Documentations

e-Agriculture Easari. Parusha Ramu1, Prof. K.Ashok Babu2 Assistant Professor, 2Proffesor & HOD

1

1,2Department

of Electrical Engineering, 1,2 Sri Indu College of Engineering &Technology. Abstract: Nowadays science and technology has been changes in to simplicity, complicated applications are also develop very easy. So people no need to put physical efforts, they can do work in very smart. In all fields applying advanced science and technologies, but still no one apply this in to the agriculture. In India about 80% of population depends upon farming and one third of the nation’s capital comes from farming. Issues concerning agriculture have been always hindering the development of the country. The only solution to this problem is smart agriculture by modernizing the current traditional methods of agriculture. Hence the project aims at making agriculture smart using automation and IoT technologies. So synopsis presents advanced science and technology used to implement smart system for the agriculture. Keywords: Wireless sensor networks, Raspberry Pi, NodeMCU, Sensors. I. INTRODUCTION Background: Technology had been enabling and enhancing various Utilities of society through it’s exactness and efficiency. Agriculture, which depends on Nature & it’s tendencies in a very sensitive way makes us to act accurately in order to get expected productivity. Today contemperory environment is uncertain in terms of atmospheric conditions, geographic conditions, adapdability of seeds to land & lack of measurement of combined parameters essential for defining and assessing productivity. This inturn is limiting all stakeholders (Farmers, Government agencies responsible for agriculture, Agricultural scientists, Agriculture product manufacturers and NGO’s in agriculture field) of agriculture ecosystem of country while making decision making on how to adapt Agriculture as per environment conditions. It’s time to converge capability of Technology(Especially Internet and communication Technologies) to needs of agriculture. Origin of the research Problem: Agriculture is Primary Sector, which is major economic source of India. But compared to secondary and tertiary sectors the rate of advancement in agriculture is low. One of the major reason behind is that Secondary (Industrial sector) & Tertiary(Services sector) had been technologized and Connected more compared to Agriculture. Most of the Indian Corporate & Engineers are not inclined towards Implementing fruits of Information and communication technology to agriculture due to lack of profitability comparatively in other sectors and risk involved in it. But cost of technology becomes much cheaper as it grows and saturates. With the advancement in Internet of Things, which is convergence of Sensors, Internet, Communication systems, Low power hardware chipsets & portable power, Agriculture is the next profitable sector to commercialize in

corporate perspective. There are major four problems which we want to focus and overcome through IoT (Internet of things). Day by day science and technology has been changes in to simplicity, complicated applications are also develop very easy. So people no need to put physical efforts, they can do work in very smart. In all fields we are applying advanced science and technologies, but still no one apply this in to the agriculture. In India about 80% of population depends upon farming and one third of the nation’s capital comes from farming. Issues concerning agriculture have been always hindering the agriculture. Hence the project aims at making agriculture smart using automation and IoT(Internet of Things) technologies. So this paper presents advanced science and technology used to implement smart system for the agriculture. Smart precision based agriculture makes use of wireless sensor networks to monitor the agricultural environment. Wi-Fi, Raspberry pi and Node MCU based agriculture monitoring system serves as a reliable and efficient method for monitoring agricultural parameters. Wireless monitoring of field not only allows user to reduce the human power, but it also allows user to see accurate changes in it. It focuses on developing devices and tools to manage, display and alert the users like farmers and agriculture experts using the advantages of a wireless sensor network[4] system and internet. The highlighting features of this project includes smart GPS based remote sensing system, cloud computing, data analysing, moisture sensing, bird and animal scaring, keeping vigilance, etc. For this concept node MCU 8266 boards are using for the nodes. Hear node intension is to collect the data from the particular place soil moister details, weather conditions, plant conditions and exactly place like latitude and longitude. Each node consists of four to six sensors, every sensor have its own specification with that it gives generated sensor data to node MCU. These nodes are cover entire land of a farmer, from every place data will be collect and send to the sever control system. II. LITERATURE SURVEY Wireless sensor networks[3] operate in open environments with potentially rough conditions. The utilized protocols to implement routing decisions in such networks are typically designed to work under challenging external influences. Following this, a survey on the most popular representative, the Routing Protocol for Low Power and Lossy Networks (RPL), and propose a set of techniques to improve the routing schemes. We transfer the concepts of computational trust and forgiveness as known from the Organic Computing domain and equip participating nodes with dynamic decision schemes to choose interaction partners in RPL. This research conducted wide ranging simulation to evaluate the changing topological structure and different attacks. Based on this setting, we identify which concept promises the highest benefit and outline how this can be utilized in RPL. Internet of Things (IoT)[2] in today’s smart environments has many applications in gerontology, health care, transportation, and smart cities. Many challenges still exist in developing IoT-based smart environments. Dynamic generation of action strategies based on multiple IoT object’s input is one of the major challenges. The stochastic Petri nets and game theory are combined

to create stochastic game nets (SGNs) for IoT-based smart environment where each IoT device acts as a player with pre-defined place and action sets. Complete SG N will be created dynamically using individual sensor SGNs which will make IoT-based smart environments highly inter operable and scalable. Focus on generic attacks in wireless sensor network installed in agriculture field, various sensors nodes are collect the data from the sensors that information forward to server module. The processing and control system are used to data processing smoothing the data. These IoT[2] values are stored in the cloud as shown in fig.1. III. Implementation: The server control system is Wi-Fi and Raspberry Pi[1] module, Raspberry Pi is the Highperformance ARM cortex-A53 Processing system. It generates graphs and tabular form values through analyzing all nodes data. A means of communication is formed in between number of nodes and server module Raspberry Pi through Wi-Fi. Raspberry Pi is a mini computer; it collects the data from the nodes for processing and analyzing and to send the data to cloud. Cloud computing is an information technology paradigm that enables ubiquitous access to shared pools of configurable system resources and higher level services which can be rapidly provisioned with minimal management effort. Every node contains six different sensors, which gathers the data like soil moisture and weather conditions like dry or wet and fog in a particular area, and the snaps from plants are taken. GPS module shows the exact location of the node. The data collected from all nodes is transferred to server control system where analyzing and data processing takes place. From the below block diagram processing and Controlling the System is done by Raspberry Pi-3 board which has an inbuilt Wi-Fi module. The total sensor data is collected through wireless WiFi communication from Node MCU 8266[5][6]. The Raspberry Pi-3 board analyzes all the data nodes, which collects the same sensor data from individual nodes and then calculates the average of all nodes at a particular single sensor. Accordingly, calculate all the separate sensor values from all the nodes to generate 3D mesh type graph for display. Based on GPS module the exact location like latitude and longitude values are also presented on the graph. The sensors average data and GPS module information are uploaded in the internet which is nothing but cloud shown in the below figure. From this cloud only authorized persons can access the data; here authorized persons are farmers, researchers and fertilizer developers. Sensor Nodes:-Sensor Control System (NODE) module consists of Node MCU8266, different types of sensors used are: i.

Precision agriculture : Leaf wetness sensors, fruit diameter

ii. Irrigation system

: Soil moister sensors, leaf wetness sensors

iii. Greenhouse

: Solar radiation, humidity, temperature sensors

iv. Weather Stations

: Anemometer, Wind Vane

Above sensors are connected to Node MCU8266 board, where data is converted from analog to digital form. These digital values are transfered to processing & control system shown in below fig.2. Node MCU8266[5] consists of in-built microcontroller and Wi-Fi module. Microcontroller collects sensors data convert into digital format.

Fig.1 The block diagram of Ubiquitous Agriculture

This processed data is transferred to processing & control system through Wi-Fi module. Node MCU8266 is a Wi-Fi SoC board, from the GPIO pin sensors[8].

Fig.2 Internal Structure of Node

Processing and Control System:- Raspberry Pi module acts as a Processing and Control System as shown in below figure. The existent data which is in the form of sensors in the nodes is given as input to the module which is stored in RAM. From the pre-defined threshold values this temporary memory data is analysed to find the normal and abnormal conditions of the weather, soil, temperature etc. This operation is done by python(high level language) and raspberry pi. In this data mining, all the data present in sensors is added and average value is generated. Based on this average value 3D mesh graphs are developed. Every sensor gives some values at present situation from all the nodes; they are

summed and get average value. Like this from all sensors generate average values, this data and 3D graph are upload in cloud. This information can be observed by clients like farmers, researchers and fertilizers developers. Clients are accessing information from mobiles, tabs, or computers through internet. For this services in mobile and Tabs Ubiquitous Agriculture App was developed. If we login to this App it will shows all kinds of data including soil moister levels, temperature percentages and leaves condition photos etc., available in this App as follow as: 1. Tabular form representation of all types of sensors averages 2. Graphical representations of all types of sensors averages 3. Globally representation through GPS System 4. Suggestions, comments and chat each other.

Fig.3 Block Diagram of Processing & Control System

Hardware:Raspberry Pi:-Several generations of Raspberry Pi[1] have been released. All models feature a Broadcom system on a chip (SoC) with an integrated ARM compatible central processing unit (CPU) and on-chip graphics processing unit (GPU). Processor speed ranges from 700 MHz to 1.4 GHz for the Pi 3 Model B+; on-board memory ranges from 256 MB to 1 GB RAM. Secure Digital (SD) cards are used to store the operating system and program memory in either SDHC or MicroSDHC sizes. The boards have one to four USB ports. For video output, HDMI and composite video are supported, with a standard 3.5 mm phono jack for audio output. Lower-level output is provided by a number of GPIO pins which support common protocols like I²C. The B-models have an 8P8C Ethernetport and the Pi 3 and Pi Zero W have onboard Wi-Fi 802.11n and Bluetooth. NodeMCU:- NodeMCU is an open source IoT platform.[4][5] It includes firmware which runs on the ESP8266 Wi-Fi SoC from Expressive Systems, and hardware which is based on the ESP12 module.[6][7] The term "NodeMCU" by default refers to the firmware rather than the dev kits.

The firmware uses the Lau scripting language. NodeMCU was created shortly after the ESP8266 came out. On December 30, 2013, Express if Systems began production of the ESP8266[10].

Fig.4 Raspberry Pi3

The ESP8266 is a WiFi SoC integrated with a Ten silica Xtensa LX106 core,[citation needed] widely used in IoT applications NodeMCU started on 13 Oct 2014, when Hong committed the first file of nodemcu-firmware to Get Hub. Two months later, the project expanded to include an openhardware platform when developer Huang R committed the Gerber file of an ESP8266 board, named devkit v0.9 .

Fig.5 NodeMCU Module

Moisture Sensor:- This Moisture Sensor can be used to detect the moisture of soil or judge if there is water around the sensor, let the plants in your garden reach out for human help. They can be very easy to use, just insert it into the soil and then read it. With the help of this sensor, it will be realizable to make the plant remind you. Its features are a) Soil moisture sensor based on soil resistivity measurement, b) Easy to use, c) 2.0cmX6.0cm grove module.

Fig.6 Moisture Sensor module

LDR Sensor:- A Light Dependent Resistor (LDR) or a photo resistor is a device whose resistivity is a function of the incident electromagnetic radiation. Hence, they are light sensitive devices. They are also called as photo conductors, photo conductive cells or simply photocells. They are made up of semiconductor materials having high resistance.

Fig.7 LDR sensor

DHT 11 Humidity & Temperature Sensor:- This DHT11 Temperature & Humidity Sensor features a temperature & humidity sensor complex with a calibrated digital signal output. By using the exclusive digital-signal acquisition technique and temperature & humidity sensing technology, it ensures high reliability and excellent long-term stability. This sensor includes a resistive-type humidity measurement component and an NTC temperature measurement component, and connects to a high performance 8-bit microcontroller, offering excellent quality, fast response, antiinterference ability and cost effectiveness.

Fig.8 DHT 11 Humidity & Temperature Sensor

Software: Thing Speak:- ThingSpeak is an Internet of Things (IoT) platform that lets you collect and store sensor data in the cloud and develop IoT applications. The ThingSpeak™ IoT platform provides apps that let you analyze and visualize your data in MATLAB®, and then act on the data. Sensor data can be sent to ThingSpeak from Arduino®, Raspberry Pi™, BeagleBone Black, and other hardware. To read and write to a ThingSpeak channel, your application sends requests to the ThingSpeak server, either by issuing HTTP requests or using MATLAB functions. Each ThingSpeak channel can have up to eight fields of data, in either numeric or alphanumeric format. A channel also has location information and a status update field. IV. RESULTS:

Fig.9 ON State

ON STATE: In this case power supply is given to the circuit as shown in fig.9 all the sensors and lights in ON state. These are ready to work in the farm. By dumping the software into the kit can work properly. We can read the current state values of moisture, humidity &temperature and air quality anywhere from the world using the web application. Below showing sensor Nodes are at ON state all Nodes are collect the data from different sensors and send date to Processing control system. Processing control system analyzes the Nodes collected data to transmit Graphical representation in Thingspeek website in internet. In Thingspeek web page all windows represents different sensor average values from four Node modules shown fig.10.

Node 1

Node 3

Node 2

Node 4

Fig.10 Graphical Representation of all sensor values

V. CONCLUSION Thus our project creates awareness about the automation in agricultural field. Here the manual intervention can be reduced by irrigating the plants automatically and the whole information about the

agricultural field can be viewed in android application. This technology will help’s a landlord plantation to monitor and manage the status of their plantation such as fruit plants or other plant in order to increase the production and improvement of quality at various agriculture sites. There are much benefits using this technology which it is consider as a low cost and reliable system compared to the other system that used such as GSM module which is need more money to build up the system. From the study comparison, there are several technique that are used to monitor a specific area whether inside or outside fields such as using a WSN (wireless sensor network), wireless CCTV, Android Smartphone based on wireless network and these system also may be employ at home, office, public, agriculture environment and other. VI. FUTURE SCOPE Our project can be improvised by adding a Webscaper which can predict the weather and water the plants/crops accordingly. If rain is forecasted, less water is let out for the plants. Also, a GSM module can be included so that the user can control the system via smart phone. A water meter can be installed to estimate the amount of water used for irrigation and thus giving cost estimation. A solenoid valve can be used for varying the volume of water flow. REFERENCES [1] Piyare, R. 2013. Internet of Things: Ubiquitous Home Control and Monitoring System Using Android Based Smart Phone. International Journal of Internet of Things. 2(1): 5-11. [2] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. 2013. Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions. Future Generation Computer Systems. 29(7):1645-1660. [3] Hwang, J., Shin, C., & Yoe, H. 2010. Study on an Agricultural Environment Monitoring Server System Using Wireless Sensor Networks. Sensors. 10(12): 11189-11211. [4] Mendez, G. R., Yunus, M. A. M., & Mukhopadhyay, S. C. 2012, May. A WiFi Based Smart Wireless Sensor Network for Monitoring an Agricultural Environment. In Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International IEEE. 2640-2645. [5] Mahmood, D. M. F. M. B. 2014. Data Acquisition of Greenhouse Using Arduino. [6] Liu, H., Meng, Z., & Cui, S. 2007, September. A Wireless Sensor Network Prototype for Environmental Monitoring in Greenhouses. In Wireless Communications, Networking and Mobile Computing, 2007. WiCom 2007. International Conference on IEEE. 2344-2347. [7] Hamrita, T. K., & Hoffacker, E. C. 2005. Development of a" smart" Wireless Soil Monitoring Sensor Prototype Using RFID Technology. Applied Engineering in Agriculture. 21(1): 139143. [8] Jahromi, H. N., Hamedani, M. J., Dolatabadi, S. F., & Abbasi, P. 2014. Smart Energy and Water Meter: A Novel Vision to Groundwater Monitoring and Management. Procedia Engineering. 70: 877-881. [9] Millan-Almaraz, J. R., Torres-Pacheco, I., Duarte-Galvan, C., Guevara-Gonzalez, R. G., Contreras-Medina, L. M., de Jesus Romero-Troncoso, R., & Rivera-Guillen, J. R. 2013.