用粪便图像识别禽类疾病，模型轻量高效且无需GPU
Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning
- 融合多种颜色空间特征，结合机器学习提取关键判别信息
- 95.85%准确率，638秒内完成推理，无需GPU
- 适合资源有限的农村养殖场实时部署
家禽养殖是全球食物供应链的重要环节，但极易受到球虫病、沙门氏菌病和新城疫等传染病影响。本研究提出一种基于轻量级机器学习的粪便图像疾病检测方法。通过多颜色空间特征提取（RGB、HSV、LAB），结合颜色、纹理和形状描述符（如颜色直方图、局部二值模式LBP、小波变换、边缘检测器），经系统消融实验与PCA、XGBoost特征选择进行降维，获得兼顾精度与效率的紧凑特征集。使用人工神经网络（ANN）分类器在该特征集上训练，达到95.85%准确率，仅需638秒执行时间且无需GPU。相比Xception和MobileNetV3等深度学习模型，本方法实现相当精度的同时大幅降低资源消耗。本工作为低资源农业环境下的实时禽类疾病检测提供了低成本、可解释、可扩展的替代方案。
原文摘要 · Abstract (English)
Poultry farming is a vital component of the global food supply chain, yet it remains highly vulnerable to infectious diseases such as coccidiosis, salmonellosis, and Newcastle disease. This study proposes a lightweight machine learning-based approach to detect these diseases by analyzing poultry fecal images. We utilize multi-color space feature extraction (RGB, HSV, LAB) and explore a wide range of color, texture, and shape-based descriptors, including color histograms, local binary patterns (LBP), wavelet transforms, and edge detectors. Through a systematic ablation study and dimensionality reduction using PCA and XGBoost feature selection, we identify a compact global feature set that balances accuracy and computational efficiency. An artificial neural network (ANN) classifier trained on these features achieved 95.85% accuracy while requiring no GPU and only 638 seconds of execution time in Google Colab. Compared to deep learning models such as Xception and MobileNetV3, our proposed model offers comparable accuracy with drastically lower resource usage. This work demonstrates a cost-effective, interpretable, and scalable alternative to deep learning for real-time poultry disease detection in low-resource agricultural settings.
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