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Batteryless image sensors present an opportunity for long-life, long-range sensor deployments that require zero maintenance, and have low cost. Such deployments are critical for enabling remote sensing applications, e.g., instrumenting national highways, where individual devices are deployed far (kms away) from supporting infrastructure. In this work, we develop and characterize Camaroptera, the first batteryless image-sensing platform to combine energy-harvesting with active, long-range (LoRa) communication. We also equip Camaroptera with a Machine Learning-based processing pipeline to mitigate costly, long-distance communication of image data. This processing pipeline filters out uninteresting images and only transmits the images interesting to the application. We show that compared to running a traditional Sense-and-Send workload, Camaroptera’s Local Inference pipeline captures and sends upto \( 12\times \) more images of interest to an application. By performing Local Inference , Camaroptera also sends upto \( 6.5\times \) fewer uninteresting images, instead using that energy to capture upto \( 14.7\times \) more new images, increasing its sensing effectiveness and availability. We fully prototype the Camaroptera hardware platform in a compact, 2 cm \( \times \) 3 cm \( \times \) 5 cm volume. Our evaluation demonstrates the viability of a batteryless, remote, visual-sensing platform in a small package that collects and usefully processes acquired data and transmits it over long distances (kms), while being deployed for multiple decades with zero maintenance.