A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments

Abstract

Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.

Image-Based Insect Counting Embedded in E-traps that Learn Without Manual Image Annotation and Self-Dispose Captured Insects

“…For these reasons, in recent years we have witnessed a significant advancement in the field of automated vision-based insect traps (a.k.a e-traps see [11][12][13] for thorough reviews). In [14][15][16][17][18] the authors use cameras attached to various platforms for biodiversity assessment in the field, while in this study we are particularly interested in agricultural moth pests [19][20][21][22][23][24]. Biodiversity assessment aims to count and identify a diverse range of flying insects that are representative of the local insect fauna, preferably without killing the insects.…”

Image-Based Insect Counting Embedded in E-Traps That Learn without Manual Image Annotation and Self-Dispose Captured Insects

“…For these reasons, in recent years we have witnessed a significant advancement in the field of automated vision-based insect traps (also known as e-traps, see [11][12][13] for thorough reviews). In [14][15][16][17][18] the authors use cameras attached to various platforms for biodiversity assessment in the field, while in this study we are particularly interested in agricultural moth pests [19][20][21][22][23][24]. Biodiversity assessment aims to count and identify a diverse range of flying insects that are representative of the local insect fauna, preferably without eliminating the insects.…”