Abstract

Wireless sensor networks (WSNs) are suitable for the continuous monitoring of crop information in large-scale farmland. The information obtained is great for regulation of crop growth and achieving high yields in precision agriculture (PA). In order to realize full coverage and k-connectivity WSN deployment for monitoring crop growth information of farmland on a large scale and to ensure the accuracy of the monitored data, a new WSN deployment method using a genetic algorithm (GA) is here proposed. The fitness function of GA was constructed based on the following WSN deployment criteria: (1) nodes must be located in the corresponding plots; (2) WSN must have k-connectivity; (3) WSN must have no communication silos; (4) the minimum distance between node and plot boundary must be greater than a specific value to prevent each node from being affected by the farmland edge effect. The deployment experiments were performed on natural farmland and on irregular farmland divided based on spatial differences of soil nutrients. Results showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k. The deployment was tested for different values of k and transmission distance (d) to the node. The results showed that, when d was set to 200 m, as k increased from 2 to 4 the minimum connectivity of nodes increases and is equal to k. When k was set to 2, the average connectivity of all nodes increased in a linear manner with the increase of d from 140 m to 250 m, and the minimum connectivity does not change.

Highlights

  • Real-time, large-scale assessment of crop conditions is conducive to precise control of crop growth and development [1,2,3,4]

  • Precision agriculture uses information technology to realize the efficient management of agriculture according to the temporal and spatial differences in farmland and crop growth [5,6]

  • With the development of quantitative spectral analysis techniques, the relationships between crop canopy reflectance spectra and crop growth information have been assessed [8,9,10], and the monitoring models of different crop information such as yield, biomass, nitrogen content, and chlorophyll a content were constructed based on crop spectra [11,12,13,14,15,16]

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Summary

Introduction

Real-time, large-scale assessment of crop conditions is conducive to precise control of crop growth and development [1,2,3,4]. It has important guiding significance in increasing production and improving crop quality. Precision agriculture uses information technology to realize the efficient management of agriculture according to the temporal and spatial differences in farmland and crop growth [5,6]. These differences include soil nutrients, soil water, crop biomass, and chlorophyll. They can achieve long-term monitoring of the target area by deploying a large number of nodes in the area [28,29]

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