IoT-enabled devices are making it easier and cheaper than ever to capture in situ environmental data and deliver these data—in the form of graphical visualisations—to farmers in a matter of seconds. In this work we describe an aquaculture focused environmental monitoring network consisting of LoRaWAN-enabled atmospheric and marine sensors attached to buoys on Clyde River, located on the South Coast of New South Wales, Australia. This sensor network provides oyster farmers operating on the river with the capacity to make informed, accurate and rapid decisions that enhance their ability to respond to adverse environmental events—typically flooding and heat waves. The system represents an end-to-end approach that involves deploying a sensor network, analysing the data, creating visualisations in collaboration with farmers and delivering them to them in real-time via a website known as FarmDecisionTECH®. We compared this network with previously available infrastructure, the results of which demonstrate that an in situ weather station was ∼5 ∘C hotter than the closest available real-time weather station (∼20 km away from Clyde River) during a summertime heat wave. Heat waves can result in oysters dying due to exposure if temperatures rise above 30 ∘C for extended periods of time (such as heat waves), which will mean a loss in income for the farmers; thus, this work stresses the need for accurate in situ monitoring to prevent the loss of oysters through informed farm management practices. Finally, an approach is proposed to present high-dimensional datasets captured from the sensor network to oyster farmers in a clear and informative manner.

No abstract

The need to identify oversized fragments in underground mining operations is discussed. The relationship between relative permittivity and bulk density is examined using established mixture models. A method of measuring the relative permittivity of ore in an industrial context is presented using a two channel Ground Penetrating Radar system in a trans-illumination arrangement. This method also provides a solution to the problem of time zero drift which affects spatially and/or temporally separated antennas. Results of experiments using sifted samples of fragmented ore show there is a relationship between bulk density and relative permittivity for fragmented ore.

No abstract

This paper presents results on detecting large rock fragments in rock piles using ground penetrating radar (GPR). The authors are researching sensors to augment the image provided to the remote operator, assisting with detection of oversized fragments beneath the surface. The attenuation of the GPR signal was calculated over the band 0-3 GHz from the signal energy in the frequency domain. The results show that attenuation measurement allowed the samples which included a large fragment to be differentiated from samples only containing smaller rock fragments. By combining velocity analysis with attenuation the rock samples can be further differentiated from samples containing air pockets or irregular surfaces. The combination of attenuation and signal velocity allowed prediction rates to detect the presence of large rock fragments over 93% correctly in laboratory testing.