Two-Phase Sensor Decision: Machine-Learning for Bird Sound Recognition and Vineyard Protection

• Published in: IEEE sensors journal 2022-06, Vol.22 (12), p.1-1
• Main Authors: Cinkler, Tibor, Nagy, Kristof, Simon, Csaba, Vida, Rolland, Rajab, Husam
• Format: Article
• Language: English
• Subjects: Actuators; Agriculture; Bird Classification; Bird Identification; Bird of Prey; Bird Recognition; Bird Song; Birds; Cloud Computation; Cloud computing; Clusters; Decision; Deep Learning; Delays; False Negative; False Positive; Game Damage; Games; Grape; Machine Learning; Microcontrollers; Microphone; Modules; Noise monitoring; Phase matching; Pipelines; Power consumption; Preprocessing; Reliability; Sensing; Sensor; Sensors; Sound; Starlings; Two-Phase; Vineyards; Viticulture; Wine; Wireless sensor networks
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3134817

For a wireless sensor network consisting of numerous sensors, spread over a large area with no direct power supply, energy efficiency is of paramount importance. As most power is consumed by the communication module, we should pay special attention to reduce communication needs as much as possible. The more data we send, the larger the power requirement of the sensor module. Preprocessing can help in reducing the amount of data to send. However, it also consumes energy. This paper focuses on this tradeoff between preprocessing, pre-filtering and preselecting of sensor data on one hand, and uploading of unprocessed and unfiltered raw data on the other hand, for the special case of protecting vineyards from starlings. We propose a two-phase decision mechanism based on machine learning: the less complex first phase is executed on the microcontroller of the sensor module, while the more complex, more accurate second phase is performed in the cloud. Individual noise sensors monitor the environment, and try to detect starling songs, using a simple, SVM-based classification. These sensors are grouped into clusters, through a mechanism similar to the well-known LEACH protocol, and signal to the current cluster-head the likelihood of starling presence. If several alerts are received to justify further investigation, the cluster-head asks the node with highest starling detection likelihood to upload a 1 s sound sample to the cloud. There, the more complex and more accurate second phase sound matching is performed, and the actuators deployed in the field are remotely triggered, if needed.