## 4 Discussion
### Recommendations to Mitigate Influential Factors
More advanced CTs using network power tend to consume more battery, due to the WiFi SD cards drawing power, but advancements in rechargeable batteries and solar energy usage should see improvements with future models [50]. A wide FOV is essential for optimal target detection and classification [54]. The average CT FOV ranges between 40-60\({}^{\circ}\) wide and from 5 m up to 30 m depending on the camera height positioning and vegetation density. We also focus on optimal resolution quality, a novel yet important factor for increasing the detection and classification of targets in CT images. Most networked CTs are of high-resolution quality, as standard with 4K video resolution and an average of 30-megapixel images, we recommend such high quality, providing optimum clarity for target detection and classification probabilities. However, one of the most important factors to consider when recommending a CT for near-real-time threat monitoring is the CT's network capabilities. Commonly, CTs with remote network capabilities rely on cellular connections or WiFi connectivity to send images. However, more recent developments are integrating AI into the CT system to send pre-processed near-real-time information from anywhere to end users [51]. [PERSON] [53] tested this using a commercial Bushnell Core 24 MP Low Glow 119936C CT and customised open-source hardware. The main frame hardware consists of a smart-bridge controller with a custom circuit board, LoRa STM32L0 ultra-low-power microcontroller, and RockBLOCK satellite modem, connecting to a Raspberry Pi 4 Compute module. The system is designed to only consume power when necessary, so it only activates the Raspberry Pi to download and classify images using AI when it receives a message from LoRa (the microcontroller within the smart bridge). After classification of the images, the Raspberry Pi sends the results via satellite and powers down to save battery. This type of open-source method is a reasonably cost-effective option. Users and developers would need to understand the mechanics of integrating and altering open-source hardware, including utilising Raspberry Pi's for scripting and ML connectivity. However, if users could overcome this issue, this type of CT approach would be highly effective for real-time threat monitoring. CT distribution and abundance will vary depending on many conditions, including individual objectives, CT detection range, target range, funding availability, and environmental conditions. However, we simply provide guideline recommendations of CT placement methods for optimum resource use and increased detection probability. Based on this paper's findings we determine that CTs should ideally be placed within 30 m of one another throughout the intended site for optimum detection and classification probability of both humans and wildlife. However, this is not likely feasible for real-world applications. Furthermore, in most cases, this is not required when users apply methods to optimise CT placement. Therefore, we instead offer recommendations for CT placement and distribution methods to increase detection and classification probability. Opportunistic CT placement is the most common approach for long-term wildlife monitoring [55]. The primary focus of CT sites would ideally be within the following landmark areas: feeding and drinking sites, game trails, downed logs, and other minor landmark points, all of which have been found to increase trigger probabilities [55]. However, an alternative method for optimising CT placement methods would be to utilise the spatial monitoring and reporting tool (SMART) as an additional measure to complement optimal CT placement [56]. Thistool allows rangers and other users to collaborate and share data including recording wildlife tracking, illegal activity monitoring, poaching camps, traps, and patrol routes, along with current CT and alternative sensor placements.