FOCUS: A cost-effective approach for large-scale crop monitoring with sensor networks

Current investment in crop monitoring consumes a large amount of financial cost, and how to reduce this cost has been a long-standing problem in agriculture. Traditional crop monitoring approaches are not cost-effective, because they rely on either heavy human labor or intensive computation with expensive instruments. In this paper, we explore the possibility of deploying networked sensor nodes for low-cost crop monitoring. As an example, we compute an important agricultural metric called global leaf area index (LAI) to illustrate the benefit of using sensor networks. We propose an approach called FOCUS that incrementally deploys sensor nodes into farmland to improve the accuracy of global LAI measurements. We design and implement a novel algorithm that calculates the total size of crop leaves with light intensity readings captured by the sensors under the crop canopies. FOCUS not only lowers the deployment cost considerably but also reduces the number of sensors for the long-term monitoring. Through a small-scale field test and large-scale simulations, we validate our design and show its effectiveness in crop monitoring.

1. Introduction

As reported by the U.S. Department of Agriculture, the national financial cost for agriculture rises continuously these years. It came up to a new record of 279.2 billion dollars in 2008, and counted for 75% of the gross income of agriculture. Crop monitoring is an integral part of agriculture and plays an important role for resources saving and yields increasing. Traditional crop monitoring uses machines or human resources to collect data from crop ecosystem to guide farmers in irrigation and fertilization. These methods are very costly and reducing the cost of crop monitoring has become an urgent problem in agriculture. With the availability of cheap sensor nodes and the progress of wireless technology, sensor networks have been widely deployed in many large-scale applications, such as environment monitoring [1] and surveillance [2]. In this paper, as shown in Figure 1, we explore the possibility of deploying networked sensor nodes in crop monitoring and use an important agricultural metric called global leaf area index (LAI) as an example to illustrate the benefit of sensor networks for cost reduction in agriculture. Figure 1. Crop monitoring with a sensor network