Wildfires are one of the most catastrophic natural disasters, causing increasingly severe ecological and economic damage. Early response is critically important for wildfire management, but also difficult due to the wide geographical area to monitor, often far from utility infrastructures such as stable power and high-bandwidth network. In this work, we present Xyloni, a very low-cost, low-power neural network accelerator for sensor nodes, which improves the cost-effectiveness and scalability of real-time wildfire detection by drastically reducing wireless data transmission and overall power consumption. Xyloni uses low-power flash and FeRAM memories to store a hardware co-optimized Neural Network model for fire and smoke detection, as well as intermediate activations during inference. It also time-shares a Field-Programmable Gate Array across different model layers for power-efficient computation. The detection model prevents benign images from consuming network traffic, allowing the use of low-bandwidth, low-power network fabrics such as a LoRa mesh network with enough range for the necessary geographical coverage. Compared to a wide range of edge and sensor platforms capable of real-time data collection, Xyloni demonstrated an order of magnitude reduction in power consumption for the network transmission reduction task, leading to a corresponding reduction in battery and deployment cost.