Table 1. Comparative Analysis
Now-a-days it is required to have techniques and systems which detect and prevent the crops from being affected by different diseases. Agriculture environment is a dynamic entity and changing continuously. Groundwater depletion, soil erosion, attack of new pest and diseases, fragmentation of land, rural-urban migration, and power supply availability for farming are some of the new challenges presently being faced in the agricultural field. The field conditions at the farm in terms of Temperature, Soil Moisture, Humidity, and Water level are acquired with the help of corresponding sensors and the real time values of the parameters is stored on the cloud. The values of the parameters is communicated to the cloud with the help of NodeMCU Microcontroller Unit, which is the main component of the system that acts as a gateway between field and the Internet. This system helps the farmer in accessing the field parameters on the go, thus promotes smart Agriculture.
India is the leading country for pomegranate production. In last decade, there is sizable growth in area and production. Pomegranate exportation from country has increased by 3-5 times in this period. It can be taken under varied conditions of country, but better in arid and semiarid regions. Also it is one of the fruit crops consumed in semi-arid and arid regions of the world.
Crop losses for pomegranate due to diseases and pests are quite normal in case of semi-arid region conditions. Bacterial blight, thrips, fruit borer, and wilt in pomegranate are considered powerful attacks leading to economical loss and force farmers to repetitive sprays. As these issues exist, there is a need to take corrective actions to combat them. So with the help of sensors, such as Temperature, Humidity, Soil Moisture, and Water, it gives necessary inputs to farmer so that he can take actions.
To mitigate the adverse climate changes and environmental conditions precise irrigation, effective use of fertilizers and nutrient management is required. It also prevents the crops from any type of diseases. To make it possible, farmers should know the field conditions in terms of Temperature, Soil Moisture, Humidity and Water level. The field conditions can be acquired with the help of sensors and it enables to take corrective actions as and when necessary.
In order to have smart agriculture, various new technologies such as IoT can be implemented and the data can be transported to the end points for the analysis. It also includes energy efficient way to develop a smart system for Agriculture.
The Wireless Sensor Network will provide a gateway to convey the sensor information to farmer over internet and real time monitoring of the field conditions is possible. With the help of NodeMCU, low cost and efficient system can be developed.
Many researchers have worked in this domain of monitoring the quality of crops for fruits, its climate, Soil, and its water level. In order to measure the Temperature, Humidity, Soil, and water level, the sensing system is used. Wireless Sensor Network field on sensor nodes are directly communicating with Node MCU (Microcontroller Unit), which is also placed near to the application field. Real time temperature, relative humidity, amount of irrigation, and soil moisture will be sensed by on-field sensor nodes. Sensed data will be sent to the Node MCU, which does the task of receiving data. NodeMCU Microcontroller Unit collects the data from four different sensors and it analyzes the data. Each sensor node has a number of sensors which does the task of sensing the environment, such as temperature, relative humidity, amount of irrigation and soil moisture. Power supply can be provided to the node by using a battery. Also sensor node communicates with NodeMCU (Microcontroller Unit) to transmit the data.
The scientific sensors are deployed in water and the real time data is fetched by the base station. So due to this technology, different parameters are monitored remotely. The sensor data can be temporarily stored in Arduino or any other Node microcontroller and the data of interfaced sensors can be transmitted through wireless medium.
Akshay et al. (2012) propose that the system basically comprises of CPU for monitoring the data in LABVIEW platform and ZigBee module along with PIC microcontroller to establish wireless communication between two distant locations. This purpose of the work is to sense the monitor and control the temperature, humidity, and irrigation in the greenhouse from remote location using the ZigBee technology at low cost.
Al-Fuqaha, Guizani, Mohammadi, Aledhari, and Ayyash (2015) suggest that the IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and Machine-to-Machine (M2M) technologies can be seen in IoT. The IoT is an expected technologies to enable new applications by connecting physical objects together in support of intelligent decision making.
Bhanu, Rao, Ramesh, and Hussain (2014) claim that the continuous monitoring of many environmental parameters, such as temperature, humidity, and carbon dioxide can help in analyzing the optimal environmental conditions to achieve more crop productiveness, for the high productivity, and to achieve remarkable energy savings.
Bhange and Hingoliwala (2015) present modern agricultural techniques and systems that are needed to detect and prevent the crops from being effected by different diseases. This helps farmers for identifying fruit disease by uploading fruit image to the system.
Chang et al. (2014) present a new agriculture monitoring system based on WSNs with IP cameras, which can be controlled remotely to have close vision of plants. WSNs can be deployed freely, collect sensor data periodically to the control centre, process and store historical data, which could facilitate clients and experts in agriculture to monitor the conditions in a large field.
Deepika and Rajapiriyan (2016) explain that the Wireless Sensor Networks (WSNs) consist of multiple unassisted embedded devices. This processes the transmitted data collected from different on-board physical sensors, such as Temperature Humidity, and Pressure. This technology utilization would be allowed for the remote measurement of factors, such as plant growth condition including temperature, humidity, atmospheric pressure, soil moisture, and water level. The wireless system also improves crop productivity.
Grace, Kharim, and Sivasakthi (2015) present the automated system to make effective utilization of water resources for agriculture and crop growth monitoring using GSM. The effective utilization of drip water resources process is improved by using the signals obtained from soil moisture sensor. The microcontroller is coordinated by the output signals of the sensors and transmitted to the user with the help of GSM Modem.
Mittal et al. (2012) present a sensor network platform, which is specifically designed to build a low cost wireless sensor network for precision agriculture applications. The IEEE 802.15.4 ZigBee compliant system-on-chip, has been used to provide a long range communication while making the sensor nodes more power efficient. Ojha Tamoghna et al. suggest the current state-of-the-art in WSNs and their applicability in agricultural and farming applications. These are analyzed with respect to communication and networking technologies, standards, and hardware. The microcontroller provides few advantages, such as low cost, flexibility to communicate with other nodes, ease of programming, and low power consumption over the traditional processors.
Divya, Sonkiya, Das, Manjusha, and Ramesh (2014) present to reduce the water usage in irrigation processes, the context aware wireless sensor network system for irrigation management. This multi-sensor system will continuously monitor the relevant environmental parameters soil parameters, hydrological parameters, and crop parameters to derive the context. These derived contexts will be used for automatic control and adaptation of the water resources system. This context aware system uses the real-time sensor data to reduce the wastage of water used in the irrigation process.
Vanaja Jyostsna et al. present smart agriculture using IoT to monitor the values of humidity and temperature successfully. It also stores the sensor parameters in the timely manner, which controls the electronic devices using a smart phone. It also offers an efficient use of energy. It is used in all areas of industry, smart building environmental monitoring, including smart agriculture, healthcare transportation, smart parking, and many more. Table 1 gives the comparative analysis of the related papers.
Table 1. Comparative Analysis
It is essential to know the real time environmental conditions in terms of Temperature, Soil Moisture, Humidity, and Water level. So with the help of sensors, this data can be communicated over Internet taking help of energy efficient and low coat system such as NodeMCU.
When the farm is dry without human presence and avoiding water wastage in irrigation process. Also monitors the soil parameters like temperature, humidity, and soil moisture level. It will also be possible to control various operations of the field remotely from anywhere, anytime by mobile.
To collect the real time information about field conditions, we need to sense Temperature, Soil Moisture, Humidity, and Water Level. To achieve this objective, following actions are considered.
The proposed work is based on collecting the data on the NodeMCU platform analyzing it there and then communicated to the end user with the help of cloud to monitor the field conditions remotely.
NodeMCU is an open source IoT platform. It includes firmware which runs on the ESP8266 Wi-Fi Serial Wireless Module from Expressive Systems, and hardware which is based on the ESP-12 module.
The programming code is being written for ESP8266 Wi-Fi chip using Arduino IDE, for which installation of ESP8266 library is required.
Wireless Sensor Network field on sensor nodes are directly communicating with NodeMCU, which is also placed near to application field. Real time temperature, relative humidity, amount of irrigation, and soil moisture will be sensed by on-field sensor nodes. Sensed data will be sent to the NodeMCU, which does the task of receiving data.
NodeMCU Microcontroller Unit collects the data from four different sensors and it analyzes the data. Each sensor node have a number of sensors, which do the task of sensing the environment as temperature, relative humidity, amount of irrigation, and soil moisture. Power supply can be provided to the node by using battery. Also sensor node communicates with NodeMCU to transmit the data.
Figure 1 shows that single-tier architecture is applied for Wireless Sensor Network on field sensor nodes are directly communicating with Node MCU, which is also placed near to application field. Real time temperature, relative humidity, amount of irrigation, and soil moisture will be sensed by on field sensor nodes. Sensed data will be sent to the NodeMCU, which does the task of receiving data and forwarding it to the remote centralized server (ThingSpeak) by using communication medium (Internet). Cloud application which is deployed on server can be accessed by user from remote location. Different algorithms are stored on server which will be executed automatically when required conditions are satisfied.
Figure 1. System Architecture
As shown in Figure 2, NodeMCU Microcontroller Unit collects the data from four different sensors and analyzes it and takes necessary actions to pass it on to the cloud so that the end user can monitor the parameters remotely.
Figure 2. Block Diagram of the System
Each sensor node has a number of sensors which does the task of sensing the environment as temperature, relative humidity, amount of irrigation, and soil moisture. Power supply can be provided to the node by using battery. Also sensor node communicates with NodeMCU to transmit the data.
Figure 3 shows water level sensor which is an easy to use, low cost high level drop recognition sensor. It is obtained by having a series of parallel wires traces measured droplets water level volume in order to determine the water level. Easy to complete water to analog signal conversion and output analog values can directly read NodeMCU .
Figure 3. Water Depth Level Sensor
Water level sensor is a conductive-type sensor, where the change in resistance of parallel wires over varying depths of water level is converted to voltage. The module has three pins: S (Signal ) + (5V), - and GND). The Signal pin S outputs voltage corresponding to water level.
The Water Level Depth Detection Sensor for Arduino has Operating voltage DC 3V to 5V and Operating current less than 20 mA. The sensor is the analog type, the analog output signals according to the water pressure with its Detection Area of 40 x 16 mm.
Figure 4 shows a simple soil moisture sensor for gardeners. Soil moisture sensors measure the soil water content. The measurement of free soil moisture includes removing, drying, and weighting of a sample, soil moisture sensors measure the volumetric water content indirectly by using some other property of the soil, such as dielectric constant, electrical resistance, or interaction with neutrons, as a proxy for the moisture content. The environmental factors such as soil type, electric conductivity, or temperature affect the soil and the measured soil type should be calibrated. The reflected radiation is affected by the soil moisture and is used for remote sensing in hydrology and agriculture. The Portable probe instruments can be used by farmers or gardeners.
Figure 4. Soil Moisture Sensor
The sensor has a built-in potentiometer for sensitivity adjustment of the digital output (D0), a power LED, and a digital output LED. The voltage that the sensor outputs changes accordingly to the water content in the soil.
Figure 5 shows Humidity Sensor which is most important of the devices that has been widely used in consumer, industrial, biomedical, and environmental. applications for measuring and monitoring Humidity values. For monitoring the temperature and humidity we use the DHT11 sensor. The DHT11 Humidity sensor detects the water vapor by measuring the electrical resistance between two electrodes. When water vapor is absorbed by the substrate, ions are released by the substrate which increases the conductivity between the electrodes. The change in resistance between the two electrodes is proportional to the relative humidity. The higher humidity decreases the resistance between the electrodes, then lower humidity increases the resistance between the electrodes. The humidity sensor is consists of a humidity sensing component, a NTC temperature sensor (or thermistor), and an IC on the back side of the sensor. For measuring humidity, they use the humidity sensing component which has two electrodes with Humidity moisture holding substrate between them. Humidity indicates the likelihood of predications, dew, or fog.
Figure 5. DHT11 Sensor
In this research work, we can control the field parameters based on humidity, temperature, and Soil moisture level. The Moisture level of soil, Temperature and Humidity, Water Depth level are measured or sensed by the sensors. The flowchart for real time data collection and Dynamic Climate change are given in Figures 6 and 7, respectively.
Figure 6. Flow Chart for Real Time Data Collection (Temperature and Relative Humidity)
Figure 7. Flow Chart for Dynamic Climate Change
Algorithm for real time data collection (Temperature and relative humidity)
Input: Real time soil moisture.
Output: Decision of irrigation (Yes/No).
X=Sense the real time soil moisture.
If(X >Maximum Threshold).
Alert farmer to stop irrigation.
Else if(X< Minimum Threshold).
Alert farmer to start irrigation.
Do nothing.
Algorithm for dynamic climate change
Input: Real time temperature (T) and relative humidity (RH).
Output: Dynamic advisory to farmer.
Send the received real time data of temperature (T) and relative humidity (RH) to dynamic advisory module.
Compare the real time values with threshold values stored in database.
Cumulative count of weather data is calculated continuously everyday.
Farmers can check the data on the cloud.
The NodeMCU is a multipurpose microcontroller unit, which has the capability to store the data and it has inbuilt Wi-Fi and Bluetooth capability to send the data wirelessly to the end point or cloud.
In this work, we can control the field parameters based on humidity, temperature, and Soil moisture level. The Moisture level of soil, Temperature and Humidity, Water Depth level are measured or sensed by the sensors.
Values of Soil Moisture printed on Serial Monitor of NodeMCU IDE are shown in Figure 8.
Figure 8. Values of Soil Moisture Printed on Serial Moniter of Node MCUIDE
Threshold for wet =<50
Threshold for Dry =>50
Table 2 provides the values of moisture level and types of soil. The Real time Values of all the parameters is uploaded on ThingSpeak Cloud (Figure 9). Upload the code. Once it is connected to Wi-Fi, the data will start uploading to the ThingSpeak Channel. You can now open your Channel and see the data changes plotted on the ThingSpeak.
Table 2. Values of Moisture Level and Type of Soil
Figure 9. Realtime Values
The new technology involves detecting some environmental conditions and crop diseases effectively. NodeMCU is used instead of WiFi serial wireless module. This new WSN technology is helpful for farmers in enhancing the productivity and increasing the net margin. This system will help in obtaining exact data of farm parameters and as these are shared over the internet, experts' advice will be available easily.
Temperature and Humidity sensor (DHT11) is used. Soil Moisture sensor and Water Depth Level sensors are used in the experimental setup. The soil moisture sensor output comments on the type of soil (Wet or Dry) while water depth level sensor gives information about water level content in the soil.
ThingSpeak cloud enables to save the parameter values in graphical form with a time stamp. Different fields in the channel store different parameter values. This research work gives a low cost solution for the farmers to monitor the field parameters remotely and by analyzing the values, corrective measures can be taken.
We would like to thank Department of Agriculture, Kolhapur for providing the valuable information related to domain. The information and motivation provided will be more useful to farmers and Agro Advisory.