Enhanced Agricultural Environment Using Smart WSN

A Wi-Fi based Smart Wireless Sensor Network for an Agricultural Environment which is capable of intelligently monitoring agricultural conditions in a pre-programmed manner was developed by a group of researchers at Massey University in New Zealand. The research was principally conducted by Gerard Rudolph Mendez and supervised by Dr. Subhas Chandra Mukhopadhyay, a Professor of Sensing Technology at the School of Engineering and Advanced Technology. The main objective of their research was to develop a smart wireless sensor network (WSN) for an agricultural environment.

It is a thing of fact that monitoring of environmental factors has increased lately in the last decade while the effects of climate change and global warming affects on the natural environment have increased, especially affecting agriculture; thus globally causing great impacts on poverty, particularly in low-income communities and third world countries.

In particular monitoring agricultural environments for various factors such as temperature and humidity along with other factors can be of significance. A traditional approach to measuring these factors in an agricultural environment meant individuals manually taking measurements and checking them at various times. The ability to document and detail changes in parameters of interest have become increasingly valuable, to such an extent that unattended monitoring systems have been investigated for this function. Their study investigated a remote monitoring system using WiFi, where the wireless sensor nodes are based on WSN802G modules. These nodes send data wirelessly to a central server, which collects the data, stores it and will allow it to be analyzed then displayed as needed

Their proposed system was made up of three stations: Sensor Node, Router, and Server. These stations according to the researchers targets sensor elements such as Temperature, humidity, light, air pressure, soil moisture and water level using communication between the sensor node and the server is achieved via 802.11g wireless modules. The overall system architecture shows advantages in cost, size, flexibility and power.

block diagram 1

The system developed was based on the WSN802G WiFi/802.11 module in order to communicate data to a selected Server. The WSN802G module was connected to the various sensors with analogue outputs via a multiplexer used for signal gating. Where two particular signals can be selected based on the General Purpose Input/Output (GPIO) values from the WSN module. The signals were measured and converted to values that are then transferred to a selected server connected to same network via a standard Wireless-G router. The server can be connected to the network either Wireless itself or through a wired Ethernet connection. The server then stores the received data into a comma-separated values (CSV) file format which was imported into a database excel file or other software in order to perform analysis and displaying of data. An important aspect of the design according to Gerard was keeping the size of the node compact as shown from the PCB design of the sending node

block diagram 2

The current system still in the development stages performs well for transferring and logging of values from the various sensor nodes. It allows for relatively easy connection to nodes and communication. Further work is required with regards to battery and self-powering from solar panels or other renewable sources. The system will allow for additional or interchangeable sensors to be connected as the need occurs in the future. Further investigation is ongoing for integrating the measurement of nitrates in water sources near agricultural environments. This is of interest due to the health concerns connected with nitrates for example Methemoglobinemia (is a disorder characterized by the presence of a higher than normal level of methemoglobin (metHb, i.e., ferric [Fe3+] rather than ferrous [Fe2+] haemoglobin) in the blood.) as regards to the creation of application to perform analysis of the data sent and received, where the calculation of the corresponding ADC values are converted to sensor measurement values with minimal user intervention.

The analysis application should account for sensor value correction, such as pressure might require adjustment with height above or below sea- level in order to be comparable over various areas. The system allows for relatively easy use and can be operated with standard commercial products that are widely in use allowing users to utilize equipment already in use.

Feel free to contact Dr. Subhas Chandra Mukhopadhyay for collaboration and business opportunities for this work

Source: Massey University