Coccidiosis, a disease caused by the Eimeria parasite, represents a major threat to the poultry and rabbit industries, demanding rapid and accurate diagnostic tools. While deep learning models offer high precision, their significant energy consumption limits their deployment in resource-constrained environments. This paper introduces a novel two-stage Spiking Neural Network (SNN) architecture, where a pre-trained Convolutional Neural Network is first converted into a spiking feature extractor and then coupled with a lightweight, unsupervised SNN classifier trained with Spike-Timing-Dependent Plasticity (STDP). The proposed model sets a new state-of-the-art, achieving 98.32\% accuracy in Eimeria classification. Remarkably, this performance is accomplished with a significant reduction in energy consumption, showing an improvement of more than 223 times compared to its traditional ANN counterpart. This work demonstrates a powerful synergy between high accuracy and extreme energy efficiency, paving the way for autonomous, low-power diagnostic systems on neuromorphic hardware.

翻译：球虫病是一种由艾美耳球虫寄生虫引起的疾病，对家禽和兔类养殖业构成重大威胁，亟需快速准确的诊断工具。尽管深度学习模型能够提供高精度检测，但其巨大的能耗限制了它们在资源受限环境中的部署。本文提出了一种新颖的两阶段脉冲神经网络架构：首先将预训练的卷积神经网络转换为脉冲特征提取器，随后与采用脉冲时序依赖可塑性训练的轻量级无监督SNN分类器相结合。该模型在艾美耳球虫分类任务中取得了98.32%的准确率，创造了新的性能记录。值得注意的是，这一卓越性能是在显著降低能耗的前提下实现的，与传统人工神经网络相比能效提升超过223倍。本研究展示了高精度与极致能效之间的强大协同效应，为在神经形态硬件上实现自主低功耗诊断系统开辟了道路。