scholarly journals Smart Crop Monitoring and Automation Irrigation System Using Machine Learning and IOT

2021 ◽  
Vol 11 (4) ◽  
pp. 2747-2757
Author(s):  
Y.S.V. Raman ◽  
P. Suresh ◽  
P. Jnana Sudheer ◽  
P. Prudhvi ◽  
S. Gopi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rural Areas ◽  
Mobile Application ◽  
Learning Algorithm ◽  
Irrigation System ◽  
Control Unit ◽  
Plant Diseases ◽  
Raspberry Pi ◽  
Moving Vehicle ◽  
Crop Monitoring

Agriculture is still a major occupation in rural areas of India. Increase in technology creates many opportunities in different fields and attracts human resources from rural areas. Farmers are facing sever human and natural resource problems. Monitoring crops with low man power is the major problem. Smart crop monitoring and automation irrigation system deals with these problems by developing a mobile application which helps farmer to get detailed information about plant diseases and to use irrigation system efficiently. This model uses image processing techniques to identify the picture of the leaf and also provides information about temperature and moisture on field. The Raspberry Pi is the project's control unit, which controls and executes the entire system's operation. Pi camera is placed at the face of the moving vehicle to take the pictures of the leaves. These pictures are analyzed using convolution neural network which is an efficient machine learning algorithm. If the captured leaf image has a disease that is already in the given dataset then farmer will get output message which contains disease cause and pesticide or fertilizers we need to provide to eradicate the disease. Mobile application also sends the data which is sensed using sensors.

Deep Learning-Based Mobile Application for Plant Disease Diagnosis

2019 ◽  
pp. 242-271 ◽  
Author(s):  
Shradha Verma ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Shubham Sharma ◽  
Puranjay Rajvanshi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Application ◽  
Plant Disease ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Learning Tools ◽  
Class Labels

With the increasing computational power, areas such as machine learning, image processing, deep learning, etc. have been extensively applied in agriculture. This chapter investigates the applications of the said areas and various prediction models in plant pathology for accurate classification, identification, and quantification of plant diseases. The authors aim to automate the plant disease identification process. To accomplish this objective, CNN has been utilized for image classification. Research shows that deep learning architectures outperform other machine learning tools significantly. To this effect, the authors have implemented and trained five CNN models, namely Inception ResNet v2, VGG16, VGG19, ResNet50, and Xception, on PlantVillage dataset for tomato leaf images. The authors analyzed 18,160 tomato leaf images spread across 10 class labels. After comparing their performance measures, ResNet50 proved to be the most accurate prediction tool. It was employed to create a mobile application to classify and identify tomato plant diseases successfully.

scholarly journals Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System

Scientifica ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Marion Olubunmi Adebiyi ◽  
Roseline Oluwaseun Ogundokun ◽  
Aneoghena Amarachi Abokhai
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Mobile Application ◽  
Mobile System ◽  
Random Forest Algorithm ◽  
User Input ◽  
Crop Monitoring ◽  
Integration Of Technology ◽  
Crop Type ◽  
Using Data

E-agriculture is the integration of technology and digital mechanisms into agricultural processes for more efficient output. This study provided a machine learning–aided mobile system for farmland optimization, using various inputs such as location, crop type, soil type, soil pH, and spacing. Random forest algorithm and BigML were employed to analyze and classify datasets containing crop features that generated subclasses based on random crop feature parameters. The subclasses were further grouped into three main classes to match the crops using data from the companion crops. The study concluded that the approach aided decision making and also assisted in the design of a mobile application using Appery.io. This Appery.io then took in some user input parameters, thereby offering various optimization sets. It was also deduced that the system led to users’ optimization of information when implemented on their farmlands.

scholarly journals IoT Based Cloud Agent System for Adult Health Care Monitoring

2020 ◽  
pp. 35-41 ◽  
Author(s):  
Prasanna kumari V ◽  
Amutha C
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Learning Algorithm ◽  
Vital Signs ◽  
Bed Rest ◽  
Adult Health ◽  
Cloud Server ◽  
Health Care Monitoring ◽  
Monitoring Service

A continuous health monitoring system for patients with long term sickness and older age people in bed rest in-home or hospital. The system is developed to provide a solution for the problem by continuously monitor the patient's health using wireless sensor networks (WSN) and Machine learning. The system provides visual monitoring service through live video. The vital signs of the patient can be monitored such as temperature, humidity, pulse, breathing rate, etc. and provides the live monitoring using an MQTT mobile application and losant dashboard cloud server. From the collected data by using a machine learning algorithm like a random forest we can able to target the risk causing factors and rescues the patient immediately.

scholarly journals Multi-Model Biometric Authentication System for Smart Attendance

2020 ◽  
Vol 8 (6) ◽  
pp. 4284-4287
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Raspberry Pi ◽  
Machine Learning Algorithm ◽  
Biometric Authentication ◽  
Biometric System ◽  
Authentication System ◽  
Paper Method

To increase the success rate in academics, attendance is an essential aspect for every student in schools and degree colleges. In olden days, this attendance is manually taken by teachers with pen and paper method, which consumes more amount of time in their busy management scheduling era. To make this attendance taking more comfortable and more accurate, a multi model biometric system for attendance monitoring system is proposed using a Raspberry Pi single-board computer. The camera and biometric device which is connected to the system gathers Information regarding the students by recognizing their faces and their fingerprint simultaneously. If both of them match with the student details stored in the database, then the system will be sending an alert about the student presence in the class. The student details which is stored into the database is collected from the students initially. By using these details like images and fingerprints the system is trained by using a Convolutional Neural Network (CNN) Machine Learning Algorithm.

scholarly journals Smart Irrigation System for Urban Gardening using Logistic Regression algorithm and Raspberry Pi

2021 ◽  
Vol 2129 (1) ◽  
pp. 012044
Author(s):  
R Aminuddin ◽  
A S Sahrom ◽  
M H A Halim
Keyword(s):  
Logistic Regression ◽  
Learning Algorithm ◽  
Irrigation System ◽  
Binary Logistic Regression ◽  
Raspberry Pi ◽  
Long Period ◽  
Urban Gardening ◽  
The Common ◽  
Logistic Regression Algorithm ◽  
Physical Access

Abstract People have shown an increasing interest in urban gardening. Irrigation is one of the common methods used to take care of the plant growth. However, the proper irrigation timing of plant is much unclear for most people. Moreover, the manual irrigation is impossible when people do not have physical access to the plant in a long period of time. Hence, a smart irrigation system using Raspberry Pi has been proposed to ease the irrigation. In this system, three different sensors, including moisture, humidity and temperature sensors are installed in the soil of the plant. The collected data from the sensors will be used to predict whether the plant need to be watered or not. This system implements a machine-learning algorithm called Binary Logistic Regression using Python library to test the accuracy of the system. The accuracy of the algorithm to predict the irrigation is 82%. The finding from this study is believed to be helpful as it may contribute to the development of better irrigation system.

scholarly journals Application of machine learning for real-time evaluation of salinity (or TDS) in drinking water using photonic sensors

2016 ◽  
Vol 9 (2) ◽  
pp. 37-45 ◽  
Author(s):  
Sandip Kumar Roy ◽  
Preeta Sharan
Keyword(s):  
Machine Learning ◽  
Drinking Water ◽  
Real Time ◽  
Learning Algorithm ◽  
Training Data ◽  
Raspberry Pi ◽  
Highly Sensitive ◽  
Sample Water ◽  
Photonic Sensors ◽  
Time Evaluation

Abstract. The world is facing an unprecedented problem in safeguarding 0.4 % of potable water, which is gradually depleting day-by-day. From a literature survey it has been observed that the refractive index (RI) of water changes with a change in salinity or total dissolved solids (TDS). In this paper we have proposed an automatic system that can be used for real-time evaluation of salinity or TDS in drinking water. A photonic crystal (PhC) based ring resonator sensor has been designed and simulated using the MEEP (MIT Electromagnetic Equation Propagation) tool and the finite difference time domain (FDTD) algorithm. The modelled and designed sensor is highly sensitive to the changes in the RI of a water sample. This work includes a real-time-based natural sequence follower, which is a machine learning algorithm of the naive Bayesian type, a sequence of statistical algorithms implemented in MATLAB with reference to training data to analyse the sample water. Further interfacing has been done using the Raspberry Pi device to provide an easy display to show the result of water analysis. The main advantage of the designed sensor with an interface is to check whether the salinity or TDS in drinking water is less than 1000 ppm or not. If it is greater than or equal to 2000 ppm, the display shows “High Salinity/TDS Observed”, and if ppm are less than or equal to 1000 ppm, then the display shows “Low salinity/TDS Observed”. The proposed sensor is highly sensitive and it can detect changes in TDS level because of the influence of any dissolved substance in water.

scholarly journals Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley

2021 ◽  
Vol 11 (1) ◽  
pp. 6609-6613
Author(s):  
A. H. Blasi ◽  
M. A. Abbadi ◽  
R. Al-Huweimel
Keyword(s):  
Machine Learning ◽  
Irrigation Water ◽  
Agricultural Sector ◽  
Learning Algorithm ◽  
Irrigation System ◽  
Jordan Valley ◽  
Agriculture Sector ◽  
Machine Learning Approach ◽  
Baseline Values ◽  
The Right

The agriculture sector is the most water-consuming sector. Due to the critical situation of available water resources in Jordan, attention should be paid to the issues of water demand and appropriate irrigation in order to spread the right management ways of modern irrigation to the farmers. The objectives of this paper are to improve the irrigation process and provide irrigation water to the highest possible extent through the use of artificial intelligence to construct a smart irrigation system that controls the irrigation mechanism using the necessary tools for sensing soil moisture and temperature, giving alerts of any change in the parameters entered as the baseline values for comparison, and installing system sensors buried at a depth of 3-5 inches below the roots to measure the moisture content in the soil. The sensors measure the humidity and temperature in the soil every ten minutes. They prevent the automatic irrigation process if the humidity is high, and permit it if the humidity is low. The smart automatic irrigation system model was built using the Decision Tree (DT) algorithm, which is a machine learning algorithm that trains the system on a part of the collected data to build the model that will be used to examine and predict the remaining data. The system had a prediction accuracy of 97.86%, which means that it may be successfully used in providing irrigation water for the agricultural sector.

scholarly journals Application of Machine Learning For Real-time Evaluation of Salinity (or TDS) in Drinking Water Using Photonic Sensor

2016 ◽  
Author(s):  
Sandip Kumar Roy ◽  
Preeta Sharan
Keyword(s):  
Machine Learning ◽  
Drinking Water ◽  
Real Time ◽  
Learning Algorithm ◽  
Training Data ◽  
Raspberry Pi ◽  
Photonic Sensor ◽  
Highly Sensitive ◽  
Sample Water ◽  
Time Evaluation

Abstract. World is facing unprecedented problem to safeguard 0.4 percent of potable water, which is eventually depleting day-by-day. From literature survey it has been observed that the refractive index (RI) of water changes with change in salinity or total dissolved solids (TDS). In this paper we have proposed an automatic system that can be used for real-time evaluation of salinity or TDS in drinking water. Photonic Crystal (PhC) based ring resonator sensor has been designed and simulated using MEEP tool and Finite Difference Time Domain (FDTD) algorithm. The modelled and designed sensor is highly sensitive to the changes in RI of water sample. This work includes a real-time based natural sequence follower which is a machine learning algorithm of Naïve Bayesian type. A sequence of statistical algorithm implemented in MATLAB with reference to training data to analyse the sample water. Further interfacing has been done using Raspberry Pi device to provide an easy display to show the result of water analysis. The main advantage of the designed sensor with interface is to check whether the salinity or TDS in drinking water is less than 1000 ppm or not.

scholarly journals Arduino based Machine Learning and IoT Smart Irrigation System

2021 ◽  
Vol 10 (4) ◽  
pp. 1-5
Author(s):  
Prakash Kanade ◽  
Jai Prakash Prasad
Keyword(s):  
Machine Learning ◽  
Soil Salinity ◽  
Temperature Sensor ◽  
Irrigation System ◽  
Ph Sensor ◽  
Drainage System ◽  
Weather Condition ◽  
Soil Surface ◽  
Raspberry Pi ◽  
Lack Of Control

We all depend on farmers in today's world. But is anybody aware of who the farmers rely on? They don't suffer from various irrigation issues, such as over-irrigation, under irrigation, underwater depletion, floods, etc. We are trying to build a project to solve some of the problems that will help farmers overcome the challenges. Owing to inadequate distribution or lack of control, irrigation happens because of waste water, chemicals, which can contribute to water contamination. Under irrigation, only enough water is provided to the plant, which gives low soil salinity, leading to increased soil salinity with a consequent build-up of toxic salts in areas with high evaporation on the soil surface. This requires either leaching to remove these salts or a drainage system to remove the salts. We have developed a project using IoT (Internet of Things) and ML to solve these irrigation problems (machine learning). The hardware consists of different sensors, such as the temperature sensor, the humidity sensor, the pH sensor, the raspberry pi or Arduino module controlled pressure sensor and the bolt IOT module. Our temperature sensor will predict the area's weather condition, through which farmers will make less use of field water. At a regular interval, our pH sensor can sense the pH of the soil and predict whether or not this soil needs more water. Our main aim is to automatically build an irrigation system and to conserve water for future purposes

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.

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