Andreadis Alessandro, Giambene Giovanni, Zambon Riccardo
Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy.
Sensors (Basel). 2021 Nov 16;21(22):7593. doi: 10.3390/s21227593.
Forests play a fundamental role in preserving the environment and fighting global warming. Unfortunately, they are continuously reduced by human interventions such as deforestation, fires, etc. This paper proposes and evaluates a framework for automatically detecting illegal tree-cutting activity in forests through audio event classification. We envisage ultra-low-power tiny devices, embedding edge-computing microcontrollers and long-range wireless communication to cover vast areas in the forest. To reduce the energy footprint and resource consumption for effective and pervasive detection of illegal tree cutting, an efficient and accurate audio classification solution based on convolutional neural networks is proposed, designed specifically for resource-constrained wireless edge devices. With respect to previous works, the proposed system allows for recognizing a wider range of threats related to deforestation through a distributed and pervasive edge-computing technique. Different pre-processing techniques have been evaluated, focusing on a trade-off between classification accuracy with respect to computational resources, memory, and energy footprint. Furthermore, experimental long-range communication tests have been conducted in real environments. Data obtained from the experimental results show that the proposed solution can detect and notify tree-cutting events for efficient and cost-effective forest monitoring through smart IoT, with an accuracy of 85%.
森林在保护环境和应对全球变暖方面发挥着至关重要的作用。不幸的是，由于森林砍伐、火灾等人类干预活动，森林面积在不断减少。本文提出并评估了一个通过音频事件分类自动检测森林中非法砍伐树木活动的框架。我们设想使用超低功耗的微型设备，嵌入边缘计算微控制器和远程无线通信，以覆盖森林中的大片区域。为了减少有效且全面检测非法砍伐树木的能源消耗和资源使用，我们提出了一种基于卷积神经网络的高效且准确的音频分类解决方案，该方案专为资源受限的无线边缘设备设计。与先前的工作相比，所提出的系统通过分布式和全面的边缘计算技术，能够识别更广泛的与森林砍伐相关的威胁。我们评估了不同的预处理技术，重点在于在分类准确率与计算资源、内存和能源消耗之间进行权衡。此外，还在实际环境中进行了实验性的远程通信测试。实验结果所获得的数据表明，所提出的解决方案能够通过智能物联网检测并通知树木砍伐事件，以实现高效且经济高效的森林监测，准确率达到85%。
