fine-tuning the weights so as able to predict
the call label of input samples during testing phase. Artificial
neural network is a new technique used in flood forecast. The
advantage of ANN approach in modeling the rain fall and run
off relationship over the conventional techniques flood fore-
cast. Neural network has several advantages over conventional
method in computing. Any problem having more time for
getting solution, ANN is highly suitable states that the neural
network method successfully predicts the pest attack inci-
dences for one week in advance. Pedo transfer
functions(PTFs) provide an alternative by estimating soil
parameters from more readily available soil data. The two
common methods used to develop PTFs are multiple-linear
regression method and ANN. Multiple linear regression and
neural network model (feed-forward back propagation
network) were employed to develop a pedo transfer function
for predicting soil parameters using easily measurable
characteristics of clay, sand, silt, SP, Bd and organic carbon.
Artificial Neural Networks have been successful in the
classification of other soil properties, such as dry land salinity
(Spencer et al. 2004). Due to their ability to solve complex or
noisy problems, Artificial Neural Networks are considered to
be a suitable tool for a difficult problem such as the estimation
of organic carbon in soil.
Support Vector Machines: Support Vector Machines (SVM)
is binary classifiers (Burges, 1998; Cortes and Vapnik,1995).
SVM is able to classify data samples in two disjoint classes.
The basic idea behind is classifying the sample data into lin-
early separable. Support Vector Machines (SVMs) are a set of
related supervised learning methods used for classification and
regression. In simple words given a set of training examples,
each marked as belonging to one of two categories, an SVM
training algorithm builds a model that predicts whether a new
example falls into one category or the other.SVM is used to
assess the spatiotemporal characteristics of the soil moisture
products.
Decision trees: The decision tree is one of the popular
classification algorithms in current use in Data Mining and
Machine Learning. Decision tree is a new field of machine
learning which is involving the algorithmic acquisition of
structured knowledge in forms such as concepts, decision trees
and discrimination nets or product ion rules. Application of
data mining techniques on drought related data for drought risk
management shows the success on Advanced Geospatial
Decision Support System (GDSS). Leisa J Armstrong states
that data mining approach is one of the approaches used for
crop decision-making.
Research has been conducted in Australia to estimate a range
of soil properties, including organic carbon (Hendersonetal.
2001). The nation-wide database had 11,483 soil points
available to predict organic carbon in the soil. An enhanced
decision trees tool (Cubist), catering for continuous outputs
was used for this study. A correlation of up to 0.64 was
obtained between the predicted and actual organic carbon
levels.
K nearest neighbor: K nearest neighbor techniques is one
of the classification techniques in data mining. It does not
have any learning phase because it uses the training set every
time a classification performed. The Nearest Neighbor
search(NN) also known as proximity search, similarity search
or closest point search is an optimization problem for finding
the closest points in metric spaces. K nearest neighbor is
applied for simulating daily precipitation and other weather
variables (Rajagopalan and Lall,1999).
Bayesian networks: A Bayesian network is a graphical model
that encodes probabilistic relationships among variables of
interest. When used in conjunction with statistical techniques,
the graphical model has several advantages for data analysis.
One, because the model encodes dependencies among all vari-
ables, it readily handles situations where some data entries are
missing. Two, a Bayesian network can be used to learn causal
relationships and hence can be used to gain understanding
about a problem domain and to predict the consequences
of intervention. Three, because the model has both a causal
and probabilistic semantics, it is an ideal representation for
combining prior knowledge (which often comes in causal
form) and data. Four, Bayesian statistical methods in conjunc-
tion with Bayesian networks offer an efficient and principled
approach for avoiding the over fitting of data Development of
a data mining application for agriculture based on Bayesian
networks were studied by Huang et al. (2008). According to
him, Bayesian network isa
powerful tool for dealing uncertainties and widely used inagri-
culture data sets. He developed the model for agriculture ap-
plication based on the Bayesian network learning method.The
results indicate that Bayesian Networks are a feasible and
efficient. Support Vector Machines Support Vector Machines
IV. RELATEDWORK
A. History of agricultural systems
Agricultural system’s science generates knowledge that
3. International Journal of Trendy Research in Engineering andTechnology
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allows researchers to consider complex problems or take
informed agricultural decisions. Modeling, an essential tool
in agricultural system’s science, has been accomplished by
sci- entists from a wide range of disciplines, who have
contributed concepts and tools over more than six decades.
As agricultural scientists now consider the“ next generation”
models, data, and knowledge products needed to meet the
increasingly complex systems problems faced by society, it is
important to take stock of this history and its lessons to
ensure that avoid re- invention and strive to consider all
dimensions of associated challenges. To this end, we
summarize here the history of agricultural systems modeling
and identify lessons learned that can help guide the design
and development of next generation of agricultural system
tools and methods.
Recent trends in broader collaboration across institutions,
across disciplines, and between the public and private sec-
tors suggest that the stage is set for the major advances in
agricultural system’s science that are needed for the next
generation of models, databases, knowledge products and
decision support systems.
B. Agricultural Systems on global food production and con-
sumption
Over the next decade’s mankind will demand more food
from fewer land and water resources. This study quantifies
the food production impacts of four alternative development
scenarios from the Millennium Ecosystem Assessment and the
Special Report on Emission Scenarios. Partially and jointly
considered are land and water supply impacts from population
growth, and technical change, as well as forest and
agricultural commodity demand shifts from population growth
and economic development. The income impacts on food
demand are computed with dynamic elasticity’s. Simulations
with a global, partial equilibrium model of the agricultural and
forest sectors show that per capita food levels increase in all
examined development scenarios with minor impacts on food
prices.
Global agricultural land increases by up to 14% between
2010 and 2030. Deforestation restrictions strongly impact the
price of land and water resources but have little consequences
for the global level of food production and food prices. While
projected income changes have the highest partial impact on
per capita food consumption levels, population growth leads
to the highest increase in total food production. The impact of
technical change is amplified or mitigated by adaptations of
land management intensities.
C. Predicting farmer uptake of new agricultural practices: A
tool for research, extension and policy, Agricultural systems.
There is much existing knowledge about the factors that
influence adoption of new practices in agriculture but few
attempts have been made to construct predictive quantitative
models of adoption for use by those planning agricultural
research, development, extension and policy. ADOPT (Adop-
tion and Diffusion Outcome Prediction Tool) is the result of
such an attempt, providing predictions of a practice’s likely rate
and peak level of adoption as well as estimating the importance
of various factors influencing adoption. It employs a conceptual
framework that incorporates a range of variables, including
variables related to economics, risk, environmental outcomes,
and farmer networks, characteristics of the farm and the farmer,
and the ease and convenience of the new practice. The ability
to learn about the relative advantage of the practice, as
influenced by characteristics of both the practice and the
potential adopters ,plays a central role.
ADOPT provides a prediction of the diffusion curve of
the practice and sensitivity analyses of the factors influencing
the speed and peak level of adoption. In this paper the
model is described and its ability to predict the diffusion of
agricultural practices is demonstrated using examples of new
crop types, new cropping technology and grazing options.
As well as providing predictions, ADOPT is designed to
increase the conceptual understanding and consideration of the
adoption process by those involved in agricultural research,
development, extension and policy.
V. SYSTEMDESGIN
Fig. 1. Architecture Diagram
A. USER INTERFACEDESIGN
User interface design, in this module user will the soil type
on UI. To develop our application we use net beans as an IDE
and MYSQL as a back end. All inputs and output will put and
get through this IDE only. Creating a user registration to get
their information. After registration user will get login id. In
this two different login one for admin and another for user.
Because admin can only add the crop details initially on the
training set.
B. MAINTAINING TRAININGDATASET
The Server will monitor the entire data set (set of crop)
information in their database and verify them if required. Also,
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the Server has to establish the connection to communicate
with the Users. The Server will update each soil and input
details into its database. The soil and crop data sets are the
main input of the user. Based on that system will compare
and predict the best crop to the user. The Server will store
the entire information in their database. Admin will feed
location, weather condition of that location; water source of
that location, crop type etc., and those details will be stored
on training set.
C. SOILESTIMATION
In this module, have to analyze the soil types. Soil type
usually refers to the different sizes of mineral particles in a
particular sample. Soil is made up in part of finely ground
rock. Hard surface of base is called hard strata soil particles,
grouped according to size as sand and silt in addition to clay,
organic material such as decomposed plant matter. We have
to feed different types of soil and their features on dataset.
D. WATER SOURCE AND WEATHERANALYSIS
Here have to gather the information about water and tem-
perature land of particular area. Because based on weather and
water facility only the best crop will cultivate. So the source
of weather that land is depending on well or rain fall. Through
this can easily predict the crop type.
E. BEST CROPRECOMMENDATION
In this module system will compare the new input with
existing training set data. Here it will generate a new set
output for given input. User will get the output based on input.
If user gives soil as input they will output as type of crop
which is to be cultivated on that land. If they give crop name,
output will become soil as an output. Output will be like, in
which sand those crops will cultivate.
VI. RESEARCHMETHODOLOGIES
A. UNDERSTANDING OFDATASET
Importantly, the use of Information Technology is now the
basic part of our lives and is increasing day by day in almost
every industry to accomplish important tasks in every business
organization. Emerging technologies have a greater impact on
our lives in different ways.
Technology is being implemented in almost every section of
our lives and business structures. Specifically, in agriculture
new applications, technologies and methods are developed to
get the efficient results; to cut down the time and to increase
the crop productivity. But in agriculture, the collection of such
big-data is not an easy task. Not having any computerized
system is making it worse in Pakistan and in the past decades,
expert opinions were taken into consideration to identify the
soil properties and recommendation for crops and the better
fertility.
In this research, the soil samples that are being used were
collected from different fields and the surrounding of Tamil
Nadu district and Andra. We have acquired the test center data
from Soil Fertility Department, Tamil Nadu in the form of
unstructured and manual format. The data was collected by
surveying different locations on different dates and containing
the test samples of soilf or different properties. After the
acquisition, the digitization of record has been made to convert
data into the structured format for further processing. This
digitized dataset included different attributes that are defined
here under: Soil-Basic, included the basic information,
specifically the village and district name and the date of the
sample was collected; this information will be a good resource
for getting the date wise soil properties and the change in
properties for different seasons.
Soil-Basic {GridID, VillageID, Sample Date, Village
Name, DistrictName}
Soil-Location, contained the GPS (Global Positioning
System) record; Zone Number and GridID for the unique
identification of locality. ELocation and N-Location are used to
identify the spatial coordinates of East and North physical
location of the respective field.
Soil-Location {GridID, ZoneNumber, ELocation,N-
Location}
Soil-Analytical, This part of the dataset is the most
important and essential part of our research based on
soil properties. The soil consists of different physical(i.e.,
texture, weight and density, etc.), chemical(Organic and
inorganic matter, i.e. magnesium (MN),copper (Cu), zinc
(Zn), Phosphorous (P), Potassium(K), Iron (Fe) etc.) And
biological properties(microbial and faunal activities in the
soil) these characteristics describe the productivity and
fertility ratios of higher yield in crops. [12] However,
thereare many other important factors, i.e. pH level, Soil
Electricity Conductivity (EC) and temperature of the soil
also have significant importance. These properties are the
part of our dataset as well. The major attributes arehereunder:
Soil-Analytical {GridID, VillageID, pH, OM,AvgK(ppm),
EC(uS/cm),Zn(ppm),Fe(ppm),Mn(ppm),Cu(ppm),texture}
In the first phase of our research, we have understood the
soil dataset, this dataset included more than 800 instances
of soil samples from different regions in Tamil Nadu and
Andra. In order to optimized prediction, we have to clean and
prune the dataset as the preprocessing and selection has the
greater influence on the computational efficiency and
predictive accuracy. Incomplete and inconsistent information
have the significant impact on analysis and may lead the worst
prediction.
B. PREPROCESSING OFDATASET
Data preprocessing has the significant and substantial role
in data mining tasks for better results. Physiognomies of soil
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data sets may include multiple noisy, incomplete, inconsistent
and irrelevant features that should be addressed. Importantly,
the selection of proper dataset for classification has the con-
siderable impact on prediction accuracy. Waikato
Environment for Knowledge Analysis (WEKA) is an open
source machine learning to tool, consists of different data
mining algorithm including, classifying data algorithms. In
this research, we are using WEKA for data mining functions
and methodologies to extract and construct the rules. More
than 1000 data entries are collected for this research. These
entries are then converted to the ARFF format, a suitable
format for WEKA. The filters available for preprocessing
in WEKA i.e. Remove R- 1, Replace Missing Values and
Discretize has helped to convert this data into coherent and
noise free state. The resulted dataset consists of 760 data
entries of different attributes as described above. Soil Dataset
consists of different attributes that have complex relationships
between dynamic variables. Therefore, before implementing
any algorithm the soil distinguished properties must be
encountered. We have split the dataset into two data sets, (i)
training and (ii) test data. (i) Training data, 40% of the
dataset (304 instances), will be used as the tuning and
validation of our data model and this will formulate the
association between the predictive lasses.(ii)Test data
(456 Instances) will be used for evaluating the strength of our
classification model. Moreover, we have to reduce the number
of parameters for construction of soil classification model for
efficiency and accuracy. The Level of pH, Texture, Electricity
Conductivity (EC) and average Potassium (K) will be used for
this internment as the targeted considerations.
C. CLASSIFICATION
Distinguished properties (i.e. PH level, organic and
inorganic matter, texture and temperature, etc.) of soil have
made the classification very critical and dynamic in nature.
Therefore, we need a robust systematic categorization of
soil with objectively efficient and effective algorithms and
methods. Besides, the structural complexity a closer analysis
is likely to lead to an improved prediction process that can be
helpful in the future. Rule-Based classifiers, Bayesian
Networks (BN),Decision Tree (DT), the Nearest Neighbor
(NN), Artificial Neural Network (ANN), Support Vector
Machine (SVM),Rough Sets, Fuzzy Logic, and Genetic
Algorithms etc.
D. RESULTS ANDANALYSIS
Specifically, in Weka, we can train data by different meth-
ods (i) Learning by examples included the nth-folds cross-
validation scheme, by supplying test data or by giving the
percentage of splitting. (ii) Lazy Learning which doesn’t
need any explicit learning model for classification also have
significance, majority predictor and neural networks are the
examples. (iii) Regression learning by giving numeric values
to classifier is also a useful tool to plot the resultant points in
linear,polynomial, single or multinomial logistic plane and the
resultant outputs of these classifiers will predict the class of the
specified instance.
The Table given below is the initial method of summarizing
the large dataset on the basis of pH, EC,texture, and level of
potassium required for different crops, so the relevant crop
class can be predicted for different soil samples.
Fig. 2. Soil Class Labels
Arc GIS is very well-known software for mapping and
analyzing spatial data. For this study, we have used the ArcGIS
tool to map the spatial data and this has helped us to visualize
our results on a Thanjavur District map; figure(iii) is the visual
representation of the NaïveBayes classification result.
Fig. 3. Barchart for Thanjavur Crop
Thanjavur crop data is viewed with soil and growth of
agriculture. This diagram shows that agriculture growth is
mainly based on the soil and the crop which is used for
agriculture.
Fig. 4. Performance Evolution Comparisons
Finally, by the results of our experiment we can conclude
that the Naïve Bayes classifier can predict the classified soil
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⇑
data for more accurate prediction as the comparison to other
classifiers used in the experiment. The comparative analysis
of selected classifiers is visualizing
the best results of Naïve Bayes in both tables and graphical
format figure (i) and these predictions will/can help the agri-
culturalist to find out the best crop fertilization with reference
to the properties of soil without
conducting traditional tests and only depending on expert
opinions.
The above results can be mapped to remote sensing and
Geographical Information System (GIS) Software for the
better understanding of the classification. Fortunately, our
soil data samples contain their Latitude and Longitude value
in Soil-Location table.
VII. CONCLUSION
Thus, the paper infer that using machine learning we
implement a system to predict the crop and yield for that
crop. Through this app farmers and normal people can get
more advantages. The experiment was conducted on data
instances from Thanjavur district and Andra.We have
observed the comparative analysis of these algorithms have
the different level of accuracy to determine the effectiveness
and efficiency of predictions. However, the benefits of the
better understanding of soils classes can improve the
productivity in farming, reduce de- pendence on fertilizers
and create better predictive rules for the recommendation of
the increase in yield. In the future, we contrive to create a
Soil Management and Recommendation System, which can
be utilized effectively by agriculturist and laboratories for
Soil Testing. This System will help to recommend a suitable
fertilizer and predict for better yield.
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