Integrated wireless sensor/actuator networks in an agricultural application
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Stochastic coverage in heterogeneous sensor networks
ACM Transactions on Sensor Networks (TOSN)
Wireless Sensor Network for Soil Moisture Applications
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Topology Optimization in Wireless Sensor Networks for Precision Agriculture Applications
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Vineyard Computing: Sensor Networks in Agricultural Production
IEEE Pervasive Computing
Canopy closure estimates with GreenOrbs: sustainable sensing in the forest
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Review: Wireless sensors in agriculture and food industry-Recent development and future perspective
Computers and Electronics in Agriculture
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Land monitoring is a critical task to ensure the quality of agricultural production. Traditional precision agriculture techniques require intensive computation and expensive hardware devices. This paper explores the techniques of deploying sensor networks in the field for practical land monitoring. As an example, we accurately measure the dark-area/light-area ratios in agriculture fields based on the Monte Carlo theory. We formulate the minimum sensor deployment problem, whose aim is to minimize the number of sensor nodes needed to achieve measurement precision requirements while satisfying size limitation requirements. Size limitation requirements are specified so that manual treatments can be carried on sub-regions that have an extraordinary dark/light ratio. We propose an incremental deployment solution - INCOME - to solve the problem, which does not require any prior knowledge of the dark/light distribution of the field. A split/merge algorithm is designed in INCOME to divide the monitored field into sub-regions satisfying both requirements. We formally prove that the sensor number needed in INCOME is less than that of regular division, and analyze the sensors needed in the ideal case and worst case. Comprehensive simulation studies demonstrate that the performance of INCOME is close to the optimal solution.