###### Abstract  
Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation. citation scientists; poacher; wildlife; biodiversity conservation; automation 202222020202202202202202202202202202202202202220222022202222220  
average, two to four weeks [10]. This is a considerable advantage compared to similar surveillance and monitoring tools, including UAVs with an average of 20-30 minutes flight time. Except for more advanced UAVs, including solar powered, reaching 25 days, this is not a widely utilised approach due to their current payload capacities and fragile components [11]. In addition, the advancement in automated networked CTs for image communications allows for more efficient data collection and transmission [9], without the need for full-time human operation as with UAV applications. Such advancements in CT image transmissions, coupled with long-term battery life, enable continuous, near-real-time data transmission. However, even with CT advancements, the vast number of images that often result from deployments pose a challenge to users. This hinders the application of CTs as a full near-real-time tool, as image analysts, who are usually experts but increasingly also citizen scientists (trained volunteers) [12], must assess images manually to detect, identify, and count targets, which is a time-consuming process [12; 13; 14; 15]. This prevents the effective application of CTs in time-sensitive situations, such as monitoring threats including poachers, as it is unrealistic to expect analysts to be on standby for image evaluation, regardless of image transmission platforms, e.g., satellites or GSM links [16]. With the intention to increase CT image processing speeds for time-sensitive applications, some research groups have integrated machine learning (ML) with citizen scientists to automatically detect and identify humans/animals (targets) within CT images [16; 17; 18]. Several groups have taken a further step towards automation and predominantly or solely used ML for such tasks [19; 20; 21; 22; 23]. These studies have indicated overall detection accuracies ranging from 68-93% [20; 22], illustrating the capacity for ML to detect targets within previously tested conditions. Ideally, we would independently apply ML for object detection in CT images irrespective of potential influences on detection probability (DP) and correct classification (CC).