This study examined the potential for Active Optical Sensors (AOS) to provide estimates of green biomass in improved and native pastures. Two sampling campaigns were undertaken across two pasture types (improved and native) in September and October 2011. The AOS was found to correlate poorly with the green fraction of the sward in September when senescent material dominated the sward, particularly in the improved pastures. In contrast the AOS demonstrated good correlations with green dry matter in October when there was a higher proportion of green material in the sward. The correlation was particularly sound for the improved pasture (r2 0.91) and better than expected for the native pasture (r2 0.63). This study demonstrates the potential for AOS to provide rapid estimates of biomass in both improved and native pastures and suggests that further research be undertaken to further quantify the value of these sensors. ; Associated Grant:Meat and Livestock Australia; Australian Government's Climate Change Research Program; CRC for Spatial Information (CRCSI)
Context Ground baiting is a strategic method for reducing vertebrate pest populations. Best practice involves maximising bait availability to the target species, although sustaining this availability is resource intensive because baits need to be replaced each time they are taken. This study focused on improving pest population management through the novel baiting technique outlined in this manuscript, although there is potential use across other species and applications (e.g. disease management). Aims To develop and test an automated, intelligent, and semi-permanent, multi-bait dispenser that detects target species before distributing baits and provides another bait when a target species revisits the site. Methods We designed and field tested the Sentinel Bait Station, which comprises a camera trap with in-built species-recognition capacity, wireless communication and a dispenser with the capacity for five baits. A proof-of-concept prototype was developed and validated via laboratory simulation with images collected by the camera. The prototype was then evaluated in the field under real-world conditions with wild-living canids, using non-toxic baits. Key results Field testing achieved 19 automatically offered baits with seven bait removals by canids. The underlying image recognition algorithm yielded an accuracy of 90%, precision of 83%, sensitivity of 68% and a specificity of 96% throughout field testing. The response time of the system, from the point of motion detection (within 6–10 m and the field-of-view of the camera) to a bait being offered to a target species, was 9.81 ± 2.63 s. Conclusion The Sentinel Bait Station was able to distinguish target species from non-target species. Consequently, baits were successfully deployed to target species and withheld from non-target species. Therefore, this proof-of-concept device is able to successfully provide baits to successive targets from secure on-board storage, thereby overcoming the need for daily bait replacement. Implications The proof-of-concept Sentinel Bait Station design, together with the findings and observations from field trials, confirmed the system can deliver multiple baits and increase the specificity in which baits are presented to the target species using artificial intelligence. With further refinement and operational field trials, this device will provide another tool for practitioners to utilise in pest management programs.
