Network Design and Performance Evaluation of Wireless Sensor Network for Precision Agriculture

Network Design and Performance Evaluation of Wireless Sensor Network for Precision Agriculture

Herman S. Sahota (IBM, USA), Ratnesh Kumar (Iowa State University, USA) and Ahmed E. Kamal (Iowa State University, USA)
Copyright: © 2015 |Pages: 27
DOI: 10.4018/978-1-4666-8251-1.ch003
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Abstract

This chapter explores the design of wireless sensor networks for applications in precision agriculture. A short review of developments in precision agriculture and recent applications of wireless sensor networks in the area is presented. The authors present their design of medium access control and network layer protocols exploring the challenges and opportunities associated with the design of such a networked system. The physical layer in their network allows multiple power modes in both receive and transmit operations. The MAC layer employs these multiple power modes to implement a novel wake-up synchronization mechanism to reduce the energy overhead. The network layer ensures reliable collection of data while balancing the energy consumption among the nodes. Finally, the authors present an analytical approach to model the behavior of the MAC protocol developed and compare it against the duty-cycle based S-MAC protocol. The results are also confirmed using simulations.
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1. Introduction

Agricultural efficiency increased in the eighteenth and nineteenth centuries with the use of new technologies developed during the industrial revolution. The seed drill, the Dutch plough with iron parts and the threshing machine were among the most important inventions for agriculture. The twentieth century saw the widespread use of self-propelled machines such as tractors, harvesters, etc., and synthetic fertilizers, which allowed agricultural practices at a scale and speed, which was unheard of. Precision agriculture was developed in the mid-twentieth century to provide cost effective methods to improve the quantity and quality of food production. However, the increased crop yields due to these inventions came at a certain toll on the environment. The use of heavy machinery is suspected to have increased the risk of soil erosion (Lal et al., 2007). Also, while fertilizers improved the agricultural production their excessive application without proper knowledge of the soil conditions caused pollution of water bodies. Fertilizer uptake within a field depends on factors such as variability in plant population, nitrogen mineralization from organic matter, water stress, soil properties, pests, etc. These factors vary in space and time. Fertilizers must be applied according to the needs of the crop. If under-applied, the crop yield suffers, while if over-applied, contamination of ground and surface water resources may take place leading to issues such as aquatic hypoxia.

We must predict the quantities of agricultural inputs (such as fertilizers, lime, and other soil amendments) needed to boost the crop yield based on the current soil composition and weather conditions. Thus, a method to collect such data about soil is needed before we can predict the quantities of the agricultural inputs to be applied. Traditionally, one of the methods of soil data collection has been laboratory testing of representative soil samples. Laboratory testing methods allowed us a tool to analyze samples of soil from farmlands to determine soil composition (in terms of available phosphorus, exchangeable potassium, calcium and magnesium, cation exchange capacity, pH, etc.) and predict the optimal quantities of agricultural inputs to be applied (Adamchuk et al., 2004). However, there are many disadvantages of this approach:

  • 1.

    The method is cumbersome in that it requires the agronomist to collect samples of soil from various locations in the farm-field.

  • 2.

    It does not allow data collection at arbitrarily low spatial and time resolutions due to limitations on account of human labor and to avoid disturbing the growing crop.

  • 3.

    It can be a time consuming operation and not suitable for near real-time decision making to apply control inputs.

  • 4.

    The method is intrusive and interferes with the growth of crops.

Another alternative, remote sensing, can monitor large areas of land for long periods of time but the spatial resolution is low. The use of on-the-go sensors is an alternative to improve the spatial density of the measurements at a relatively low cost. However, in order to apply precision agriculture to production scales the spatio-temporal resolution and reliability of soil data must be improved by automating the data collection process. Recent developments in System on chip (SoC) and wireless communication technologies allow the integration of sensors with wireless transceivers. A network of such wireless sensor nodes, deployed in a farm-field can allow us to gather soil data at much greater scales and resolutions. This data can be used to make near real-time farming decisions.

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