There exist many biological sensing applications where direct measurement is either impossible, extremely invasive, or extremely time consuming. For example, measuring the presence/absence of birds at a feeder station currently requires a human to watch a camera pointed at the feeder, identifying when birds arrive and leave. Similarly, measuring CO2 flux from a plant requires placing the plant inside a growth chamber, destructively modifying the environment. We propose using imagers as biological sensors by constructing a procedure that uses images to obtain approximate measurements of these phenomena. This procedure, composed of state-of-the-art computer vision, image processing, and statistical learning algorithms, will be evaluated in the context of a specific application and shown to be general through multiple instantiations. Through application, it has been found that many of these algorithms make unacceptable assumptions about their input. Providing accurate data to biologists and ecologists, though the appropriate modification of these algorithms, is the ultimate goal of this work.